Index

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z _

A

a - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.T_FRM: a value.
a() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyInterval: Returns the lower extreme of the interval fuzzy set.
A() - Method in class keel.Algorithms.Hyperrectangles.INNER.Pair: Returns the first rule of the pair
a - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network: Transfer function parameters
A - Variable in class keel.Algorithms.Instance_Generation.PNN.MatrixOfDistances: One prototype set
a - Variable in class keel.Algorithms.Neural_Networks.gann.Network: Transfer function parameters
a - Variable in class keel.Algorithms.Neural_Networks.gmdh.Network: Transfer function parameters
a - Variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus: Current coefficient array
a - Variable in class keel.Algorithms.Neural_Networks.net.Data: Scaling parameters (a)
a - Variable in class keel.Algorithms.Neural_Networks.net.Network: Transfer function parameters
A - Static variable in class keel.Algorithms.Rule_Learning.Ripper.Ripper: Flag ('Accuracy'metric)
A - Static variable in class keel.Algorithms.Rule_Learning.Slipper.Slipper: 'Accuracy'metric
A - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Array for internal storage of elements.
ABB - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_IEP: Automatic Branch and Bound
ABB(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_IEP.ABB: Creates a new instance of ABB
ABB - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_LIU: Automatic Branch and Bound
ABB(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_LIU.ABB: Creates a new instance of ABB
ABB - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_MI: Automatic Branch and Bound
ABB(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_MI.ABB: Creates a new instance of ABB
aboutHeight - Static variable in class keel.GraphInterKeel.experiments.Credits
aboutLabel - Variable in class keel.GraphInterKeel.experiments.Credits
aboutLeft - Static variable in class keel.GraphInterKeel.experiments.Credits
aboutTop - Static variable in class keel.GraphInterKeel.experiments.Credits
aboutWidth - Static variable in class keel.GraphInterKeel.experiments.Credits
abs(fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
absDev(int, M5Instances) - Static method in class keel.Algorithms.Decision_Trees.M5.M5: Returns the absolute deviation value of the instances values of an attribute
absDev(int, MyDataset) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: Returns the absolute deviation value of the itemsets values of an attribute
absoluteAccuracy(PrototypeSet, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Calculate the absolute accuracy between two sets
absoluteAccuracy(PrototypeSet, PrototypeSet) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Calculate the absolute accuracy between two sets
absoluteAccuracyAndError(PrototypeSet, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Calculate the absolute accuracy between two sets
absoluteAccuracyAndError(PrototypeSet, PrototypeSet) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Calculate the absolute accuracy between two sets
absoluteAccuracyKNN(PrototypeSet, PrototypeSet, int) - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Calculate the absolute accuracy between two sets
absoluteAccuracyKNN(PrototypeSet, PrototypeSet, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Calculate the absolute accuracy between two sets
absoluteDistance(Prototype, Prototype) - Static method in class keel.Algorithms.Instance_Generation.utilities.Distance: Compute the Absolute Distance between two prototypes.
absoluteDistance(Prototype, Prototype) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Distance: Compute the Absolute Distance between two prototypes.
AbstractAPR - Class in keel.Algorithms.MIL.APR
AbstractAPR() - Constructor for class keel.Algorithms.MIL.APR.AbstractAPR
AbstractAttribute - Class in keel.Algorithms.Neural_Networks.NNEP_Common.data: IAttribute abstract implementation
AbstractAttribute() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractAttribute: Empty constructor
AbstractAttribute(String) - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractAttribute: Constructor that sets attribute name
AbstractDataset - Class in keel.Algorithms.Neural_Networks.NNEP_Common.data: IDataset abstract implementation
AbstractDataset() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset: Empty constructor
AbstractDataset.Instance - Class in keel.Algorithms.Neural_Networks.NNEP_Common.data: Implementation of the IInstance interface
AbstractMIAlgorithm - Class in keel.Algorithms.MIL
AbstractMIAlgorithm() - Constructor for class keel.Algorithms.MIL.AbstractMIAlgorithm
AbstractNearestNeighbour - Class in keel.Algorithms.MIL.Nearest_Neighbour
AbstractNearestNeighbour() - Constructor for class keel.Algorithms.MIL.Nearest_Neighbour.AbstractNearestNeighbour
AbstractNeuralNet - Class in keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet: Implementation of a neural net
AbstractNeuralNet() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Empty constructor
AbstractNeuralNetSpecies - Class in keel.Algorithms.Neural_Networks.NNEP_Common: Abstract implementation for INeuralNetSpecies.
AbstractNeuralNetSpecies() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Empty constructor
ACC(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.ICPL.ICPLGenerator: Retaining Center Instances
acc_0 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Parameter of the accuracy function (Is the accuracy threshold beyond which the accuracy of the classifier is set to 1.
AccAUC - Class in keel.Algorithms.ImbalancedClassification.Auxiliar.AUC: This class represents, where one element is the accuracy of a classifier and the other one the AUC.
AccAUC(double, double) - Constructor for class keel.Algorithms.ImbalancedClassification.Auxiliar.AUC.AccAUC: Constructor
accept(File) - Method in class keel.GraphInterKeel.datacf.util.KeelFileFilter: Overriding the accept method for accepting directory names
accept(File, String) - Method in class keel.GraphInterKeel.experiments.ArchiveFilter: Test if the file shoudl be accepted
accept(File) - Method in class keel.GraphInterKeel.experiments.ArchiveFilter2: Tests if a file is accepted
accept(File) - Method in class keel.GraphInterKeel.experiments.KeelFileFilter: Test if the file is accepted
accept(File) - Method in class keel.GraphInterKeel.statistical.CSVFileFilter: Test if the file is accepted
accionExit_mouseEntered(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Enter in exit button
accionExit_mouseEntered(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Enter in exit button
accionExit_mouseExited(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Exit from teaching button
accionExit_mouseExited(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Exit from exit button
accionExit_mouseReleased(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Exit from the application
accionExit_mouseReleased(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Releasing exit button
accu - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Antd: The accurate data for this antecedent in the growing data
accuracy - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Percentage describing classification accuarcy.
accuracy - Variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
accuracy - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
accuracy(PrototypeSet, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Calculate the percetage of well classificated prototypes of one set using 1NN in a reduced set.
accuracy() - Static method in class keel.Algorithms.RST_Learning.KNNClassifier: Estimates the LVO (Leave-one-out) accuracy of the classifier over the training data.
accuracy(PrototypeSet, PrototypeSet) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Calculate the percetage of well classificated prototypes of one set using 1NN in a reduced set.
accuracy2(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Calculate the percetage of well classificated prototypes of one set using 1NN in a reduced set.
accuracy2(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Calculate the percetage of well classificated prototypes of one set using 1NN in a reduced set.
AccuracyMeter - Class in keel.Algorithms.Instance_Generation.Basic: Measures classification accuracy of a reduced set.
AccuracyMeter() - Constructor for class keel.Algorithms.Instance_Generation.Basic.AccuracyMeter
AccuracyMeter - Class in keel.Algorithms.Semi_Supervised_Learning.Basic: Measures classification accuracy of a reduced set.
AccuracyMeter() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.AccuracyMeter
accuracyOfPosition(int, matchProfileAgent) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ruleOrderAgent
accuRate - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Antd: The accurate rate of this antecedent test on the growing data
ACO - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner: Title: Ant Colony Optimization Description: Classification Algorithm by ACO (Advanced Ant Miner).
ACO() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ACO: Default constructor.
ACO(String, String, String, String, String, String, String, int, int, int, int, float, float, float, float, long) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ACO: Parameter constructor.
ACO - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus: Title: Ant Colony Optimization Description: Classification Algorithm by ACO (Advanced Ant Miner +).
ACO() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ACO: Default constructor.
ACO(String, String, String, String, String, String, String, int, int, int, int, float, float, float, float, long) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ACO: Parameter constructor.
ACO - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner: Title: Ant Colony Optimization Description: Classification Algorithm by ACO (Ant Miner).
ACO() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ACO: Default constructor.
ACO(String, String, String, String, String, String, int, int, int, int, long) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ACO: Parameter constructor.
ACO - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus: Title: Ant Colony Optimization Description: Classification Algorithm by ACO (Advanced Ant Miner +).
ACO() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ACO: Default constructor.
ACO(String, String, String, String, String, String, String, int, int, int, int, float, float, float, float, long) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ACO: Parameter constructor.
ACT_MIN - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
actInputOutput(ExternalObjectDescription, GraphPanel) - Method in class keel.GraphInterKeel.experiments.Algorithm: Check the constraints defined by the node described by 'dsc' contained in the graph
actInputOutput(ExternalObjectDescription, GraphPanel) - Method in class keel.GraphInterKeel.experiments.DataSet: Updates input and output variables
actInputOutput(ExternalObjectDescription, GraphPanel) - Method in class keel.GraphInterKeel.experiments.Node: Test the input/output capabilities described by the ExternalObjectDescriptor and stores them
actInputOutputLQD(ExternalObjectDescription, GraphPanel, DatasetXML[]) - Method in class keel.GraphInterKeel.experiments.DataSet: Updates input and output variables
actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.Credits: Action performed
actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.EducationalRun
actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.statistical.ExcelAdapter: This method is activated on the Keystrokes we are listening to in this implementation.
activate() - Method in class keel.Algorithms.Decision_Trees.C45.Attribute: Sets the attribute as used.
activate() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Attribute: Sets the attribute as used.
activate() - Method in class keel.Algorithms.Decision_Trees.ID3.Attribute: Sets the attribute as used.
activate() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Attribute: Sets the attribute as used.
activate() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Sets the attribute as used.
activate() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA_Attribute: Sets the attribute as used.
activate() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyAttribute: Sets the attribute as used.
activate() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Attribute: Sets the attribute as used.
activate() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Attribute: Sets the attribute as used.
activate() - Method in class keel.Algorithms.Rule_Learning.ART.Attribute: Sets the attribute as used.
activate() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyAttribute: Sets the attribute as used.
activate() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyAttribute: Sets the attribute as used.
activate() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Attribute: Sets the attribute as used.
activate() - Method in class keel.Algorithms.Rule_Learning.PART.MyAttribute: Sets the attribute as used.
activateModified() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
activateModifiedFlag() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.populationWrapper
activateUpperMenu_principals() - Method in class keel.GraphInterKeel.experiments.Experiments: Activates main elements of upper menu
activation - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network: Output of each node
activation - Variable in class keel.Algorithms.Neural_Networks.gann.Network: Output of each node
activation - Variable in class keel.Algorithms.Neural_Networks.gmdh.Network: Output of each node
activation - Variable in class keel.Algorithms.Neural_Networks.net.Network: Output of each node
activationsAtt() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL.activationsAtt
Active() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
Active() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
Active() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
actualiza(boolean[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseR
actualiza(Poblacion) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Poblacion
actualizaClasePrincipal() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Actualizes the main class of the node by considering the frecuency of each class.
actualizaFeromona(Vector, int, int, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Updates the pheromone values following the environment of particles.
actualizaHistograma(int) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Changes an element with the class given from the left leaf to the right one.
actualizarUorg(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Organizacion: Selectes N examples with opposing classes and checks the combination of their attributes/values
AD - Class in keel.Algorithms.PSO_Learning.PSOLDA: Title: AD.
AD(double[][], double[][]) - Constructor for class keel.Algorithms.PSO_Learning.PSOLDA.AD: Parameter constructor.
AD - Class in keel.Algorithms.Statistical_Classifiers.Shared.DiscrAnalysis: Linear and Quadratic discriminant analysis, with a normal distribution of the examples for each class.
AD(double[][], double[][]) - Constructor for class keel.Algorithms.Statistical_Classifiers.Shared.DiscrAnalysis.AD: Parameter constructor.
Adaptation(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
Adaptation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
Adaptation(double, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
Adaptation(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.fuzzy_t: Calculates the adaptation degree of "x" with a label
Adaptation(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t: Calculates the adaptation degree of "x" with the variable
Adaptation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
Adaptation(double, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
Adaptation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Adaptation(double, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Adaptation(double, int, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Adaptation(vectordouble, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Adaptation(vectordouble, String, double[], double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Adaptation(vectordouble, String, double[], double, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Adaptation(vectordouble, genetcode) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Adaptation(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Returns the adaptation degree of a value x to the domain.
Adaptation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Returns the adaptation degree of a value x to a specific label in the domain (this label is given by its position in the domain).
Adaptation(double, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Returns the adaptation degree of a value x to a set of label in the domain.
Adaptation(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.fuzzy_t: Returns the adaptation degree of a value x to the label.
Adaptation(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t: Returns the adaptation degree of a certain value x to the variable.
Adaptation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t: Returns the adaptation degree of a certain value x to the label "etiqueta" of the variable.
Adaptation(double, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t: Returns the adaptation degree of a certain value x to a set of label "etiquetas" of the variable.
Adaptation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Returns the adaptation degree of a certain value x to the variable in position "variable" of the list.
Adaptation(double, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Returns the adaptation degree of a certain value x to the label "dominio" of the variable in position "variable" of the list.
Adaptation(double, int, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Returns the adaptation degree of a certain value x to a set of label "dominio" of the variable in position "variable" of the list.
Adaptation(vectordouble, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Returns the adaptation degree of set of values in "x" to a set rules enconded in a String
Adaptation(vectordouble, String, double[], double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Returns the adaptation degree of set of values in "x" to a set rules enconded in a String, taking into account the activation threshold for the variables
Adaptation(vectordouble, String, double[], double, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Returns the adaptation degree of set of values in "x" to a set rules enconded in a String, taking into account the activation threshold for the variables
Adaptation(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
Adaptation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
Adaptation(double, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
Adaptation(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.fuzzy_t: Calculates the adaptation degree of "x" with a label
Adaptation(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t: Calculates the adaptation degree of "x" with the variable
Adaptation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
Adaptation(double, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
Adaptation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
Adaptation(double, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
Adaptation(double, int, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
Adaptation(vectordouble, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
Adaptation(vectordouble, String, double[], double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
Adaptation(vectordouble, String, double[], double, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
Adaptation(vectordouble, genetcode) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
Adaptation(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
Adaptation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
Adaptation(double, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
Adaptation(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.fuzzy_t: Calculates the adaptation degree of "x" with a label
Adaptation(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t: Calculates the adaptation degree of "x" with the variable
Adaptation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
Adaptation(double, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
Adaptation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
Adaptation(double, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
Adaptation(double, int, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
Adaptation(vectordouble, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
Adaptation(vectordouble, String, double[], double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
Adaptation(vectordouble, String, double[], double, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
Adaptation(vectordouble, genetcode) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
AdaptationC(vectordouble, int, Double_t, Double_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
AdaptationC(vectordouble, int, ArrayList<double[]>) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Calculates the adaptation degree of set of values in "x" to a certain label "etiq".
AdaptationC(vectordouble, int, Double_t, Double_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
AdaptationC(vectordouble, int, Double_t, Double_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
AdaptiveAttribute - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist
AdaptiveAttribute() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveAttribute
AdaptiveAttribute - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS
AdaptiveAttribute() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveAttribute
AdaptiveRule - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist
AdaptiveRule() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveRule
AdaptiveRule - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS
AdaptiveRule() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveRule
adaptiveRules(double, double, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.RuleBase: Function to adjust fuzzy confidences (Nozaki method)
ADASYN - Class in keel.Algorithms.ImbalancedClassification.Resampling.ADASYN: File: ADASYN.java The ADASYN algorithm is an oversampling method used to deal with the imbalanced problem.
ADASYN(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.ADASYN.ADASYN: Constructor of the class.
add(Item) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset: Function to add an item to our itemset.
add(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.RuleBase: It adds a rule to the rule base
add(Itemset, long) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.RuleBase: It adds a rule to the rule base from an itemset and a time
add(Item) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset: Function to add an item to our itemset
add(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.RuleBase: It adds a rule to the rule base
add(Itemset, long) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.RuleBase: It adds a rule to the rule base from an itemset and a time
add(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.RuleBase: It adds a rule to the rule base
add(Item) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Itemset: Function to add an item to our itemset
add(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.RuleBase: It adds a rule to the rule base
add(Itemset, long) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.RuleBase: It adds a rule to the rule base
add(Item) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Itemset: Function to add an item to our itemset
add(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: It adds a rule to the rule base
add(RuleBase) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: It adds the rules of the RuleBase given.
add(Itemset) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: It adds a rule to the rule base
add(Item) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Itemset: Function to add an item to our itemset
add(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.RuleBase: It adds a rule to the rule base
add(Itemset) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.RuleBase: It adds a rule to the rule base
add(int, Itemset) - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Function to add the given itemset to given the value.
add(int, Itemset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Function to add the given itemset to given the value.
add(M5Instance) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Adds one instance to the end of the set.
add(double) - Method in class keel.Algorithms.Decision_Trees.M5.SimpleStatistics: Adds a value to the observed values
add(double, double) - Method in class keel.Algorithms.Decision_Trees.M5.SimpleStatistics: Adds a value that has been seen n times to the observed values
add(Rule) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase: It adds a new rule to the rule set
Add(genetcode, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.ruleset: Add a new rule "x" to the last position of the set of learned rules with its weight associated "weight".
Add(genetcode, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.ruleset: Add a new rule "x" to the last position of the set of learned rules with its weight associated "weight".
Add(genetcode, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.ruleset: Add a new rule "x" to the last position of the set of learned rules with its weight associated "weight".
add(Item) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Itemset
add(Rule) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
add(RuleBase) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
add(Itemset) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
add(Instance) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Adds one instance to the end of the set.
add(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleStatistics: Adds a value to the observed values
add(double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleStatistics: Adds a value that has been seen n times to the observed values
add(int, Itemset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Function to add the given itemset to given the value.
add(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Roulette: Enter a new probability in the roulette.
add(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Roulette: Enter a new probability in the roulette.
add(int, Itemset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Function to add the given itemset to given the value.
add(int, Itemset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Function to add the given itemset to given the value.
add(Instance) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Adds one instance to the end of the set.
add(double) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Stats: Adds a value to the observed values
add(double, double) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Stats: Adds a value that has been seen n times to the observed values
add(double) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Add an increment to all the inputs of the prototype
add(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Performs add operation between two prototypes.
add(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Add the elements of a set.
add(Prototype) - Method in class keel.Algorithms.Instance_Generation.GMCA.Cluster: Add prototype to the cluster.
add(Cluster) - Method in class keel.Algorithms.Instance_Generation.GMCA.ClusterSet: Adds a cluster of the set.
add(Prototype) - Method in class keel.Algorithms.Instance_Generation.PNN.MatrixOfDistances: Add a new prototype to the matrix of distances.
add(IndexType) - Method in class keel.Algorithms.Instance_Generation.utilities.OneSideFloatMatrix
add(Prototype) - Method in class keel.Algorithms.Instance_Generation.VQ.Cluster: Add one element to the cluster.
add(int) - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster: Add an element into the cluster
add(Function, Function) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.Function: the sum of two functions
add(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Histogram: Add one to the bins summation of th one determinated by the given number
add(double, double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Add the given numbers to the summation and squares summation for mean and squares summation.
add(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Sample: Add the given number to the summation and squares summation for mean and squares summation.
add(int, Itemset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Function to add the given itemset to given the value.
add(int, Itemset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Function to add the given itemset to given the value.
add(int, Itemset) - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Function to add the given itemset to given the value.
add(double) - Method in class keel.Algorithms.Rule_Learning.UnoR.Interval: Adds a new value to the interval.
add(int, Itemset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Function to add the given itemset to given the value.
add(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Add an increment to all the inputs of the prototype
add(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Performs add operation between two prototypes.
add(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Add the elements of a set.
add(IndexType) - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.OneSideFloatMatrix: Adds a new index (row) to the matrix.
add(Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Returns the sum of this matrix with another.
add(TechnicalInformation) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation: adds the given information to the list of additional technical informations
add(TechnicalInformation.Type) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation: Adds an empty technical information with the given type to the list of additional informations and returns the instance.
add(Instance) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Adds one instance to the end of the set.
add(TechnicalInformation) - Method in class keel.Algorithms.SVM.SMO.core.TechnicalInformation: adds the given information to the list of additional technical informations
add(TechnicalInformation.Type) - Method in class keel.Algorithms.SVM.SMO.core.TechnicalInformation: Adds an empty technical information with the given type to the list of additional informations and returns the instance.
add(Item) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Itemset: It allows to add an item into an itemset
add(Item) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Itemset: It allows to add an item into an itemset
add(Item) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Itemset: It allows to add an item into an itemset
add(Item) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Itemset: It allows to add an item into an itemset
add(Item) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Itemset: It allows to add an item into an itemset
add(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
add(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
add_escapes(String) - Method in exception keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParseException: Used to convert raw characters to their escaped version when these raw version cannot be used as part of an ASCII string literal.
add_escapes(String) - Method in exception keel.Algorithms.Rule_Learning.Swap1.ParseException: Used to convert raw characters to their escaped version when these raw version cannot be used as part of an ASCII string literal.
add_escapes(String) - Method in exception keel.Dataset.ParseException: Used to convert raw characters to their escaped version when these raw version cannot be used as part of an ASCII string literal.
Add_Weight(int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.ruleset: Associates the weight "weight" to the rule "rule"
Add_Weight(int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.ruleset: Associates the weight "weight" to the rule "rule"
Add_Weight(int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.ruleset: Associates the weight "weight" to the rule "rule"
addAndUpdate(Rule) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Add a rule to the ruleset and update the stats
addAntecedent(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule: It adds a single antecedent term to an association rule
addAntecedent(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
addAntecedent(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
addAntecedent(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule: It adds a single antecedent term to an association rule
addAntecedent(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule
addAntecedent(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
addAntecedent(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
addAntecedente(Atributo_valor) - Method in class keel.Algorithms.Rule_Learning.Rules6.Regla: Adds the given antecent to the list of the rule.
addAntecedente(Atributo_valor) - Method in class keel.Algorithms.Rule_Learning.SRI.Regla: Adds the given antecent to the list of the rule.
addAtribute1(String) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelCharts2D: Adds attributes to the first combo box
addAtribute2(String) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelCharts2D: Adds attributes to the second combo box
addAtributo(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Adds a new antecedent (attribute + value).
addAttribute(IAttribute) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.Metadata: Adds an attribute to this metadata If the name of the new attribute is empty or there already exists an attribute with the same name, it is not added to the name hashtable.
addAttribute(Attribute) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: This method adds an attribute definition.
addAttribute(Attribute) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: Appends the given attribute to the non-static list of the current InstanceSet
addAttribute(Attribute) - Method in class keel.Dataset.InstanceAttributes: This method adds an attribute definition.
addAttribute(Attribute) - Method in class keel.Dataset.InstanceSet: Appends the given attribute to the non-static list of the current InstanceSet
addAttributeNames(String[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Rule: It stores the names of the input attributes
addAttributeNames(String[]) - Method in class keel.Algorithms.Rule_Learning.AQ.Complex: It performs a local copy of the name of the input variables
addAttributeNames(String[]) - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: It performs a local copy of the name of the input variables
addCache(Class, String, Vector) - Static method in class keel.Algorithms.SVM.SMO.core.ClassDiscovery: adds the list of classnames to the cache
addCellEditorListener(CellEditorListener) - Method in class keel.GraphInterKeel.datacf.util.EachRowEditor: Adds a cell editor listener
addChild(Item) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.Item: It adds a child item to a parent item
addChild(Item) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.Item: It adds a child item to a parent item
addChildren(Node) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Adds a child to the node.
addChromosome(Chromosome) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Cochromosome: Adds a chromosome to this list
addClassDistribution(int) - Method in class keel.Algorithms.Rule_Learning.AQ.Complex: Increments the class distribution
addClassDistribution(int) - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: Increments the class distribution
addClassifier(classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_aggregated
addClassifier(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Adds a classifier in the population.
addClassifier(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Adds a classifier in the population.
addClassName(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Rule: It stores the name for the class attribute
addClassName(String) - Method in class keel.Algorithms.Rule_Learning.AQ.ruleSet: It performs a local copy of the name of the output class
addClassName(String) - Method in class keel.Algorithms.Rule_Learning.CN2.ruleSet: It performs a local copy of the name of the output class
addClassNames(String[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Rule: It stores the names of the output attribute
addClassNames(String[]) - Method in class keel.Algorithms.Rule_Learning.AQ.ruleSet: It performs a local copy of the names of the output classes
addClassNames(String[]) - Method in class keel.Algorithms.Rule_Learning.CN2.ruleSet: It performs a local copy of the names of the output classes
addConsequent(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule: It adds a single consequent term to an association rule
addConsequent(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
addConsequent(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
addConsequent(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule: It adds a single consequent term to an association rule
addConsequent(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule
addConsequent(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
addConsequent(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
addCovered() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Instance: Increments the "covered" value
addCovered() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Instance: Increments the "covered" value
addCovered() - Method in class keel.Algorithms.Rule_Learning.AQ.Instance: Increments the "covered" value
addCovered() - Method in class keel.Algorithms.Rule_Learning.CN2.Instance: Increments the "covered" value
addCovered() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Instance: Increments the "covered" value
addCoveredTID(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome: It adds a dataset records to the list of records being covered by a chromosome
addData(Instance) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.myDataset: It adds a data
addData(Instance) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.myDataset: It adds a data
addData(Sample) - Method in class keel.Algorithms.Hyperrectangles.EACH.EachDataSet: Removes a data item
addData(Instance) - Method in class keel.Algorithms.Rule_Learning.AQ.myDataset: It adds a data
addData(Instance) - Method in class keel.Algorithms.Rule_Learning.CN2.myDataset: It adds a data
addDato(Muestra) - Method in class keel.Algorithms.Rule_Learning.Prism.ConjDatos: Add a data item
addDato(Muestra) - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjDatos: Add a data item
addDato(Muestra) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjDatos: Add a data item
addDato(Muestra) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjDatos: Add a data item
addDato(Instance) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetData: Add a data
addDiscretizer(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.DiscretizationManager
addDiscretizer(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.DiscretizationManager
addDistinct(double, int) - Method in class keel.Algorithms.Decision_Trees.M5.M5AttrStats: Updates the counters for one more observed distinct value.
addDistinct(double, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.AttributeStats: Updates the counters for one more observed distinct value.
addDiv(Prototype, double) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Performs add and divide operation between two prototypes.
addDiv(Prototype, double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Performs add and divide operation between two prototypes.
AddDomain(vectorvar) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.ruleset
AddDomain(vectorvar) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.ruleset
AddDomain(vectorvar) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.ruleset
addEditTab() - Method in class keel.GraphInterKeel.datacf.DataCFFrame: Adds Edit Tab
addEditTab(File) - Method in class keel.GraphInterKeel.datacf.DataCFFrame: Adds Edit Tab
addElement(Object) - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector: Adds an element to this vector.
addElement(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector: Adds an element to this vector.
addElement(Object) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector: Adds an element to this vector.
AddElement(String) - Method in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.FreqList: Adds an item to the list, increasing it frequency by one.
AddElement(String) - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.FreqList: Adds an item to the list, increasing it frequency by one.
addElement(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.valueAssociations: This method adds an element (a class) to the list
AddElement(String) - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.FreqList: Adds an item to the list, increasing it frequency by one.
addElement(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.valueAssociations: This method adds an element (a class) to the list
AddElement(String) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqList: Adds an item to the list, increasing it frequency by one.
AddElement(String, String) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqListPair: Adds a new pair, increasing its frequency if already exists, or creating a new pair if not
addElement(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.valueAssociations: This method adds an element (a class) to the list
AddElement(String) - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.FreqList: Adds an item to the list, increasing it frequency by one.
addElement(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.valueAssociations: This method adds an element (a class) to the list
AddElement(String) - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.FreqList: Adds an item to the list, increasing it frequency by one.
addElement(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.valueAssociations: This method adds an element (a class) to the list
AddElement(String) - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.FreqList: Adds an item to the list, increasing it frequency by one.
addElement(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.valueAssociations: This method adds an element (a class) to the list
AddElement(String) - Method in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.FreqList: Adds an item to the list, increasing it frequency by one.
AddElement(String) - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.FreqList: Adds an item to the list, increasing it frequency by one.
addElement(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.valueAssociations: This method adds an element (a class) to the list
AddElement(String) - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.FreqList: Adds an item to the list, increasing it frequency by one.
addElement(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.valueAssociations: This method adds an element (a class) to the list
addElement(Object) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector: Adds an element to this vector.
addElement(int, int, double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Add a value to an element.
addElement(double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Adds an element into the vector
addElement(Object) - Method in class keel.Algorithms.SVM.SMO.core.FastVector: Adds an element to this vector.
adder(Rbfn) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation: Performs the DELETER mutator operator: modifies C_DELETER% of the Radius of the net
addEscapes(String) - Static method in error keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.TokenMgrError: Replaces unprintable characters by their espaced (or unicode escaped) equivalents in the given string
addEscapes(String) - Static method in error keel.Algorithms.Rule_Learning.Swap1.TokenMgrError: Replaces unprintable characters by their espaced (or unicode escaped) equivalents in the given string
addEscapes(String) - Static method in error keel.Dataset.TokenMgrError: Replaces unprintable characters by their espaced (or unicode escaped) equivalents in the given string
addExportTab() - Method in class keel.GraphInterKeel.datacf.DataCFFrame: Adds Export Tab
addExtension(String) - Method in class keel.GraphInterKeel.datacf.util.KeelFileFilter: Adds extendion to the filter
addExtension(String) - Method in class keel.GraphInterKeel.experiments.KeelFileFilter: Adds an extension to the filter
addExtension(String) - Method in class keel.GraphInterKeel.statistical.CSVFileFilter: Adds an extension to the filter
addFila(int) - Method in class keel.Algorithms.Rule_Learning.LEM2.Atributo_valor
addFilas(LinkedList<Integer>) - Method in class keel.Algorithms.Rule_Learning.LEM2.Atributo_valor
addHlayer(LinkedLayer) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Adds a new layer to the neural net
addHlayer(LinkedLayer) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuralNet: Adds a new layer to the neural net
addImportTab(boolean, boolean) - Method in class keel.GraphInterKeel.datacf.DataCFFrame: Adds Import Tab
addInMeanValue(int, double) - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It adds the new value to the mean values vector
addInMeanValue(int, double) - Method in class keel.Dataset.Attribute: It adds the new value to the mean values vector
addInput(int) - Method in class keel.GraphInterKeel.experiments.Multiplexor: Add input to the multiplexor
addInstance(Instance) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Cluster: Adds one instance to the cluster (referencing it)
addInstance(Instance) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: It adds the passed instance at the end of the present InstanceSet
addInstance(Instance) - Method in class keel.Dataset.InstanceSet: It adds the passed instance at the end of the present InstanceSet
addInstanceInfo(Instance) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets: Adds the given instance info.
addInstanceInfo(Instance) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Arrays: Adds the given instance info.
addInterval(Interval) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.IntegerNumericalAttribute: Adds an interval
addInterval(Interval) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.RealNumericalAttribute: Adds an interval
addItemset(Itemset) - Method in class keel.Algorithms.Decision_Trees.C45.Dataset: Function to add one itemset.
addItemset(Itemset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Function to add one itemset.
addItemset(Itemset) - Method in class keel.Algorithms.Decision_Trees.ID3.Dataset: Function to add one itemset.
addItemset(Itemset) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Function to add one itemset.
addItemset(Itemset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Function to add one itemset.
addItemset(Itemset) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Function to add one itemset.
addItemset(Itemset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Function to add one itemset.
addItemset(Itemset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Function to add one itemset.
addItemset(Itemset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Function to add one itemset.
addItemset(Itemset) - Method in class keel.Algorithms.Rule_Learning.ART.Dataset: Function to add one itemset.
addItemset(Itemset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Function to add one itemset.
addItemset(Itemset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Function to add one itemset.
addItemset(Itemset) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Function to add one itemset.
addItemset(Itemset) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Function to add one itemset.
addItemset(Itemset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Function to add one itemset.
addItemset(Itemset) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Itemset: It allows to add an item into an itemset
additional() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation: returns an enumeration of all the additional technical informations (if there are any)
additional() - Method in class keel.Algorithms.SVM.SMO.core.TechnicalInformation: returns an enumeration of all the additional technical informations (if there are any)
additional_outputs - Variable in class keel.GraphInterKeel.experiments.Parameters
AdditionalMeasureProducer - Interface in keel.Algorithms.SVM.SMO.core: Interface to something that can produce measures other than those calculated by evaluation modules.
ADDITIVE - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Fuzzy_Ish: Configuration flags.
ADDITIVE_COMBINATION - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Fuzzy_Chi: Configuration flags.
ADDITIVE_COMBINATION - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Fuzzy_Ish: Configuration flag (ADDITIVE_COMBINATION).
addKey(double) - Method in class keel.Algorithms.Rule_Learning.Ripper.Score: Adds a new value to the vector.
addKey(double, int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Score: Adds a new value to the vector.
addKey(double) - Method in class keel.Algorithms.Rule_Learning.Slipper.Score: Adds a new value to the vector.
addKey(double, double, int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Score: Adds a new value to the vector.
addKO(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.matchProfileAgent
addLabel(DataBase) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.FuzzyAntecedent: Adds randomly a label to the label set of this fuzzy antecedent
addLabel(int, DataBase) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Adds a label to the fuzzy antecedent of the given variable
addLabel(DataBase) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Adds randomly a label to the label set of this fuzzy antecedent
addLabel(int, DataBase, int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Adds a label to the fuzzy antecedent of the given variable
addLink(ExpNeuron, LinkedLayer, ILayer<? extends INeuron>, int, int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.ExpNeuronStructuralMutator: Adds a link to a neuron of an specific layer from a specific origin neuron
addLink(N, LinkedLayer, ILayer<? extends INeuron>, int, int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.INeuronStructuralMutator: Adds a link to a neuron of an specific layer from a specific origin neuron
addLink(LinearNeuron, LinkedLayer, ILayer<? extends INeuron>, int, int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.LinearNeuronStructuralMutator: Adds a link to a neuron of an specific layer from a specific origin neuron
addLink(SigmNeuron, LinkedLayer, ILayer<? extends INeuron>, int, int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.SigmNeuronStructuralMutator: Adds a link to a neuron of an specific layer from a specific origin neuron
addMatch(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformanceTraining
addMissingPartition(String, int) - Method in class keel.GraphInterKeel.experiments.DataSet: Adds a missing partition to the vector at a given position
addMul(Prototype, double) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Performs add and product operation between two prototypes.
addMul(Prototype, double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Performs add and product operation between two prototypes.
addNameAttributes(String[]) - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Do a local copy of the name of the input variables
addNameClass(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetRules: Copy the name of the class
addNameClasses(String[]) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetRules: Copy the complete names of the classes
addNegative(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Score: Increases the number of negatives instances in a given vector's position.
addNegative(double) - Method in class keel.Algorithms.Rule_Learning.Ripper.Score: Increases the number of negatives instances of a given values.
addNegative() - Method in class keel.Algorithms.Rule_Learning.Ripper.Trio: Increases the number of negative instances of a given dataset that contains the value.
addNegative(int, double) - Method in class keel.Algorithms.Rule_Learning.Slipper.Score: Increases the number of negatives instances in a given vector's position.
addNegative(double, double) - Method in class keel.Algorithms.Rule_Learning.Slipper.Score: Increases the number of negatives instances of a given values.
addNegative(double) - Method in class keel.Algorithms.Rule_Learning.Slipper.Trio: Increases the number of negative instances of a given dataset that contains the value.
addNeuron(ExpNeuron, LinkedLayer, ILayer<? extends INeuron>, LinkedLayer) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.ExpNeuronStructuralMutator: Adds a neuron into a specific layer
addNeuron(N, LinkedLayer, ILayer<? extends INeuron>, LinkedLayer) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.INeuronStructuralMutator: Adds a neuron into a specific layer
addNeuron(LinearNeuron, LinkedLayer, ILayer<? extends INeuron>, LinkedLayer) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.LinearNeuronStructuralMutator: Adds a neuron into a specific layer
addNeuron(SigmNeuron, LinkedLayer, ILayer<? extends INeuron>, LinkedLayer) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.SigmNeuronStructuralMutator: Adds a neuron into a specific layer
addNeuron(N) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.ILayer: Add a neuron to the layer
addNeuron(InputNeuron) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputLayer: Add a neuron to the layer
addNeuron(LinkedNeuron) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Add a neuron to the layer
addNew(Item) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Itemset: It allows to add an item into an itemset
addNoMatch(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformanceTraining
addNominalGene(int, Attribute, int, ArrayList<String>) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome: Adds a nominal gene to this chromosome (rule)
addNominalValue(String) - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: This method add a new value to the list of possible values in a nominal attribute.
addNominalValue(String) - Method in class keel.Dataset.Attribute: This method add a new value to the list of possible values in a nominal attribute.
addNominalValues(ArrayList<String>) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Gene: add a set of nominal values, so their status are now active
addNoRepetition(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Add the elements of a set.
addOK(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.matchProfileAgent
addParameter(String) - Method in class keel.GraphInterKeel.experiments.UseCase: Sets the method's parameters
addPartitionTab() - Method in class keel.GraphInterKeel.datacf.DataCFFrame: Adds Partition Tab
addPartitionTab(File) - Method in class keel.GraphInterKeel.datacf.DataCFFrame: Adds Partiton Tab
addPosicion(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Adds an attribute to the rule.
addPositive(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Score: Increases the number of positives instances in a given vector's position.
addPositive(double) - Method in class keel.Algorithms.Rule_Learning.Ripper.Score: Increases the number of positives instances of a given values.
addPositive() - Method in class keel.Algorithms.Rule_Learning.Ripper.Trio: Increases the number of positive instances of a given dataset that contains the value.
addPositive(int, double) - Method in class keel.Algorithms.Rule_Learning.Slipper.Score: Increases the number of positives instances in a given vector's position.
addPositive(double, double) - Method in class keel.Algorithms.Rule_Learning.Slipper.Score: Increases the number of positives instances of a given values.
addPositive(double) - Method in class keel.Algorithms.Rule_Learning.Slipper.Trio: Increases the number of positive instances of a given dataset that contains the value.
addPositiveExamples(int, int[], int, matchProfileAgent) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ruleOrderAgent
addPrediction(int, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformance
addPrediction(int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PerformanceAgent: Function used to inform PerformanceAgent of each example classified during the training stage
addPrediction(int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PerformanceAgent: Function used to inform PerformanceAgent of each example classified during the training stage
addPredictionTest(int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PerformanceAgent: Function used to inform PerformanceAgent of each example classified during the test stage
addPredictionTest(int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PerformanceAgent: Function used to inform PerformanceAgent of each example classified during the test stage
addProperty(String, String) - Method in class keel.GraphInterKeel.datacf.util.OptionsDialog: Adds a option to the panel
addPrototype(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Add one prototype to the set..
addPrototype(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Add one prototype to the set..
addPrototype2(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: this + other.
addPrototype2(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: this + other.
addPrototypeNoRepetition(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet
addRange(int, Dataset, int, int) - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Function to add all itemsets in given range to given value.
addRange(int, Dataset, int, int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Function to add all itemsets in given range to given value.
addRange(int, MyDataset, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Function to add all itemsets in given range to given value.
addRange(int, Dataset, int, int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Function to add all itemsets in given range to given value.
addRange(int, Dataset, int, int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Function to add all itemsets in given range to given value.
addRange(int, MyDataset, int, int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Function to add all itemsets in given range to given value.
addRange(int, MyDataset, int, int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Function to add all itemsets in given range to given value.
addRange(int, MyDataset, int, int) - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Function to add all itemsets in given range to given value.
addRange(int, Dataset, int, int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Function to add all itemsets in given range to given value.
addRealBoundedGene(int, Attribute, int, double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome: Adds a real-valued gene, and sets its bounds
addRealGene(int, Attribute, int, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome: Adds a real valued gene
addReference(String) - Method in class keel.GraphInterKeel.experiments.UseCase: Adds a new method's reference
addRegla(Complejo) - Method in class keel.Algorithms.Rule_Learning.Prism.ConjReglas: Add a rule to the list
addRegla(Complejo) - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjReglas: Add a rule to the list
addRegla(Regla) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjReglas: Add a rule to the list
addRegla(Complejo) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjReglas: Add a rule to the list
addRegla(Complex) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetRules: Add a rule to the list
addReglas(ConjReglas) - Method in class keel.Algorithms.Rule_Learning.Prism.ConjReglas: Add a set of rules to the list
addReglas(ConjReglas) - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjReglas: Add a set of rules to the list
addReglas(ConjReglas) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjReglas: Add a set of rules to the list
addReglas(SetRules) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetRules: Add a set of rules to the list
addRelation(Instances) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Adds a relation to a relation-valued attribute.
addRelation(Instances) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Adds a relation to a relation-valued attribute.
addReplace(Replace) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: Function to add a replace entry in the "Replace" list.
addReplace(Replace) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: Function to add a replace entry in the "Replace" list
addRow(int, double[]) - Method in class keel.GraphInterKeel.statistical.tests.Distribution2KeyTable: Add a row to the table
addRule(Cochromosome) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Copopulation: Adds a cochromsome to this population (cannot be latter deleted!)
addRule(int, Chromosome) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Population: Adds a rule to the specified subpopulation
addRule(Chromosome) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Subpopulation: Adds a rule to this subpopulation
addRule(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Ruleset: Adds a new rule to the ruleset.
addRule(int, matchProfileAgent, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ruleOrderAgent
addRule(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Ruleset: Adds a new rule to the ruleset.
addRule(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.ruleSet: It add a new rule to the set
addRule(Complex) - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleSet: Add a rule to the list
addRule(Complex) - Method in class keel.Algorithms.Rule_Learning.AQ.ruleSet: It adds one rule to the list
addRule(Rule) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Ruleset: Adds a new rule to the ruleset.
addRule(Rule) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset: Adds a new rule to the ruleset.
addRule(Complex) - Method in class keel.Algorithms.Rule_Learning.CN2.ruleSet: It adds one rule to the list
addRule(Rule) - Method in class keel.Algorithms.Rule_Learning.PART.Ruleset: Adds a new rule to the ruleset.
addRule(Rule) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset: Adds a new rule to the ruleset.
addRule(Rule) - Method in class keel.Algorithms.Rule_Learning.Slipper.Ruleset: Adds a new rule to the ruleset.
addRules(long[], FuzzyRule[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase: Adds new rules to BaseRule.
addRules(RuleSet) - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleSet: Add a set of rules to the list
addRules(ruleSet) - Method in class keel.Algorithms.Rule_Learning.CN2.ruleSet: It adds a whole rule set to the list
addSeed(String) - Method in class keel.GraphInterKeel.experiments.Parameters: adds seed
addSelector(Selector) - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Add the selector to the list
addSelector(Selector) - Method in class keel.Algorithms.Rule_Learning.AQ.Complex: Id adds the selector if it is not in the list
addSelector(Selector) - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: Id adds the selector if it is not in the list
addSelector(Selector) - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Add the selector to the list
addSelector(Selector) - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Add the selector into the selector list
addSelector(Selector) - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Add the selector into the selector list
addSelector(Selector) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Add the selector into the selector list
addSelector(Selector) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Add the selector to the complex
addStringValue(String) - Method in class keel.Algorithms.Decision_Trees.M5.M5Attribute: Adds a string value to the list of valid strings for attributes of type STRING and returns the index of the string.
addStringValue(M5Attribute, int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Attribute: Adds a string value to the list of valid strings for attributes of type STRING and returns the index of the string.
addStringValue(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Adds a string value to the list of valid strings for attributes of type STRING and returns the index of the string.
addStringValue(AttributeWeka, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Adds a string value to the list of valid strings for attributes of type STRING and returns the index of the string.
addStringValue(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Adds a string value to the list of valid strings for attributes of type STRING and returns the index of the string.
addStringValue(Attribute, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Adds a string value to the list of valid strings for attributes of type STRING and returns the index of the string.
addSupportToTtreeLevelN(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree: Continues process of adding support alues to further levels of the T-tree (not the top level) by stepping through the Ptree table from the current required level upto the maximum level that may be contained in the table.
addTestNominalValue(String) - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: Adds a new value for a nominal that has been read in the test file.
addTestNominalValue(String) - Method in class keel.Dataset.Attribute: Adds a new value for a nominal that has been read in the test file.
addToA(Prototype) - Method in class keel.Algorithms.Instance_Generation.PNN.MatrixOfDistances: Add a new prototype to the matrix of distances (subset A).
addToB(Prototype) - Method in class keel.Algorithms.Instance_Generation.PNN.MatrixOfDistances: Add a new prototype to the matrix of distances (subset B).
addToCoveredInstances(int) - Method in class keel.Algorithms.Discretizers.MVD.Interval: Adds the index of the instance to the list of covered instances.
addToDefaultCr(double) - Method in class keel.Algorithms.Rule_Learning.Slipper.Ruleset: Adds a given value to the confidence of the default rule.
addToFile(String, String) - Static method in class keel.GraphInterKeel.experiments.Files: Appends a text to a file
addToFile(String, String) - Static method in class keel.GraphInterKeel.statistical.Files: Adds data in the file, avoiding overwrite previous content
addToFile(String, String) - Static method in class org.core.Files: Adds data in the file, avoiding overwrite previous content
AddtoMyFile(String, String) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyFile: Function for adding a String Object to a file
addToPtree(int, int, int, PtreeNode, short[], int, PtreeNode) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree: Inserts given itemset into P-tree.
addToReport(String) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.ReportTool: Adds external information to the report
addToTtree(short[], int) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TotalSupportTree: Commences process of adding an itemset (with its support value) to a T-tree when using a T-tree either as a storage mechanism, or when adding to an existing T-tree.
addToTtree(TtreeNode[], int, int, short[], int) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TotalSupportTree: Inserts a node into a T-tree.
addToTtree(short[], int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TotalSupportTree: Commences process of adding an itemset (with its support value) to a T-tree when using a T-tree either as a storage mechanism, or when adding to an existing T-tree.
addToTtree(TtreeNode[], int, int, short[], int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TotalSupportTree: Inserts a node into a T-tree.
addU(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Organizacion
addUsefulTimes(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Adds the value passed to the usefulTimes parameter of the classifier
addUsefulTimes(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Adds the given value to the usefulTimes parameter of the classifier
addValoresInv(Integer, Double) - Method in class keel.Algorithms.Rule_Learning.Rules6.Regla: Adds an attribute with its value to the invalid values list.
addValoresInv(Integer, Double) - Method in class keel.Algorithms.Rule_Learning.SRI.Regla: Adds an attribute with its value to the invalid values list.
addValue(double, double) - Method in class keel.Algorithms.Decision_Trees.M5.M5Kernel: Add a new data value to the current estimator.
addValue(Object) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.CategoricalAttribute: Adds a new value
addValue(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.IntegerSet: It adds a value to the set if it is not present
addValue(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
addValue(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
addValue(int, int, double) - Method in class keel.GraphInterKeel.statistical.tests.Distribution2KeyTable: Modifies a value in the table
addValueNClass(double, double) - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.VAList: Adds an attribute value with its associated class to this list.
addValueNClass(double, double) - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.VAList: Adds an attribute value with its associated class to this list.
addValueNClass(double, double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.VAList: Adds an attribute value with its associated class to this list.
addValueNClass(double, double) - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.VAList: Adds an attribute value with its associated class to this list.
addValueNClass(double, double) - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.VAList: Adds an attribute value with its associated class to this list.
addValueNClass(double, double) - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.VAList: Adds an attribute value with its associated class to this list.
addValueNClass(double, double) - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.VAList: Adds an attribute value with its associated class to this list.
addValueNClass(double, double) - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.VAList: Adds an attribute value with its associated class to this list.
addValuesNames(String[][]) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Rule: It stores the name for the different values in the data-set
addVar(DataBase) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Adds a new variable to the fuzzy antecedent set of this rule
addVar(DataBase, int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Adds a new variable to the fuzzy antecedent set of this rule
addVisualizeTab() - Method in class keel.GraphInterKeel.datacf.DataCFFrame: Adds Visualize Tab
addWeights(Itemset, double[]) - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Funtion to add the given itemset to all values weighting it according to given weights.
addWeights(Itemset, double[]) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Funtion to add the given itemset to all values weighting it according to given weights.
addWeights(Itemset, double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Funtion to add the given itemset to all values weighting it according to given weights.
addWeights(Itemset, double[]) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Funtion to add the given itemset to all values weighting it according to given weights.
addWeights(Itemset, double[]) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Funtion to add the given itemset to all values weighting it according to given weights.
addWeights(Itemset, double[]) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Funtion to add the given itemset to all values weighting it according to given weights.
addWeights(Itemset, double[]) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Funtion to add the given itemset to all values weighting it according to given weights.
addWeights(Itemset, double[]) - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Funtion to add the given itemset to all values weighting it according to given weights.
addWeights(Itemset, double[]) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Funtion to add the given itemset to all values weighting it according to given weights.
addWithUnknownValue(Dataset, int) - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Function to add all itemsets with unknown values for given attribute.
addWithUnknownValue(Dataset, int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Function to add all itemsets with unknown values for given attribute.
addWithUnknownValue(MyDataset, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Function to add all itemsets with unknown values for given attribute.
addWithUnknownValue(Dataset, int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Function to add all itemsets with unknown values for given attribute.
addWithUnknownValue(Dataset, int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Function to add all itemsets with unknown values for given attribute.
addWithUnknownValue(MyDataset, int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Function to add all itemsets with unknown values for given attribute.
addWithUnknownValue(MyDataset, int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Function to add all itemsets with unknown values for given attribute.
addWithUnknownValue(MyDataset, int) - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Function to add all itemsets with unknown values for given attribute.
addWithUnknownValue(Dataset, int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Function to add all itemsets with unknown values for given attribute.
ADE_CoForestAlgorithm - Class in keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest: ADE_CoForest algorithm calling.
ADE_CoForestAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ADE_CoForestAlgorithm
ADE_CoForestGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest: This class implements the Tri-training.
ADE_CoForestGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ADE_CoForestGenerator: Build a new ADE_CoForestGenerator Algorithm
ADE_CoForestGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ADE_CoForestGenerator: Build a new ADE_CoForestGenerator Algorithm
adiKR - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
adiKR - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
adjuntaNombreAtributos(String[]) - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Do a local copy of the name of the in-put variables
adjuntaNombreAtributos(String[]) - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Local copy of the name of variables
adjuntaNombreAtributos(String[]) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Local copy of the name of variables
adjuntaNombreAtributos(String[]) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Local copy of the name of variables
adjuntaNombreAtributos(String[]) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Copy the name of the attributes
adjuntaNombreClase(String) - Method in class keel.Algorithms.Rule_Learning.Prism.ConjReglas: Do a clocal copy of the name of the class variable
adjuntaNombreClase(String) - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjReglas: Do a clocal copy of the name of the class variable
adjuntaNombreClase(String) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjReglas: Do a clocal copy of the name of the class variable
adjuntaNombreClase(String) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjReglas: Do a clocal copy of the name of the class variable
adjuntaNombreClases(String[]) - Method in class keel.Algorithms.Rule_Learning.Prism.ConjReglas: Do a local copy of the name of the values of the class
adjuntaNombreClases(String[]) - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjReglas: Do a local copy of the name of the values of the class
adjuntaNombreClases(String[]) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjReglas: Do a local copy of the name of the values of the class
adjuntaNombreClases(String[]) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjReglas: Do a local copy of the name of the values of the class
adjuntaNombres(String[]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Selector: Sets the attributes names inside the Selector.
adjuntaNombres(String[]) - Method in class keel.Algorithms.Decision_Trees.DT_GA.Selector: Sets the attributes names with the given ones.
adjuntaNombres(String[]) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Selector: Sets the attributes names inside the Selector.
adjuntClassName(String) - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleSet: Do a clocal copy of the name of the class variable
adjuntClassNames(String[]) - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleSet: Do a local copy of the name of the values of the class
adjuntNameAttributes(String[]) - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Local copy of the name of variables
adjustBeginLineColumn(int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream: Method to adjust line and column numbers for the start of a token.
adjustBeginLineColumn(int, int) - Static method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream: Method to adjust line and column numbers for the start of a token.
adjustBeginLineColumn(int, int) - Static method in class keel.Dataset.SimpleCharStream: Method to adjust line and column numbers for the start of a token.
adjustDistrib() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Convert the distribution between 0 and 1
adjustDistrib() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.AssociationRule: Convert the distribution between 0 and 1
adjustFeatureWeights(double[], int) - Method in class keel.Algorithms.Hyperrectangles.EACH.HyperrectangleSet: Adjust the weights of the attributes
adjustFitness() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
adjustWeight(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Regla: It modifies the rule weight adding a given quantity
adjustWeightFailure() - Method in class keel.Algorithms.Hyperrectangles.EACH.Hyperrectangle: Adjust the weight of the hyperrectangle by a miss
adjustWeightSuccess() - Method in class keel.Algorithms.Hyperrectangles.EACH.Hyperrectangle: Adjust the weight of the hyperrectangle by a goal
AdministrativeStaff - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams
AdministrativeStaff() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.AdministrativeStaff
ADOMS - Class in keel.Algorithms.ImbalancedClassification.Resampling.ADOMS: File: ADOMS.java The ADOMS algorithm is an oversampling method used to deal with the imbalanced problem.
ADOMS(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.ADOMS.ADOMS: Constructor of the class.
adress - Variable in class keel.GraphInterKeel.help.HelpSheet: URL (address of the sheet).
afuzzy(String) - Static method in class keel.Algorithms.LQD.tests.IntermediateBoost.fun_aux: Convert the number in fuzzy
agentPerformance - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
agentPerformance(int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformance
agentPerformanceTraining - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
agentPerformanceTraining(int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformanceTraining
Agrega(double[], double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
agregaDatos(Node, Node) - Method in class keel.Algorithms.Decision_Trees.SLIQ.SLIQ: Aggregates the data of the given child that will be pruned into the father given.
agregaElemento(int) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Adds an element of the class given to the node.
AHCClustering - Class in keel.Algorithms.ImbalancedClassification.Resampling.AHCClustering: File: AHCClustering.java The AHCClustering algorithm is an oversampling method used to deal with the imbalanced problem.
AHCClustering(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.AHCClustering.AHCClustering: Constructor of the class.
ajusta(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseD
ajusta(Individuo) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseR
ajusta(double[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseD
ajusta(int[]) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjReglas: Do a local copy of the values of the rules.
ajusta(int[]) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Local copy of the values of the attributes of the rule.
ajustaDistrib() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Normalizes the distribution values [0, 1]
ajustaDistribucion(ConjDatos) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.EvaluaCalidadReglas: Updates the dataset distribution.
AjustColumnas(int[][]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Arrays
AjustColumnasD(double[][]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Arrays
AjustFilas(int[][]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Arrays
AjustFilasD(double[][]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Arrays
AjustVector(int[]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Arrays
AjustVector(double[]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Arrays
Alatasetal - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal: It gathers all the parameters, launches the algorithm, and prints out the results
Alatasetal() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Alatasetal: Default constructor
Alatasetal(parseParameters) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Alatasetal: It reads the data from the input files and parse all the parameters from the parameters array
AlatasetalProcess - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal: It provides the implementation of the algorithm to be run in a process
AlatasetalProcess(myDataset, int, int, int, int, double, double, double, double, double, double, double, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AlatasetalProcess: It creates a new process for the algorithm by setting up its parameters
Alcalaetal - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal: It gathers all the parameters, launches the algorithm, and prints out the results
Alcalaetal() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Alcalaetal: Default constructor
Alcalaetal(parseParameters) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Alcalaetal: It reads the data from the input files and parse all the parameters from the parameters array.
AlcalaetalProcess - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal: It provides the implementation of the algorithm to be run in a process
AlcalaetalProcess(myDataset, int, int, int, double, double, int, boolean, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.AlcalaetalProcess: It creates a new process for the algorithm by setting up its parameters
ALFA - Variable in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
AlgGenetic - Class in keel.Algorithms.LQD.methods.FGFS_costInstances: File: AlgGenetic.java This genetic algorithm obtains fuzzy rule from imprecise or low quality using a cost-sensitive learning based in a cost matrix defined by linguistic terms or interval.
AlgGenetic(int, float, float, Vector<fuzzyPartition>, int, fuzzy[][], Vector<Vector<Float>>, Vector<fuzzy>, int, int, int, String, int, String, String, Vector<Float>, Vector<Vector<fuzzy>>, int, int, int, int, String, int, String, String) - Constructor for class keel.Algorithms.LQD.methods.FGFS_costInstances.AlgGenetic
AlgGenetic - Class in keel.Algorithms.LQD.methods.FGFS_Minimum_Risk: File: AlgGenetic.java This genetic algorithm obtains fuzzy rule from imprecise or low quality using a cost-sensitive learning based in a cost matrix defined by linguistic terms or interval.
AlgGenetic(int, float, float, Vector<fuzzyPartition>, int, fuzzy[][], Vector<Vector<Float>>, Vector<Float>, int, int, int, String, int, String, String, Vector<Float>, Vector<Vector<fuzzy>>, int, int, int, int, String, int, String, String) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.AlgGenetic
AlgGenetic - Class in keel.Algorithms.LQD.methods.FGFS_Original: File: AlgGenetic.java This genetic algorithm obtains fuzzy rule from imprecise or low quality
AlgGenetic(int, float, float, Vector<partition>, int, Interval[][], Vector<Vector<Float>>, int, int, String, int, String, String, Vector<Float>, int, int, int, int, String) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Original.AlgGenetic
AlgGenetic - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight: File: AlgGenetic.java This genetic algorithm obtains fuzzy rule from imprecise or low quality.
AlgGenetic(int, float, float, Vector<partition>, int, fuzzy[][], Vector<Vector<Float>>, int, int, String, int, String, String, Vector<Float>, int, int, int, int, int, String, String) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.AlgGenetic
AlgGenetic - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty: File: AlgGenetic.java This genetic algorithm obtains fuzzy rule from imprecise or low quality.
AlgGenetic(int, float, float, Vector<partition>, int, fuzzy[][], Vector<Vector<Float>>, int, int, String, int, String, String, Vector<Float>, int, int, int, int, int, String, String) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.AlgGenetic
algoritEvol(int, int, double, Vector, Vector, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion: Implements Evolutionary Learning
Algorithm - Class in keel.Algorithms.Decision_Trees.C45: This is the main class
Algorithm() - Constructor for class keel.Algorithms.Decision_Trees.C45.Algorithm
Algorithm - Class in keel.Algorithms.Decision_Trees.DT_GA.C45: This class implements the interface Algorithm
Algorithm() - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm
Algorithm - Class in keel.Algorithms.Decision_Trees.ID3: Abstract class that implements the Algorithm interface
Algorithm() - Constructor for class keel.Algorithms.Decision_Trees.ID3.Algorithm
Algorithm - Class in keel.Algorithms.Decision_Trees.SLIQ: Abstract class of the algorithm implemented.
Algorithm() - Constructor for class keel.Algorithms.Decision_Trees.SLIQ.Algorithm
Algorithm - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99: It contains the implementation of the algorithm
Algorithm() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen: It contains the implementation of the algorithm
Algorithm() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE: It contains the implementation of the algorithm.
Algorithm() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.Genetic_Rule_Learning.LogenPro: It contains the implementation of the algorithm
Algorithm() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.Genetic_Rule_Learning.PART: Abstract class implementing the functions of a Algorithm
Algorithm() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm
ALGORITHM - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
Algorithm - Class in keel.Algorithms.ImbalancedClassification.CSMethods.C45CS: Abstract class for the CS methods
Algorithm() - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm
Algorithm - Class in keel.Algorithms.ImbalancedClassification.Ensembles.C45: The abstract class to be implemented by C45 based algorithms
Algorithm() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm
algorithm - Variable in class keel.Algorithms.MIL.Diverse_Density.Optimization.DDoptimization
algorithm - Variable in class keel.Algorithms.MIL.Diverse_Density.Optimization.EMDDoptimization
algorithm - Static variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.KEELIRPropPlusWrapperClas: Wrapped algorithm
algorithm - Static variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.KEELIRPropPlusWrapperRegr: Wrapped algorithm
algorithm - Static variable in class keel.Algorithms.Neural_Networks.NNEP_Clas.KEELWrapperClas: Wrapped algorithm
algorithm - Static variable in class keel.Algorithms.Neural_Networks.NNEP_Regr.KEELWrapperRegr: Wrapped algorithm
Algorithm - Class in keel.Algorithms.RE_SL_Methods.P_FCS1: It contains the implementation of the algorithm
Algorithm() - Constructor for class keel.Algorithms.RE_SL_Methods.P_FCS1.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.RE_SL_Methods.P_FCS1.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.RE_SL_Methods.SEFC: It contains the implementation of the algorithm
Algorithm() - Constructor for class keel.Algorithms.RE_SL_Methods.SEFC.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.RE_SL_Methods.SEFC.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs: Title: Algorithm Description: It contains the implementation of the algorithm Company: KEEL
Algorithm() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs: Title: Algorithm Description: It contains the implementation of the algorithm Company: KEEL
Algorithm() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs: Title: Algorithm Description: It contains the implementation of the algorithm Company: KEEL
Algorithm() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs: Title: Algorithm Description: It contains the implementation of the algorithm Company: KEEL
Algorithm() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs: Title: Algorithm Description: It contains the implementation of the algorithm Company: KEEL
Algorithm() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs: Title: Algorithm Description: It contains the implementation of the algorithm Company: KEEL
Algorithm() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs: Title: Algorithm Description: It contains the implementation of the algorithm Company: KEEL
Algorithm() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs: Title: Algorithm Description: It contains the implementation of the algorithm Company: KEEL
Algorithm() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.Rule_Learning.ART: Abstract class that contains the basic structures to implements Rule learining algorithms.
Algorithm() - Constructor for class keel.Algorithms.Rule_Learning.ART.Algorithm
Algorithm - Class in keel.Algorithms.Rule_Learning.C45Rules: Abstract class that contains the basic structures to implements Rule learining algorithms.
Algorithm() - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.Algorithm
Algorithm - Class in keel.Algorithms.Rule_Learning.C45RulesSA: Abstract class that contains the basic structures to implements Rule learining algorithms.
Algorithm() - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.Algorithm
Algorithm - Class in keel.Algorithms.Rule_Learning.DataSqueezer: Abstract class that contains the basic structures to implements Rule learining algorithms.
Algorithm() - Constructor for class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm
Algorithm - Class in keel.Algorithms.Rule_Learning.LEM1: Title: Algorithm Description: It contains the implementation of the algorithm Company: KEEL
Algorithm() - Constructor for class keel.Algorithms.Rule_Learning.LEM1.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.Rule_Learning.LEM1.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.Rule_Learning.LEM2: Title: Algorithm Description: It contains the implementation of the algorithm Company: KEEL
Algorithm() - Constructor for class keel.Algorithms.Rule_Learning.LEM2.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.Rule_Learning.LEM2.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.Rule_Learning.PART: Abstract class that contains the basic structures to implements Rule learining algorithms.
Algorithm() - Constructor for class keel.Algorithms.Rule_Learning.PART.Algorithm
Algorithm - Class in keel.Algorithms.Rule_Learning.Ritio: Title: Algorithm Description: It contains the implementation of the algorithm Company: KEEL
Algorithm() - Constructor for class keel.Algorithms.Rule_Learning.Ritio.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.Rule_Learning.Ritio.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.Rule_Learning.Rules6: Title: Algorithm Description: It contains the implementation of the algorithm Company: KEEL
Algorithm() - Constructor for class keel.Algorithms.Rule_Learning.Rules6.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.Rule_Learning.Rules6.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.Rule_Learning.SRI: Title: Algorithm Description: It contains the implementation of the algorithm Company: KEEL
Algorithm() - Constructor for class keel.Algorithms.Rule_Learning.SRI.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.Rule_Learning.SRI.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.Algorithms.Statistical_Classifiers.Naive_Bayes: Title: Algorithm Description: It contains the implementation of the algorithm Naive-Bayes Company: KEEL
Algorithm() - Constructor for class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.Algorithm: Default constructor
Algorithm(parseParameters) - Constructor for class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.Algorithm: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Algorithm - Class in keel.GraphInterKeel.experiments
Algorithm() - Constructor for class keel.GraphInterKeel.experiments.Algorithm: Builder
Algorithm(ExternalObjectDescription, Point, GraphPanel) - Constructor for class keel.GraphInterKeel.experiments.Algorithm: Builder
Algorithm(ExternalObjectDescription, Point, GraphPanel, Vector, int, int, Vector<Joint>) - Constructor for class keel.GraphInterKeel.experiments.Algorithm: Builder
algorithmFinished(<any>, ProblemEvaluator) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlusReporterClas: This method is called when the algorithm has finished its execution.
algorithmFinished(<any>, ProblemEvaluator) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.IRPropPlusReporterRegr: This method is called when the algorithm has finished its execution.
algorithmFinished(<any>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.listener.NeuralNetReporterClas: This event is fired when the algorithm has finished its execution.
algorithmFinished(<any>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.listener.NeuralNetReporterRegr: This event is fired when the algorithm has finished its execution.
AlgorithmGAPGen - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms: AlgorithmGAPGen is the genetic algorithm and programming (GAP) algorithm when the generational option is chosen, that is, the Steady parameter of the given method is not marked.
AlgorithmGAPGen(GeneticIndividual, int, int, double, double, double, double, double, double, int, int, Randomize, int, int, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms.AlgorithmGAPGen: Class constructor with the following parameters:
AlgorithmGAPNiches - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms
AlgorithmGAPNiches(GeneticIndividual, int, int, double, double, double, double, double, int, double, int, int, Randomize, int, int, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms.AlgorithmGAPNiches: Class constructor with the following parameters:
AlgorithmGAPSteady - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms: AlgorithmGAPSteady is the genetic algorithm and programming (GAP) algorithm when the Steady option is chosen, that is, the Steady parameter of the given method is marked but the Niches is not.
AlgorithmGAPSteady(GeneticIndividual, int, int, double, double, double, double, double, int, double, int, int, Randomize, int, int, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms.AlgorithmGAPSteady: Class constructor with the following parameters:
algorithmName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
algorithmName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
algorithmName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
algorithmName - Variable in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Name of the reduction tecnique
algorithmName - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters: Algorithm name.
algorithmName - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Parameters: Algorithm name.
algorithmName - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters: Algorithm name.
algorithmName - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters: Algorithm name.
algorithmName - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Parameters: Algorithm name.
algorithmName - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters: Algorithm name.
algorithmName - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Parameters: Algorithm name.
algorithmName - Static variable in class keel.Algorithms.Rule_Learning.Swap1.Parameters
algorithmName - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Name of the reduction tecnique
algorithmStarted(<any>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.listener.NeuralNetReporterClas: This event is fired when the algorithm has started its execution.
algorithmStarted(<any>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.listener.NeuralNetReporterRegr: This event is fired when the algorithm has started its execution.
AlgorithmXML - Class in keel.GraphInterKeel.experiments
AlgorithmXML(Element) - Constructor for class keel.GraphInterKeel.experiments.AlgorithmXML: Builder
allInstances - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper
allInstances - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper
AllKNN - Class in keel.Algorithms.Instance_Selection.AllKNN: File: AllKNN.java The AllKNN Instance Selection algorithm.
AllKNN(String) - Constructor for class keel.Algorithms.Instance_Selection.AllKNN.AllKNN: Default constructor.
AllKNN - Class in keel.Algorithms.Preprocess.Instance_Selection.AllKNN: File: AllKNN.java The AllKNN Instance Selection algorithm.
AllKNN(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.AllKNN.AllKNN: Default constructor.
allowUnclassifiedInstancesTipText() - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Returns the tip text for this property
allowUnclassifiedInstancesTipText() - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Returns the tip text for this property
allowUnclassifiedInstancesTipText() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Returns the tip text for this property
AllPossibleValues - Class in keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues: This class computes all the possible values found in the data set for a given missing value
AllPossibleValues(String) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.AllPossibleValues: Creates a new instance of AllPossibleValues
AlmacenaParametros(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
AlmacenaParametros(double[]) - Method in class keel.Algorithms.Statistical_Classifiers.Shared.DiscrAnalysis.AD: Stores the parameters of the LDA or QDA in the array given.
almostSame(Complex) - Method in class keel.Algorithms.Rule_Learning.AQ.Complex: It checks if a complex is almost the same to another
almostSame(Complex) - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: It checks if a complex is almost the same to another
alnorm(double, int) - Static method in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Quoted from original Fortran documentation: Evaluates the tail area of the standardised normal curve from x to infinity if upper is true or from minus infinity to x if upper is false.
alpha - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoostingMaxMin
alpha - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
alpha - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Parameter of accuracy function (the fall of rate in the fitness evaluation).
alpha - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Parameter of acurcy function (the fall of rate in the fitness evaluation).
ALPHA - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
alpha - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters: Problem coefficients
alpha - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters: Problem coefficients
alpha - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters: Problem coefficients
alpha - Variable in class keel.Algorithms.Neural_Networks.net.Parameters: Problem coefficients
alpha_0 - Variable in class keel.Algorithms.Instance_Generation.HYB.HYBGenerator: Alpha0 parameter of the LVQ3 methods.
alpha_0 - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQ1: Alpha parameter.
alpha_0 - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQPRU: Alpha constant of the internal LVQ2.1 mapping.
ALPHA_DEFAULT_VALUE - Static variable in class keel.Algorithms.Instance_Generation.LVQ.LVQ1: Default alpha_0 learning parameter of the algorithm LVQ1
alphaControlParametersUpdate(double, double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Updates alpha control parameters at the end of each neuron mutation, if neccesary
alphaControlParametersUpdate(double, double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricSAMutator: Updates alpha control parameters at the end of each neuron mutation, if neccesary
alphaControlParametersUpdate(double, double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricSRMutator: Updates alpha control parameters at the end of each neuron mutation, if neccesary
alphaCut(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut: Returns the alpha-cut interval (le, ri) for alpha a.
alphaInit() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Init the values of alpha parameters used in the mutations
alphaInit() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricSAMutator: Init the values of alpha parameters used in the mutations
alphaInit() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricSRMutator: Init the values of alpha parameters used in the mutations
alphaInput - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Current alpha coeficient for the input weigths
alphaLEQZero - Variable in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
alphaOutput - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Current alpha coeficient for the output weigths
alphaUpdate(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Updates the values of alpha parameters at the beginning of a generation
alphaUpdate(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricSAMutator: Updates the values of alpha parameters used in the mutations
alphaUpdate(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricSRMutator: Updates the values of alpha parameters used in the mutations
alreadyUsedToDecompose(Node, int, int) - Method in class keel.Algorithms.Decision_Trees.ID3.ID3: Function to check if the specified attribute and value are already used to decompose the data.
Ameba - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal: Main class of the Ameba algorithm.
Ameba() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.Ameba
AMEBA - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OperatorIdent: Identifier for local optimization (AMEBA).
AMEBA - Static variable in class keel.Algorithms.Shared.Parsing.OperatorIdent: Identifier for local optimization (AMEBA).
AmevaDiscretizer - Class in keel.Algorithms.Discretizers.Ameva_Discretizer: This is the class with the operations of the Ameva discretization.
AmevaDiscretizer() - Constructor for class keel.Algorithms.Discretizers.Ameva_Discretizer.AmevaDiscretizer
amevaR - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
amplitude - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ExpNeuronParametricMutator: Amplitude coefficient for allowed weights
amplitude - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.LinearNeuronParametricMutator: Amplitude coefficient for allowed weights
amplitude - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Amplitude coefficient for allowed weights
amplitude - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.SigmNeuronParametricMutator: Amplitude coefficient for allowed weights
AMPSOAlgorithm - Class in keel.Algorithms.Instance_Generation.AMPSO: PSO algorithm calling.
AMPSOAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.AMPSO.AMPSOAlgorithm
AMPSOGenerator - Class in keel.Algorithms.Instance_Generation.AMPSO: AMPSOGenerator.java
AMPSOGenerator(PrototypeSet, int, int, int, double, double, double, double, double, double, double, double) - Constructor for class keel.Algorithms.Instance_Generation.AMPSO.AMPSOGenerator: Build a new PSOGenerator Algorithm
AMPSOGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.AMPSO.AMPSOGenerator: Build a new PSOGenerator Algorithm
anadirReglas(LinkedList<LinkedList<Atributo_valor>>, int) - Method in class keel.Algorithms.Rule_Learning.LEM2.BaseReglas
anadirReglas(TreeMap<Integer, LinkedList<Integer>>) - Method in class keel.Algorithms.Rule_Learning.Ritio.BaseReglas
AnadirtoFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Methods.LEL_TSK.Fichero: Function for adding a String Object to a file
AnadirtoFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Methods.MamWM.Fichero: Function for adding a String Object to a file
AnadirtoFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Methods.mogulIRL.Fichero: Function for adding a String Object to a file
AnadirtoFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.Fichero: Function for adding a String Object to a file
AnadirtoFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.Fichero: Function for adding a String Object to a file
AnadirtoFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.Fichero: Function for adding a String Object to a file
AnadirtoFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamSelect.Fichero: Function for adding a String Object to a file
AnadirtoFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamWSelect.Fichero: Function for adding a String Object to a file
AnadirtoFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamWTuning.Fichero: Function for adding a String Object to a file
AnadirtoFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.Fichero: Function for adding a String Object to a file
AnadirtoFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Postprocess.TSKSelect.Fichero: Function for adding a String Object to a file
AnadirtoFichero(String, String) - Static method in class org.core.Fichero
and(Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask: Returns the mask that it's the outcome of the bolean operation 'and' between this and a given mask.
and(Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Returns the mask that it's the outcome of the bolean operation 'and' between this and a given mask.
and(IncrementalMask) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Returns the mask that it's the outcome of the bolean operation 'and' between this and a given mask.
and(Mask) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask: Returns the mask that it's the outcome of the bolean operation 'and' between this and a given mask.
and(IncrementalMask) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask: Returns the mask that it's the outcome of the bolean operation 'and' between this and a given mask.
and(Mask) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Returns the mask that it's the outcome of the bolean operation 'and' between this and a given mask.
and(IncrementalMask) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Returns the mask that it's the outcome of the bolean operation 'and' between this and a given mask.
and(Mask) - Method in class keel.Algorithms.Rule_Learning.PART.Mask: Returns the mask that it's the outcome of the bolean operation 'and' between this and a given mask.
and(IncrementalMask) - Method in class keel.Algorithms.Rule_Learning.PART.Mask: Returns the mask that it's the outcome of the bolean operation 'and' between this and a given mask.
and(Mask) - Method in class keel.Algorithms.Rule_Learning.Ripper.Mask: Returns the mask that it's the outcome of the bolean operation 'and' between this and a given mask.
and(Mask) - Method in class keel.Algorithms.Rule_Learning.Slipper.Mask: Returns the mask that it's the outcome of the bolean operation 'and' between this and a given mask.
AND_operator(int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary: Given the individuals "indiv1" and "indiv2", it selects two points and makes the AND operation between their central zones.
AND_operator(int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary: Given the individuals "indiv1" and "indiv2", it selects two points and makes the AND operation between their central zones.
AND_OR_Stationary(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary: Given the individuals "indiv1" and "indiv2", it selects two points and makes the AND/OR operation between their central zones.
AND_OR_Stationary(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal: Given the individuals "indiv1" and "indiv2", it selects two points and makes the AND/OR operation between their central zones.
ANORMAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Organizacion
Antd - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: The single antecedent in the rule, which is composed of an attribute and the corresponding value.
Antd(AttributeWeka) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Antd: Constructor
antecedent - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList.RuleNode: Antecedent of AR.
antecedent - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList.RuleNodeCMAR: Antecedent of AR.
Antecedent() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
Antecedent() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
Antecedent() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
antecedent - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining.RuleNode: Antecedent of AR.
ANTECEDENT - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene
antecedent - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining.RuleNode: Antecedent of AR.
ANTECEDENT - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene
antecedent - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule: It is used for representing and handling an Association Rule.
ANTECEDENT - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene
antecedent - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule: It is used for representing and handling an Association Rule.
ANTECEDENT - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene
antecedent - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule: It is used for representing and handling an Association Rule.
ANTECEDENT - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene
antecedente - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyRule
antiRadius(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Returns the smallest distance between center and other prototype and that prototype.
antiRadius(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Returns the smallest distance between center and other prototype and that prototype.
antsSupport - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
antsSupport - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
antsSupport - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
antSupport - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
antSupport - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
antSupport - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
ANY - Static variable in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Condition: Tag (ANY).
aplicaMejorCorte(Node) - Method in class keel.Algorithms.Decision_Trees.SLIQ.SLIQ: Método que aplica en un nodo el mejor corte obtenido previamente Apply the best cut fouded to divide the given node.
aplicaMejorCorteContinuo(Node) - Method in class keel.Algorithms.Decision_Trees.SLIQ.SLIQ: Divides the node in the more optimal way possible (Continuous attribute).
aplicaMejorCorteDiscreto(Node) - Method in class keel.Algorithms.Decision_Trees.SLIQ.SLIQ: Divides the node in the more optimal way possible (Discrete attribute).
append(short[], short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Concatinates two itemSets --- resizes given array so that its length is increased by size of second array and second array added.
Append(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.vectordouble: Add the "x" value to the vector
append(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Rule: Adds all the antecedents attributes of the rule s to this rule (to perform the IGA)
append(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Rule: Adds all the antecedents attributes of the rule s to this rule (to perform the IGA)
append(String, String) - Static method in class keel.Algorithms.Instance_Generation.utilities.KeelFile: Append text to a Keel-style file.
append(String, String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KeelFile: Append text to a Keel-style file.
appendElements(M5Vector) - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector: Appends all elements of the supplied vector to this vector.
appendElements(FastVector) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector: Appends all elements of the supplied vector to this vector.
appendElements(FastVector) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector: Appends all elements of the supplied vector to this vector.
appendElements(FastVector) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector: Appends all elements of the supplied vector to this vector.
appendElements(FastVector) - Method in class keel.Algorithms.SVM.SMO.core.FastVector: Appends all elements of the supplied vector to this vector.
appendResults(String, String[], double[], double[], double[][][], double[][], double[]) - Static method in class keel.Algorithms.Statistical_Tests.Shared.outputFile: This method appends the results of an experiment to a file
applicableDropping(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual: It check it is possible to apply the dropping condition operator, which only can be applied if the number of "ANY" condition is the tree is less than number of input variables minus one.
Application1 - Class in keel.RunKeelGraph: File: Application1.java Application to process the execution of a experiment.
Application1() - Constructor for class keel.RunKeelGraph.Application1: Default builder
apply(MyDataset, Mask, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule: Returns the number of the instances covered by the rule in a given dataset.
apply(MyDataset, Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule: Returns the number of the instances covered by the rule in a given dataset
apply(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule: It returns the number of the instances covered by the rule in a given dataset
apply(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule: It returns the number of true positives,true negatives,false positives and false negatives of the rule in a given dataset
apply(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Ruleset: It returns the number of true positives,true negatives,false positives and false negatives of the whole ruleset in a given dataset.
apply(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Ruleset: It returns the number of true positives,true negatives,false positives and false negatives of the whole ruleset in a given dataset.
apply(MyDataset, Mask, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule: Returns the number of the instances covered by the rule in a given dataset.
apply(MyDataset, Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule: Returns the number of the instances covered by the rule in a given dataset
apply(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule: It returns the number of the instances covered by the rule in a given dataset
apply(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule: It returns the number of true positives,true negatives,false positives and false negatives of the rule in a given dataset
apply(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Ruleset: It returns the number of true positives,true negatives,false positives and false negatives of the whole ruleset in a given dataset.
apply(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Ruleset: It returns the number of true positives,true negatives,false positives and false negatives of the whole ruleset in a given dataset.
apply(MyDataset, Mask, int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Rule: Returns the number of the instances covered by the rule in a given dataset.
apply(MyDataset, Mask) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Rule: Returns the number of the instances covered by the rule in a given dataset
apply(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Rule: It returns the number of the instances covered by the rule in a given dataset
apply(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Rule: It returns the number of true positives,true negatives,false positives and false negatives of the rule in a given dataset
apply(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Ruleset: It returns the number of true positives,true negatives,false positives and false negatives of the whole ruleset in a given dataset.
apply(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Ruleset: It returns the number of true positives,true negatives,false positives and false negatives of the whole ruleset in a given dataset.
apply(MyDataset, Mask, int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule: Returns the number of the instances covered by the rule in a given dataset.
apply(MyDataset, Mask) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule: Returns the number of the instances covered by the rule in a given dataset
apply(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule: It returns the number of the instances covered by the rule in a given dataset
apply(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule: It returns the number of true positives,true negatives,false positives and false negatives of the rule in a given dataset
apply(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset: It returns the number of true positives,true negatives,false positives and false negatives of the whole ruleset in a given dataset.
apply(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset: It returns the number of true positives,true negatives,false positives and false negatives of the whole ruleset in a given dataset.
apply(MyDataset, Mask, int) - Method in class keel.Algorithms.Rule_Learning.PART.Rule: Returns the number of the instances covered by the rule in a given dataset.
apply(MyDataset, Mask) - Method in class keel.Algorithms.Rule_Learning.PART.Rule: Returns the number of the instances covered by the rule in a given dataset
apply(MyDataset) - Method in class keel.Algorithms.Rule_Learning.PART.Rule: It returns the number of the instances covered by the rule in a given dataset
apply(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.PART.Rule: It returns the number of true positives,true negatives,false positives and false negatives of the rule in a given dataset
apply(MyDataset) - Method in class keel.Algorithms.Rule_Learning.PART.Ruleset: It returns the number of true positives,true negatives,false positives and false negatives of the whole ruleset in a given dataset.
apply(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.PART.Ruleset: It returns the number of true positives,true negatives,false positives and false negatives of the whole ruleset in a given dataset.
apply(MyDataset, Mask, int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Rule: Returns the number of the instances covered by the rule in a given dataset.
apply(MyDataset, Mask) - Method in class keel.Algorithms.Rule_Learning.Ripper.Rule: Returns the number of the instances covered by the rule in a given dataset
apply(MyDataset) - Method in class keel.Algorithms.Rule_Learning.Ripper.Rule: It returns the number of the instances covered by the rule in a given dataset
apply(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.Ripper.Rule: It returns the number of true positives,true negatives,false positives and false negatives of the rule in a given dataset
apply(MyDataset) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset: It returns the number of true positives,true negatives,false positives and false negatives of the whole ruleset in a given dataset.
apply(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset: It returns the number of true positives,true negatives,false positives and false negatives of the whole ruleset in a given dataset.
apply(MyDataset, Mask, int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Rule: Returns the number of the instances covered by the rule in a given dataset.
apply(MyDataset, Mask) - Method in class keel.Algorithms.Rule_Learning.Slipper.Rule: Returns the number of the instances covered by the rule in a given dataset
apply(MyDataset) - Method in class keel.Algorithms.Rule_Learning.Slipper.Rule: It returns the number of the instances covered by the rule in a given dataset
apply(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.Slipper.Rule: It returns the number of true positives,true negatives,false positives and false negatives of the rule in a given dataset
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ADE_CoForestGenerator: Apply the ADE_CoForestGenerator method.
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.APSSC.APSSCGenerator: Apply the Generator method.
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Makes the trivial reduction.
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.C45SSL.C45SSLGenerator: Apply the C45SSLGenerator method.
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCGenerator: Apply the CLCCGenerator method.
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoBC.CoBCGenerator: Apply the CoBCGenerator method with 3 classifiers: C45, NB, and 3NN
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.CoForestGenerator: Apply the CoForestGenerator method.
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoTraining.CoTrainingGenerator: Apply the CoTraining method.
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.DE_TriTraining.DE_TriTrainingGenerator: Apply the DE_TriTrainingGenerator method.
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.Democratic.DemocraticGenerator: Apply the DemocraticGenerator method with 3 classifiers: C45, NB, and 3NN
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.NBSSL.NBSSLGenerator: Apply the NBSSLGenerator method.
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.NNSSL.NNSSLGenerator: Apply the NNSSLGenerator method.
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.RASCO.RASCOGenerator: Apply the RASCOGenerator method.
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.Rel_RASCO.Rel_RASCOGenerator: Apply the Rel_RASCOGenerator method.
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.SelfTraining.SelfTrainingGenerator: Apply the SelfTrainingGenerator method.
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.SETRED.SETREDGenerator: Apply the SelfTrainingGenerator method.
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.SMOSSL.SMOSSLGenerator: Apply the SMOSSLGenerator method.
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.SNNRCE.SNNRCEGenerator: Apply the SelfTrainingGenerator method.
applyAlgorithm() - Method in class keel.Algorithms.Semi_Supervised_Learning.TriTraining.TriTrainingGenerator: Apply the TriTrainingGenerator method.
applyC45(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.C45SSL.C45SSLGenerator: Applies the C4.5 algorithm for the dataset given.
applyClean() - Method in class keel.Algorithms.Preprocess.Transformations.CleanAttributes.CleanAttributes: Process the training and test files provided in the parameters file to the constructor.
applydiffs(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Gene: Transforms the current gene, so the attribute value is now covered by this gene
applyDiscretization(String, String) - Method in class keel.Algorithms.Discretizers.Basic.Discretizer: Applies the discretization stored on the cut points.
applyDiscretization(String, String) - Method in class keel.Algorithms.Discretizers.Chi2_Discretizer.Discretizer: Applies the discretization stored on the cut points.
applyDiscretization(String, String) - Method in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Discretizer: Applies the discretization stored on the cut points.
applyDiscretization(String, String) - Method in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Discretizer: Applies the discretization stored on the cut points.
applyDiscretization(String, String) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Basic.Discretizer
applyNB(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.NBSSL.NBSSLGenerator: Builds a Naive Bayes model with the labeled dataset and classifies the unlabeled dataset.
applyNeighbour(Vector, Neighbour) - Method in class keel.Algorithms.Discretizers.MODL.MODL: Apply the neighbour to the current interval set.
applyOperators(double, double) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation: Applies operator according to their probabilities
applySMO(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.SMOSSL.SMOSSLGenerator: Builds a SMO model with the labeled dataset and classifies the unlabeled dataset.
applyThresholds() - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Change attribute values that are not in [0.0, 1.0].
applyThresholds() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Change values of the prototypes that are not in the values domain.
applyThresholds() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Change attribute values that are not in [0.0, 1.0].
applyThresholds() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Change values of the prototypes that are not in the values domain.
ApproximateSets - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets
ApproximateSets() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets
ApproximateSqrt - Class in keel.Algorithms.Instance_Generation.utilities
ApproximateSqrt() - Constructor for class keel.Algorithms.Instance_Generation.utilities.ApproximateSqrt
ApproximateSqrt - Class in keel.Algorithms.Semi_Supervised_Learning.utilities: Implements an approximation of the squared root operator.
ApproximateSqrt() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.utilities.ApproximateSqrt
Apriori - Class in keel.Algorithms.Associative_Classification.ClassifierCBA: This class mines the frecuent non-fuzzy itemsets and the non-fuzzy classification association rules.
Apriori() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.Apriori: Default Constructor
Apriori(DataBase, myDataset, double, double, int, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.Apriori: Parameters Constructor: Generates an Apriori objects from a list of parameters
Apriori - Class in keel.Algorithms.Associative_Classification.ClassifierCBA2: This class mines the frecuent non-fuzzy itemsets and the non-fuzzy classification association rules
Apriori() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.Apriori: Default Constructor
Apriori(DataBase, myDataset, double, double, int, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.Apriori: Parameters Constructor: Generates an Apriori objects from a list of parameters
Apriori - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR: This class mines the frecuent fuzzy itemsets and the fuzzy classification associacion rules
Apriori() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Apriori: Default Constructor.
Apriori(double, double, double, RuleBase) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Apriori: Parameters Constructor: Generates an Apriori objects from a list of parameters
Apriori - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD: Title: Apriori Description: This class mines the frecuent fuzzy itemsets and the fuzzy classification associacion rules Copyright: Copyright KEEL (c) 2007 Company: KEEL
Apriori() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Apriori: Default Constructor.
Apriori(RuleBase, DataBase, myDataset, double, double, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Apriori: Builder
Apriori - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA: This class mines the frecuent fuzzy itemsets and the fuzzy classification associacion rules
Apriori() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Apriori: Default Constructor
Apriori(DataBase, myDataset) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Apriori: Parameters Constructor: Generates an Apriori objects from a list of parameters
Apriori - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS: Title: Apriori Description: This class mines the frecuent fuzzy itemsets and the fuzzy classification associacion rules Copyright: Copyright KEEL (c) 2007 Company: KEEL
Apriori() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Apriori
Apriori(RuleBase, DataBase, myDataset, myDataset, double, double, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Apriori: Builder
Apriori - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori: It gathers all the parameters, launches the algorithm, and prints out the results
Apriori() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.Apriori: Default constructor
Apriori(parseParameters) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.Apriori: It reads the data from the input files and parse all the parameters from the parameters array.
AprioriProcess - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori: It provides the implementation of the Apriori algorithm to be run in a process
AprioriProcess(myDataset, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AprioriProcess: It creates a new process for the algorithm by setting up its parameters
aprioriSD - Class in keel.Algorithms.Subgroup_Discovery.aprioriSD: Contents the principal methods of the APRIORISD algorithm
aprioriSD() - Constructor for class keel.Algorithms.Subgroup_Discovery.aprioriSD.aprioriSD: Default constructor.
aprioriSD(String, String, String, String, String, String, double, double, int, int) - Constructor for class keel.Algorithms.Subgroup_Discovery.aprioriSD.aprioriSD: Parameter constructor.
AprioriTFP_CMAR - Class in keel.Algorithms.Associative_Classification.ClassifierCMAR: Methods to produce classification rules using Wenmin Li, Jiawei Han and Jian Pei's CMAR (Classification based on Multiple associate Rules) algorithm but founded on Apriori-TFP.
AprioriTFP_CMAR(double, double, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFP_CMAR: Processes command line arguments.
AprioriTFPclass - Class in keel.Algorithms.Associative_Classification.ClassifierCMAR: Methods to produce classification rules using a Apriori-T appraoch.
AprioriTFPclass(double, double, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Processes command line arguments.
aproximation(int, fuzzy) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyPartition
aproximation(int, fuzzy) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyPartition
aproximation(int, fuzzy) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.partition
aproximation(int, fuzzy) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.partition
aproximation(int, fuzzy) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzypartition
aproximation(int, fuzzy) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzypartition
APSSCAlgorithm - Class in keel.Algorithms.Semi_Supervised_Learning.APSSC: APSSC algorithm calling.
APSSCAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.APSSC.APSSCAlgorithm
APSSCGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.APSSC: This class implements the Self-traning wrapper.
APSSCGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.APSSC.APSSCGenerator: Build a new APSSCGenerator Algorithm
APSSCGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.APSSC.APSSCGenerator: Build a new APSSCGenerator Algorithm
AQ - Class in keel.Algorithms.Rule_Learning.AQ: Title: Main class of the algorithm Description: It contains the esential methods for the AQ algorithm Created: November 26th 2004 Copyright: Copyright (c) 2004 Company: KEEL
AQ() - Constructor for class keel.Algorithms.Rule_Learning.AQ.AQ: Default builder
AQ(String, String, String, String, String, String, long, int, int) - Constructor for class keel.Algorithms.Rule_Learning.AQ.AQ: AQ class builder It does a local copy of the filenames for their posterior use.
aRange - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters
aRange - Variable in class keel.Algorithms.Neural_Networks.gmdh.SetupParameters
arbol_valueChanged(TreeSelectionEvent) - Method in class keel.GraphInterKeel.experiments.Experiments: Tree changed method
arbol_valueChanged(TreeSelectionEvent) - Method in class keel.GraphInterKeel.help.HelpOptions: Manages events in tree
Arc - Class in keel.GraphInterKeel.experiments
Arc() - Constructor for class keel.GraphInterKeel.experiments.Arc: Builder
Arc(GraphPanel) - Constructor for class keel.GraphInterKeel.experiments.Arc: Builder
Arc(int, int, GraphPanel) - Constructor for class keel.GraphInterKeel.experiments.Arc: Builder
arc_selected - Variable in class keel.GraphInterKeel.experiments.GraphPanel
ArchiveFilter - Class in keel.GraphInterKeel.experiments
ArchiveFilter(String[]) - Constructor for class keel.GraphInterKeel.experiments.ArchiveFilter: Builder
ArchiveFilter2 - Class in keel.GraphInterKeel.experiments
ArchiveFilter2(String[], String) - Constructor for class keel.GraphInterKeel.experiments.ArchiveFilter2: Builder
Area(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
Area() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.fuzzy_t
Area(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
Area(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Area(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Returns the area of the label number l in the domain
Area() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.fuzzy_t: Returns the area of the label.
Area(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t: Returns the area of the label number "l" in the variable's domain.
Area(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Returns the area of the label number "lab" in the variable in position "var" of the list.
Area(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
Area() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.fuzzy_t
Area(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
Area(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
Area(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
Area() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.fuzzy_t
Area(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
Area(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
areAllDefinedAsOutputs(Vector) - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: This method checks if all the output names vector corresponds with all the attributes in output vector.
areAllDefinedAsOutputs(Vector) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: This method checks if all the output names vector corresponds with all the attributes in output vector.
areAllDefinedAsOutputs(Vector) - Static method in class keel.Dataset.Attributes: This method checks if all the output names vector corresponds with all the attributes in output vector.
areAllDefinedAsOutputs(Vector) - Method in class keel.Dataset.InstanceAttributes: This method checks if all the output names vector corresponds with all the attributes in output vector.
AreaTrapecio(double, double, double, double, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseR
AreaTrapecioX(double, double, double, double, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseR: Functions to calculate the centre of gravity
areNewNominalValuesInTest() - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It returns true if in test have appeared new values.
areNewNominalValuesInTest() - Method in class keel.Dataset.Attribute: It returns true if in test have appeared new values.
areSimilar(Vector<Itemset>, Vector<Itemset>) - Method in class keel.Algorithms.Discretizers.UCPD.UCPD: It checks if two frequents itemsets are similar
ARFF_ATTRIBUTE - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: The keyword used to denote the start of an arff attribute declaration
ARFF_ATTRIBUTE_DATE - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: The keyword used to denote a date attribute
ARFF_ATTRIBUTE_INTEGER - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: A keyword used to denote a numeric attribute
ARFF_ATTRIBUTE_NUMERIC - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: A keyword used to denote a numeric attribute
ARFF_ATTRIBUTE_REAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: A keyword used to denote a numeric attribute
ARFF_ATTRIBUTE_RELATIONAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: The keyword used to denote a relation-valued attribute
ARFF_ATTRIBUTE_STRING - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: The keyword used to denote a string attribute
ARFF_DATA - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: The keyword used to denote the start of the arff data section
ARFF_DATA - Static variable in class keel.Algorithms.SVM.SMO.core.Instances: The keyword used to denote the start of the arff data section
ARFF_END_SUBRELATION - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: The keyword used to denote the end of the declaration of a subrelation
ARFF_RELATION - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: The keyword used to denote the start of an arff header
ARFF_RELATION - Static variable in class keel.Algorithms.SVM.SMO.core.Instances: The keyword used to denote the start of an arff header
ArffDataSet - Class in keel.Algorithms.Neural_Networks.NNEP_Common.data: ArffDataset implementation (Weka dataset).
ArffDataSet(String, String...) - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArffDataSet: Constructor with the filename and the specification file
ArffDataSet() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArffDataSet: Constructor without arguments
arg - Variable in class keel.GraphInterKeel.experiments.ExternalObjectDescription
argmax(double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FB
argmax(double[]) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.AD: Returns the index of the maximum element of the array given.
argmax(double[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Shared.DiscrAnalysis.AD: Returns the index of the maximum element of the array given.
ARITHMETIC_MEAN - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.GP_COACH: Configuration flag (ARITHMETIC_MEAN).
ARMMGA - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA: It gathers all the parameters, launches the algorithm, and prints out the results.
ARMMGA() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.ARMMGA: Default constructor
ARMMGA(parseParameters) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.ARMMGA: It reads the data from the input files and parse all the parameters from the parameters array.
ARMMGAProcess - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA: It provides the implementation of the ARMMGA algorithm to be run in a process
ARMMGAProcess(myDataset, DataB, int, int, int, double, double, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.ARMMGAProcess: It creates a new process for the algorithm by setting up its parameters
array - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Array with all data
array2string(double[]) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils: Creates a string from an array of doubles
array2string(int[]) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils: Creates a string from an array of integer
array2string(double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils: Creates a string from an array of doubles
array2string(double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils: Creates a string from an array of doubles
array2string(double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils: Creates a string from an array of doubles
array2string(double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils: Creates a string from an array of doubles
array2string(double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils: Creates a string from an array of doubles
array2string(double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils: Creates a string from an array of doubles
ArrayDataset - Class in keel.Algorithms.Neural_Networks.NNEP_Common.data: ArrayDataset implementation (dataset populated with an array)
ArrayDataset() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArrayDataset: Empty constructor
arrayLeftDivide(Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Element-by-element left division, C = A.
arrayLeftDivideEquals(Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Element-by-element left division in place, A = A.
arrayRightDivide(Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Element-by-element right division, C = A.
arrayRightDivideEquals(Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Element-by-element right division in place, A = A.
Arrays - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets
Arrays() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Arrays
arrayTimes(Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Element-by-element multiplication, C = A.
arrayTimesEquals(Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Element-by-element multiplication in place, A = A.
arrayToString(Object) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Returns the given Array in a string representation.
arrayToString(Object) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Returns the given Array in a string representation.
arrayToString(Object) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Returns the given Array in a string representation.
arrayToString(Object) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Returns the given Array in a string representation.
arrayToString(Object) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Returns the given Array in a string representation.
arrayToString(Object) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Returns the given Array in a string representation.
arrayToString(Object) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Returns the given Array in a string representation.
ARS - Class in keel.Algorithms.Instance_Generation.BasicMethods: Implements a random selector but taking proportional number of prototypes in order to their occurences (that is, their a priory probabilities).
ARS(PrototypeSet, int) - Constructor for class keel.Algorithms.Instance_Generation.BasicMethods.ARS: Creates a new AdvanceRandomSelector
ARS(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.BasicMethods.ARS: Creates a new AdvanceRandomSelector
ARSAlgorithm - Class in keel.Algorithms.Instance_Generation.BasicMethods: Main class.
ARSAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.BasicMethods.ARSAlgorithm
ART - Class in keel.Algorithms.Rule_Learning.ART: A Java implementation of the ART algorithm
ART(String) - Constructor for class keel.Algorithms.Rule_Learning.ART.ART: Constructor.
Asigna(FuzzyInterval) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyInterval: Copies the FuzzyInterval parameter over the present instance.
Asigna(FuzzyNumberTRIANG) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyNumberTRIANG: Copies the FuzzyNumberTRIANG parameter over the present instance.
Asigna(FuzzyNumberTRLEFT) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyNumberTRLEFT: Copies the FuzzyNumberTRLEFT parameter over the present instance.
asignaAntecedente(int[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: It sets the rule's antecedent.
asignaAntecedente(int[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: It sets the rule's antecedent
asignaAntecedente(int[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule: It sets the antecedent of the rule
asignaAntecedente(int[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Rule: It sets the antecedent of the rule
asignaAntecedente(int[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Rule: It sets the antecedent of the rule
asignaAntecedente(int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Regla
asignaAntecedente(int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Regla
asignaAntecedente(int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule
asignaAntecedente(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Regla
asignaConsecuente(myDataset, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Regla: It assigns the rule weight to the rule
asignaConsecuente(myDataset, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Regla
asignaejemplos(double[][], double[], double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForSymbRegr: This method assign examples based on a level of tolerance
asignaejemplos(fuzzy[][], Vector<Vector<Float>>) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.IndMichigan
asignaejemplos(fuzzy[][], Vector<Vector<Float>>) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.IndMichigan
asignaejemplos(Interval[][], Vector<Vector<Float>>) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.IndMichigan
asignaejemplos(fuzzy[][], Vector<Vector<Float>>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.IndMichigan
asignainstances(Interval[][], Vector<Vector<Float>>) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.AlgGenetic
asignAntecedent(int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule: It assings the ancedent of the Rule
asignaPesos(double) - Method in class keel.Algorithms.Decision_Trees.Target.Nodo: Sets weights to the attributes within a given probability.
asignarCalidad(float) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Assigns the given quality to the rule.
asignarCalidad(float) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Assigns the given quality to the rule.
asignarCalidad(float) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Assigns the given quality to the rule.
asignarCalidad(float) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Assigns the given quality to the rule.
asignarCalidad(float) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Assigns the given quality to the rule.
asignarCalidadMPosicion(float) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Assigns the given quality to the best position of the particle.
asignarCalidadPosActual(float) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Assigns the given quality to the actual position of the particle.
asignarClasePosicionActual(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Assigns the class for the actual position of the particle.
asignarClasePosicionMejor(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Assigns the class to the best position of the particle.
asignarCondicionContinuaPosicionActual(Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Assigns the given continuous condition to the actual position of the particle.
asignarCondicionContinuaPosicionMejor(Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Assigns the given continuous condition to the best position of the particle.
asignarCondicionNominalPosicionActual(Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Assigns the given nominal condition to the actual position of the particle.
asignarCondicionNominalPosicionMejor(Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Assigns the given nominal condition to the best position of the particle.
asignarMuestrasCubiertas(float) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Sets the number of examples covered by the rule.
asignarMuestrasCubiertas(float) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Sets the number of examples covered by the rule.
asignarMuestrasCubiertas(float) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Sets the number of examples covered by the rule.
asignarMuestrasCubiertas(float) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Sets the number of examples covered by the rule.
asignarMuestrasCubiertasActual(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Assigns the number of examples covered by the actual position of the particle.
asignarMuestrasCubiertasPasado(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Assigns the number of examples covered by the best position of the particle.
asignarNombres(String, String[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Regla
asignarNumeroMuestrasCubiertas(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Assign the number of examples covered by the rule with the value given.
asigninstances(fuzzy[][], Vector<Vector<Float>>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.IndMichigan
asKeelDataFileString(String) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Converts data set into a Keelish-String.
asKeelDataFileString() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Converts data set into a Keelish-String.
asKeelDataFileString(String) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Converts data set into a Keelish-String.
asKeelDataFileString() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Converts data set into a Keelish-String.
asList(int[]) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Returns a List with the elements of the array given.
assertArguments(String[]) - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm: Assert keel-style arguments
assertArguments(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm: Assert keel-style arguments
assertBasicArgs(String[]) - Static method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Assert the program arguments
assertBasicArgs(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Assert the program arguments
assertExtendedArg(String[], int, String, double, double) - Static method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Assert the program arguments which depends on the specific algorithm.
assertExtendedArg(String[], String, double, double) - Static method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Assert the program arguments which depends on the specific algorithm.
assertExtendedArg(String[], int, String, double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Assert the program arguments which depends on the specific algorithm.
assertExtendedArg(String[], String, double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Assert the program arguments which depends on the specific algorithm.
assertExtendedArgAsDouble(String[], int, String, double, double) - Static method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Assert a double arguments which depends on the specific algorithm.
assertExtendedArgAsDouble(String[], String, double, double) - Static method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Assert a double arguments which depends on the specific algorithm.
assertExtendedArgAsDouble(String[], int, String, double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Assert a double arguments which depends on the specific algorithm.
assertExtendedArgAsDouble(String[], String, double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Assert a double arguments which depends on the specific algorithm.
assertExtendedArgAsInt(String[], int, String, double, double) - Static method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Assert an integer argument which depends on the specific algorithm.
assertExtendedArgAsInt(String[], String, double, double) - Static method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Assert an integer argument which depends on the specific algorithm.
assertExtendedArgAsInt(String[], int, String, double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Assert an integer argument which depends on the specific algorithm.
assertExtendedArgAsInt(String[], String, double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Assert an integer argument which depends on the specific algorithm.
assertExtendedArgAsString(String[], int, String, ArrayList<String>) - Static method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Assert the program arguments which depends on the specific algorithm.
assertExtendedArgAsString(String[], int, String, String[]) - Static method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Assert the program arguments which depends on the specific algorithm.
assertExtendedArgAsString(String[], int, String, ArrayList<String>) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Assert the program arguments which depends on the specific algorithm.
assertExtendedArgAsString(String[], int, String, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Assert the program arguments which depends on the specific algorithm.
Assessment(int, double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
Assessment(int, double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
Assessment(int, double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
Assign(int, double, double, boolean, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
Assign(int, double, double, double[], double[], double[], double[], String[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
Assign(double, double, double, double, String, boolean, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.fuzzy_t
Assign(double, double, double, double, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.fuzzy_t
Assign(int, double, double, boolean, boolean, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
Assign(int, String, int, double, double, double[], double[], double[], double[], String[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
Assign(int, variable_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Assign(int, double, double, boolean, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
Assign(int, double, double, double[], double[], double[], double[], String[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
Assign(double, double, double, double, String, boolean, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.fuzzy_t
Assign(double, double, double, double, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.fuzzy_t
Assign(int, double, double, boolean, boolean, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
Assign(int, String, int, double, double, double[], double[], double[], double[], String[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
Assign(int, variable_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
Assign(int, double, double, boolean, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
Assign(int, double, double, double[], double[], double[], double[], String[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
Assign(double, double, double, double, String, boolean, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.fuzzy_t
Assign(double, double, double, double, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.fuzzy_t
Assign(int, double, double, boolean, boolean, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
Assign(int, String, int, double, double, double[], double[], double[], double[], String[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
Assign(int, variable_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
assignAntecedent(int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Regla: Sets the antecedents with the given array.
assignConditionNoAny(int, myDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual: It assigns a condition to the atribute in position "atributo", but different to "ANY"
assignConsequent(myDataset, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Rule: It assigns the rule Weigth
Assigned(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Utils: Rounds the generated value for the semantics when necesary
Assigned(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Utils: Rounds the generated value for the semantics when necesary
Assigned(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Utils: Rounds the generated value for the semantics when necesary
Assigned(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Utils: Rounds the generated value for the semantics when necesary
Assigned(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Utils: Rounds the generated value for the semantics when necesary
Assigned(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Utils: Rounds the generated value for the semantics when necesary
Assigned(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Utils: Rounds the generated value for the semantics when necesary
assignedClass() - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Returns the assigned class of the prototype (same value as the first output of the protoype).
assignedClass() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Returns the assigned class of the prototype (same value as the first output of the protoype).
assignNewClass(myDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual: It changes the class in the consequent of the individual by a new one
assignOutputClass() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Initializes the output class stored in the node with a valid output class, in a standard way, with the class stored in the node if it is pure or with the class of the majority of the instances where if there are two majority classes, it selects randomly one of them
assingConsequent(myDataset, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Rule: It assigns the rule weight to the rule
associatedInstanceSet - Variable in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Associated instance set to the prototype set.
associatedInstanceSet - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Associated instance set to the prototype set.
associatesPrototype(PrototypeSet, Prototype) - Method in class keel.Algorithms.Instance_Generation.ICPL.ICPLGenerator: Return all the prototype in (this) that has other like the nearest neighbor
Association - Class in keel.Algorithms.Decision_Trees.M5: An Association simply associates a numeric ID with a String description.
Association(int, String) - Constructor for class keel.Algorithms.Decision_Trees.M5.Association: Creates a new Association instance.
AssociationRule - Class in keel.Algorithms.Subgroup_Discovery.SDMap.SDMap: It is used for representing and handling an Association Rule
AssociationRule(short[], short[], double, double, double, int) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.AssociationRule: It creates a new association rule by setting up its properties
AssociationRule(short[], short[], double, double, double, int, int[]) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.AssociationRule: It creates a new association rule by setting up its properties
AssociationRule - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal
AssociationRule(Itemset, Itemset, double, double, double, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.AssociationRule: It creates a new association rule by setting up its properties
AssociationRule - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori
AssociationRule(Itemset, Itemset, double, double, double, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.AssociationRule: It creates a new association rule by setting up its properties
AssociationRule - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori: It is used for representing and handling an Association Rule
AssociationRule(Itemset, Itemset, double, double, double, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.AssociationRule: It creates a new association rule by setting up its properties
AssociationRule - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC
AssociationRule(Itemset, Itemset, double, double, double, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.AssociationRule: It creates a new association rule by setting up its properties
AssociationRule - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal: It is used for representing and handling an Association Rule.
AssociationRule(Chromosome) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AssociationRule: It creates a new association rule by setting up the chromosome which is based on
AssociationRule - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori: It is used for representing and handling an Association Rule
AssociationRule() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule: Default constructor
AssociationRule - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA: It is used for representing and handling an Association Rule
AssociationRule() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
AssociationRule - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA
AssociationRule() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
AssociationRule - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat
AssociationRule() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule: Default constructor
AssociationRule - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth: It is used for representing and handling an Association Rule
AssociationRule(short[], short[], double, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.AssociationRule: It creates a new association rule by setting up its properties
AssociationRule - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR
AssociationRule() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule
AssociationRule - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR
AssociationRule() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
AssociationRule - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR
AssociationRule() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
AssociationRule - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh
AssociationRule() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
AssociationRule(Chromosome) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule: It creates a new association rule by setting up the chromosome which is based on
AssociationRule - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR
AssociationRule() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
AssociationRule(Chromosome) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule: It creates a new association rule by setting up the chromosome which is based on
AssociationRule - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII
AssociationRule() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
AssociationRule(Chromosome) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule: It creates a new association rule by setting up the chromosome which is based on
AssocRuleMining - Class in keel.Algorithms.Associative_Classification.ClassifierCMAR: Set of utilities to support various Association Rule Mining (ARM) algorithms included in the LUCS-KDD suite of ARM programs.
AssocRuleMining(double, double) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Processes command line arguments.
AssocRuleMining() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Default constructor.
AssocRuleMining - Class in keel.Algorithms.Subgroup_Discovery.SDMap.FPTree: Set of utilities to support various Association Rule Mining (ARM)
AssocRuleMining(myDataset, double, double) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Constructor to process dataset and parameters.
AssocRuleMining - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD
AssocRuleMining(myDataset, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Constructor to process dataset and parameters.
AssocRuleMining.RuleNode - Class in keel.Algorithms.Subgroup_Discovery.SDMap.FPTree: Inner class for storing linked list of ARs or CARs as appropriate.
AssocRuleMining.RuleNode - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD: Set of utilities to support various Association Rule Mining (ARM)
AString(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
AString(int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
AString(int[][]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
AString(double[][]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
AString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoosting
AString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoostingMaxMin
aString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.IntDouble: This method displays the consequent and the weight of the rule as a string
aString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.Fuzzy: Returns a printable version of the instance.
aString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut: Returns a printable version of the instance.
aString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyInterval: Returns a printable version of the instance.
aString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyNumberTRIANG: Returns a printable version of the instance.
aString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyNumberTRLEFT: Returns a printable version of the instance.
aString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyPartition: Returns a printable version of the instance.
aString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzySingleton: Returns a printable version of the instance.
AString(double[]) - Method in class keel.Algorithms.Statistical_Classifiers.Shared.DiscrAnalysis.AD: Returns a String representation of the array given.
AString(double[][]) - Method in class keel.Algorithms.Statistical_Classifiers.Shared.DiscrAnalysis.AD: Returns a String representation of the array of arrays given.
At(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectordouble
At(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.vectordouble: Returns the value in the position "pos" of the vector (or -99999999 if the position is not valid)
At(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectordouble
At(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectordouble
atanh(double) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ConjGradNN: Computes the atanh of the given number.
Atributo - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner: Title: Atributo (Attribute).
Atributo() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Atributo: Default constructor.
Atributo(String, int, boolean) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Atributo: Parameter constructor.
Atributo(Atributo) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Atributo: Copy Constructor.
Atributo - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus: Title: Atributo (Attribute) Description: Implements the attributes representation used by the ACO algorithm.
Atributo() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Atributo: Default constructor.
Atributo(String, int, boolean) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Atributo: Parameter constructor.
Atributo(Atributo) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Atributo: Copy Constructor.
Atributo - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner: Title: Atributo (Attribute).
Atributo() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Atributo: Default constructor.
Atributo(String, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Atributo: Parameter constructor.
Atributo(Atributo) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Atributo: Copy Constructor.
Atributo - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus: Title: Atributo (Attribute).
Atributo() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Atributo: Default constructor.
Atributo(String, int, boolean) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Atributo: Parameter constructor.
Atributo(Atributo) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Atributo: Copy Constructor.
Atributo - Class in keel.Algorithms.Genetic_Rule_Learning.PSO_ACO: This class implements an attribute.
Atributo() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Atributo: Default constructor.
Atributo(float, int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Atributo: Parameter constructor.
Atributo(Atributo) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Atributo: Copy Constructor.
Atributo_valor - Class in keel.Algorithms.Rule_Learning.LEM2
Atributo_valor() - Constructor for class keel.Algorithms.Rule_Learning.LEM2.Atributo_valor
Atributo_valor(Integer, Double) - Constructor for class keel.Algorithms.Rule_Learning.LEM2.Atributo_valor
Atributo_valor(Integer, Double, LinkedList<Integer>) - Constructor for class keel.Algorithms.Rule_Learning.LEM2.Atributo_valor
Atributo_valor - Class in keel.Algorithms.Rule_Learning.Rules6: Title: Atributo_Valor class (Attribute_value) Description: It stores the index of an attribute and a certein value of it.
Atributo_valor() - Constructor for class keel.Algorithms.Rule_Learning.Rules6.Atributo_valor: Default constructor.
Atributo_valor(Integer, Double) - Constructor for class keel.Algorithms.Rule_Learning.Rules6.Atributo_valor: Parameter constructor.
Atributo_valor - Class in keel.Algorithms.Rule_Learning.SRI: Title: Atributo_Valor class (Attribute_value) Description: It stores the index of an attribute and a certein value of it.
Atributo_valor() - Constructor for class keel.Algorithms.Rule_Learning.SRI.Atributo_valor: Default constructor.
Atributo_valor(Integer, Double) - Constructor for class keel.Algorithms.Rule_Learning.SRI.Atributo_valor: Parameter constructor.
AtributoComparator - Class in keel.Algorithms.Genetic_Rule_Learning.Hider
AtributoComparator(int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.AtributoComparator: Constructor
att - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Antd: The attribute of the antecedent
ATT_INPUT - Static variable in class keel.Algorithms.Rule_Learning.Swap1.Instance: Input attributes location
ATT_INPUT - Static variable in class keel.Dataset.Instance: Input attributes location
ATT_NONDEF - Static variable in class keel.Algorithms.Rule_Learning.Swap1.Instance: Non-defined direction attributes location
ATT_NONDEF - Static variable in class keel.Dataset.Instance: Non-defined direction attributes location
ATT_OUTPUT - Static variable in class keel.Algorithms.Rule_Learning.Swap1.Instance: Output attributes location
ATT_OUTPUT - Static variable in class keel.Dataset.Instance: Output attributes location
attBounds - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
attBounds - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
attDirection - Variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo: It keeps if the attribute is an input, output or non-defined attribute
attDirection - Variable in class keel.Dataset.ErrorInfo: It keeps if the attribute is an input, output or non-defined attribute
ATTEMPTS - Static variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
attemptsTipText() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
attInstances() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL.attInstances
attNames - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
attNames - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
AttribPr - Variable in class keel.Algorithms.PSO_Learning.CPSO.Particle
AttribPr - Variable in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
Attribute - Class in keel.Algorithms.Decision_Trees.C45: Class to implement an attribute
Attribute(String, int) - Constructor for class keel.Algorithms.Decision_Trees.C45.Attribute: Constructor for continuous attributes.
Attribute(String, Vector, int) - Constructor for class keel.Algorithms.Decision_Trees.C45.Attribute: Constructor for discret attributes.
Attribute - Class in keel.Algorithms.Decision_Trees.DT_GA.C45: Class to implement an attribute
Attribute(String, int) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.C45.Attribute: Constructor for continuous attributes.
Attribute(String, Vector, int) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.C45.Attribute: Constructor for discret attributes.
Attribute - Class in keel.Algorithms.Decision_Trees.ID3: Class to implement an attribute
Attribute(String, int) - Constructor for class keel.Algorithms.Decision_Trees.ID3.Attribute: Constructor for continuous attributes.
Attribute(String, Vector, int) - Constructor for class keel.Algorithms.Decision_Trees.ID3.Attribute: Constructor for discret attributes.
attribute(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Returns the attribute with the given index.
attribute(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Returns an attribute.
attribute(String) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Returns an attribute given its name.
Attribute - Class in keel.Algorithms.Decision_Trees.SLIQ: Class to implement an attribute
Attribute(String, int) - Constructor for class keel.Algorithms.Decision_Trees.SLIQ.Attribute: Constructor for continuous attributes.
Attribute(String, Vector, int) - Constructor for class keel.Algorithms.Decision_Trees.SLIQ.Attribute: Constructor for discret attributes.
attribute(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Returns the attribute with the given index.
attribute(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns an attribute.
attribute(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns an attribute given its name.
attribute - Variable in class keel.Algorithms.Genetic_Rule_Learning.ILGA.AttributeCR: Attribute id.
Attribute - Class in keel.Algorithms.Genetic_Rule_Learning.OCEC: Title: Description: Copyright: Copyright (c) 2007 Company:
Attribute(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OCEC.Attribute
Attribute(String[]) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OCEC.Attribute
attribute - Variable in class keel.Algorithms.Genetic_Rule_Learning.OIGA.AttributeCR: Attribute ID.
Attribute - Interface in keel.Algorithms.Genetic_Rule_Learning.UCS: This interface has to be implemented for all attributes representation.
Attribute - Interface in keel.Algorithms.Genetic_Rule_Learning.XCS: This interface has to be implemented for all attributes representation.
Attribute - Class in keel.Algorithms.ImbalancedClassification.CSMethods.C45CS: Class to implement an attribute
Attribute(String, int) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Attribute: Constructor for continuous attributes.
Attribute(String, Vector, int) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Attribute: Constructor for discret attributes.
Attribute - Class in keel.Algorithms.ImbalancedClassification.Ensembles.C45: Class to implement an attribute
Attribute(String, int) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Attribute: Constructor for continuous attributes.
Attribute(String, Vector, int) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Attribute: Constructor for discret attributes.
Attribute - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets: Class for handling an attribute.
Attribute(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Constructor for a numeric attribute.
Attribute(String, ProtectedProperties) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Constructor for a numeric attribute, where metadata is supplied.
Attribute(String, String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Constructor for a date attribute.
Attribute(String, String, ProtectedProperties) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Constructor for a date attribute, where metadata is supplied.
Attribute(String, FastVector) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Constructor for nominal attributes and string attributes.
Attribute(String, FastVector, ProtectedProperties) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Constructor for nominal attributes and string attributes, where metadata is supplied.
Attribute(String, Instances) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Constructor for relation-valued attributes.
Attribute(String, Instances, ProtectedProperties) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Constructor for relation-valued attributes.
attribute(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Returns the attribute with the given index.
attribute(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns an attribute.
attribute(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns an attribute given its name.
Attribute - Class in keel.Algorithms.Rule_Learning.ART: Class to implement an attribute
Attribute(String, int) - Constructor for class keel.Algorithms.Rule_Learning.ART.Attribute: Constructor for continuous attributes.
Attribute(String, Vector, int) - Constructor for class keel.Algorithms.Rule_Learning.ART.Attribute: Constructor for discret attributes.
Attribute - Class in keel.Algorithms.Rule_Learning.DataSqueezer: Class to implement an attribute
Attribute(String, int) - Constructor for class keel.Algorithms.Rule_Learning.DataSqueezer.Attribute: Constructor for continuous attributes.
Attribute(String, Vector, int) - Constructor for class keel.Algorithms.Rule_Learning.DataSqueezer.Attribute: Constructor for discret attributes.
Attribute - Class in keel.Algorithms.Rule_Learning.Swap1: Attribute It contains an attribute representation.
Attribute() - Constructor for class keel.Algorithms.Rule_Learning.Swap1.Attribute: Attribute Constructor.
ATTRIBUTE - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for attributes lines.
attribute(int) - Method in class keel.Algorithms.SVM.SMO.core.Instance: Returns the attribute with the given index.
attribute(int) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns an attribute.
attribute(String) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns an attribute given its name.
Attribute - Class in keel.Dataset: Attribute It contains an attribute representation.
Attribute() - Constructor for class keel.Dataset.Attribute: Attribute Constructor.
ATTRIBUTE - Static variable in interface keel.Dataset.DataParserConstants
Attribute - Class in keel.GraphInterKeel.datacf.util: Class representing an attribute in a dataset
Attribute(int, String, String) - Constructor for class keel.GraphInterKeel.datacf.util.Attribute: Constructor with attribute type and min and max bounds
Attribute(int) - Constructor for class keel.GraphInterKeel.datacf.util.Attribute: Constructor with attribute type
Attribute(int, String[]) - Constructor for class keel.GraphInterKeel.datacf.util.Attribute: Constructor with attribute type and nominal values
AttributeCR - Class in keel.Algorithms.Genetic_Rule_Learning.ILGA: This class implements the relation between an attribute and its classification rate for sorting purposes
AttributeCR(int, double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.ILGA.AttributeCR: Assigns the attribute number and the CR to this object
AttributeCR - Class in keel.Algorithms.Genetic_Rule_Learning.OIGA: This class implements the relation between an attribute and its classification rate for sorting purposes
AttributeCR(int, double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OIGA.AttributeCR: Assigns the attribute number and the CR to this object
attributeIndex() - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Returns the index of the attribute to cut on.
attributeIndex() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Returns the index of the attribute to cut on.
attributeIndex() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Returns the index of the attribute to cut on.
attributeIndex() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Returns the index of the attribute to cut on.
attributeIndex() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Returns the index of the attribute to cut on.
attributeIndex() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Returns the index of the attribute to cut on.
attributeIndex() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Returns the index of the attribute to cut on.
attributeIndex() - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Returns the index of the attribute to cut on.
attributeIndex() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Returns the index of the attribute to cut on.
AttributeLocator - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: This class locates and records the indices of a certain type of attributes, recursively in case of Relational attributes.
AttributeLocator(Instances, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: Initializes the AttributeLocator with the given data for the specified type of attribute.
AttributeLocator(Instances, int, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: Initializes the AttributeLocator with the given data for the specified type of attribute.
AttributeLocator(Instances, int, int[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: initializes the AttributeLocator with the given data for the specified type of attribute.
attributeNames() - Method in class keel.Algorithms.SVM.SMO.SMO: Returns the attribute names.
AttributeNotDefinedInTrain - Static variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo
AttributeNotDefinedInTrain - Static variable in class keel.Dataset.ErrorInfo
attributeNum - Variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo: It stores the attribute number where the error has appeared.
attributeNum - Variable in class keel.Dataset.ErrorInfo: It stores the attribute number where the error has appeared.
attributes - Variable in class keel.Algorithms.Decision_Trees.C45.Dataset: The attributes.
attributes - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: The attributes.
attributes - Variable in class keel.Algorithms.Decision_Trees.ID3.Dataset: The attributes.
attributes - Variable in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: The attributes.
attributes - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: The attributes.
attributes - Variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: The attributes.
attributes - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: The attributes.
attributes - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: The attributes.
attributes - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: The attributes.
attributes - Variable in class keel.Algorithms.Rule_Learning.ART.Dataset: The attributes.
attributes - Variable in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: The attributes.
attributes - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: The attributes.
attributes - Variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: The attributes.
attributes - Variable in class keel.Algorithms.Rule_Learning.PART.MyDataset: The attributes.
Attributes - Class in keel.Algorithms.Rule_Learning.Swap1: Attributes This class is a static class that, basically, contains a Vector of defined attributes in the train file.
Attributes() - Constructor for class keel.Algorithms.Rule_Learning.Swap1.Attributes
attributes - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: The attributes.
Attributes - Class in keel.Dataset: Attributes This class is a static class that, basically, contains a Vector of defined attributes in the train file.
Attributes() - Constructor for class keel.Dataset.Attributes
attributeSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
attributesList - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.Metadata: Array list containing all attributes of this meta data.
attributesMap - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.Metadata: Mapping of attribute names to attributes.
attributeSparse(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Returns the attribute with the given index.
attributeSparse(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5SparseInstance: Returns the attribute associated with the internal index.
attributeSparse(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Returns the attribute with the given index.
attributeSparse(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Returns the attribute with the given index.
attributeSparse(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Returns the attribute with the given index.
attributeSparse(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance: Returns the attribute associated with the internal index.
attributeSparse(int) - Method in class keel.Algorithms.SVM.SMO.core.Instance: Returns the attribute with the given index.
attributeStats(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Calculates summary statistics on the values that appear in this set of instances for a specified attribute.
AttributeStats - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets: A Utility class that contains summary information on an the values that appear in a dataset for a particular attribute.
AttributeStats() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.AttributeStats
attributeStats(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Calculates summary statistics on the values that appear in this set of instances for a specified attribute.
AttributeTable - Class in keel.GraphInterKeel.datacf.util
AttributeTable(String[], Object[], int) - Constructor for class keel.GraphInterKeel.datacf.util.AttributeTable: Constructor
attributeToDoubleArray(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Gets the value of all instances in this dataset for a particular attribute.
attributeToDoubleArray(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Gets the value of all instances in this dataset for a particular attribute.
attributeToDoubleArray(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Gets the value of all itemsets in this dataset for a particular attribute.
attributeToDoubleArray(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Gets the value of all instances in this dataset for a particular attribute.
attributeToDoubleArray(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Gets the value of all instances in this dataset for a particular attribute.
attributeToDoubleArray(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Gets the value of all instances in this dataset for a particular attribute.
attributeToDoubleArray(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: Gets the value of all instances in this dataset for a particular attribute.
attributeToDoubleArray(int) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Gets the value of all instances in this dataset for a particular attribute.
AttributeType - Enum in keel.Algorithms.Neural_Networks.NNEP_Common.data: Enumeration with attribute types
attributeType(Attribute) - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
attributeType(Attribute) - Static method in class keel.Dataset.DataParser
AttributeWeka - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: Class for handling an attribute.
AttributeWeka(String) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constructor for a numeric attribute.
AttributeWeka(String, ProtectedProperties) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constructor for a numeric attribute, where metadata is supplied.
AttributeWeka(String, String) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constructor for a date attribute.
AttributeWeka(String, String, ProtectedProperties) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constructor for a date attribute, where metadata is supplied.
AttributeWeka(String, FastVector) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constructor for nominal attributes and string attributes.
AttributeWeka(String, FastVector, ProtectedProperties) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constructor for nominal attributes and string attributes, where metadata is supplied.
AttributeWeka(String, Instances) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constructor for relation-valued attributes.
AttributeWeka(String, Instances, ProtectedProperties) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constructor for relation-valued attributes.
AttributeWeka(String, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constructor for a numeric attribute with a particular index.
AttributeWeka(String, String, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constructor for date attributes with a particular index.
AttributeWeka(String, FastVector, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constructor for nominal attributes and string attributes with a particular index.
AttributeWeka(String, Instances, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constructor for a relation-valued attribute with a particular index.
attrSplit(int, M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.SplitInfo: Finds the best splitting point for an attribute in the instances
attrSplit(int, MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SplitInfo: Finds the best splitting point for an attribute in the instances
AUC - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: AUC measurement identifier.
AUTO - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_DefaultC
AUTO - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_DefaultC
autoSeed - Variable in class keel.GraphInterKeel.experiments.Graph
avegDistance(double[][]) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils: Computes the average distance between vectors in a double[][]
avegDistance(double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils: Computes the average distance between vectors in a double[][]
avegDistance(double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils: Computes the average distance between vectors in a double[][]
avegDistance(double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils: Computes the average distance between vectors in a double[][]
avegDistance(double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils: Computes the average distance between vectors in a double[][]
avegDistance(double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils: Computes the average distance between vectors in a double[][]
avegDistance(double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils: Computes the average distance between vectors in a double[][]
average(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It return the average of an specific attribute
Average() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.vectordouble: Returns the average value for all the values in the vector (or position -999999999 if the vector is empty)
average(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It return the average of an specific attribute
average(int) - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It return the average of an specific attribute
averageAccuracy - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Average accuracy as the result of TCV.
averageAccuracy - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Statistics
averageAccuracy - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Statistics
averageAmplitude() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut: Returns the average amplitude of a fuzzy set.
averageClassValue() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns the average class value of the set.
averageClassValue(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns the average class value of the instances covered by a rule.
averageFitness - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Statistics: Computes and stores several statistics about the learning process
averageFitness - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Statistics
averageNumCRs - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Average accuracy number of callsification rules as the result of TCV.
averageNumFreqSets - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Average number of frequent sets as the result of TCV.
averageNumRules - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Statistics
averageNumRules - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Statistics
averageNumRulesUtils - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Statistics
averageNumRulesUtils - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Statistics
averageNumUpdates - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Average number of updates as the result of TCV.
averagePredictedClassValue(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns the average predicted class value of the instances covered by a rule.
averageValue(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns the average value for a given attribute of the set.
avg(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Performs average operation between two prototypes.
avg(Prototype, Prototype) - Static method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Performs average operation between two prototypes.
avg(Prototype, double, Prototype, double) - Static method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Performs averaged-based explicit operation between two prototypes.
avg() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Performs avg operation of the prototype set.
AVG - Class in keel.Algorithms.Instance_Generation.BasicMethods: Implements the reduction of the prototype set, making a centroid for each class.
AVG(PrototypeSet) - Constructor for class keel.Algorithms.Instance_Generation.BasicMethods.AVG: Constructs the AVG
AVG(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.BasicMethods.AVG: Constructs the AVG
avg() - Method in class keel.Algorithms.Instance_Generation.VQ.Cluster: Computes the average prototype of the cluster.
avg(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Performs average operation between two prototypes.
avg(Prototype, Prototype) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Performs average operation between two prototypes.
avg(Prototype, double, Prototype, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Performs averaged-based explicit operation between two prototypes.
avg() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Performs avg operation of the prototype set.
AVGAlgorithm - Class in keel.Algorithms.Instance_Generation.BasicMethods: Main class.
AVGAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.BasicMethods.AVGAlgorithm
avgCost() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Gets the average cost, that is, total cost of misclassifications (incorrect plus unclassified) over the total number of instances.
AVQAlgorithm - Class in keel.Algorithms.Instance_Generation.VQ: AVQ algorithm calling.
AVQAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.VQ.AVQAlgorithm
AVQGenerator - Class in keel.Algorithms.Instance_Generation.VQ: AVQ prototype generator.
AVQGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.VQ.AVQGenerator: Constructor of the AVQGenerator.
AVQGenerator(PrototypeSet, double, int, double) - Constructor for class keel.Algorithms.Instance_Generation.VQ.AVQGenerator: Constructor of the AVQGenerator.

B

b - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.T_FRM: a value.
b() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyInterval: Returns the upper extreme of the interval fuzzy set.
B() - Method in class keel.Algorithms.Hyperrectangles.INNER.Pair: Returns the second rule of the pair
B - Variable in class keel.Algorithms.Instance_Generation.PNN.MatrixOfDistances: Other prototype set
b - Variable in class keel.Algorithms.Neural_Networks.net.Data: Scaling parameters (b)
B - Variable in class keel.Algorithms.PSO_Learning.CPSO.Particle
B - Variable in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
B - Variable in class keel.Algorithms.PSO_Learning.PSOLDA.Particle
B - Variable in class keel.Algorithms.PSO_Learning.REPSO.Particle
b_htan - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network: Transfer function parameters
b_htan - Variable in class keel.Algorithms.Neural_Networks.gann.Network: Transfer function parameters
b_htan - Variable in class keel.Algorithms.Neural_Networks.gmdh.Network: Transfer function parameters
b_htan - Variable in class keel.Algorithms.Neural_Networks.net.Network: Transfer function parameters
b_log - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network: Transfer function parameters
b_log - Variable in class keel.Algorithms.Neural_Networks.gann.Network: Transfer function parameters
b_log - Variable in class keel.Algorithms.Neural_Networks.gmdh.Network: Transfer function parameters
b_log - Variable in class keel.Algorithms.Neural_Networks.net.Network: Transfer function parameters
back_mouseEntered(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Enter in back button
back_mouseExited(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Exit from back button
back_mouseReleased(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Releasing back button
backfitData(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Backfits the given data into the tree.
backfitData(PrototypeSet, double[]) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Recursively backfits data into the tree.
backfitData(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Backfits the given data into the tree.
backfitData(PrototypeSet, double[]) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Recursively backfits data into the tree.
backfitData(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Backfits the given data into the tree.
backfitData(PrototypeSet, double[]) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Recursively backfits data into the tree.
BackPropagation(Parameters, int, double[][], int) - Method in class keel.Algorithms.Neural_Networks.gann.ConnNetwork: Method that implements the backpropagation algorithm
backQuoteChars(String) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Converts carriage returns and new lines in a string into \r and \n.
backQuoteChars(String) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Converts carriage returns and new lines in a string into \r and \n.
backQuoteChars(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Converts carriage returns and new lines in a string into \r and \n.
backQuoteChars(String) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Converts carriage returns and new lines in a string into \r and \n.
backQuoteChars(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Converts carriage returns and new lines in a string into \r and \n.
backQuoteChars(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Converts carriage returns and new lines in a string into \r and \n.
backQuoteChars(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Converts carriage returns and new lines in a string into \r and \n.
backQuoteChars(String) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Converts carriage returns and new lines in a string into \r and \n.
backQuoteChars(String) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Converts carriage returns and new lines in a string into \r and \n.
backup(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
backup(int) - Static method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
backup(int) - Static method in class keel.Dataset.SimpleCharStream
BackwardIncon - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.BACKWARD.filter: MAIN CLASS OF BACKWARD FEATURE SELECTION ALGORITHM USING INCONSISTENCY COUNT
BackwardIncon(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.BACKWARD.filter.BackwardIncon: Creates a new instance of BackwardIncon
BackwardLVO - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.BACKWARD.wrapper: MAIN CLASS OF BACKWARD FEATURE SELECTION ALGORITHM USING LVO AS WRAPPER ALGORITHM
BackwardLVO(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.BACKWARD.wrapper.BackwardLVO: Creates a new instance of BackwardLVO
BadNumberOfValues - Static variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo: Definitions of possible ERRORS
BadNumberOfValues - Static variable in class keel.Dataset.ErrorInfo: Definitions of possible ERRORS
BadNumericValue - Static variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo
BadNumericValue - Static variable in class keel.Dataset.ErrorInfo
Base - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs: The class which contains the functions to do the decodification
Base(int, int, int, double, double[]) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base: It is the constructor of the class
base() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Ecm: Returns the fuzzy rules base.
base - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.EscribeBCLing: Rules base vector.
base - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.LeerWm: Rules base vector.
Base - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs: The class which contains the functions to do the decodification
Base(int, int, int, double, double[]) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base: It is the constructor of the class
base() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Ecm: Returns the fuzzy rules base.
base - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.EscribeBCLing: Rules base vector.
base - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.LeerWm: Rules base vector.
Base - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs: The class which contains the functions to do the decodification
Base(int, int, int, double, double[]) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base: It is the constructor of the class
base() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Ecm: Returns the fuzzy rules base.
base - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.EscribeBCLing: Rules base vector.
base - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.LeerWm: Rule base vector.
Base - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs: The class which contains the functions to do the decodification
Base(int, int, int, double, double[]) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base: It is the constructor of the class
base() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Ecm: Returns the fuzzy rules base.
base - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.EscribeBCLing: Rules base vector.
base - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.LeerWm: Rules base vector.
Base - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs: The class which contains the functions to do the decodification
Base(int, int, int, double, double[]) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base: It is the constructor of the class
base() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Ecm: Returns the fuzzy rules base.
base - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.EscribeBCLing: Rules base vector.
base - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.LeerWm: Rules base vector.
Base - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs: The class which contains the functions to do the decodification
Base(int, int, int, double, double[]) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base: It is the constructor of the class
base() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Ecm: Returns the fuzzy rules base.
base - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.EscribeBCLing: Rules base vector.
base - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.LeerWm: Rules base vector.
Base - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs: The class which contains the functions to do the decodification
Base(int, int, int, double, double[]) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base: It is the constructor of the class
base() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Ecm: Returns the fuzzy rules base.
base - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.EscribeBCLing: Rules base vector.
base - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.LeerWm: Rules base vector.
Base - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs: The class which contains the functions to do the decodification
Base(int, int, int, double, double[]) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base: It is the constructor of the class
base() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Ecm: Returns the fuzzy rules base.
base - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.EscribeBCLing: Rules base vector.
base - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.LeerWm: Rules base vector.
BaseConocimiento - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
BaseD - Class in keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted: Title: BaseD Description: Contains the definition of the data base Copyright: Copyright (c) 2009 Company: KEEL
BaseD() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseD: Default constructor
BaseD(int, int, double[][], String[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseD: Constructor with parameters.
BaseD - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec: Title: BaseD Description: data set.
BaseD() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseD: Default constructor.
BaseD(int, int, double[][], String[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseD
BaseD - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF
BaseD() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseD
BaseD(int, int, double[][]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseD
BaseD - Class in keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM
BaseD() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseD
BaseD(String, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseD
BaseDatos - Class in keel.Algorithms.Genetic_Rule_Learning.Hider
BaseDatos(Dataset) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos: Constructor
basePrototype - Static variable in class keel.Algorithms.Instance_Generation.utilities.Distance: Base prototype of the sortings.
basePrototype - Static variable in class keel.Algorithms.Semi_Supervised_Learning.utilities.Distance: Base prototype of the sortings.
BaseR - Class in keel.Algorithms.Decision_Trees.DT_GA: This class contains the representation of a Rule Set.
BaseR() - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.BaseR: Default constructor.
BaseR(myDataset, String) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.BaseR: Creates a rulebase using the rules of a existing decision tree given as an argument.
BaseR - Class in keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted: Title: BaseR Description: Contains the definition of the rule base Copyright: Copyright (c) 2009 Company: KEEL
BaseR() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseR: Default constructor
BaseR(BaseD, myDataset, int, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseR: Rule Base Constructor
BaseR - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec
BaseR() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseR
BaseR(BaseD, myDataset, int, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseR
BaseR - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF
BaseR() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseR
BaseR(BaseD, myDataset) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseR
BaseR - Class in keel.Algorithms.Genetic_Rule_Learning.OCEC
BaseR() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OCEC.BaseR
BaseR(myDataset) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OCEC.BaseR
BaseR - Class in keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM
BaseR() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseR
BaseR(String, BaseD) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseR
BaseReglas - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Sel: Rules Array that represents the rulebase.
BaseReglas - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Tun: Rules Array that represents the rulebase.
BaseReglas - Class in keel.Algorithms.Rule_Learning.LEM1
BaseReglas(LinkedList<Integer>, myDataset) - Constructor for class keel.Algorithms.Rule_Learning.LEM1.BaseReglas
BaseReglas - Class in keel.Algorithms.Rule_Learning.LEM2
BaseReglas() - Constructor for class keel.Algorithms.Rule_Learning.LEM2.BaseReglas
BaseReglas - Class in keel.Algorithms.Rule_Learning.Ritio
BaseReglas(myDataset) - Constructor for class keel.Algorithms.Rule_Learning.Ritio.BaseReglas
BaseReglas - Class in keel.Algorithms.Rule_Learning.Rules6: Title: BaseReglas (Rule base) Description: Stores the rules and checks the test file to evaluates them
BaseReglas() - Constructor for class keel.Algorithms.Rule_Learning.Rules6.BaseReglas: Default constructor.
BaseReglas(LinkedList<Regla>) - Constructor for class keel.Algorithms.Rule_Learning.Rules6.BaseReglas: Parameter constructor.
BaseReglas - Class in keel.Algorithms.Rule_Learning.SRI: Title: BaseReglas (Rule base) Description: Stores the rules and checks the test file to evaluates them
BaseReglas() - Constructor for class keel.Algorithms.Rule_Learning.SRI.BaseReglas
BaseReglas(LinkedList<Regla>) - Constructor for class keel.Algorithms.Rule_Learning.SRI.BaseReglas: Parameter constructor.
basicDE(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.IPLDE.IPLDEGenerator
batchFilterFile(NominalToBinaryFilter, String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Method for testing filters ability to process multiple batches.
batchFinished() - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter: Signify that this batch of input to the filter is finished.
BayesianDiscretizer - Class in keel.Algorithms.Discretizers.Bayesian_Discretizer: This is the class with the operations of the Bayesian discretization.
BayesianDiscretizer() - Constructor for class keel.Algorithms.Discretizers.Bayesian_Discretizer.BayesianDiscretizer
before - Variable in class keel.GraphInterKeel.experiments.Joint
BEGIN - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
beginColumn - Variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Token: beginLine and beginColumn describe the position of the first character of this token; endLine and endColumn describe the position of the last character of this token.
beginColumn - Variable in class keel.Algorithms.Rule_Learning.Swap1.Token: beginLine and beginColumn describe the position of the first character of this token; endLine and endColumn describe the position of the last character of this token.
beginColumn - Variable in class keel.Dataset.Token: beginLine and beginColumn describe the position of the first character of this token; endLine and endColumn describe the position of the last character of this token.
beginLine - Variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Token: beginLine and beginColumn describe the position of the first character of this token; endLine and endColumn describe the position of the last character of this token.
beginLine - Variable in class keel.Algorithms.Rule_Learning.Swap1.Token: beginLine and beginColumn describe the position of the first character of this token; endLine and endColumn describe the position of the last character of this token.
beginLine - Variable in class keel.Dataset.Token: beginLine and beginColumn describe the position of the first character of this token; endLine and endColumn describe the position of the last character of this token.
beginSequentialExamples() - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Environment: It initializes the first example.
beginSequentialExamples() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.MPEnvironment: It initializes the first example.
beginSequentialExamples() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.SSFileEnvironment: It initializes at the first example.
beginSequentialExamples() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.DECEnvironment: It initializes the first example.
beginSequentialExamples() - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Environment: It initializes the first example.
beginSequentialExamples() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MPEnvironment: It initializes the first example.
beginSequentialExamples() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MSEnvironment: It initializes the first example.
beginSequentialExamples() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.PAREnvironment: It initializes the first example.
beginSequentialExamples() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.POSEnvironment: It initializes the first example.
beginSequentialExamples() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.SSFileEnvironment: It initializes at the first example.
BeginToken() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
BeginToken() - Static method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
BeginToken() - Static method in class keel.Dataset.SimpleCharStream
belongs(String) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqList: Test if the element passed as argument belongs to this list
Berg - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Apply Berg flag.
bernuilli(double, int, int) - Method in class keel.Algorithms.Semi_Supervised_Learning.SETRED.SETREDGenerator: Computes the bernuilli value for a given number with the given probability and value of n.
best - Variable in class keel.Algorithms.MIL.Diverse_Density.DD.DD
best - Variable in class keel.Algorithms.MIL.Diverse_Density.EMDD.EMDD
Best_current_perf - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
Best_guy - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
bestAccuracy - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Statistics
bestAccuracy - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Statistics
bestAliveRules - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Statistics
bestAliveRules - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Statistics
bestCCRIndividual - Variable in class keel.Algorithms.Neural_Networks.NNEP_Clas.algorithm.classification.CCRElitistNeuralNetAlgorithm: Best individual of last generation
bestEvaluation - Variable in class keel.Algorithms.PSO_Learning.CPSO.Particle
bestEvaluation - Variable in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
bestEvaluation - Variable in class keel.Algorithms.PSO_Learning.PSOLDA.Particle
bestEvaluation - Variable in class keel.Algorithms.PSO_Learning.REPSO.Particle
bestFeatures - Variable in class keel.Algorithms.MIL.APR.AbstractAPR
bestFitness - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Statistics
bestFitness - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Statistics
bestIndividual - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Best individual of last generation
bestOfIteration(double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Statistics
bestOfIteration(double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Statistics
bestPreviousFitness - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricSAMutator: Best fitness of the previous generation
bestRB() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Population: It returns the best rule base obtained
bestRB() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Population: It returns the best RB found so far
bestRules - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Statistics
bestRules - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Statistics
beta - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
beta - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Learning rate for prediction, prediction error, fitness, and action set estimation updates
beta - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Learning rate for prediction, prediction error, fitness, and action set estimation updates
BETA - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
BETA - Variable in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
betainv(double, double, double) - Static method in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Quoted from original Fortran documentation: Computes incomplete beta function ratio for arguments x between zero and one, p and q positive.
betta - Variable in class keel.Algorithms.Neural_Networks.ensemble.Ensemble: Ensemble weights.
BETTER(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.RuleBase: Maximization
BETTER(double, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Population: Maximization
BETTER(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: Maximization
BETTER(double, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Population: Maximization
BETTER(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.RuleBase: Maximization
BETTER(double, double) - Method in class keel.Algorithms.Decision_Trees.DT_GA.Poblacion: Checks if the double a is greater than b.
BETTER(double, double) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.Poblacion: Checks if the double a is greater than b.
BETTER(double, double) - Method in class keel.Algorithms.Decision_Trees.Target.Poblacion: Checks if the double a is greater than b.
BETTER(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Population: Maximization
BETTER(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase: Maximization
Better(int, int[], Int_t, int[][]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
Better(int, int[], Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
Better(int, int[], Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
BETTER(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Population: Maximization
BETTER(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.RuleBase: Maximization
BETTER(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseR
BETTER(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Poblacion
BETTER(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseR
BETTER(double, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Poblacion: Checks if the double a is greater than b.
BETTER(double, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Population
BETTER(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
BETTER(double, double) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.GA
BETTER(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseR
BETTER(double, double) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Poblacion: Checks if the double a is greater than b.
Better(Double, Double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion: It compares 2 values which is better
Better(Double, Double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion: It compares 2 values which is better
Better(Double, Double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion: It compares 2 values which is better
Better(Double, Double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion: It compares 2 values which is better
Better(Double, Double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion: It compares 2 values which is better
Better(Double, Double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion: It compares 2 values which is better
Better(Double, Double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion: It compares 2 values which is better
Better(Double, Double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion: It compares 2 values which is better
BETTER(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Utils: Returns if the first float argument is better than the second
BETTER(int, int) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Utils: Returns if the first integer argument is better than the second
BETTER(double, double) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Utils: Returns if the first double argument is better than the second
BETTER(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Utils: Returns if the first float argument is better than the second
BETTER(int, int) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Utils: Returns if the first integer argument is better than the second
BETTER(double, double) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Utils: Returns if the first double argument is better than the second
BETTER(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Utils: Returns if the first float argument is better than the second
BETTER(int, int) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Utils: Returns if the first integer argument is better than the second
BETTER(double, double) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Utils: Returns if the first double argument is better than the second
BETTER(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Utils: Returns if the first float argument is better than the second
BETTER(int, int) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Utils: Returns if the first integer argument is better than the second
BETTER(double, double) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Utils: Returns if the first double argument is better than the second
BETTER(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Utils: Returns if the first float argument is better than the second
BETTER(int, int) - Static method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Utils: Returns if the first integer argument is better than the second
BETTER(double, double) - Static method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Utils: Returns if the first double argument is better than the second
BETTER(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Utils: Returns if the first float argument is better than the second
BETTER(int, int) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Utils: Returns if the first integer argument is better than the second
BETTER(double, double) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Utils: Returns if the first double argument is better than the second
BETTER(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Utils: Returns if the first float argument is better than the second
BETTER(int, int) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Utils: Returns if the first integer argument is better than the second
BETTER(double, double) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Utils: Returns if the first double argument is better than the second
Beval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeAdd: This method evaluate the alphacut of two nodes with the sum
Beval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeExp: This method evaluate the alphacut of a node
Beval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeExprArit: This abstract method evaluate the alphacut of two nodes
Beval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeExprHold: This method evaluate the alphacut of the nodes
Beval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeLog: This method evaluate two nodes with the fuzzy alpha cut log
Beval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeMinus: This method evaluates two nodes with the fuzzy alpha cut minus
Beval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeProduct: This method evaluates two nodes with the fuzzy alpha cut product
Beval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeSquareRoot: This method evaluates the square root node
Beval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeValue: this method return the fuzzy alpha cuts of a node
Beval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeVariable: This method returns the fuzzy alpha cut
bias() - Method in class keel.Algorithms.SVM.SMO.SMO: Returns the bias of each binary SMO.
biased - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Boolean indicating if each linked layer of neural nets are biased
biased - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Is biased?
biased - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron: Is biased?
biased(double, double, double) - Static method in class keel.Algorithms.Rule_Learning.C45Rules.Rule: Biased function.
biased(double, double, double) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Ruleset: Biased function.
biased(double, double, double) - Static method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule: Biased function.
biased(double, double, double) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset: Biased function.
bienCubiertos - Static variable in class keel.Algorithms.Decision_Trees.Target.Tree: Number of correct classified examples.
big - Static variable in class keel.Algorithms.Lazy_Learning.Statistics: Big number
big - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics
biginv - Static variable in class keel.Algorithms.Lazy_Learning.Statistics: Inverse big number
biginv - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics
bin2nominal() - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Gene: This method converts from the status of the gene to a list of nominals values of the attribute which are currently covered
BinarySMO() - Constructor for class keel.Algorithms.SVM.SMO.SMO.BinarySMO
binConversion(StringTokenizer, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Produce an item set (array of elements) from input line.
binomialLog(int, int) - Static method in class keel.Algorithms.Discretizers.MODL.MODL: Returns the natural logarithm of m over n.
binomialStandardError(double, int) - Static method in class keel.Algorithms.Decision_Trees.M5.Distributions: Computes standard error for observed values of a binomial random variable.
binomialStandardError(double, int) - Static method in class keel.Algorithms.Lazy_Learning.Statistics: Computes standard error for observed values of a binomial random variable.
binomialStandardError(double, int) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics: Computes standard error for observed values of a binomial random variable.
binomP(double, double, double) - Method in class keel.Algorithms.Lazy_Learning.LBR.LBR: Performs a binomial test
binsearch(DoubleFunc, double, double) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Numeric: find inverse x of fun.F so that a = F(x), where x>=0, and fun.F is monotonically increasing.
BinTournSelect() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: BinTournSelect Applies the selection schema of the genetic algorithm Binary tournament selection from elite to inter
BioHEL - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL: Class of BioHEL algorithm
BioHEL() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.BioHEL: Default constructor.
bivariateDensity(double, double, double) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.StatFunc: Density function of the Bivariate Standard Normal Distribution.
bloatControlDone - Variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
bloatControlDone - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
BNGE - Class in keel.Algorithms.Hyperrectangles.BNGE: File: BNGE.java The BNGE Algorithm.
BNGE(String) - Constructor for class keel.Algorithms.Hyperrectangles.BNGE.BNGE: The main method of the class
bodyFont - Variable in class keel.GraphInterKeel.experiments.Credits
Bon - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Bon boolean
Bon - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Apply Bon flag.
BOOLEAN_CONST - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
BOOLEAN_CONST - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for boolean constant.
BOOLEAN_CONST - Static variable in interface keel.Dataset.DataParserConstants
bootstrap(FileWriter, FileWriter, int, int, int, fuzzy[][], Vector<Float>, Vector<fuzzy>) - Static method in class keel.Algorithms.LQD.tests.IntermediateBoost.Main
Borderline_SMOTE - Class in keel.Algorithms.ImbalancedClassification.Resampling.Borderline_SMOTE: File: Borderline_SMOTE.java The Borderline SMOTE algorithm is an oversampling method used to deal with the imbalanced problem.
Borderline_SMOTE(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.Borderline_SMOTE.Borderline_SMOTE: Constructor of the class.
borraDistrib() - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Reset the value of the distribution for the complex
borraDistrib() - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: reset the distribution value for the complex
borraDistrib() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: reset the distribution value for the complex
borrar() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseR: It removes those rules from the RB whose rule weight (confidence) is lower than 0
borrar() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseR
borrar() - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Cromosoma: Marks a chromosome for deletion.
borrar() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.Chromosome
borrar() - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma: Marks a chromosome for deletion.
borrar() - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma: Marks a chromosome for deletion.
borrar() - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma: Marks a chromosome for deletion.
borrar() - Method in class keel.Algorithms.Instance_Selection.CHC.Cromosoma: Marks a chromosome for deletion
borrar() - Method in class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Marks a chromosome for deletion
borrar() - Method in class keel.Algorithms.Instance_Selection.GGA.Cromosoma: Marks a chromosome for deletion.
borrar() - Method in class keel.Algorithms.Instance_Selection.PBIL.Cromosoma: Marks a chromosome for deletion
borrar() - Method in class keel.Algorithms.Instance_Selection.SGA.Cromosoma: Marks a chromosome for deletion
borrar() - Method in class keel.Algorithms.Instance_Selection.SSMA.Cromosoma: Marks a chromosome for deletion
borrar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.CHC.Cromosoma: Marks a chromosome for deletion
borrar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Marks a chromosome for deletion
borrar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.GGA.Cromosoma: Marks a chromosome for deletion
borrar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Cromosoma: Marks a chromosome for deletion
borrar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.SGA.Cromosoma: Marks a chromosome for deletion
borrar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma: Marks a chromosome for deletion
borrar(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Returns a copy of the set without an prototype.
borrosorectangular(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
borrosorectangular(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
borrosorectangular(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.fuzzy
borrosorectangular(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.fuzzy
borrosorectangular(float, float) - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
borrosorectangular(float, float) - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
borrosorectangular(float, float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
borrosorectangular(float, float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
borrosorectangular(float, float) - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
borrosotrapdcha(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
borrosotrapdcha(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
borrosotrapdcha(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.fuzzy
borrosotrapdcha(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.fuzzy
borrosotrapdcha(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.fuzzy
borrosotrapdcha(float, float) - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
borrosotrapdcha(float, float) - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
borrosotrapdcha(float, float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
borrosotrapdcha(float, float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
borrosotrapdcha(float, float) - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
borrosotrapezoidal(float, float, float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
borrosotrapezoidal(float, float, float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
borrosotrapezoidal(float, float, float, float) - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
borrosotrapezoidal(float, float, float, float) - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
borrosotrapezoidal(float, float, float, float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
borrosotrapezoidal(float, float, float, float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
borrosotrapezoidal(float, float, float, float) - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
borrosotrapizda(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
borrosotrapizda(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
borrosotrapizda(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.fuzzy
borrosotrapizda(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.fuzzy
borrosotrapizda(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.fuzzy
borrosotrapizda(float, float) - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
borrosotrapizda(float, float) - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
borrosotrapizda(float, float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
borrosotrapizda(float, float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
borrosotrapizda(float, float) - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
borrosotriangular(float, float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
borrosotriangular(float, float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
borrosotriangular(float, float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.fuzzy
borrosotriangular(float, float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.fuzzy
borrosotriangular(float, float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.fuzzy
borrosotriangular(float, float, float) - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
borrosotriangular(float, float, float) - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
borrosotriangular(float, float, float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
borrosotriangular(float, float, float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
borrosotriangular(float, float, float) - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
boundary() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
boundary - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal
boundary() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal
boundary_Class(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff: Compute the boundary of the set (Cases in the upper aproximation and not in the lower aproximation)
boundary_Class(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal: Compute the boundary of the set (Cases in the upper aproximation and not in the lower aproximation)
boundValueToAttributeLimits(double, Attribute) - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.ConceptMostCommonValue: Takes a value and checks if it belongs to the attribute interval.
boundValueToAttributeLimits(double, Attribute) - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.knnImpute: Takes a value and checks if it belongs to the attribute interval.
boundValueToAttributeLimits(double, Attribute) - Method in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.MostCommonValue: Takes a value and checks if it belongs to the attribute interval.
boundValueToAttributeLimits(double, Attribute) - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.wknnImpute: Takes a value and checks if it belongs to the attribute interval.
boxplot() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Histogram: Return the boxplot (median, lowerquartile, upperquartile, P0.05, P0.95).
bp_type - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters: Not used
bp_type - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters: Not used
bp_type - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters: Not used
bp_type - Variable in class keel.Algorithms.Neural_Networks.net.Parameters: Not used
BPCA - Class in keel.Algorithms.Preprocess.Missing_Values.BPCA: File: BPCA.java The BPCA Missing Values Imputation algorithm.
BPCA(String) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.BPCA.BPCA: Creates a new object of BPCA using the parameter file indicated
br - Variable in class keel.GraphInterKeel.experiments.EducationalReport
broken - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.Link: Link state
BrowserControl - Class in keel.GraphInterKeel.menu: A simple, static class to display a URL in the system browser.
BrowserControl() - Constructor for class keel.GraphInterKeel.menu.BrowserControl
BRtoString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Sel: Prints the RB to a String
BRtoString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Tun: Prints the RB to a String
BsdInitCrom(TableVar, float) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.CromCAN: Biased Random initialization of an existing chromosome
BsdInitCrom(TableVar, float) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.CromDNF: Biased Random initialization of an existing chromosome
BsdInitInd(TableVar, float, int, String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndCAN: Creates biased instance of Canonical individual
BsdInitInd(TableVar, float, int, String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndDNF: Creates biased instance of DNF individual
BsdInitInd(TableVar, float, int, String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Creates biased instance of individual
BsdInitPob(TableVar, float, float, int, String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Population: Biased random population initialization
BsdInitPop(TableVar, float, float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Biased random population initialization
BSE - Class in keel.Algorithms.Instance_Selection.BSE: BSE method
BSE(String) - Constructor for class keel.Algorithms.Instance_Selection.BSE.BSE: Constructor
BSE - Class in keel.Algorithms.Preprocess.Instance_Selection.BSE: BSE method
BSE(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.BSE.BSE: Constructor
bset - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Current individuals set
BTS - Class in keel.Algorithms.Decision_Trees.C45_Binarization: Title: BTS Description: This class implements the BTS OVO scheme Company: KEEL
BTS(Multiclassifier, float, OVO) - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.BTS: Binary Tree of Classifiers constructor
BTS3Algorithm - Class in keel.Algorithms.Instance_Generation.BTS3: BTS3Algorithm.
BTS3Algorithm() - Constructor for class keel.Algorithms.Instance_Generation.BTS3.BTS3Algorithm
BTS3Generator - Class in keel.Algorithms.Instance_Generation.BTS3: Prototoype generator by the Boostrap algorithm (BST3)
BTS3Generator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.BTS3.BTS3Generator: Constructor
BTS3Generator(PrototypeSet, int, int, int) - Constructor for class keel.Algorithms.Instance_Generation.BTS3.BTS3Generator: Constructor
BTS3Generator(PrototypeSet, double, int, int) - Constructor for class keel.Algorithms.Instance_Generation.BTS3.BTS3Generator: Constructor
bufcolumn - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
bufcolumn - Static variable in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
bufcolumn - Static variable in class keel.Dataset.SimpleCharStream
buffer - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
buffer - Static variable in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
buffer - Static variable in class keel.Dataset.SimpleCharStream
bufferInput(M5Instance) - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Adds the supplied input instance to the inputformat dataset for later processing.
bufferInput(M5Instance) - Method in class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter: Buffers a input format.
bufferInstance - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArffDataSet: Buffer Instance
bufferInstance - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.KeelDataSet: Buffer Instance
bufline - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
bufline - Static variable in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
bufline - Static variable in class keel.Dataset.SimpleCharStream
bufpos - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
bufpos - Static variable in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
bufpos - Static variable in class keel.Dataset.SimpleCharStream
build_tree() - Method in class keel.Algorithms.Decision_Trees.CART.CART: Constructs decision tree
buildClassifier(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.M5: Construct a model tree by training instances
buildClassifier(Instances) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Classifier: Generates a classifier.
buildClassifier(Instances) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Builds Ripper in the order of class frequencies.
buildClassifier(Instances) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.PDFC: Method for building the classifier.
buildClassifier(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: Construct a model tree by training itemsets
buildClassifier(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA: Builds the model
buildClassifier(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Builds classifier.
buildClassifier(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Builds classifier.
buildClassifier(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Builds classifier.
buildClassifier(InstanceSet) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Builds the classifier
buildClassifier(InstanceSet, InstanceAttributes) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Builds the classifier
buildClassifier(Instances, int, int, boolean, int, int) - Method in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: Method for building the binary classifier.
buildClassifier(Instances) - Method in class keel.Algorithms.SVM.SMO.SMO: Method for building the classifier.
buildClassifier(Instances) - Method in class keel.Algorithms.SVM.SMO.SMOreg: Method for building the classifier.
buildClassifier(Instances) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: learn SVM parameters from data.
buildClassifier(Instances) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMO: learn SVM parameters from data using Smola's SMO algorithm.
buildClassifier(Instances) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: learn SVM parameters from data using Keerthi's SMO algorithm.
buildClassifier(Instances) - Method in class keel.Algorithms.SVM.SMO.SVMreg: Method for building the classifier.
buildConfGraphDotFile(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams
buildCutPoints(InstanceSet) - Method in class keel.Algorithms.Discretizers.Basic.Discretizer: Creates the cut points for each attribute with the given dataset.
buildCutPoints(InstanceSet) - Method in class keel.Algorithms.Discretizers.Chi2_Discretizer.Discretizer: Creates the cut points for each attribute with the given dataset.
buildCutPoints(InstanceSet) - Method in class keel.Algorithms.Discretizers.Cluster_Analysis.Cluster_Analysis: It computes the cutpoints of the given dataset
buildCutPoints(InstanceSet) - Method in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Discretizer: Creates the cut points for each attribute with the given dataset.
buildCutPoints(InstanceSet) - Method in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Discretizer: Creates the cut points for each attribute with the given dataset.
buildCutPoints(InstanceSet) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Basic.Discretizer
buildCutPoints(InstanceSet) - Method in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Discretizer
buildHeuristic(myDataset, int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Rule: Builds a new rule from a heuristic function
buildKernel(Instances) - Method in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: builds the kernel with the given data.
buildKernel(Instances) - Method in class keel.Algorithms.SVM.SMO.supportVector.Kernel: builds the kernel with the given data
buildKernel(Instances) - Method in class keel.Algorithms.SVM.SMO.supportVector.Puk: builds the kernel with the given data.
buildKernel(Instances) - Method in class keel.Algorithms.SVM.SMO.supportVector.RBFKernel: builds the kernel with the given data.
buildKernel(Instances) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: builds the kernel with the given data.
buildLogisticModelsTipText() - Method in class keel.Algorithms.SVM.SMO.SMO: Returns the tip text for this property
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.AMPSO.AMPSOAlgorithm: Builds a new ChenGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm: Build a new generator object.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.BasicMethods.ARSAlgorithm
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.BasicMethods.AVGAlgorithm
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.BasicMethods.SAVGAlgorithm
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.BTS3.BTS3Algorithm
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.Chen.ChenAlgorithm: Builds a new ChenGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.DE.DEAlgorithm: Builds a new DEGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.DEGL.DEGLAlgorithm: Builds a new DEGLGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.DSM.DSMAlgorithm: Builds a new DSMGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.ENPC.ENPCAlgorithm: Builds a new ENPCGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.GENN.GENNAlgorithm: Builds a new GENNGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.GMCA.GMCAAlgorithm: Builds a new GMCAGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.HYB.HYBAlgorithm: Builds a new HYBGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.ICPL.ICPLAlgorithm: Builds a new ICPLGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.IPLDE.IPLDEAlgorithm: Builds a new IPADEGenerator
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.JADE.JADEAlgorithm: Builds a new JADEGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQ1Algorithm: Builds a new LVQ1 object.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQ2_1Algorithm: Builds a new LVQ2.1 object.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQ2Algorithm: Builds a new LVQ2 object.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQ3Algorithm: Builds a new LVQ3 object.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQPRUAlgorithm: Builds a new LVQPRU object.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQTCAlgorithm: Builds a new LVQTC object.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.MCA.MCAAlgorithm: Builds a new MCAGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.MixtGauss.MixtGaussAlgorithm: Builds a new MixtGaussGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.MSE.MSEAlgorithm: Builds a new MSEGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.OBDE.OBDEAlgorithm: Builds a new OBDEGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.PNN.PNNAlgorithm: Builds a new PNNGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.POC.POCAlgorithm: Builds a new POCGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.PSCSA.PSCSAAlgorithm: Builds a new PSCSAGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.PSO.PSOAlgorithm: Builds a new PSOGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.RSP.RSPAlgorithm: Builds a new RSPGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.SADE.SADEAlgorithm: Builds a new SADEGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.SFLSDE.SFLSDEAlgorithm: Builds a new SFLSDEGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.SGP.SGPAlgorithm: Builds a new SGPGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.Trivial.TrivialAlgorithm: Builds a new PrototypeGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.VQ.AVQAlgorithm: Builds a new AVQGenerator.
buildNewPrototypeGenerator(PrototypeSet, Parameters) - Method in class keel.Algorithms.Instance_Generation.VQ.VQAlgorithm: Builds a new VQGenerator.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ADE_CoForestAlgorithm: Builds a new ADE_CoForestGenerator.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.APSSC.APSSCAlgorithm: Builds a new APSSCGenerator.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm: Build a new generator object for SSL.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.C45SSL.C45SSLAlgorithm: Builds a new C45SSLGenerator.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCAlgorithm: Builds a new CLCCGenerator.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoBC.CoBCAlgorithm: Builds a new CoBCGenerator.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.CoForestAlgorithm: Builds a new CoForestGenerator.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoTraining.CoTrainingAlgorithm: Builds a new CoTrainingGenerator.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.DE_TriTraining.DE_TriTrainingAlgorithm: Builds a new DE_TriTrainingGenerator.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.Democratic.DemocraticAlgorithm: Builds a new DemocraticGenerator.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.NBSSL.NBSSLAlgorithm: /** Builds a new NBSSLGenerator.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.NNSSL.NNSSLAlgorithm: Builds a new NNSSLGenerator.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.RASCO.RASCOAlgorithm: Builds a new RASCOGenerator.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.Rel_RASCO.Rel_RASCOAlgorithm: Builds a new Rel_RASCOGenerator.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.SelfTraining.SelfTrainingAlgorithm: Builds a new SelfTrainingGenerator.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.SETRED.SETREDAlgorithm: Builds a new SETREDGenerator.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.SMOSSL.SMOSSLAlgorithm: Builds a new SMOSSLGenerator.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.SNNRCE.SNNRCEAlgorithm: Builds a new SNNRCEGenerator.
buildNewPrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Method in class keel.Algorithms.Semi_Supervised_Learning.TriTraining.TriTrainingAlgorithm: Builds a new TriTrainingGenerator.
buildNode(Dataset) - Method in class keel.Algorithms.Decision_Trees.C45.Tree: Adds one new node.
buildNode(Dataset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Tree: Adds one new node.
buildNode(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree: Adds one new node.
buildNode(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Tree: Adds one new node.
buildNode(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree: Adds one new node.
buildNode(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Tree: Adds one new node.
buildNode(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree: Adds one new node.
buildNode(MyDataset) - Method in class keel.Algorithms.Rule_Learning.PART.Tree: Adds one new node.
buildNode(Dataset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: Adds one new node.
buildTree(Dataset) - Method in class keel.Algorithms.Decision_Trees.C45.Tree: Function to build the classifier tree.
buildTree(Dataset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Tree: Function to build the classifier tree.
buildTree(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree: Function to build the classifier tree.
buildTree(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Tree: Function to build the classifier tree.
buildTree(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree: Function to build the classifier tree.
buildTree(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Tree: Function to build the classifier tree.
buildTree(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree: Function to build the classifier tree.
buildTree(MyDataset) - Method in class keel.Algorithms.Rule_Learning.PART.Tree: Function to build the classifier tree.
buildTree(PrototypeSet, double[], PrototypeSet, double, boolean, int[], int, boolean) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Recursively generates a tree.
buildTree(Dataset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: Function to build the classifier tree.
buildTree(PrototypeSet, double[], PrototypeSet, double, boolean, int[], int, boolean) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Recursively generates a tree.
buildTree(PrototypeSet, double[], PrototypeSet, double, boolean, int[], int, boolean) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Recursively generates a tree.
busca_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.help.HelpOptions: Search event
buttonPressed - Static variable in class keel.GraphInterKeel.menu.Frame: EDUCATIONAL KEEL ***************************
buttonPressed - Static variable in class keel.GraphInterKeel.menu.FrameModules: EDUCATIONAL KEEL ***************************
ButtonTabComponent - Class in keel.GraphInterKeel.datacf.util
ButtonTabComponent(JTabbedPane) - Constructor for class keel.GraphInterKeel.datacf.util.ButtonTabComponent: Constructor
bw - Variable in class keel.GraphInterKeel.experiments.EducationalReport
byClasses() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets
byClasses() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal
byClasses() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SInstances

C

C - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForClassification
c - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForClassification
C - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
C - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Cluster: Vector that stores the representation of the cluster.
C - Variable in class org.libsvm.svm_parameter
C45 - Class in keel.Algorithms.Decision_Trees.C45: Class to implement the C4.5 algorithm
C45(String) - Constructor for class keel.Algorithms.Decision_Trees.C45.C45: Constructor.
C45(String, boolean, float, int, boolean) - Constructor for class keel.Algorithms.Decision_Trees.C45.C45: Constructor.
C45 - Class in keel.Algorithms.Decision_Trees.DT_GA.C45: Class to implement the C4.5 algorithm
C45(String) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.C45.C45: Constructor.
C45(String, boolean, float, int) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.C45.C45: Constructor.
C45 - Class in keel.Algorithms.Genetic_Rule_Learning.PART: Class to implement the C4.5 algorithm
C45(parseParameters) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.C45: Constructor.
C45(MyDataset, boolean, float, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.C45: Constructor with given paramters.
C45 - Class in keel.Algorithms.ImbalancedClassification.Ensembles.C45: Class to implement the C4.5 algorithm
C45(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.C45: Constructor.
C45(String, boolean, float, int, double[]) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.C45: Constructor.
C45(InstanceSet, boolean, float, int, double[]) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.C45: Constructor.
C45 - Class in keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter: Class to implement the C4.5 algorithm
C45(String, String) - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.C45: Constructor.
C45 - Class in keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter: Class to implement the C4.5 algorithm
C45(String, String) - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.C45: Constructor.
C45 - Class in keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter: Class to implement the C4.5 algorithm
C45(String, String) - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.C45: Constructor.
C45 - Class in keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter: Class to implement the C4.5 algorithm
C45(String, String) - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.C45: Constructor.
C45 - Class in keel.Algorithms.Rule_Learning.C45Rules: para commons.configuration import org.apache.commons.configuration
C45(parseParameters) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.C45: Constructor.
C45 - Class in keel.Algorithms.Rule_Learning.C45RulesSA: para commons.configuration import org.apache.commons.configuration
C45(parseParameters) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.C45: Constructor.
C45 - Class in keel.Algorithms.Rule_Learning.PART: para commons.configuration import org.apache.commons.configuration
C45(parseParameters) - Constructor for class keel.Algorithms.Rule_Learning.PART.C45: Constructor.
C45(MyDataset, boolean, float, int) - Constructor for class keel.Algorithms.Rule_Learning.PART.C45: Parameter constructor.
C45 - Class in keel.Algorithms.Semi_Supervised_Learning.Basic.C45: Class to implement the C4.5 algorithm
C45(String, String) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45: Constructor.
C45(InstanceSet, InstanceSet) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45: Constructor.
C45CS - Class in keel.Algorithms.ImbalancedClassification.CSMethods.C45CS: Class to implement the C4.5 algorithm
C45CS(String) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.C45CS: Constructor.
C45SSLAlgorithm - Class in keel.Algorithms.Semi_Supervised_Learning.C45SSL: C45SSL algorithm calling.
C45SSLAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.C45SSL.C45SSLAlgorithm
C45SSLGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.C45SSL: This class implements the Self-traning wrapper.
C45SSLGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.C45SSL.C45SSLGenerator: Build a new C45SSLGenerator Algorithm
C45SSLGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.C45SSL.C45SSLGenerator: Build a new C45SSLGenerator Algorithm
c_random(Rbfn) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation: Performs the C_RANDOM mutator operator: modifies MUTATORS_INTERNAL_PROB % of the centers of the net
C_SVC - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
C_SVC - Static variable in class org.libsvm.svm_parameter
CACC - Class in keel.Algorithms.Discretizers.CACC: This class implements the CACC discretizer
CACC() - Constructor for class keel.Algorithms.Discretizers.CACC.CACC: Constructor of the class
cache - Variable in class keel.Algorithms.Neural_Networks.ensemble.Ensemble: Ensemble weights.
cache_size - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
cache_size - Variable in class org.libsvm.svm_parameter
CachedKernel - Class in keel.Algorithms.SVM.SMO.supportVector: Base class for RBFKernel and PolyKernel that implements a simple LRU.
CachedKernel() - Constructor for class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: default constructor - does nothing.
CachedKernel(Instances, int) - Constructor for class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: Initializes the kernel cache.
cacheSizeTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: Returns the tip text for this property
cacheSizeTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Returns the tip text for this property
CAD_CONST - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
CAD_CONST - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for string constant.
CAD_CONST - Static variable in interface keel.Dataset.DataParserConstants
CADDDiscretizer - Class in keel.Algorithms.Discretizers.CADD_Discretizer: This class implements the CADD discretizer.
CADDDiscretizer(double, int) - Constructor for class keel.Algorithms.Discretizers.CADD_Discretizer.CADDDiscretizer: Builder
CAIMDiscretizer - Class in keel.Algorithms.Discretizers.CAIM_Discretizer: This class implements the CADD discretizer.
CAIMDiscretizer() - Constructor for class keel.Algorithms.Discretizers.CAIM_Discretizer.CAIMDiscretizer: Builder
CalAproxInfSup(int, double) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets
calcDist(Individual) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Computes the distance between this individual and other NOTE: this function can not be used before the "original support" measures (if used) is computed
CalcFitness(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Computes the fitness of the individuals in the population, until number max First we compute strength, then rawFitness, distances, density and finally fitness All the individuals are suposed to have been evaluated We don't compute for all the population but the number inicated as a parameter
CalcInd(float[], int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual: Evaluate the individual
CalcInd(float[], int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual: Evaluate the individual
CalcInd(float[], int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual: Evaluate the individual
CalcPob(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population: Evaluates the population
CalcPob(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population: Evaluates the population
CalcPob(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population: Evaluates the population
CalcPobOutput(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population: Evaluates the population to obtain the output files of training and test for classification
CalcPobOutput(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population: Evaluates the population to obtain the output files of training and test for classification
CalcPobOutput(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population: Evaluates the population to obtain the output files of training and test for classification
calcula_consecuente(myDataset) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Regla: It computes the best consequent (class and rule weight) for the given rule
calcula_consecuente(myDataset) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Regla
calculaCoste(int) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Computes the cost of each node of the tree.
calculaFitness() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Organizacion
calculaGini() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Computes and returns the Gini index for the node (continuous attributes).
calculaGini(int, int[][], int, int, int) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Computes and returns the Gini index for the node (discrete attributes).
CalculaI(double[][][], FuzzyRule[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
calculaLaplaciano() - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Calculate the LaPlaces's value for a complex
calculaLaplaciano() - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Computes the laplacian value of the complex.
calculaMasComunes() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: Computes the most frequent values for every attribute.
calculaMasComunes() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: Computes the most frequent values for every attribute.
calculaMasComunes() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: Computes the most frequent values for every attribute.
calculaMasComunes() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: Computes the most frequent values for every attribute.
calculaMasComunes() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: It computes the most common values for each attribute
calculaMasComunes() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: Computes the most frequent values for every attribute.
calculaMasComunes() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: It computes the most common values for each attribute
calculaMasComunes() - Method in class keel.Algorithms.Rule_Learning.AQ.Dataset: It computes the most common values for each attribute
calculaMasComunes() - Method in class keel.Algorithms.Rule_Learning.CN2.Dataset: It computes the most common values for each attribute
calculaMasComunes() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Calculate the values most commons for each column or attribute
calculaMasComunes() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Calculate the values most commons for each column or attribute
calculaMasComunes() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: It computes the most common values for each attribute
calculaMasComunes() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset: It computes the most common values for each attribute
calculaMasComunes() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset: It computes the most common values for each attribute
calculaMejorCorte(int, Node) - Method in class keel.Algorithms.Decision_Trees.SLIQ.SLIQ: Computes the best cut of the given attribute for the given node.
CalculaParametros() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.AD: Computes the different parameters for the algorithm PSOLDA
calcular_clean(int, boolean, String, Vector) - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.ChooseExamples
calculaRangos() - Method in class keel.Algorithms.RE_SL_Methods.LEL_TSK.MiDataset: Is reads the extremes
calculaRangos() - Method in class keel.Algorithms.RE_SL_Methods.mogulHC.MiDataset: Is reads the extremes
calculaRangos() - Method in class keel.Algorithms.RE_SL_Methods.mogulSC.MiDataset: Is reads the extremes
calculaRangos() - Method in class keel.Algorithms.RE_SL_Methods.TSK_IRL.MiDataset: It reads the extremes
calculaRelativeSupport(myDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Regla
calcularROM(double, double[], double[]) - Static method in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Friedman: Computes ROM adjusted p-values
calcularROM(double, double[], double[]) - Static method in class keel.GraphInterKeel.statistical.tests.Friedman: Computes ROM adjusted p-values
CalculaSignoDeseado(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
calculate(PosProb[]) - Static method in class keel.Algorithms.ImbalancedClassification.Auxiliar.AUC.CalculateAUC: Method to calculate the Area Under The Curve (AUC)
calculate(Matrix, Matrix, double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.LinearRegression: performs the actual regression.
Calculate - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate: Calculate the values of subgroup discovery quality measures with respect to the rules extracted by the algorithm
Calculate() - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Calculate
Calculate(String, String, String, String, String, String, int) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Calculate: Calculate is the main method of the Calculate
Calculate - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate: Calculate the values of subgroup discovery quality measures with respect to the rules extracted by the algorithm
Calculate() - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Calculate
Calculate(String, String, String, String, String, String, int) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Calculate: Calculate is the main method of the Calculate
Calculate - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate: Calculate the values of subgroup discovery quality measures with respect to the rules extracted by the algorithm.
Calculate() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Calculate
Calculate(String, String, String, String, String, String, int) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Calculate: Calculate is the main method of the Calculate
Calculate_Increase(double, double) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Calculate_Increase(double, double) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
calculateAc(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.PRM: It computes the PNArray A' from a rule r
CalculateAccuracy(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.MixtGauss.MixtGaussGenerator: Accuracy per class.
calculateActualSeed() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: Method to calculate seed for actual partition The config.txt file is inspected
CalculateAUC - Class in keel.Algorithms.ImbalancedClassification.Auxiliar.AUC: Class to compute the AUC values
CalculateAUC() - Constructor for class keel.Algorithms.ImbalancedClassification.Auxiliar.AUC.CalculateAUC
calculateConfidence(fuzzy[][], Vector<Vector<Float>>, Vector<fuzzyPartition>, int, int, int, Vector<Float>, Vector<Vector<fuzzy>>, Vector<fuzzy>, String, Vector<fuzzy>) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
calculateConfidence(fuzzy[][], Vector<Vector<Float>>, Vector<fuzzyPartition>, int, int, int, Vector<Float>, Vector<Vector<fuzzy>>, Vector<fuzzy>, String, Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
calculateConfidence(fuzzy[][], Vector<Vector<Float>>, Vector<partition>, int, int, int, Vector<Float>, Vector<Vector<Float>>, Vector<Interval>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
calculateConfidence(fuzzy[][], Vector<Vector<Float>>, Vector<partition>, int, int, int, Vector<Float>, Vector<Vector<Float>>, Vector<Interval>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
calculateConfidences(Instances) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule: Computes the confidences of the given data.
calculateCoverageFactor(double, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Gene: It computes the coverage factor for the membership functions involved in a gene
calculateCoverageFactor(double, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Gene: It computes the coverage factor for the membership functions involved in a gene
calculateDerived() - Method in class keel.Algorithms.Decision_Trees.M5.SimpleStatistics: Tells the object to calculate any statistics that don't have their values automatically updated during add.
calculateDerived() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleStatistics: Tells the object to calculate any statistics that don't have their values automatically updated during add.
calculateDerived() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Stats: Tells the object to calculate any statistics that don't have their values automatically updated during add.
calculateError(byte[][], byte[][]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.errorfunctions.ClassificationAccuracyErrorFunction: Returns the classification error of a matrix of obtained values for classification, compared with a matrix of expected values
calculateError(double[][], double[][]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.errorfunctions.LogisticErrorFunction: Returns the logistic error of a matrix of obtained values for classification, compared with a matrix of expected values
calculateError(T, T) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.problem.errorfunctions.IErrorFunction: Calculate error of obtained entity, comparated with expected entity
calculateError(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.problem.errorfunctions.MSEErrorFunction: Returns the MSE of an array of obtained values, compared with an array of expected values
calculateError(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.problem.errorfunctions.SEPErrorFunction: Returns the MSE of an array of obtained values, compared with an array of expected values
calculateGenerality() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Computes the generality of the classifier and stores it in it corresponding parameter in the class Parameters.
calculateGenerality() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Computes the generality of the classifier.
calculateInfoNode(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams: Constructs and returns an InfoNode of the node with the given id.
calculateInfoNode(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Constructs and returns an InfoNode of the node with the given id.
calculateLaplace(myDataset) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.Rule: Function to calculate the Laplace accuracy to our rule from a train dataset
calculateLift(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams
calculateMeans() - Method in class keel.Algorithms.Lazy_Learning.NM.NM: Calculate the mean (centroid) of each class
calculateMeans() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Obtain the means of all the outputs
calculateMostCommon() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Calculates the values for each column and attribute
CalculateNoiseFactor() - Method in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.PANDA: Computes the noise factor.
calculateOverlapFactor(FuzzyAttribute) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Gene: It computes the overlap factor for the membership functions involved in a gene
calculateOverlapFactor() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Gene: It computes the overlap factor for the membership functions involved in a gene
calculateOverlapFactor() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Gene: It computes the overlap factor for the membership functions involved in a gene
calculateOverlapLength(MembershipFunction) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.MembershipFunction: It computes the overlap length of a membership functions with respect to another one
calculateOverlapLength(MembershipFunction) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.MembershipFunction: It computes the overlap length of a membership functions with respect to another one
calculatePartitionDatNameFile(int) - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: Return the path for PartitionDat File Method to calculate name of partition (training or test) The config.txt file is inspected
calculatePNc(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.PRM: It computes positive and negative values for each example in the training set by means a given rule
calculateRadius() - Method in class keel.Algorithms.Lazy_Learning.KNNAdaptive.KNNAdaptive: Precalculates the radius of each train instance
calculateStorage() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Commences process of calculating storage requirements for T-tree.
calculateSumInterval(myDataset) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
calculateSumInterval(myDataset) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
calculateSupport(FuzzyDataset) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Itemset: It computes the support of an itemset
calculateSupport(myDataset, DataBase, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Itemset: It computes the support of an itemset
calculateSupport(myDataset) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Itemset: It computes the support of an itemset
calculateSupport(FuzzyDataset) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Itemset: It computes the support of an itemset
calculateSupport(FuzzyDataset) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Itemset: It computes the support of an itemset
calculateSupports(myDataset) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset: It computes the support, rule support, hits, misses and PER of our itemset for a given dataset.
calculateSupports(myDataset) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset: It computes the support, rule support, hits, misses and PER of our itemset for a given dataset
calculateSupports(myDataset) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Itemset: It computes the support, rule support, hits, misses and PER of our itemset for a given dataset
calculateSupports(DataBase, myDataset) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Itemset: It computes the support, rule support, hits, misses and PER of our itemset for a given dataset
calculateSupports(myDataset) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Itemset: It computes the support, rule support, hits, misses and PER of our itemset for a given dataset
calculateSupports(DataBase, myDataset) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Itemset
calculateWeights() - Method in class keel.Algorithms.Lazy_Learning.CPW.CPW: Algorithm to calculate weights.
calculateWeights() - Method in class keel.Algorithms.Lazy_Learning.CW.CW: Algorithm to calculate weights.
calculateWeights() - Method in class keel.Algorithms.Lazy_Learning.PW.PW: Algorithm to calculate weights.
calculateWracc(myDataset, ArrayList<ExampleWeight>) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Rule: Calculate Wracc for this rule.
calculateWracc(myDataset, ArrayList<ExampleWeight>) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule: Calculate Wracc for this rule
calculation_fitness_total(Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.AlgGenetic
calculation_fitness_total(Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.AlgGenetic
calculaVelocidad(int, Vector, int, int, float[][], float, float, float, Randomize) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Computes the velocity of the particle.
calculeVolume() - Method in class keel.Algorithms.Hyperrectangles.EACH.Hyperrectangle: Calculates the area, volumn, ... of the hyperrectangle
calculo_fitness_ltf(Vector<fuzzy>) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.AlgGenetic
calculo_fitness_ltf(Vector<fuzzy>) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.AlgGenetic
calculo_fitness_regla(Vector<Float>, Interval) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.AlgGenetic
calculo_fitness_total(Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.AlgGenetic
calculo_fitness_total(Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.AlgGenetic
calculo_fitness_total(Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.AlgGenetic
calculo_previo_hvdm() - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Distance
calculo_previo_hvdm() - Method in class keel.GraphInterKeel.datacf.partitionData.PartitioningSchemes.DistanceBased_best
cambiarContextoAttributes() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoTraining.CoTrainingGenerator: Changes the context of the attributes.
cambiarContextoAttributes() - Method in class keel.Algorithms.Semi_Supervised_Learning.RASCO.RASCOGenerator: Changes the attributes to a different context.
cambiarContextoAttributes() - Method in class keel.Algorithms.Semi_Supervised_Learning.Rel_RASCO.Rel_RASCOGenerator: Changes the context of the attributes.
cambiarGen(int, int) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.Cromosoma: modifies the ith value of a gene
cambiarGen(int, int) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaBinario: modifies the ith value of a gene
cambiarGen(int, int) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaEntero: modifies the ith value of a gene
CamNN - Class in keel.Algorithms.Lazy_Learning.CamNN: File: CamNN.java The Cam NN Algorithm.
CamNN(String) - Constructor for class keel.Algorithms.Lazy_Learning.CamNN.CamNN: The main method of the class
cancelCellEditing() - Method in class keel.GraphInterKeel.datacf.util.EachRowEditor: Cancels the cell editing
capacity() - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector: Returns the capacity of the vector.
capacity() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector: Returns the capacity of the vector.
capacity() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector: Returns the capacity of the vector.
capacity() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector: Returns the capacity of the vector.
capacity() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Gets the capacity of the vector.
capacity() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector: Returns the capacity of the vector
capacity() - Method in class keel.Algorithms.SVM.SMO.core.FastVector: Returns the capacity of the vector.
capturaDataset() - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.MESDIF: Read the dataset and stores the values
capturaDataset() - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.SDIGA: Read the dataset and stores the values
CaptureDataset(String) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Calculate: Dataset interpretation - read the dataset and stores the values The attribute designed in "outputs" at the dataset file is the target variable If it is not established, the last one is taken as output - defined in the methods that manages the dataset.
CaptureDataset(String) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Calculate: Dataset interpretation - read the dataset and stores the values The attribute designed in "outputs" at the dataset file is the target variable If it is not established, the last one is taken as output - defined in the methods that manages the dataset.
CaptureDataset() - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.NMEEFSD: Read the dataset and stores the values
CaptureDataset(String) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Calculate: Dataset interpretation - read the dataset and stores the values The attribute designed in "outputs" at the dataset file is the target variable If it is not established, the last one is taken as output - defined in the methods that manages the dataset.
CaptureNumRules(String) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Calculate: Return the number of rules obtained by the algorithm
CaptureNumRules(String) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Calculate: Return the number of rules obtained by the algorithm
CaptureNumRules(String) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Calculate: Return the number of rules obtained by the algorithm
CaptureRules(String) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Calculate: Generate the population with the rules obtained by the algorithm
CaptureRules(String) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Calculate: Generate the population with the rules obtained by the algorithm
CaptureRules(String) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Calculate: Generate the population with the rules obtained by the algorithm
CAR_CONST - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
CART - Class in keel.Algorithms.Decision_Trees.CART: Main class of algorithm CART: Classification And Regression Trees (Breiman and al., 1984) CART are binary trees
CART(DoubleTransposedDataSet) - Constructor for class keel.Algorithms.Decision_Trees.CART.CART: Default constructor
CART(DoubleTransposedDataSet, IImpurityFunction) - Constructor for class keel.Algorithms.Decision_Trees.CART.CART: Constructor with impurity function
cartAlgorithm - Variable in class keel.Algorithms.Decision_Trees.CART.RunCART: Algorithm
cast(Object) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Casting an object without "unchecked" compile-time warnings.
cast(Object) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Casting an object without "unchecked" compile-time warnings.
cast(Object) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Casting an object without "unchecked" compile-time warnings.
cat(DoubleVector) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Combine two vectors together
CategoricalAttribute - Class in keel.Algorithms.Neural_Networks.NNEP_Common.data: Categorical attributes
CategoricalAttribute() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.data.CategoricalAttribute: Empty constructor
categories - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.CategoricalAttribute: Categories list (external values)
Cauchy - Static variable in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: Type of Positive Definite Functions supported (Cauchy)
cauchy(double) - Method in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: Computes the result of the Cauchy PDRF
CBA - Class in keel.Algorithms.Associative_Classification.ClassifierCBA: It contains the implementation of the CBA (Classification Based on Association) algorithm.
CBA() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.CBA: Default constructor
CBA(parseParameters) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.CBA: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
CBA2 - Class in keel.Algorithms.Associative_Classification.ClassifierCBA2: It contains the implementation of the CBA2 (Classification Based on Association 2) algorithm
CBA2() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.CBA2: Default constructor
CBA2(parseParameters) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.CBA2: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
CBACBM2() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.RuleBase: Classifier Builder (Method 2)
CBACBM2() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.RuleBase: Classifier Builder (Method 2)
CCIS - Class in keel.Algorithms.Instance_Selection.CCIS: CCIS method
CCIS(String) - Constructor for class keel.Algorithms.Instance_Selection.CCIS.CCIS: Constructor
CCIS - Class in keel.Algorithms.Preprocess.Instance_Selection.CCIS: CCIS method
CCIS(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.CCIS.CCIS: Constructor
CCLOSED - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for closed squared bracket "]".
CCLOSED - Static variable in interface keel.Dataset.DataParserConstants
CCRElitistNeuralNetAlgorithm> - Class in keel.Algorithms.Neural_Networks.NNEP_Clas.algorithm.classification: Base implementation for all classification neural net algorithms.
CCRElitistNeuralNetAlgorithm() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Clas.algorithm.classification.CCRElitistNeuralNetAlgorithm: Empty constructor
CDF_Normal - Class in keel.Algorithms.Statistical_Tests.Shared.nonParametric: File: Main.java This class contains routines to calculate the normal cumulative distribution function (CDF) and its inverse.
CDF_Normal() - Constructor for class keel.Algorithms.Statistical_Tests.Shared.nonParametric.CDF_Normal
CDF_Normal - Class in keel.GraphInterKeel.statistical.tests: File: Main.java This class contains routines to calculate the normal cumulative distribution function (CDF) and its inverse.
CDF_Normal() - Constructor for class keel.GraphInterKeel.statistical.tests.CDF_Normal
center() - Method in class keel.Algorithms.Instance_Generation.VQ.Cluster: Returns the center of the cluster.
centerDataMatrix(DenseMatrix, InstanceSet) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Centers data by subtraction of the mean
CenterLabel(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
CenterLabel() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.fuzzy_t
CenterLabel(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
CenterLabel(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
CenterLabel(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Returns the central value of the label number i in the domain
CenterLabel() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.fuzzy_t: Returns the central value of the label
CenterLabel(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t: Returns the central value of the label number "i" in the variable's domain.
CenterLabel(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Returns the central value of the label number "lab" in the variable in position "var" of the list.
CenterLabel(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
CenterLabel() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.fuzzy_t
CenterLabel(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
CenterLabel(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
CenterLabel(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
CenterLabel() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.fuzzy_t
CenterLabel(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
CenterLabel(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
CenterNN - Class in keel.Algorithms.Lazy_Learning.CenterNN: File: CenterNN.java The CenterNN Algorithm.
CenterNN(String) - Constructor for class keel.Algorithms.Lazy_Learning.CenterNN.CenterNN: The main method of the class
centersOfLBGCLuster(double) - Method in class keel.Algorithms.Instance_Generation.VQ.Cluster: Returns the centers of the cluster obtained by LBG method.
centre - Variable in class keel.GraphInterKeel.experiments.Node
centroid - Variable in class keel.Algorithms.Instance_Generation.VQ.Cluster: Centroid/Center of the cluster.
CertezaAntecedente(FuzzyRule, double[][][]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
CertezaAntecedenteEj(int, FuzzyRule, double[][][]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
CF - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Fuzzy_Chi: Configuration flags.
CF - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Fuzzy_Ish: Configuration flags.
CF - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Fuzzy_Ish: Configuration flag (CF).
CF - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Fuzzy_Ish
CF - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.GP_COACH: Configuration flag (CF).
CF - Static variable in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.GP_COACH_H: Configuration flag (CF)
CF - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
CF - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
CF - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
CF - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
CF - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
CF - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
cFac - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
CFAR - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR: It contains the implementation of the algorithm
CFAR() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.CFAR: Default constructor
CFAR(parseParameters) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.CFAR: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
CFKNN - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.CFKNN: File: CFKNN.java The CFKNN algorithm.
CFKNN(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.CFKNN.CFKNN: Main builder.
change(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask: Changes the value of a given position (if it was activated, it is now deactivated and vicecersa).
change(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Changes the value of a given position (if it was activated, it is now deactivated and vicecersa).
change(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask: Changes the value of a given position (if it was activated, it is now deactivated and vicecersa).
change(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Changes the value of a given position (if it was activated, it is now deactivated and vicecersa).
change(int) - Method in class keel.Algorithms.Rule_Learning.PART.Mask: Changes the value of a given position (if it was activated, it is now deactivated and vicecersa).
change(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Mask: Changes the value of a given position (if it was activated, it is now deactivated and vicecersa).
change(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Mask: Changes the value of a given position (if it was activated, it is now deactivated and vicecersa).
changeChild(Node, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node: This method modify the children node in the i position, assigning the node that is passed as parameter
changeIdAttr(FuzzyDataset) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Itemset: Change attribute ID with the given fuzzy dataset.
changeLabel(DataBase) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.FuzzyAntecedent: Changes randomly a label from the label set of this fuzzy antecedent to a non-existing label in this fuzzy antecedent
changeLabel(int, DataBase) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Changes a label in the fuzzy antecedent of the given variable from an existing value to a non-existing value
changeLabel(DataBase) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Changes randomly a label from the label set of this fuzzy antecedent to a non-existing label in this fuzzy antecedent
changeLabel(int, DataBase, int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Changes a label in the fuzzy antecedent of the given variable from an existing value to a non-existing value
changeLabels(FuzzyAntecedent, DataBase) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.FuzzyAntecedent: Change the labels associated to this fuzzy antecedent with the labels of the given antecedent
changeLabels(FuzzyAntecedent, DataBase) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Change the labels associated to this fuzzy antecedent with the labels of the given antecedent
changePNAc(Literal) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.PRM: It modifies positive and negative values and the PNArray of each example from a given literal
changeSign(double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method implements the operation of changing the sing applied to a vector
changeSign(double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method implements the operation of changing the sing applied to a matrix
changeSign(double[][][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method implements the operation of changing the sing applied to a cubic matrix
changeWeights(double) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ConjGradNN: Updates the matrix of weights with the addition of random valued in the range [-x,x].
changeWeights(double) - Method in class keel.Algorithms.Shared.ClassicalOptim.ConjGradNN: Updates the matrix of weights with the addition of random valued in the range [-x,x].
characters(char[], int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.FuncionEvaluacionBeanHandler
characters(char[], int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.OperacionHandler
chart - Variable in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelAttribute: Chart
chart2 - Variable in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelCharts2D: JFreeChart
charVector - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: It contains all the characters that can take a problem with character representation.
charVector - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It contains all the characters that can take a problem with character representation.
CHC - Class in keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H: Title: CHC Description: Uses a CHC algorithm to select the rules used in the GP-COACH-H algorithm Company: KEEL
CHC() - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC: Default constructor
CHC(myDataset, GP_COACH_H, double, ArrayList<Rule>, int, int, int, int, int) - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC: Creates a CHC object with its parameters
CHC - Class in keel.Algorithms.Instance_Selection.CHC: File: CHC.java The CHC evolutionary model for Instance Selection.
CHC(String) - Constructor for class keel.Algorithms.Instance_Selection.CHC.CHC: Default builder.
CHC - Class in keel.Algorithms.Preprocess.Instance_Selection.CHC: File: CHC.java The CHC evolutionary model for Instance Selection.
CHC(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.CHC.CHC: Default builder.
Chc - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs: Title: the cycle class of the algorithm Description: It do CHC cycle
Chc(int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Chc
Chc - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs: Title: the cycle class of the algorithm Description: It do CHC cycle
Chc(int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Chc
Chc - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs: Title: the cycle class of the algorithm Description: It do CHC cycle
Chc(int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Chc
Chc - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs: Title: the cycle class of the algorithm Description: It do CHC cycle
Chc(int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Chc
Chc - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs: Title: the cycle class of the algorithm Description: It do CHC cycle
Chc(int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Chc
Chc - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs: Title: the Main class of the algorithm Description: It do CHC cycle
Chc(int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Chc
Chc - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs: Title: the cycle class of the algorithm Description: It do CHC cycle
Chc(int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Chc
Chc - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs: Title: the Main class of the algorithm Description: It do CHC cycle
Chc(int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Chc
CHC_Chromosome - Class in keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H: Title: CHC_RuleBase Description: Chromosome that represents a modified data base and rule base used in the CHC algorithm Company: KEEL
CHC_Chromosome() - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC_Chromosome: Default constructor
CHC_Chromosome(CHC_Chromosome) - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC_Chromosome: Creates a CHC chromosome from another chromosome (copies a chromosome)
CHC_Chromosome(int, int) - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC_Chromosome: Creates a random CHC_Chromosome of specified size
CHC_Chromosome(int, boolean, int, double) - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC_Chromosome: Creates a CHC_Chromosome of specified size with all its elements set to the specified value
CHC_Chromosome(boolean[], double[]) - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC_Chromosome: Creates a CHC chromosome from a boolean array representing a chromosome
CHCBinaryIncon - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CHC.filter: MAIN CLASS OF THE CHC FEATURE SELECTION ALGORITHM
CHCBinaryIncon(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CHC.filter.CHCBinaryIncon: Creates a new instance of CHCBinaryIncon
CHCBinaryLVO - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CHC.wrapper: MAIN CLASS OF THE CHC FEATURE SELECTION ALGORITHM
CHCBinaryLVO(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CHC.wrapper.CHCBinaryLVO: Creates a new instance of CHCBinaryLVO
check(double) - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Function to check if at least two values contain a minimum number of itemsets.
check(double) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Function to check if at least two values contain a minimum number of itemsets.
check(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Function to check if at least two values contain a minimum number of itemsets.
check(double) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Function to check if at least two values contain a minimum number of itemsets.
check(double) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Function to check if at least two values contain a minimum number of itemsets.
check(double) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Function to check if at least two values contain a minimum number of itemsets.
check(double) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Function to check if at least two values contain a minimum number of itemsets.
check(double) - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Function to check if at least two values contain a minimum number of itemsets.
check(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Function to check if at least two values contain a minimum number of itemsets.
Check - Class in keel.Algorithms.SVM.SMO.core: Abstract general class for testing in Weka.
Check() - Constructor for class keel.Algorithms.SVM.SMO.core.Check
check(ActionEvent, JCheckBox, Joint) - Method in class keel.GraphInterKeel.experiments.Container_Selected
check_show(ActionEvent, JCheckBox, Joint) - Method in class keel.GraphInterKeel.experiments.Container_Selected
Check_Values(String[], int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Check_Values(String[], int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
checkAlphas() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Debuggage function
Checks that :
alpha*alpha_=0
sum(alpha[i] - alpha_[i]) = 0
checkBestIndividual() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.geneticAlgorithm
checkBestIndividual() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.GA
checkBestIndividual() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.GA
checkClassifier() - Method in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: Quick and dirty check whether the quadratic programming problem is solved.
checkClassType(Dataset) - Static method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.DatasetChecker
checkDataset(InstanceSet) - Static method in class keel.Algorithms.Discretizers.DIBD.Main: It checks the dataset and exits the program if there are errors: - more than one output - output attribute is not nominal
checkDataset(InstanceSet) - Static method in class keel.Algorithms.Discretizers.HellingerBD.Main: It checks the dataset and exits the program if there are errors: - more than one output - output attribute is not nominal
checkDataset(InstanceSet) - Static method in class keel.Algorithms.Discretizers.HeterDisc.Main: Checks the dataset and exits the program if there are errors: - more than one output - output attribute is not nominal
checkDataset() - Static method in class keel.Algorithms.Discretizers.UCPD.Main: It checks the dataset and exits the program if there are errors: - more than one output - output attribute is not nominal
checkDataset() - Method in class keel.Algorithms.MIL.ExceptionDatasets
checkErrorRateTipText() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Returns the tip text for this property
CheckException - Exception in keel.Algorithms.ImbalancedClassification.Ensembles.Basic: CheckException This class defines the exception that will be thrown if the dataset not corresponding with classification
CheckException() - Constructor for exception keel.Algorithms.ImbalancedClassification.Ensembles.Basic.CheckException: Creates a new instance of CheckException
CheckException(String) - Constructor for exception keel.Algorithms.ImbalancedClassification.Ensembles.Basic.CheckException: Does instance a new CheckException with the message specified and the Vector with all the errors.
CheckException - Exception in keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic: CheckException This class defines the exception that will be thrown if the dataset not corresponding with classification
CheckException() - Constructor for exception keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.CheckException: Creates a new instance of CheckException
CheckException(String) - Constructor for exception keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.CheckException: Does instance a new CheckException with the message specified and the Vector with all the errors.
CheckException - Exception in keel.Algorithms.Preprocess.Basic: CheckException This class defines the exception that will be thrown if the dataset not corresponding with classification
CheckException() - Constructor for exception keel.Algorithms.Preprocess.Basic.CheckException: Creates a new instance of CheckException
CheckException(String) - Constructor for exception keel.Algorithms.Preprocess.Basic.CheckException: Does instance a new CheckException with the message specified and the Vector with all the errors.
checkForAttributeType(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Checks for attributes of the given type in the dataset
checkForAttributeType(int) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Checks for attributes of the given type in the dataset
checkForLeadingSubString(short[], short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Checks whether two itemSets share a leading substring.
checkForRemainingOptions(String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Checks if the given array contains any non-empty options.
checkForRemainingOptions(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Checks if the given array contains any non-empty options.
checkForRemainingOptions(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Checks if the given array contains any non-empty options.
checkForRemainingOptions(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Checks if the given array contains any non-empty options.
checkForRemainingOptions(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Checks if the given array contains any non-empty options.
checkForRemainingOptions(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Checks if the given array contains any non-empty options.
checkForRemainingOptions(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Checks if the given array contains any non-empty options.
checkForRemainingOptions(String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Checks if the given array contains any non-empty options.
checkForRemainingOptions(String[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Checks if the given array contains any non-empty options.
checkForStringAttributes() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Checks for string attributes in the dataset
checkForStringAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Checks for string attributes in the dataset
checkForStringAttributes() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Checks for string attributes in the dataset
checkForStringAttributes() - Method in class keel.Algorithms.SVM.SMO.core.Instances: Checks for string attributes in the dataset
checkInstance(M5Instance) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Checks if the given instance is compatible with this dataset.
checkInstance(Instance) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Checks if the given instance is compatible with this dataset.
checkInstance(Itemset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Checks if the given itemset is compatible with this dataset.
checkInstance(Instance) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Checks if the given instance is compatible with this dataset.
checkInstance(Instance) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Checks if the given instance is compatible with this dataset.
checkItemSets(short[], short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Determines relationship between two item sets (same, parent, before, child or after).
checkModel() - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Function to check if generated model is valid.
checkModel() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to check if generated model is valid.
checkModel() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Function to check if generated model is valid.
checkModel() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Function to check if generated model is valid.
checkModel() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to check if generated model is valid.
checkModel() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Function to check if generated model is valid.
checkModel() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Function to check if generated model is valid.
checkModel() - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Function to check if generated model is valid.
checkModel() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Function to check if generated model is valid.
checkOptimality() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Debuggage function.
checksDatasetsScrollPane - Variable in class keel.GraphInterKeel.experiments.Experiments
checkSets() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Debuggage function.
checksTurnedOffTipText() - Method in class keel.Algorithms.SVM.SMO.SMO: Returns the tip text for this property
checksTurnedOffTipText() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Returns the tip text for this property
checksTurnedOffTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.Kernel: Returns the tip text for this property
checkUniqueOutput(Dataset) - Static method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.DatasetChecker
ChenAlgorithm - Class in keel.Algorithms.Instance_Generation.Chen: Chen algorithm calling.
ChenAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.Chen.ChenAlgorithm
ChenGenerator - Class in keel.Algorithms.Instance_Generation.Chen: ChenGenerator prototype reducition algorithm
ChenGenerator(PrototypeSet, int) - Constructor for class keel.Algorithms.Instance_Generation.Chen.ChenGenerator: Build a new ChenGenerator Algorithm
ChenGenerator(PrototypeSet, double) - Constructor for class keel.Algorithms.Instance_Generation.Chen.ChenGenerator: Build a new ChenGenerator Algorithm
ChenGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.Chen.ChenGenerator: Build a new ChenGenerator Algorithm
Chi2 - Class in keel.Algorithms.Discretizers.MVD: This class implements the Chi2 table.
Chi2() - Constructor for class keel.Algorithms.Discretizers.MVD.Chi2
Chi2Discretizer - Class in keel.Algorithms.Discretizers.Chi2_Discretizer: This class implements the Chi2 discretizer.
Chi2Discretizer(double) - Constructor for class keel.Algorithms.Discretizers.Chi2_Discretizer.Chi2Discretizer: Parameter Constructor.
chi2RowValues(Vector, int[]) - Method in class keel.Algorithms.Discretizers.Khiops.Khiops: Creates the initial chi square value of the initial discretization scheme.
child(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node: This method return the children of the i position
child - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Lists: Child list.
child - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Lists: Child list.
child - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Lists: Child list.
childRef - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PtreeNode: Pointer to child structure.
childRef - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PtreeNodeTop: Pointer to child structure.
childRef - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TtreeNode: A reference variable to the child (if any) of the node.
childRef - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TtreeNode: A reference variable to the child (if any) of the node.
childRef - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TtreeNode: A reference variable to the child (if any) of the node.
children - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node
children() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node: The public method return the list of children of the node
ChiMergeDiscretizer - Class in keel.Algorithms.Discretizers.ChiMerge_Discretizer: This class implements the USD discretizer.
ChiMergeDiscretizer(double) - Constructor for class keel.Algorithms.Discretizers.ChiMerge_Discretizer.ChiMergeDiscretizer: Builder
ChiMergeDiscretizer - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.ChiMerge_Discretizer
ChiMergeDiscretizer(double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.ChiMerge_Discretizer.ChiMergeDiscretizer
chisqDistribution - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Distribution type: chi-squared
chiSquare(int[][]) - Method in class keel.Algorithms.Discretizers.MVD.MVD: Obtains the Chi square value of this node using the contigency table
chiSquare(double, int) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.StatFunc: Chi Square Distribution Function.
chiSquaredProbability(double, int) - Static method in class keel.Algorithms.Decision_Trees.M5.Distributions: Returns chi-squared probability for given value and degrees of freedom.
chiSquaredProbability(double, double) - Static method in class keel.Algorithms.Lazy_Learning.Statistics: Returns chi-squared probability for given value and degrees of freedom.
chiSquaredProbability(double, double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics: Returns chi-squared probability for given value and degrees of freedom.
chiSquarePercentage(double, int) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.StatFunc: Percentage point of the chi square distribution.
chiVal(double[][], boolean) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ContingencyTables: Computes chi-squared statistic for a contingency table.
chiVal(double[][], boolean) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.ContingencyTables: Computes chi-squared statistic for a contingency table.
chiVal(double[][], boolean) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.ContingencyTables: Computes chi-squared statistic for a contingency table.
chol() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Cholesky Decomposition
CholeskyDecomposition - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix: Cholesky Decomposition.
CholeskyDecomposition(Matrix) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.CholeskyDecomposition: Cholesky algorithm for symmetric and positive definite matrix.
ChooseExamples - Class in keel.Algorithms.Preprocess.NoiseFilters.INFFC
ChooseExamples() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.INFFC.ChooseExamples
chooseExploreAction() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.PredictionArray: It chooses a random action in the prediction array.
chosenRandomIndex - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQ1: Random number index
Chromosome - Class in keel.Algorithms.Genetic_Rule_Learning.COGIN: This class implements a binary chromosome as specified in the COGIN algorithm
Chromosome() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.COGIN.Chromosome: Default constructor
Chromosome(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.COGIN.Chromosome: Builds up a new chromosome with specified number of genes
Chromosome(Chromosome) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.COGIN.Chromosome: Copy constructor.
Chromosome - Class in keel.Algorithms.Genetic_Rule_Learning.CORE: This class represents the chromosome (rule) of the CORE algorithm
Chromosome() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome: Default constructor.
Chromosome(Chromosome) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome: Deep-copy constructor.
Chromosome - Class in keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat: Class to implement a chromosome for the EUS-CHC metho
Chromosome(int) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.Chromosome: Construct a random chromosome of specified size
Chromosome(int, Chromosome) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.Chromosome: It creates a copied chromosome
Chromosome - Class in keel.Algorithms.Preprocess.Feature_Selection.Shared: File: Chromosome.java A chromosome implementation for FS algorithms
Chromosome(int) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.Shared.Chromosome: Builder.
Chromosome(int[]) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.Shared.Chromosome: Builder.
Chromosome(int[], double) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.Shared.Chromosome: Builder.
Chromosome - Class in keel.Algorithms.RST_Learning.EFS_RPS: File: Chromosome.java A implementation of a chromosome class for EFS_RPS.
Chromosome() - Constructor for class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome: Default constructor.
Chromosome(int[]) - Constructor for class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome: Parameter constructor.
Chromosome(int[], double) - Constructor for class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome: Parameter constructor.
Chromosome - Class in keel.Algorithms.RST_Learning.EIS_RFS: File: Chromosome.java A implementation of a chromosome class for EIS_RFS.
Chromosome() - Constructor for class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome: Default constructor.
Chromosome(int[]) - Constructor for class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome: Parameter constructor.
Chromosome(int[], double) - Constructor for class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome: Parameter constructor.
Chromosome - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate: Defines the structure and manage the contents of a rule This implementation uses only integer values to store the gens.
Chromosome(int, TypeVar[]) - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Chromosome: Creates new instance of chromosome, no initialization
Chromosome - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate: Defines the structure and manage the contents of a rule This implementation uses only integer values to store the gens.
Chromosome(int, TypeVar[]) - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Chromosome: Creates new instance of chromosome, no initialization
Chromosome - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate: Defines the structure and manage the contents of a rule.
Chromosome(int, TypeVar[]) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Chromosome: Creates new instance of chromosome, no initialization
Chromosome - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal: It is used for representing and handling a chromosome throughout the evolutionary learning
Chromosome(Gene[]) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Chromosome: It creates a new chromosome by setting up its genes
Chromosome - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori
Chromosome(Gene[]) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Chromosome: It creates a new chromosome by setting up its genes
Chromosome - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC
Chromosome(Gene[]) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Chromosome: It creates a new chromosome by setting up its genes
Chromosome - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal: It is used for representing and handling a Chromosome throughout the evolutionary learning
Chromosome(Gene[]) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome: It creates a new chromosome by setting up its genes
Chromosome - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA
Chromosome(int, int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
Chromosome(ArrayList<Gene>, int, int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
Chromosome - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA
Chromosome(int, int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
Chromosome(ArrayList<Gene>, int, int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
Chromosome - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR
Chromosome(Gene[]) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Chromosome
Chromosome(Gene[], boolean) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Chromosome
Chromosome(Gene[], int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Chromosome
Chromosome - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR
Chromosome(Gene[]) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Chromosome
Chromosome - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR
Chromosome() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Chromosome
Chromosome(ArrayList<Gene>) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Chromosome
Chromosome - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh
Chromosome(Gene[], int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome: It creates a new chromosome by setting up its genes
Chromosome - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR
Chromosome(Gene[], int, int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome: It creates a new chromosome by setting up its genes
Chromosome - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII
Chromosome(Gene[], int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome: It creates a new chromosome by setting up its genes
Chronometer - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist: Measure and count the amount of time that the system spends in each GA stage
Chronometer() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
Chronometer - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS: Measure and count the amount of time that the system spends in each GA stage
Chronometer() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
CIW_NN - Class in keel.Algorithms.Coevolution.CIW_NN: File: CIW_NN.java The CIW-NN Algorithm.
CIW_NN(String) - Constructor for class keel.Algorithms.Coevolution.CIW_NN.CIW_NN: The main method of the class
CKNN - Class in keel.Algorithms.MIL.Nearest_Neighbour.CKNN
CKNN() - Constructor for class keel.Algorithms.MIL.Nearest_Neighbour.CKNN.CKNN
clase - Variable in class keel.Algorithms.Decision_Trees.SLIQ.ListaClases: Class index.
clase - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.T_Consequent: Consequent class.
clase - Variable in class keel.Algorithms.Hyperrectangles.EHS_CHC.Hyper
clase - Variable in class keel.Algorithms.PSO_Learning.CPSO.Particle
clase - Variable in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
clase - Variable in class keel.Algorithms.PSO_Learning.REPSO.Particle
claseMasFrecuente() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It returns the most frequent class in the dataset
claseMasFrecuente() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It returns the most frequent class in the dataset
claseMasFrecuente() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It returns the most frequent class in the dataset
claseMasFrecuente() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It returns the most frequent class in the dataset
claseNumerica(String) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Returns a numeric representation of a class nominal value given as argument.
ClasePredominante() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: Returns the majority class with the not removed classes.
ClasePredominante() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: Returns the majority class with the not removed classes.
ClasePredominante() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: Returns the majority class with the not removed classes.
clases() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Returns the different classes in the data-set.
clases() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It returns the name of the classes
clases() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It returns the name of every output values (possible classes).
clases() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It returns the name of every output values (possible classes).
clases() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It returns the name of the classes
clases() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It returns the name of the classes
clases() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It returns the name of every output values (possible classes).
clases() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It returns the name of every output values (possible classes).
clases - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyDataset: Classes values.
clases() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It returns the name of every output values (possible classes).
clases() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It returns the name of every output values (possible classes).
clasesTest - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Test output data.
clasesTest - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
clasesTest - Variable in class keel.Algorithms.Preprocess.Basic.Metodo: Test output data.
clasesTrain - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Training output data.
clasesTrain - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
clasesTrain - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
clasesTrain - Variable in class keel.Algorithms.Preprocess.Basic.Metodo: Training output data.
CLASICO - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Fuzzy_Ish
Clasif_General - Class in keel.Algorithms.Statistical_Tests.Classification.Clasif_General: This class has only a main method that calls Clasif_General output method for classification problems, defined in StatTest
Clasif_General() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.Clasif_General.Clasif_General
Clasif_Summary - Class in keel.Algorithms.Statistical_Tests.Classification.Clasif_Summary: This class has only a main method that calls Model_Summary output method for classification problems, defined in StatTest
Clasif_Summary() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.Clasif_Summary.Clasif_Summary
Clasif_Tabular - Class in keel.Algorithms.Statistical_Tests.Classification.Clasif_Tabular: This class has only a main method that calls Model_Tabular output method for classification problems, defined in StatTest
Clasif_Tabular() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.Clasif_Tabular.Clasif_Tabular
clasifica(boolean, double[], StringBuffer) - Method in class keel.Algorithms.Decision_Trees.DT_GA.BaseR: Classifies an example given as an argument using rather a decision tree based rules or genetic algorithm based.
clasifica(double[]) - Method in class keel.Algorithms.Decision_Trees.DT_GA.Clasificador: Classifies an example
clasifica(int[], int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.Individuo: Computes and returns the fitness of the individual as the product of sensitivity and specificity of the given examples classification.
clasifica() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseR
clasifica() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Individuo
clasifica(double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.BaseR
Clasificador - Class in keel.Algorithms.Decision_Trees.DT_GA: Description: It contains the implementation of the classifier DT_GA.
Clasificador() - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.Clasificador: Default Constructor.
Clasificador(BaseR, BaseR, int, int, String) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.Clasificador: Parameters Constructor.
clasificaLarge(int[], int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.Individuo: Computes and returns the fitness of the individual as the product of sensitivity and specificity of the given examples classification.
clasificar(double[]) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.Tree: Classifies a given example, returning the predicted class.
clasificar(double[]) - Method in class keel.Algorithms.Decision_Trees.Target.Tree: Classifies a given example, returning the predicted class.
clasificar(PrototypeSet[], PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoBC.CoBCGenerator: Classify the instances given.
clasificar(PrototypeSet[], PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.Democratic.DemocraticGenerator: Classify and calculing intervals of confidence
ClasifTest_5x2cv - Class in keel.Algorithms.Statistical_Tests.Classification.ClasifTest_5x2cv: This class has only a main method that calls Dietterich 5x2cv test for classification problems, defined in StatTest
ClasifTest_5x2cv() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.ClasifTest_5x2cv.ClasifTest_5x2cv
ClasifTest_f - Class in keel.Algorithms.Statistical_Tests.Classification.ClasifTest_f: This class has only a main method that calls f test for classification problems, defined in StatTest
ClasifTest_f() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.ClasifTest_f.ClasifTest_f
ClasifTest_rs - Class in keel.Algorithms.Statistical_Tests.Classification.ClasifTest_rs: This class has only a main method that calls Wilcoxon signed rank test for classification problems, defined in StatTest
ClasifTest_rs() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.ClasifTest_rs.ClasifTest_rs
ClasifTest_sw - Class in keel.Algorithms.Statistical_Tests.Classification.ClasifTest_sw: This class has only a main method that calls Shapiro Wilk test for classification problems, defined in StatTest
ClasifTest_sw() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.ClasifTest_sw.ClasifTest_sw
ClasifTest_t - Class in keel.Algorithms.Statistical_Tests.Classification.ClasifTest_t: This class has only a main method that calls t test for classification problems, defined in StatTest
ClasifTest_t() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.ClasifTest_t.ClasifTest_t
ClasifTest_u - Class in keel.Algorithms.Statistical_Tests.Classification.ClasifTest_u: This class has only a main method that calls Mann Whitney U test for classification problems, defined in StatTest
ClasifTest_u() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.ClasifTest_u.ClasifTest_u
classAttribute() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Returns class attribute.
classAttribute() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Returns the class attribute.
classAttribute() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Returns class attribute.
classAttribute() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns the class attribute.
classAttribute() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Returns class attribute.
classAttribute() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns the class attribute.
classAttribute() - Method in class keel.Algorithms.SVM.SMO.core.Instance: Returns class attribute.
classAttribute() - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns the class attribute.
classAttributeNames() - Method in class keel.Algorithms.SVM.SMO.SMO: Returns the names of the class attributes.
classConv - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
classConv - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
ClassDiscovery - Class in keel.Algorithms.SVM.SMO.core: This class is used for discovering classes that implement a certain interface or a derived from a certain class.
ClassDiscovery() - Constructor for class keel.Algorithms.SVM.SMO.core.ClassDiscovery
ClassDiscovery.StringCompare - Class in keel.Algorithms.SVM.SMO.core: compares two strings with the following order:
case insensitive german umlauts (ä , ö etc.) or other non-ASCII letters are treated as special chars special chars < numbers < letters
classes - Variable in class keel.GraphInterKeel.experiments.DatasetXML
classesWithPrototypes() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Returns classes which there are prototypes with them.
classesWithPrototypes() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Returns classes which there are prototypes with them.
classficationAccuracy(PrototypeSet, PrototypeSet, int) - Static method in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN: Informs of the classification accuracy.
classficationAccuracy(PrototypeSet, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN: Informs of the classification accuracy
classficationAccuracy(PrototypeSet, PrototypeSet, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Informs of the classification accuracy.
classficationAccuracy(PrototypeSet, PrototypeSet) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Informs of the classification accuracy
classficationAccuracy1NN(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.DROP3LVQ3.DROP3LVQ3
classficationAccuracy1NN(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.DROP3PSO.DROP3PSO: Computes the accuracy for the given test dataset with the 1NN algorithm.
classficationAccuracy1NN(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.DROP3SFLSDE.DROP3SFLSDE
classficationAccuracy1NN(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.ICFLVQ3.ICFLVQ3
classficationAccuracy1NN(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.ICFPSO.ICFPSO: Computes the accuracy for the given test dataset with the 1NN algorithm.
classficationAccuracy1NN(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.ICFSFLSDE.ICFSFLSDE
classficationAccuracy1NN(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.SSMALVQ3
classficationAccuracy1NN(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.SSMAPSO: Computes the accuracy for the given test dataset with the 1NN algorithm.
classficationAccuracy1NN(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE
classficationAccuracy1NN(PrototypeSet, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN: Informs of the number of prototypes with correct class.
classficationAccuracy1NN(PrototypeSet, PrototypeSet) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Informs of the number of prototypes with correct class.
classficationAccuracyAndError1NN(PrototypeSet, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN: Informs of the number of prototypes with correct class.
classficationAccuracyAndError1NN(PrototypeSet, PrototypeSet) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Informs of the number of prototypes with correct class.
Classification - Class in keel.Algorithms.Decision_Trees.C45: Class to handle a classification of class values.
Classification(int, int) - Constructor for class keel.Algorithms.Decision_Trees.C45.Classification: Function to create and initialize a new classification.
Classification(Dataset) - Constructor for class keel.Algorithms.Decision_Trees.C45.Classification: Function to create a new classification with only one value.
Classification(Dataset, Cut) - Constructor for class keel.Algorithms.Decision_Trees.C45.Classification: Function to create a new classification with the given dataset.
Classification(Classification) - Constructor for class keel.Algorithms.Decision_Trees.C45.Classification: Function to create a new classification with only one value by merging all ç values of given classification.
classification - Variable in class keel.Algorithms.Decision_Trees.C45.Cut: Classification of class values.
classification() - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Returns the classification created by the model.
Classification - Class in keel.Algorithms.Decision_Trees.DT_GA.C45: Class to handle a classification of class values.
Classification(int, int) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Function to create and initialize a new classification.
Classification(Dataset) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Function to create a new classification with only one value.
Classification(Dataset, Cut) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Function to create a new classification with the given dataset.
Classification(Classification) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Function to create a new classification with only one value by merging all ç values of given classification.
classification - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Classification of class values.
classification() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Returns the classification created by the model.
Classification - Class in keel.Algorithms.Genetic_Rule_Learning.PART: Class to handle a classification of class values.
Classification(int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Function to create and initialize a new classification.
Classification(MyDataset) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Function to create a new classification with only one value.
Classification(MyDataset, Cut) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Function to create a new classification with the given dataset.
Classification(Classification) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Function to create a new classification with only one value by merging all ç values of given classification.
classification - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Classification of class values.
classification() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Returns the classification created by the model.
Classification - Class in keel.Algorithms.ImbalancedClassification.CSMethods.C45CS: Class to handle a classification of class values.
Classification(int, int) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Function to create and initialize a new classification.
Classification(Dataset) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Function to create a new classification with only one value.
Classification(Dataset, Cut) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Function to create a new classification with the given dataset.
Classification(Classification) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Function to create a new classification with only one value by merging all values of given classification.
classification - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Classification of class values.
classification() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Returns the classification created by the model.
Classification - Class in keel.Algorithms.ImbalancedClassification.Ensembles.C45: Class to handle a classification of class values.
Classification(int, int) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Function to create and initialize a new classification.
Classification(Dataset) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Function to create a new classification with only one value.
Classification(Dataset, Cut) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Function to create a new classification with the given dataset.
Classification(Classification) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Function to create a new classification with only one value by merging all values of given classification.
classification - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Classification of class values.
classification() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Returns the classification created by the model.
Classification - Class in keel.Algorithms.Rule_Learning.C45Rules: Class to handle a classification of class values.
Classification(int, int) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.Classification: Function to create and initialize a new classification.
Classification(MyDataset) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.Classification: Function to create a new classification with only one value.
Classification(MyDataset, Cut) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.Classification: Function to create a new classification with the given dataset.
Classification(Classification) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.Classification: Function to create a new classification with only one value by merging all ç values of given classification.
classification - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Classification of class values.
classification() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Returns the classification created by the model.
Classification - Class in keel.Algorithms.Rule_Learning.C45RulesSA: Class to handle a classification of class values.
Classification(int, int) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Function to create and initialize a new classification.
Classification(MyDataset) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Function to create a new classification with only one value.
Classification(MyDataset, Cut) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Function to create a new classification with the given dataset.
Classification(Classification) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Function to create a new classification with only one value by merging all ç values of given classification.
classification - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Classification of class values.
classification() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Returns the classification created by the model.
Classification - Class in keel.Algorithms.Rule_Learning.PART: Class to handle a classification of class values.
Classification(int, int) - Constructor for class keel.Algorithms.Rule_Learning.PART.Classification: Function to create and initialize a new classification.
Classification(MyDataset) - Constructor for class keel.Algorithms.Rule_Learning.PART.Classification: Function to create a new classification with only one value.
Classification(MyDataset, Cut) - Constructor for class keel.Algorithms.Rule_Learning.PART.Classification: Function to create a new classification with the given dataset.
Classification(Classification) - Constructor for class keel.Algorithms.Rule_Learning.PART.Classification: Function to create a new classification with only one value by merging all ç values of given classification.
classification - Variable in class keel.Algorithms.Rule_Learning.PART.Cut: Classification of class values.
classification() - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Returns the classification created by the model.
Classification - Class in keel.Algorithms.Semi_Supervised_Learning.Basic.C45: Class to handle a classification of class values.
Classification(int, int) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Function to create and initialize a new classification.
Classification(Dataset) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Function to create a new classification with only one value.
Classification(Dataset, Cut) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Function to create a new classification with the given dataset.
Classification(Classification) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Function to create a new classification with only one value by merging all values of given classification.
classification - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Classification of class values.
classification() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Returns the classification created by the model.
ClassificationAccuracyErrorFunction - Class in keel.Algorithms.Neural_Networks.NNEP_Clas.problem.errorfunctions: Classification accuracy Error Function.
ClassificationAccuracyErrorFunction() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.errorfunctions.ClassificationAccuracyErrorFunction: Empty constructor
ClassificationFilter - Class in keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter: The Classification Filter begins with n equal-sized disjoint subsets of the training set E (done with n-fold cross validation) and the empty output set A of detected noisy examples.
ClassificationFilter() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.ClassificationFilter: It initializes the partitions from training set
classificationForItemset(Itemset) - Method in class keel.Algorithms.Decision_Trees.C45.C45: Returns class probabilities for an itemset.
classificationForItemset(Itemset) - Method in class keel.Algorithms.Decision_Trees.C45.Tree: Function to get the classification of classes.
classificationForItemset(Itemset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.C45: Returns class probabilities for an itemset.
classificationForItemset(Itemset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Tree: Function to get the classification of classes.
classificationForItemset(Itemset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.C45: Returns class probabilities for an itemset.
classificationForItemset(Itemset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree: Function to get the classification of classes.
classificationForItemset(Itemset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.C45CS: Returns class probabilities for an itemset.
classificationForItemset(Itemset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Tree: Function to get the classification of classes.
classificationForItemset(Itemset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.C45: Returns class probabilities for an itemset.
classificationForItemset(Itemset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree: Function to get the classification of classes.
classificationForItemset(Itemset) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.C45: Returns class probabilities for an itemset.
classificationForItemset(Itemset) - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.C45: Returns class probabilities for an itemset.
classificationForItemset(Itemset) - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.C45: Returns class probabilities for an itemset.
classificationForItemset(Itemset) - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.C45: Returns class probabilities for an itemset.
classificationForItemset(Itemset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.C45: Returns class probabilities for an itemset.
classificationForItemset(Itemset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Tree: Function to get the classification of classes.
classificationForItemset(Itemset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.C45: Returns class probabilities for an itemset.
classificationForItemset(Itemset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree: Function to get the classification of classes.
classificationForItemset(Itemset) - Method in class keel.Algorithms.Rule_Learning.PART.C45: Returns class probabilities for an itemset.
classificationForItemset(Itemset) - Method in class keel.Algorithms.Rule_Learning.PART.Tree: Function to get the classification of classes.
classificationForItemset(Itemset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45: Returns class probabilities for an itemset.
classificationForItemset(Itemset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: Function to get the classification of classes.
ClassificationProblemEvaluator - Class in keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax: Classification problem evaluator
ClassificationProblemEvaluator() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.ClassificationProblemEvaluator: Empty constructor
classificationTest(double[][], int, int[], int, int) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Evaluates the net for clasification problem
classifier() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.RuleBase: This function generates a classifier from the CompSet of the generated fuzzy rules
Classifier - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier: Classifier is the base clase for all fuzzy rule learned classifier.
Classifier() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.Classifier
Classifier - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: Abstract classifier.
Classifier() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Classifier
classifier - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
classifier() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
Classifier - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist: Base class for all classifiers (knowledge representations)
Classifier() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
Classifier - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS: Base class for all classifiers (knowledge representations)
Classifier() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
Classifier - Class in keel.Algorithms.Genetic_Rule_Learning.UCS: The classifier contains two classes, the representation and the parameters.
Classifier(double[], int, int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: It constructs a classifier with the condition and the action specified.
Classifier(Classifier, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: It creates a new classifier copying the representation and the parameters of the classifier passed as a parameter.
Classifier(Classifier, Classifier, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: It creates a new classifier making a crossover between the parents parametres.
Classifier - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: The classifier contains two classes, the representation and the parameters.
Classifier(double[], int, int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: It constructs a classifier with the condition and the action specified.
Classifier(Classifier, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: It creates a new classifier copying the representation and the parameters of the classifier passed as a parameter.
Classifier(Classifier, Classifier, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: It creates a new classifier making a crossover between the parents parametres.
Classifier(StringTokenizer) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: It creates a new classifier with the parameters contained in the String.
CLASSIFIER - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
classifier - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters: Classifier used (knn or c45).
classifier_aggregated - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
classifier_aggregated() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_aggregated
classifier_hyperrect_list - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
classifier_hyperrect_list() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list
classifier_hyperrect_list(classifier_hyperrect_list) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list
classifier_hyperrect_list_real - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
classifier_hyperrect_list_real(classifier_hyperrect_list_real) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
classifier_hyperrect_list_real() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
ClassifierADI - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist: Contains the classifier for the ADI knowledge representation
ClassifierADI() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierADI
ClassifierADI - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS: Contains the classifier for the ADI knowledge representation
ClassifierADI() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
ClassifierADLinear - Class in keel.Algorithms.Statistical_Classifiers.ClassifierADLinear: In this class, a classifier using Linear Discriminant Analysis is implemented
ClassifierADLinear() - Constructor for class keel.Algorithms.Statistical_Classifiers.ClassifierADLinear.ClassifierADLinear
ClassifierADQuadratic - Class in keel.Algorithms.Statistical_Classifiers.ClassifierADQuadratic: In this class, a classifier using Quadratic Discriminant Analysis is implemented
ClassifierADQuadratic() - Constructor for class keel.Algorithms.Statistical_Classifiers.ClassifierADQuadratic.ClassifierADQuadratic
classifierFactory - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
classifierFactory() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifierFactory
classifierFitness - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
classifierFitness() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifierFitness
ClassifierFuzzyAdaBoost - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyAdaBoost: ClassifierFuzzyAdaBoost generates a Fuzzy Rule Based System classifier using the Ada Boosting algorithm.
ClassifierFuzzyAdaBoost() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyAdaBoost.ClassifierFuzzyAdaBoost
ClassifierFuzzyGAP - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGAP: ClassifierFuzzyGAP is intended to generate a Fuzzy Rule Based System (FRBS) classifier using an Genetic Algorithm and Programming (GAP).
ClassifierFuzzyGAP() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGAP.ClassifierFuzzyGAP
ClassifierFuzzyGP - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGP: ClassifierFuzzyGP is intended to generate a Fuzzy Rule Based System (FRBS) classifier using an Genetic Programming (GP).
ClassifierFuzzyGP() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGP.ClassifierFuzzyGP
ClassifierFuzzyLogitBoost - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyLogitBoost: ClassifierFuzzyLogitBoost generates a Fuzzy Rule Based System classifier using the Logit Boosting algorithm.
ClassifierFuzzyLogitBoost() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyLogitBoost.ClassifierFuzzyLogitBoost
ClassifierFuzzyMaxLogitBoost - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyMaxLogitBoost: ClassifierFuzzyMaxLogitBoost generates a Fuzzy Rule Based System classifier using the Max Logit Boosting algorithm.
ClassifierFuzzyMaxLogitBoost() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyMaxLogitBoost.ClassifierFuzzyMaxLogitBoost
ClassifierFuzzyPittsBurgh - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyPittsBurgh: ClassifierFuzzyPittsBurgh is intended to generate a Fuzzy Rule Based System (FRBS) classifier using an Genetic Algorithm and Programming (GAP).
ClassifierFuzzyPittsBurgh() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyPittsBurgh.ClassifierFuzzyPittsBurgh
ClassifierFuzzySAP - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySAP: ClassifierFuzzySAP is intended to generate a Fuzzy Rule Based System (FRBS) classifier using an Simulated Annealing algorithm and Programming (SAP).
ClassifierFuzzySAP() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySAP.ClassifierFuzzySAP
ClassifierFuzzyWangMendel - Class in keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.ClassifierFuzzyWangMendel: ClassifierFuzzyWangMendel is intended to generate a Fuzzy Rule Based System (FRBS) classifier using the Wang and Mendel approach.
ClassifierFuzzyWangMendel() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.ClassifierFuzzyWangMendel.ClassifierFuzzyWangMendel
ClassifierGABIL - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist: Contains the classifier for the GABIL knowledge representation
ClassifierGABIL() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierGABIL
ClassifierGABIL - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS: Contains the classifier for the GABIL knowledge representation
ClassifierGABIL() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
ClassifierGABIL.activationsAtt - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS
ClassifierGABIL.attInstances - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS
ClassifierGABIL.cleanTarget - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS
ClassifierGABIL.splittedRule - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS
ClassifierKernel - Class in keel.Algorithms.Statistical_Classifiers.ClassifierKernel: In this class, a kernel classifier is implemented
ClassifierKernel() - Constructor for class keel.Algorithms.Statistical_Classifiers.ClassifierKernel.ClassifierKernel
ClassifierLinearLMS - Class in keel.Algorithms.Statistical_Classifiers.ClassifierLinearLMS: In this class, a linear classifier using Least Mean Squares is implemented
ClassifierLinearLMS() - Constructor for class keel.Algorithms.Statistical_Classifiers.ClassifierLinearLMS.ClassifierLinearLMS
ClassifierMLPerceptron - Class in keel.Algorithms.Neural_Networks.ClassifierMLPerceptron: Classification model by means of a multi-layered perceptron.
ClassifierMLPerceptron() - Constructor for class keel.Algorithms.Neural_Networks.ClassifierMLPerceptron.ClassifierMLPerceptron
ClassifierMLPerceptron - Class in keel.Algorithms.Preprocess.NoiseFilters.ANR: Classification model by means of a multi-layered perceptron.
ClassifierMLPerceptron() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.ANR.ClassifierMLPerceptron
ClassifierPolQuadraticLMS - Class in keel.Algorithms.Statistical_Classifiers.ClassifierPolQuadraticLMS: In this class, a quadratic polynomial classifier using Least Squares is implemented
ClassifierPolQuadraticLMS() - Constructor for class keel.Algorithms.Statistical_Classifiers.ClassifierPolQuadraticLMS.ClassifierPolQuadraticLMS
classifierTrainFinished() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.OVO: It finishes the operations needed before classifying but after constructing the classifiers (for BTS!).
ClassifierUBR - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist: Classifier for the UBR intervalar knowledge representation
ClassifierUBR() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierUBR
classify(Dataset) - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Function to create the cut point.
classify(Dataset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to create the cut point.
classify() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase: It performs the classification process with the current rule set
classify(Instance) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_aggregated
classify(Instance) - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Chromosome: classifies the provided instance with this chromosome
classify(InstanceSet, String[], String[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Cogin
classify(InstanceSet, String[], String[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Cochromosome: Performs the classification of a data set for final results printing purposes.
classify(InstanceSet) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.RuleSet: Classifies the instances of a data sets, and updates the fitness function as the number of well classified instances
classify(Instance) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.RuleSet: Classifies an instance using the set of rules
classify(Mask, Ruleset[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Classifies the entries' classes according to several sets of rules.
classify(Ruleset[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Classifies the entries' classes according to several sets of rules.
classify(Mask, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Classifies the entries' classes according to several rules.
classify(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Classifies the entries' classes according to several rules.
classify(InstanceSet) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.RuleSet: Classifies the instances of a data sets, and updates the fitness function as the number of well classified instances
classify(Instance) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.RuleSet: Classifies an instance using the set of rules
classify(Dataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
classify(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Function to create the cut point.
classify(Mask, Ruleset[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Classifies the entries' classes according to several sets of rules.
classify(Mask, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Classifies the entries' classes according to several rules.
classify(Ruleset[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Classifies the entries' classes according to several sets of rules.
classify(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Classifies the entries' classes according to several rules.
classify(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Function to create the cut point.
classify(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to create the cut point.
classify(myDataset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.multi_C45: It carries out the classification of a given dataset throughout the learning stage of the ensemble
classify(double[]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.neuralnet.NeuralNetClassifier: Obtain the associated class of one observation
classify(double[][]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.neuralnet.NeuralNetClassifier: Obtain the associated class of a set of observations, through their inputs values
classify(double[]) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.IClassifier: Obtain the associated class of one observation
classify(double[][]) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.IClassifier: Obtain the associated class of a set of observations, through their inputs values
classify(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Function to create the cut point.
classify(Mask, Ruleset[], int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Classifies the entries' classes according to several sets of rules.
classify(Ruleset[], int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Classifies the entries' classes according to several sets of rules.
classify(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Function to create the cut point.
classify(Mask, Ruleset[], int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Classifies the entries' classes according to several sets of rules.
classify(Ruleset[], int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Classifies the entries' classes according to several sets of rules.
classify(MyDataset) - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Function to create the cut point.
classify(Mask, Ruleset[], int) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Classifies the entries' classes according to several sets of rules.
classify(Mask, Vector) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Classifies the entries' classes according to several rules.
classify(Ruleset[], int) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Classifies the entries' classes according to several sets of rules.
classify(Vector) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Classifies the entries' classes according to several rules.
classify(Mask, Ruleset[], int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: Classifies the entries' classes according to several sets of rules.
classify(Ruleset[], int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: Classifies the entries' classes according to several sets of rules.
classify(Mask, Ruleset[], int) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: Classifies the entries' classes according to several sets of rules.
classify(Ruleset[], int) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: Classifies the entries' classes according to several sets of rules.
classify(Instance) - Method in class keel.Algorithms.Rule_Learning.Swap1.swap1: Classfies a given instance.
classify(Dataset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Function to create the cut point.
classify(int, PrototypeSet, PrototypeSet, boolean) - Method in class keel.Algorithms.Semi_Supervised_Learning.DE_TriTraining.DE_TriTrainingGenerator: Classify a test set with the algorithm specified.
classify(int, PrototypeSet, PrototypeSet, boolean) - Method in class keel.Algorithms.Semi_Supervised_Learning.TriTraining.TriTrainingGenerator: Classify a test set with the algorithm specified.
classify(PrototypeSet, PrototypeSet, int, double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Informs of the classification done.
classify2(PrototypeSet, PrototypeSet, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Informs of the classification done.
classifyingLeaf(Itemset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: Classifies the given item.
classifyInstance(M5Instance) - Method in class keel.Algorithms.Decision_Trees.M5.M5: Classifies the given test instance.
classifyInstance(Instance) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Classifier: Classifies the given test instance.
classifyInstance(Instance) - Method in class keel.Algorithms.SVM.SMO.SMOreg: Classifies a given instance.
classifyInstance(Instance) - Method in class keel.Algorithms.SVM.SMO.SVMreg: Classifies the given instance using the linear regression function.
classifyNewInstance(double[]) - Static method in class keel.Algorithms.RST_Learning.KNNClassifier: Classifies a new example
classifyRecordWCS(short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList: Selects the best rule in a rule list according to the Weighted Chi- Squared (WCS) Value.
classifyTest() - Method in class keel.Algorithms.Coevolution.CIW_NN.CIW_NN: Executes the classification of test dataset
classifyTest() - Method in class keel.Algorithms.Coevolution.IFS_COCO.IFS_COCO: Performs the classification of test dataset
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.CFKNN.CFKNN: Classifies the test set
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.D_SKNN.D_SKNN: Classifies the test set
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FCMKNN.FCMKNN: Classifies the test set
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FENN.FENN: Classifies the test set
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRKNNA.FRKNNA: Classifies the test set
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN.FRNN: Classifies the test set
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_FRS.FRNN_FRS: Classifies the test set
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_VQRS.FRNN_VQRS: Classifies the test set
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyKNN.FuzzyKNN: Classifies the test set
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyNPC.FuzzyNPC: Classifies the test set
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.GAFuzzyKNN.GAFuzzyKNN: Classifies the test set
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IF_KNN.IF_KNN: Classifies the test set
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFSKNN.IFSKNN: Classifies the test set
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFV_NP.IFV_NP: Classifies the test set
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IT2FKNN.IT2FKNN: Classifies the test set
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN.JFKNN: Classifies the test set
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.PFKNN.PFKNN: Classifies the test set
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.PosIBL.PosIBL: Classifies the test set
classifyTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.VWFuzzyKNN.VWFuzzyKNN: Classifies the test set
classifyTest() - Method in class keel.Algorithms.RST_Learning.EFS_RPS.EFS_RPS: Classifies the test set
classifyTest() - Method in class keel.Algorithms.RST_Learning.EIS_RFS.EIS_RFS: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Coevolution.CIW_NN.CIW_NN: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.CFKNN.CFKNN: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.D_SKNN.D_SKNN: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FCMKNN.FCMKNN: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FENN.FENN: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRKNNA.FRKNNA: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN.FRNN: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_FRS.FRNN_FRS: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_VQRS.FRNN_VQRS: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyKNN.FuzzyKNN: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyNPC.FuzzyNPC: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.GAFuzzyKNN.GAFuzzyKNN: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IF_KNN.IF_KNN: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFSKNN.IFSKNN: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFV_NP.IFV_NP: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IT2FKNN.IT2FKNN: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN.JFKNN: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.PFKNN.PFKNN: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.PosIBL.PosIBL: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.VWFuzzyKNN.VWFuzzyKNN: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.RST_Learning.EFS_RPS.EFS_RPS: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.RST_Learning.EIS_RFS.EIS_RFS: Classifies the test set
classifyTestSet() - Method in class keel.Algorithms.Rule_Learning.Swap1.swap1: Classifies the test set
classifyTrain() - Method in class keel.Algorithms.Coevolution.CIW_NN.CIW_NN: Executes the classification of train dataset
classifyTrain() - Method in class keel.Algorithms.Coevolution.IFS_COCO.IFS_COCO: Performs the classification of train dataset
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.CFKNN.CFKNN: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.D_SKNN.D_SKNN: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FCMKNN.FCMKNN: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FENN.FENN: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRKNNA.FRKNNA: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN.FRNN: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_FRS.FRNN_FRS: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_VQRS.FRNN_VQRS: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyKNN.FuzzyKNN: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyNPC.FuzzyNPC: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.GAFuzzyKNN.GAFuzzyKNN: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IF_KNN.IF_KNN: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFSKNN.IFSKNN: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFV_NP.IFV_NP: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IT2FKNN.IT2FKNN: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN.JFKNN: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.PFKNN.PFKNN: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.PosIBL.PosIBL: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.VWFuzzyKNN.VWFuzzyKNN: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.RST_Learning.EFS_RPS.EFS_RPS: Classifies the training set (leave-one-out)
classifyTrain() - Method in class keel.Algorithms.RST_Learning.EIS_RFS.EIS_RFS: Classifies the training set (leave-one-out)
classifyTraining() - Method in class keel.Algorithms.Coevolution.CIW_NN.CIW_NN: Classifies the training set
classifyTrainingInstance(int) - Static method in class keel.Algorithms.RST_Learning.KNNClassifier: Classifies a training example
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.CFKNN.CFKNN: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.D_SKNN.D_SKNN: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FCMKNN.FCMKNN: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FENN.FENN: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRKNNA.FRKNNA: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN.FRNN: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_FRS.FRNN_FRS: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_VQRS.FRNN_VQRS: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyKNN.FuzzyKNN: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyNPC.FuzzyNPC: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.GAFuzzyKNN.GAFuzzyKNN: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IF_KNN.IF_KNN: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFSKNN.IFSKNN: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFV_NP.IFV_NP: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IT2FKNN.IT2FKNN: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN.JFKNN: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.PFKNN.PFKNN: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.PosIBL.PosIBL: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.VWFuzzyKNN.VWFuzzyKNN: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.RST_Learning.EFS_RPS.EFS_RPS: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.RST_Learning.EIS_RFS.EIS_RFS: Classifies the training set
classifyTrainSet() - Method in class keel.Algorithms.Rule_Learning.Swap1.swap1: Classifies the training set
classIndex - Variable in class keel.Algorithms.Decision_Trees.C45.Dataset: The index of the class attribute.
classIndex() - Method in class keel.Algorithms.Decision_Trees.C45.Itemset: Returns the index of the class attribute.
classIndex - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: The index of the class attribute.
classIndex() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset: Returns the index of the class attribute.
classIndex - Variable in class keel.Algorithms.Decision_Trees.ID3.Dataset: The index of the class attribute.
classIndex() - Method in class keel.Algorithms.Decision_Trees.ID3.Itemset: Returns the index of the class attribute.
classIndex() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Returns the class attribute's index.
classIndex() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Returns the class attribute's index.
classIndex - Variable in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: The index of the class attribute.
classIndex() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Itemset: Returns the index of the class attribute.
classIndex() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Returns the class attribute's index.
classIndex() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns the class attribute's index.
classIndex() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Returns the index of the class attribute.
classIndex - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: The index of the class attribute.
classIndex - Variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: The index of the class attribute.
classIndex() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset: Returns the index of the class attribute.
classIndex() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset: Returns the index of the class attribute.
classIndex - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: The index of the class attribute.
classIndex - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: The index of the class attribute.
classIndex() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset: Returns the index of the class attribute.
classIndex - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: The index of the class attribute.
classIndex() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset: Returns the index of the class attribute.
classIndex() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Returns the class attribute's index.
classIndex() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns the class attribute's index.
classIndex - Variable in class keel.Algorithms.MIL.AbstractMIAlgorithm
classIndex - Variable in class keel.Algorithms.Rule_Learning.ART.Dataset: The index of the class attribute.
classIndex() - Method in class keel.Algorithms.Rule_Learning.ART.Itemset: Returns the index of the class attribute.
classIndex() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Itemset: Returns the index of the class attribute.
classIndex - Variable in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: The index of the class attribute.
classIndex() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset: Returns the index of the class attribute.
classIndex - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: The index of the class attribute.
classIndex - Variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: The index of the class attribute.
classIndex() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset: Returns the index of the class attribute.
classIndex() - Method in class keel.Algorithms.Rule_Learning.PART.Itemset: Returns the index of the class attribute.
classIndex - Variable in class keel.Algorithms.Rule_Learning.PART.MyDataset: The index of the class attribute.
classIndex - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: The index of the class attribute.
classIndex() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset: Returns the index of the class attribute.
classIndex() - Method in class keel.Algorithms.SVM.SMO.core.Instance: Returns the class attribute's index.
classIndex() - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns the class attribute's index.
ClassIndividual(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
ClassIndividual(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
ClassIndividual(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
classIsMissing() - Method in class keel.Algorithms.Decision_Trees.C45.Itemset: Function to test if the class attribute is missing.
classIsMissing() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset: Function to test if the class attribute is missing.
classIsMissing() - Method in class keel.Algorithms.Decision_Trees.ID3.Itemset: Function to test if the class attribute is missing.
classIsMissing() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Tests if an instance's class is missing.
classIsMissing() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Itemset: Function to test if the class attribute is missing.
classIsMissing() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Tests if an instance's class is missing.
classIsMissing() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Function to test if the class attribute is missing.
classIsMissing() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset: Function to test if the class attribute is missing.
classIsMissing() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset: Function to test if the class attribute is missing.
classIsMissing() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset: Function to test if the class attribute is missing.
classIsMissing() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset: Function to test if the class attribute is missing.
classIsMissing() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Tests if an instance's class is missing.
classIsMissing() - Method in class keel.Algorithms.Rule_Learning.ART.Itemset: Function to test if the class attribute is missing.
classIsMissing() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Itemset: Function to test if the class attribute is missing.
classIsMissing() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset: Function to test if the class attribute is missing.
classIsMissing() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset: Function to test if the class attribute is missing.
classIsMissing() - Method in class keel.Algorithms.Rule_Learning.PART.Itemset: Function to test if the class attribute is missing.
classIsMissing() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset: Function to test if the class attribute is missing.
classIsMissing() - Method in class keel.Algorithms.SVM.SMO.core.Instance: Tests if an instance's class is missing.
className(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: Returns the nominal value for a class represented by the integer given.
className - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
className - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
classNames() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It returns an array with the labels of the output class
classNames() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It returns an array with the labels of the output class
classNames() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It returns an array with the labels of the output class
classNames() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It returns an array with the labels of the output class
classOfInstance() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.InstanceWrapper
classOfInstance() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.InstanceWrapper
classOfInstances - Variable in class keel.Algorithms.Discretizers.Basic.Discretizer: The class of each instances.
classOfInstances - Variable in class keel.Algorithms.Discretizers.Chi2_Discretizer.Discretizer: The class of each instances.
classOfInstances - Variable in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Discretizer: The class of each instances.
classOfInstances - Variable in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Discretizer: The class of each instances.
classOfInstances - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Basic.Discretizer
classOfInstances - Variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Discretizer
classPartition() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Makes a partition of the set by class.
classPartition() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Makes a partition of the set by class.
classPredictedSTD(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Computes the standard deviation (over the predicted values) for the instances covered by a rule.
classPredictedVariance(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Computes the variance (over the predicted values) for the instances covered by a rule.
classProbability(int, Itemset, int) - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Function to compute the probability for itemset.
classProbability(int, Itemset, int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to compute the probability for itemset.
classProbability(int, Itemset, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Function to compute the probability for itemset.
classProbability(int, Itemset, int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Function to compute the probability for itemset.
classProbability(int, Itemset, int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to compute the probability for itemset.
classProbability(int, Itemset, int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Function to compute the probability for itemset.
classProbability(int, Itemset, int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Function to compute the probability for itemset.
classProbability(int, Itemset, int) - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Function to compute the probability for itemset.
classProbability(int, Itemset, int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Function to compute the probability for itemset.
classSTD() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Computes the standard deviation for the class attribute.
classSTD(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Computes the standard deviation for the instances covered by a rule.
classValue() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Returns an instance's class value in internal format.
classValue() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Returns an instance's class value in internal format.
classValue - Variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
classValue() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Returns an instance's class value in internal format.
classValue() - Method in class keel.Algorithms.SVM.SMO.core.Instance: Returns an instance's class value in internal format.
classVariance() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Computes the variance for the class attribute.
classVariance(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Computes the variance for the instances covered by a rule.
classWiseInit - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
CLCCAlgorithm - Class in keel.Algorithms.Semi_Supervised_Learning.CLCC: CLCC algorithm calling.
CLCCAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCAlgorithm
CLCCGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.CLCC: This class implements the CLCC.
CLCCGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCGenerator: Build a new CLCCGenerator Algorithm
CLCCGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCGenerator: Build a new CLCCGenerator Algorithm
CLCLOSED - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for closed bracket "}".
CLCLOSED - Static variable in interface keel.Dataset.DataParserConstants
clean() - Method in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: Frees the cache used by the kernel.
clean() - Method in class keel.Algorithms.SVM.SMO.supportVector.Kernel: Frees the memory used by the kernel.
clean() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Frees the memory used by the kernel.
CleanAttributes - Class in keel.Algorithms.Preprocess.Transformations.CleanAttributes: This class performs the decimal scaling transformation on the data.
CleanAttributes(String) - Constructor for class keel.Algorithms.Preprocess.Transformations.CleanAttributes.CleanAttributes: Creates a new instance of decimal_scaling
cleanPanels() - Method in class keel.GraphInterKeel.datacf.importData.ImportPanel: Clean import panels
cleanRule(int[], matchProfileAgent) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
cleanTarget() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL.cleanTarget
cleanTargetOfRule(int[], int, int[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
clear() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: Removes the rules stored.
clear() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
clear() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.ProtectedProperties: Overrides a method to prevent the properties from being modified.
clear() - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Leaves the list empty
clear() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ProtectedProperties: Overrides a method to prevent the properties from being modified.
clear() - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Leaves the list empty
clear() - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Cleans the list
clear() - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Cleans the list
clear() - Method in class keel.Algorithms.Rule_Learning.UnoR.Interval: Clear the interval of values.
clear() - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Clear all data estructures, and allocates new memory for them
clear() - Method in class keel.GraphInterKeel.experiments.SelectData: Clear all the data estructures of this object, and allocates new memory for them
clear() - Method in class keel.GraphInterKeel.statistical.tests.Distribution2KeyTable: Clear table
clearAll() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: clearAll This method clears all the static members of the class.
clearAll() - Static method in class keel.Dataset.Attributes: clearAll This method clears all the static members of the class.
clearAntecedent() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Deletes all the data stored in this rule antecedent
clearAntecedent() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Deletes all the data stored in this rule antecedent
clearAttributes() - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
clearAttributes() - Static method in class keel.Algorithms.RST_Learning.RSTData
clearCache() - Static method in class keel.Algorithms.SVM.SMO.core.ClassDiscovery: clears the cache for class/classnames relation
clearInstances() - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
clearInstances() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: Remove all instances from this InstanceSet
clearInstances() - Method in class keel.Dataset.InstanceSet: Remove all instances from this InstanceSet
clearMissingVector() - Method in class keel.GraphInterKeel.experiments.DataSet: Empties the missing partition vector
clearNonStaticAttributes() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: Clear the non-Static attributes.
clearNonStaticAttributes() - Method in class keel.Dataset.InstanceSet: Clear the non-Static attributes.
clearSeeds() - Method in class keel.GraphInterKeel.experiments.Parameters: removes all seeds
clearTable() - Method in class keel.GraphInterKeel.datacf.util.AttributeTable: Clears the table
clearTable() - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelAttribute: Clear a table of attributes
clearTables(boolean) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.OVO: Clear the existing tables and identifies if is test or not.
clearValue() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
clearValue() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
clLength - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Represents the lenght of the classifier.
clLength - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Represents the lenght of the classifier.
CLLENGTH - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Item: Clone function.
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset: Clone function.
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Replace: Clone function.
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: Clone function.
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Selected: Clone function.
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Item: Clone function.
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset: Clone function.
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Replace: Clone function.
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: Clone function.
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Selected: Clone function.
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.Literal: Clone function.
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.Rule: Clone function.
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Fuzzy: Clone Function.
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Item: Clone function.
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Itemset: Clone function
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule: Clone Function
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.RuleBase: Clone Function
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Cover: Returns a copy of the Cover object.
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Fuzzy: Clone Function.
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual: Clone
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Item
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Itemset: Clone
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Rule: Clone
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: Clone
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Fuzzy: Clone Function
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Individual: Clone Function
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Item: Clone function
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Itemset: Clone function
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Rule: Clone Function
clone() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.RuleBase: Clone Function
clone() - Method in class keel.Algorithms.Decision_Trees.DT_GA.Individuo
clone() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.Individuo
clone(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.DataBase: It makes a copy of a fuzzy label
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Fuzzy: It makes a copy for the object
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseR: It creates a copy of the current rule base
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Difuso: It makes a copy for the object
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Regla: It creates a copy of the rule
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGAP.FuzzyGAPClassifier: This method clones the current object.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGP.FuzzyGPClassifier: This method clones the current object.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyPittsBurgh.PittsburghClassifier: This method clones the current object.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySAP.FuzzySAPClassifier: This method clones the current object.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Fuzzy: It makes a copy for the object
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule: It carries out a copy of the current rule
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase: Clone Function
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.fuzzy_t
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.fuzzy_t
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationinteger
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.fuzzy_t
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationinteger
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationreal
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Fuzzy: It makes a copy for the object
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Rule: It carries out a copy of the current rule
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.RuleBase: It performs a copy of the current RB
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseR
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Difuso
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Individuo
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Regla
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseD
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Difuso
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Individuo
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Regla
clone(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.DataBase: It makes a copy of a fuzzy label
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Fuzzy: It makes a copy for the object
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Cover
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Fuzzy: Clone Function.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual: Clone
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Item
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Itemset: Clone
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule: Clone
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase: Clone
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.Classifier: abstract method to clone the current object.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.FuzzyClassifier: This method clones the current object.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.FuzzyFGPClassifier: This method clones the current object.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.Genotype: abstract method for generating a perfect copy of the current Genotype.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGAP: This method is intended for generating a perfect copy of the current Genotype.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGP: This method is intended for generating a perfect copy of the current Genotype.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGPRegSym: This method is intended for generating a perfect copy of the current Genotype.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypePitts: This method is intended for generating a perfect copy of the current Genotype.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividual: This abstract method clone a genetic individual
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGAPModelIndividual: This method clone a fuzzy individual for GAP model
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPModel: This method clone a fuzzy model for GP
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPModelIndividual: This method clone a fuzzy individual for GP model
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPRegSymModel: This method clone a fuzzy model
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyModel: This method clone a fuzzy model
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.Model: This abstrac method clone a model
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.PittsburghModel: This method clone a genetic individual from a Pittsburgh model
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.RegSymFuzzyGP: This method clone a fuzzy system of symbolic regession
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node: This abstract method clone a Node
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeAdd: This method generates a new NodeAdd
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeAnd: This method clone a NodeAnd
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeAssert: This abstract method clone a node
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeConsequent: This method creates a clone from the object of the class
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeExp: This method creates a clone from the object of the class
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeExprArit: This abstract method clone a node
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeExprHold: This method clone a node
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeIs: This method clone a node
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeLabel: This method clone a node
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeLog: This method clone a log node
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeMinus: This method clone a minud node
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeOr: This method clone an or node
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeProduct: This method clones a product node
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeRule: This method clones a node rule
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeRuleBase: This method clones a node from a node rule base
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeSquareRoot: This method clones a node from another square root node
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeValue: This method generate a new node from a NodeValue
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeVariable: This method clones a variable node
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.Fuzzy: Creates and returns a copy of this object.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut: Creates and returns a copy of this object.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyInterval: Creates and returns a copy of this object.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyNumberTRIANG: Creates and returns a copy of this object.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyNumberTRLEFT: Creates and returns a copy of this object.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyPartition: Creates and returns a copy of this object.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyRegressor: Creates and returns a copy of this object.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyRule: Creates and returns a copy of this object.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzySingleton: Creates and returns a copy of this object.
clone() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase: Creates and returns a copy of this object.
clone() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Condition: Make a copy of a Constructor object
clone() - Method in class keel.Algorithms.Hyperrectangles.BNGE.Rule: Clone method
clone() - Method in class keel.Algorithms.Hyperrectangles.INNER.Rule: Clone method
clone() - Method in class keel.Algorithms.Hyperrectangles.RISE.Rule: Clone method
clone() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
clone(int, int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.DataBase: It makes a copy of a fuzzy label
clone() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Fuzzy: It makes a copy for the object
clone() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Override of the clone function
clone() - Method in class keel.Algorithms.Instance_Generation.GMCA.ClusterSet: Clone the cluster set.
clone() - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster: Clone method.
clone() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbf: Clones a neuron/rbf
clone() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Clones a RBFN neural network
clone() - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbf: Clones a neuron/rbf
clone() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbf: Clones a neuron/rbf
clone() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbf: Clones a neuron/rbf
clone() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf: Clones a neuron/rbf
clone() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbf: Clones a neuron/rbf
clone() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf: Clones a neuron/rbf
clone() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseD
clone() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Difuso
clone() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Individuo
clone() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.MatrizR
clone() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Regla
clone() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Override of the clone function
clone() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Creates and returns a clone of this object.
clone() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Clones the DoubleVector object.
clone() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector: Clones the IntVector object.
clone() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Clone the Matrix object.
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyRegion: It allows to clone correctly a fuzzy region
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Gene: It allows to clone correctly a gene
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Item: It allows to clone correctly an item
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Itemset: It allows to clone correctly an itemset
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fuzzy: Clone Function.
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Itemset: It allows to clone correctly an itemset
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyRegion: It allows to clone correctly a fuzzy region
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Item: It allows to clone correctly an item
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Itemset: It allows to clone correctly an itemset
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyRegion: It allows to clone correctly a fuzzy region
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Gene: It allows to clone correctly a gene
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Item: It allows to clone correctly an item
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Itemset: It allows to clone correctly an itemset
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.MembershipFunction: It allows to clone correctly a membership function
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyRegion: It allows to clone correctly a fuzzy region
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Gene: It allows to clone correctly a gene
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Item: It allows to clone correctly an item
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Itemset: It allows to clone correctly an itemset
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.MembershipFunction: It allows to clone correctly a membership function
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Intervals
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Interval: It allows to clone correctly an interval
clone() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Intervals
clone() - Method in class org.libsvm.svm_parameter
cloneClassifier(classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifierFactory
cloneClassifier(classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.populationWrapper
cloneEmpty() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase: It performs a copy of the current RB
cloneParticle() - Method in class keel.Algorithms.PSO_Learning.CPSO.Particle
cloneParticle() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
cloneParticle() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.Particle
cloneParticle() - Method in class keel.Algorithms.PSO_Learning.REPSO.Particle
CLOPENED - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for opened bracket "{".
CLOPENED - Static variable in interface keel.Dataset.DataParserConstants
close() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset: Close dataset
close() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArffDataSet: Close dataset
close() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArrayDataset: Close dataset
close() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.FileDataset: Close dataset
close() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IDataset: Close dataset
close() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.KeelDataSet: Close dataset
close() - Method in class keel.Dataset.InstanceParser: This method closes the buffered reader used to parse the instances
closeAboutBox() - Method in class keel.GraphInterKeel.datacf.DataCFAboutBox
closedEducationalExec(EducationalRunEvent) - Method in class keel.GraphInterKeel.experiments.Experiments: EDUCATIONAL KEEL **********************
closedEducationalExec(EducationalRunEvent<A>) - Method in interface keel.GraphInterKeel.experiments.IEducationalRunListener: Window of partitions is closed by user, then this method is invoqued
closedEducationalExecWindow - Variable in class keel.GraphInterKeel.experiments.Experiments: EDUCATIONAL KEEL ***************************
closeFiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Statistic: Closes all the opened files.
closeFiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Statistic: Closes all the opened files.
closeLog() - Static method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.LogManager
closeLog() - Static method in class keel.Algorithms.Genetic_Rule_Learning.Globals.LogManager
closeLog() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.LogManager
closeRead() - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.FileManagement: Close the file we've read.
closeRead() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.FileManagement: Close the file we've read.
closestRbf(double[]) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Returns the nearest rbf/neuron to a vector v (pattern)
closeWrite() - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.FileManagement: Close the file we've writen.
closeWrite() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.FileManagement: Close the file we've writen.
Cluster - Class in keel.Algorithms.Instance_Generation.GMCA: Represents a cluster
Cluster(PrototypeSet, Prototype) - Constructor for class keel.Algorithms.Instance_Generation.GMCA.Cluster: Cluster constructor using a prototype set and its center.
Cluster(PrototypeSet) - Constructor for class keel.Algorithms.Instance_Generation.GMCA.Cluster: Cluster constructor using a prototype set and its center.
Cluster(Prototype) - Constructor for class keel.Algorithms.Instance_Generation.GMCA.Cluster: Cluster constructor using a prototype set and its center.
Cluster - Class in keel.Algorithms.Instance_Generation.VQ: Cluster of a prototype set.
Cluster(Prototype, PrototypeSet) - Constructor for class keel.Algorithms.Instance_Generation.VQ.Cluster: Construct a new cluster.
Cluster(PrototypeSet, Prototype) - Constructor for class keel.Algorithms.Instance_Generation.VQ.Cluster: Construct a new cluster.
Cluster - Class in keel.Algorithms.Lazy_Learning.NSC: File: Cluster.java A class modelling a cluster for NSC algorithm It provides basic functionality such insert and drop elements, calculates inner and outer border, variance, and more.
Cluster(int, int, int) - Constructor for class keel.Algorithms.Lazy_Learning.NSC.Cluster: Buider.Builts a empty cluster, setting its parameters
Cluster - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering: This class is a cluster C for the EventCovering method
Cluster() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Cluster: Creates a new instance of Cluster
Cluster_Analysis - Class in keel.Algorithms.Discretizers.Cluster_Analysis: This class implements the Cluster Analysis discretizer.
Cluster_Analysis() - Constructor for class keel.Algorithms.Discretizers.Cluster_Analysis.Cluster_Analysis: Builder
clusterInitation(double[]) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.EventCovering: Initializes the set of clusters using information of the data set
ClusterKMeans - Class in keel.Algorithms.Clustering_Algorithms.ClusterKMeans: ClusterKMeans is a class to cluterize a dataset using the k-means clustering algorithm.
ClusterKMeans() - Constructor for class keel.Algorithms.Clustering_Algorithms.ClusterKMeans.ClusterKMeans
ClusterSet - Class in keel.Algorithms.Instance_Generation.GMCA: Set of the clusters.
ClusterSet() - Constructor for class keel.Algorithms.Instance_Generation.GMCA.ClusterSet: Default constructor.
CMAR - Class in keel.Algorithms.Associative_Classification.ClassifierCMAR: It contains the implementation of the CMAR algorithm
CMAR(parseParameters) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCMAR.CMAR: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Cmeans(double[][], int, double[][]) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Executes the k means algorithm.
CN2 - Class in keel.Algorithms.Rule_Learning.CN2: Title: Main class of the algorithm Description: It contains the esential methods for the CN2 algorithm Created: November 26th 2004 Copyright: Copyright (c) 2004 Company: KEEL
CN2() - Constructor for class keel.Algorithms.Rule_Learning.CN2.CN2: Default builder
CN2(String, String, String, String, String, String, int, double, int) - Constructor for class keel.Algorithms.Rule_Learning.CN2.CN2: CN2 class builder It does a local copy of the filenames for their posterior use.
CN2SD - Class in keel.Algorithms.Subgroup_Discovery.CN2SD: Title: CN2SD Description: Contents the principal methods of the CN2SD algorithm
CN2SD(String, String, String, String, String, String, int, double, double, int, int) - Constructor for class keel.Algorithms.Subgroup_Discovery.CN2SD.CN2SD: Parameter constructor.
CNN - Class in keel.Algorithms.ImbalancedClassification.Resampling.CNN: File: CNN.java The CNN algorithm is an undersampling method that can be used to deal with the imbalanced problem.
CNN(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.CNN.CNN: Constructor of the class.
CNN - Class in keel.Algorithms.Instance_Generation.BasicMethods
CNN(PrototypeSet) - Constructor for class keel.Algorithms.Instance_Generation.BasicMethods.CNN: Creates a CNN algorithm.
CNN(PrototypeSet, int) - Constructor for class keel.Algorithms.Instance_Generation.BasicMethods.CNN: Creates a CNN algorithm.
CNN - Static variable in class keel.Algorithms.Instance_Generation.HYB.HYBGenerator: CNN title text
CNN - Class in keel.Algorithms.Instance_Selection.CNN: File: CNN.java The CNN Instance Selection algorithm.
CNN(String) - Constructor for class keel.Algorithms.Instance_Selection.CNN.CNN: Default builder.
CNN - Class in keel.Algorithms.Preprocess.Instance_Selection.CNN: File: CNN.java The CNN Instance Selection algorithm.
CNN(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.CNN.CNN: Default builder.
CNN_TomekLinks - Class in keel.Algorithms.ImbalancedClassification.Resampling.CNN_TomekLinks: File: CNN_TomekLinks.java The CNN + TomekLinks algorithm is an undersampling method that can be used to deal with the imbalanced problem, a chain procedure of the CNN and Tomek Links methods.
CNN_TomekLinks(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.CNN_TomekLinks.CNN_TomekLinks: Constructor of the class.
Co - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForClassification
co_forest_sim(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCGenerator: It applies a coforest-sim algorithm and fill the this.confidence matrix.
CoBCAlgorithm - Class in keel.Algorithms.Semi_Supervised_Learning.CoBC: CoBC algorithm calling.
CoBCAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.CoBC.CoBCAlgorithm
CoBCGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.CoBC: This class implements the Co-traning wrapper.
CoBCGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.CoBC.CoBCGenerator: Build a new CoBCGenerator Algorithm
CoBCGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.CoBC.CoBCGenerator: Build a new CoBCGenerator Algorithm
COBERT - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
cobertura(Vector, Vector, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion
CobInitCrom(Population, TableVar, TableDat, float, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.CromCAN: Initialization based on coverage
CobInitCrom(Population, TableVar, TableDat, float, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.CromDNF: Initialization based on coverage
CobInitInd(Population, TableVar, TableDat, float, int, String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndCAN: Creates instance of Canonical individual based on coverage
CobInitInd(Population, TableVar, TableDat, float, int, String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndDNF: Creates nstance of DNF individual based on coverage
CobInitInd(Population, TableVar, TableDat, float, int, String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Creates nstance of individual based on coverage
cochransCriterion(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ContingencyTables: Tests if Cochran's criterion is fullfilled for the given contingency table.
cochransCriterion(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.ContingencyTables: Tests if Cochran's criterion is fullfilled for the given contingency table.
cochransCriterion(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.ContingencyTables: Tests if Cochran's criterion is fullfilled for the given contingency table.
Cochromosome - Class in keel.Algorithms.Genetic_Rule_Learning.CORE: This class implements the cooperative-competitive rule-based scheme of the CORE algorithm
Cochromosome() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.CORE.Cochromosome: default constructor.
Cochromosome(Cochromosome) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.CORE.Cochromosome: Deep-copy constructor (but the arraylist of chromosomes only copies the references to the chromosome objects!)
CoCoIS - Class in keel.Algorithms.Instance_Selection.CoCoIS: File: CoCoIS.java This class implements the Cooperative Coevolutionary Instance Selection model (CoCoIS)
CoCoIS(String) - Constructor for class keel.Algorithms.Instance_Selection.CoCoIS.CoCoIS: Default builder.
CoCoIS - Class in keel.Algorithms.Preprocess.Instance_Selection.CoCoIS: File: CoCoIS.java This class implements the Cooperative Coevolutionary Instance Selection model (CoCoIS)
CoCoIS(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.CoCoIS: Default builder.
Code(int, genetcode) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation: Returns in "code" the genetic code of the individual "i"
Code(int, int[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary
Code(int, int[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger
Code(int, int[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal
Code(int, genetcode) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation: Returns in "code" the genetic code of the individual "i"
Code(int, int[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
Code(int, int[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationinteger
Code(int, int[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal
Code(int, genetcode) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation: Returns in "code" the genetic code of the individual "i"
Code(int, int[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
Code(int, int[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationinteger
Code(int, int[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationreal
codifica(int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Codificacion: Encodes a value from its row and column numbers
codificaBase(BaseDatos) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Codificacion: Encodes the elements
Codificacion - Class in keel.Algorithms.Genetic_Rule_Learning.Hider
Codificacion() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Codificacion: Empty constructor
Codificacion(Vector) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Codificacion: Constructor with a 'Vector' parameter
codifyAntecents(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase: This method finds out the label with maximum membership value for each variable.
coef0 - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
coef0 - Variable in class org.libsvm.svm_parameter
coefficient - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
Coeficientes() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.AD: Computes the Coeficients
coevolution() - Method in class keel.Algorithms.Coevolution.CIW_NN.CIW_NN: Performs the coevolutionary search
coevolution() - Method in class keel.Algorithms.Coevolution.IFS_COCO.IFS_COCO: Performs the co-evolutionary process
CoevolutionAlgorithm - Class in keel.Algorithms.Coevolution: File: CoevolutionAlgorithm.java A general framework for Coevolutionary Algorithms.
CoevolutionAlgorithm() - Constructor for class keel.Algorithms.Coevolution.CoevolutionAlgorithm
CoForestAlgorithm - Class in keel.Algorithms.Semi_Supervised_Learning.CoForest: CoForest algorithm calling.
CoForestAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.CoForest.CoForestAlgorithm
CoForestGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.CoForest: This class implements the Tri-training.
CoForestGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.CoForest.CoForestGenerator: Build a new CoForestGenerator Algorithm
CoForestGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.CoForest.CoForestGenerator: Build a new CoForestGenerator Algorithm
Cogin - Class in keel.Algorithms.Genetic_Rule_Learning.COGIN: This class implements the COGIN algorithm from: David Perry Greene and Stephen F.
Cogin() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.COGIN.Cogin: Default constructor
Cogin(String) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.COGIN.Cogin: Builds up the COGIN with the provided parameters in KEEL format
colexIncFuncRank(int[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Combinatoric: Rank of increasing function a[] in colex order
collapse() - Method in class keel.Algorithms.Decision_Trees.C45.Tree: Function to collapse a tree to a node if training error doesn't increase.
collapse() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Tree: Function to collapse a tree to a node if training error doesn't increase.
collapse() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree: Function to collapse a tree to a node if training error doesn't increase.
collapse() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Tree: Function to collapse a tree to a node if training error doesn't increase.
collapse() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree: Function to collapse a tree to a node if training error doesn't increase.
collapse() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Tree: Function to collapse a tree to a node if training error doesn't increase.
collapse() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree: Function to collapse a tree to a node if training error doesn't increase.
collapse() - Method in class keel.Algorithms.Rule_Learning.PART.Tree: Function to collapse a tree to a node if training error doesn't increase.
collapse() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: Function to collapse a tree to a node if training error doesn't increase.
COLON - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
COLON - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for coma character.
COLON - Static variable in interface keel.Dataset.DataParserConstants
column - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
column - Static variable in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
column - Static variable in class keel.Dataset.SimpleCharStream
columna(double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
columna(double[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
columns - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
COM - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for COM.
COM - Static variable in interface keel.Dataset.DataParserConstants
COM2 - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
COM2 - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for COM2.
COM2 - Static variable in interface keel.Dataset.DataParserConstants
combinations(short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Invokes combinations method to calculate all possible combinations of a given item set.
combinatoria(int, int) - Static method in class keel.Algorithms.Discretizers.MODL.MODL: Function that calculates combinatory of two integers
combinatoria(int, int) - Static method in class keel.Algorithms.Instance_Selection.Explore.EncodingLength: Function that calculates combinatory of two integers
combinatoria(int, int) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.Explore.EncodingLength: Function that calculates combinatory of two integers
combinatoria(int, int) - Static method in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Friedman: Computes the (N/M) combinatory number
combinatoria(int, int) - Static method in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Multiple: Computes the (N/M) combinatory number
combinatoria(int, int) - Static method in class keel.GraphInterKeel.statistical.tests.Friedman: Computes the (N/M) combinatory number
combinatoria(int, int) - Static method in class keel.GraphInterKeel.statistical.tests.Multiple: Computes the (N/M) combinatory number
Combinatoric - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat: combinatorical functions Source: S.Gill Williamson (1985) Combinatorics for Computer Science, Computer Science Press
Combinatoric() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Combinatoric
combine(Function, Function) - Static method in class keel.Algorithms.Decision_Trees.M5.Function: Constructs a new function of which the variable list is a combination of those of two functions
combine(Itemset) - Method in class keel.Algorithms.Discretizers.UCPD.Itemset: It combines two itemsets to get a new itemset based on the creation rules of A priori algorithm
combine(Function, Function) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Function: Constructs a new function of which the variable list is a combination of those of two functions
combinedDL(double, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Compute the combined DL of the ruleset in this class, i.e. theory DL and data DL.
COMENT - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
command - Variable in class keel.GraphInterKeel.experiments.UserMethod
commentedValue - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArffDataSet: Symbol which represents commentted values
commentedValue - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.KeelDataSet: Symbol which represents commentted values
CommonTokenAction(Token) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserTokenManager
CommonTokenAction(Token) - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParserTokenManager
CommonTokenAction(Token) - Static method in class keel.Dataset.DataParserTokenManager
compactify() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Compactifies the set of instances.
compactify() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Compactifies the set of instances.
compactify() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Compactifies the set of itemsets.
compactify() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Compactifies the set of instances.
compactify() - Method in class keel.Algorithms.SVM.SMO.core.Instances: Compactifies the set of instances.
compara(Muestra) - Method in class keel.Algorithms.Rule_Learning.Prism.Muestra: Compare if two examples are equals
compara(Muestra) - Method in class keel.Algorithms.Rule_Learning.UnoR.Muestra: Compare if two examples are equals
compara(Muestra) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Muestra: Compare if two examples are equals
compara(Muestra) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Muestra: Compare if two examples are equals
ComparadorAtributo - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner: Title: ComparadorAtributo (Attributes comparative method) Description: Attributes comparative method.
ComparadorAtributo() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ComparadorAtributo: Default constructor.
ComparadorAtributo - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus: Title: ComparadorAtributo (Attributes comparative method) Description: Attributes comparative method.
ComparadorAtributo() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ComparadorAtributo: Default constructor.
ComparadorAtributo - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner: Title: ComparadorAtributo (Attributes comparative method) Description: Attributes comparative method.
ComparadorAtributo() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ComparadorAtributo: Default constructor.
ComparadorAtributo - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus: Title: ComparadorAtributo (Attributes comparative method) Description: Attributes comparative method.
ComparadorAtributo() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ComparadorAtributo: Default constructor.
ComparadorAtributo - Class in keel.Algorithms.Genetic_Rule_Learning.PSO_ACO: Title: ComparadorAtributo (Attributes comparative method) Description: Attributes comparative method.
ComparadorAtributo() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ComparadorAtributo: Default constructor.
ComparadorCondicion - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner: Title: ComparadorCondicion (Conditions comparative method) Conditions comparative method.
ComparadorCondicion() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ComparadorCondicion: Default constructor.
ComparadorCondicion - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus: Title: ComparadoCondicion (Conditions comparative method) Conditions comparative method.
ComparadorCondicion() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ComparadorCondicion: Default constructor.
ComparadorCondicion - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus: Title: ComparadoCondicion (Conditions comparative method) Conditions comparative method.
ComparadorCondicion() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ComparadorCondicion: Default constructor.
ComparadorCondicion - Class in keel.Algorithms.Genetic_Rule_Learning.PSO_ACO: Title: ComparadoCondicion (Conditions comparative method) Conditions comparative method.
ComparadorCondicion() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ComparadorCondicion: Default constructor.
ComparadorParticulas - Class in keel.Algorithms.Genetic_Rule_Learning.PSO_ACO: Title: ComparadorParticulas (Particles comparative method) Particles comparative method.
ComparadorParticulas() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ComparadorParticulas: Default constructor.
ComparadorRegla - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner: Title: Rules comparative method.
ComparadorRegla() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ComparadorRegla: Default constructor.
ComparadorRegla - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus: Title: Rules comparative method.
ComparadorRegla() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ComparadorRegla: Default constructor.
ComparadorRegla - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner: Title: Rules comparative method.
ComparadorRegla() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ComparadorRegla: Default constructor.
ComparadorRegla - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus: Title: Rules comparative method.
ComparadorRegla() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ComparadorRegla: Default constructor.
ComparadorRegla - Class in keel.Algorithms.Genetic_Rule_Learning.PSO_ACO: Title: Rules comparative method.
ComparadorRegla() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ComparadorRegla: Default constructor.
comparator - Variable in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.ClassificationProblemEvaluator: Fitnesses comparator
comparator - Variable in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.SoftmaxClassificationProblemEvaluator: Fitnesses comparator
comparator - Variable in class keel.Algorithms.Neural_Networks.NNEP_Regr.problem.regression.RegressionProblemEvaluator: Fitnesses comparator
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Atributo: Compares two attributes.
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ComparadorAtributo: Compares two attributes
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ComparadorCondicion: Compares two conditions.
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ComparadorRegla: Compares two rules.
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Atributo: Compares two attributes.
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ComparadorAtributo: Compares two attributes
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ComparadorCondicion: Compares two condition.
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ComparadorRegla: Compares two rules.
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Atributo: Compares two attributes.
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ComparadorAtributo: Compares two attributes
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ComparadorRegla: Compares two rules.
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Atributo: Compares two attributes.
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ComparadorAtributo: Compares two attributes
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ComparadorCondicion: Compares two condition.
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ComparadorRegla: Compares two rules.
compare(Instance) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Instance: It compares if two examples are the same
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.AtributoComparator: Compare
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.IndividuoComparator: Compare
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Atributo: Compares two attributes.
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ComparadorAtributo: Compares two attributes
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ComparadorCondicion: Compares two condition.
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ComparadorParticulas: Compares two particles.
compare(Object, Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ComparadorRegla: Compares two rules.
compare(Instance) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Instance: It compares if two examples are the same
compare(Sample) - Method in class keel.Algorithms.Hyperrectangles.EACH.Sample: Checks if the Sample objects are equals
compare(Object, Object) - Method in class keel.Algorithms.Hyperrectangles.INNER.Rule: Compare two rules, regarding its impurity level
compare(Prototype, Prototype) - Method in class keel.Algorithms.Instance_Generation.utilities.Distance: Overloading of the compare function.
Compare - Class in keel.Algorithms.Neural_Networks.gann: This is a public Class that implements the Comparator interface
Compare() - Constructor for class keel.Algorithms.Neural_Networks.gann.Compare
compare(Object, Object) - Method in class keel.Algorithms.Neural_Networks.gann.Compare: Method implemented for decremental ordering
compare(Instance) - Method in class keel.Algorithms.Rule_Learning.AQ.Instance: It compares if two examples are the same
compare(Instance) - Method in class keel.Algorithms.Rule_Learning.CN2.Instance: It compares if two examples are the same
compare(Prototype, Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Distance: Overloading of the compare function.
Compare(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population: Method to Compare two individuals of the population
Compare(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population: Method to Compare two individuals of the population
compare(Instance) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Instance: It compares if two examples are the same
Compare(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population: Method to Compare two individuals of the population
compare(Object, Object) - Method in class keel.Algorithms.SVM.SMO.core.ClassDiscovery.StringCompare: Compares its two arguments for order.
compareConstraint(Individual, Individual) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Ranking: Gets the comparison constraint
compareDominance(Individual, int, Individual, int, int, String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Ranking: Gets the comparison Dominance
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Item: Function to compare objects of the Item class.
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset: Function to compare objects of the Itemset class.
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Replace: Function to compare objects of the Structure class.
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: Function to compare objects of the Rule class.
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Selected: Function to compare objects of the Selected class.
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Item: Function to compare objects of the Item class.
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset: Function to compare objects of the Itemset class.
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Replace: Function to compare objects of the Structure class.
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: Function to compare objects of the Rule class.
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Selected: Function to compare objects of the Selected class.
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.Literal: Function neccessary to sort literals.
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.Rule: Function to compare objects of the Rule class.
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Item: Function to compare objects of the Item class.
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule: Function to compare objects of the Rule class.
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Item: Function to compare objects of the Item class.
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Rule: Function to compare objects of the Rule class.
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Individual: Function to compare objects of the Individual class Necessary to be able to use "sort" function It sorts in an increasing order of fitness
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Item: Function to compare objects of the Item class Necessary to be able to use "sort" function It sorts in an decreasing order of attribute If equals, in an decreasing order of attribute's value
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Rule: Function to compare objects of the Rule class Necessary to be able to use "sort" function It sorts in an decreasing order of laplace accuracy
compareTo(Object) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Selected: Function to compare objects of the Selected class Necessary to be able to use "sort" function It sorts in an increasing order of probability
compareTo(Object) - Method in class keel.Algorithms.Decision_Trees.DT_GA.Individuo
compareTo(Object) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.Individuo: Funcion de minimizacion del fitness
compareTo(Register) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Register: Overriden function that symbolizes if the other register is equal, greater or smaller than the original register with a integer as response
compareTo(Object) - Method in class keel.Algorithms.Decision_Trees.Target.Tree
compareTo(Object) - Method in class keel.Algorithms.Discretizers.Khiops.DeltaValue: Method from interface Comparable, so this object can be sorted in Java lists
compareTo(Object) - Method in class keel.Algorithms.Discretizers.MODL.DeltaValue: Method from interface Comparable, so this object can be sorted in Java lists
compareTo(Object) - Method in class keel.Algorithms.Discretizers.MODL.Neighbour
compareTo(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule: Compares the fitness of two rules for the ordering procedure
compareTo(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.Individual: Compares the fitness value of two individuals
compareTo(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.Individual: Compares the fitness value of two individuals
compareTo(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Rule: Compares the fitness of two rules for the ordering procedure
compareTo(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.RuleBase: Compares the fitness of two RB for the ordering procedure
compareTo(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseR
compareTo(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Individuo
compareTo(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Regla
compareTo(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Selectos
compareTo(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Individuo
compareTo(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Fuzzy: Compares this object with the specified object for order, according to the number of label measure
compareTo(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.FuzzyAntecedent: Compares this object with the specified object for order, according to the number of variable measure
compareTo(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Compares this object with the specified object for order, according to the raw_fitness measure
compareTo(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual
compareTo(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Item
compareTo(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule
compareTo(AttributeLocator) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: Compares this object with the specified object for order.
compareTo(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Chromosome: Implements the Comparator method to sort chromosomes by its fitness.
compareTo(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome
compareTo(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.RuleSet: Function that lets compare cromosomes to sort easily
compareTo(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.RuleSet: Function that lets compare cromosomes to sort easily
compareTo(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.AttributeCR: Implementation of the method compareTo for sorting (by CR)
compareTo(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.RuleSet
compareTo(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual: Compares the fitness value of two individuals
compareTo(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Regla
compareTo(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.AttributeCR: Implementation of the method compareTo for sorting (by CR)
compareTo(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.RuleSet: It compares the RuleSet with other one
compareTo(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Rule: It compares the rule with respect to their strength
compareTo(Object) - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Compare two objects of the Complex class
compareTo(Object) - Method in class keel.Algorithms.Hyperrectangles.EACH.Selector: Function to compare two objects of the selector class
compareTo(Object) - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Cromosoma: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Hyperrectangles.INNER.Pair: Compare to method: Compare two pairs of rules regarding its distance
compareTo(PosProb) - Method in class keel.Algorithms.ImbalancedClassification.Auxiliar.AUC.PosProb: Compares the ordering of two different PosProb objects with respect to their associated probability values
compareTo(Object) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Referencia
compareTo(Object) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Referencia: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.Chromosome: Function that lets compare cromosomes for an easilier sort
compareTo(Object) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC_Chromosome: Compares this object with the specified object for order, according to the fitness measure
compareTo(Object) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Fuzzy: Compares this object with the specified object for order, according to the number of label measure
compareTo(Object) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Compares this object with the specified object for order, according to the number of variable measure
compareTo(Object) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Compares this object with the specified object for order, according to the raw_fitness measure
compareTo(Object) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet
compareTo(Object) - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Instance_Selection.CCIS.Pareja
compareTo(Object) - Method in class keel.Algorithms.Instance_Selection.CHC.Cromosoma: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Instance_Selection.CPruner.Trio
compareTo(Object) - Method in class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.Cromosoma
compareTo(Object) - Method in class keel.Algorithms.Instance_Selection.GGA.Cromosoma: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Instance_Selection.IGA.Cromosoma: Test if two chromosome differ in only one gene
compareTo(Object) - Method in class keel.Algorithms.Instance_Selection.MNV.ReferenciaMNV
compareTo(Object) - Method in class keel.Algorithms.Instance_Selection.PBIL.Cromosoma: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Instance_Selection.SGA.Cromosoma: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Instance_Selection.SSMA.Cromosoma: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Instance_Selection.ZhangTS.Cromosoma: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Preprocess.Basic.Referencia: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.Chromosome: Compare to method
compareTo(Object) - Method in class keel.Algorithms.Preprocess.Instance_Selection.CCIS.Pareja
compareTo(Object) - Method in class keel.Algorithms.Preprocess.Instance_Selection.CHC.Cromosoma: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Preprocess.Instance_Selection.CPruner.Trio
compareTo(Object) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GGA.Cromosoma: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Preprocess.Instance_Selection.IGA.Cromosoma: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Preprocess.Instance_Selection.MNV.ReferenciaMNV
compareTo(Object) - Method in class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Cromosoma: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SGA.Cromosoma: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.Cromosoma: Compare to Method
compareTo(Object) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EVpair
compareTo(Object) - Method in class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.IndexValuePair
compareTo(Object) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.Individual: Compares the fitness value of two individuals
compareTo(Object) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.Individual: Compares the fitness value of two individuals
compareTo(Object) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Individuo
compareTo(Object) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.MatrizR
compareTo(Chromosome) - Method in class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome
compareTo(Chromosome) - Method in class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome
compareTo(Object) - Method in class keel.Algorithms.Rule_Learning.AQ.Complex: Comparison function between two objects of the class complex
compareTo(Object) - Method in class keel.Algorithms.Rule_Learning.AQ.Selector: Comparison function between two objects of the class Selector
compareTo(Object) - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: Comparison function between two objects of the class complex
compareTo(Object) - Method in class keel.Algorithms.Rule_Learning.CN2.Selector: Comparison function between two objects of the class Selector
compareTo(Object) - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Compare two objects of the Complex class
compareTo(Object) - Method in class keel.Algorithms.Rule_Learning.Prism.Selector: Function to compare two objects of the selector class
compareTo(Object) - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Compare two objects of the class
compareTo(Object) - Method in class keel.Algorithms.Rule_Learning.Riona.Selector: Function for comparing two objects of the selector class
compareTo(Object) - Method in class keel.Algorithms.Rule_Learning.Ripper.Pair
compareTo(Object) - Method in class keel.Algorithms.Rule_Learning.Ripper.Trio
compareTo(Object) - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Compare two objects of the class
compareTo(Object) - Method in class keel.Algorithms.Rule_Learning.UnoR.Selector: Function to compare two objects of the selector class
compareTo(Object) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet
compareTo(Object) - Method in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.MultiplePair: CompareTo method
compareTo(Object) - Method in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Pair: CompareTo method
compareTo(Object) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Compares the rule with the one given.
compareTo(Object) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Compare two objects of the class
compareTo(Object) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Selector: Function to compare two objects of the selector class
compareTo(Object) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Compare two objects
compareTo(Object) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Selector: Function to compare two objects of this class
compareTo(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Chromosome: It compares a chromosome with another one in order to accomplish ordering (NOT ascending) later.
compareTo(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Item: It compares an item with another one in order to accomplish ordering later.
compareTo(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Item: It compares an item with another one in order to accomplish ordering later.
compareTo(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Chromosome: It compares a chromosome with another one in order to accomplish ordering (NOT ascending) later.
compareTo(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Item: It compares an item with another one in order to accomplish ordering later.
compareTo(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.MembershipFunction: It compares a membership function with another one in order to accomplish ordering (ascending) later.
compareTo(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Chromosome: It compares a chromosome with another one in order to accomplish ordering (NOT ascending) later.
compareTo(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Item: It compares an item with another one in order to accomplish ordering later.
compareTo(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.MembershipFunction: It compares a membership function with another one in order to accomplish ordering (ascending) later.
compareTo(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome: It compares a chromosome with another one in order to accomplish ordering later.
compareTo(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
compareTo(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
compareTo(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Chromosome
compareTo(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Chromosome
compareTo(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
compareTo(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
compareTo(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
compareTo(Object) - Method in class keel.GraphInterKeel.experiments.AlgorithmXML: Implements the lexicographic order
compareTo(Object) - Method in class keel.GraphInterKeel.experiments.DatasetXML: Implements the lexicographic order
compareTo(Object) - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription: Implements the String's lexicographic order
compareTo(Object) - Method in class keel.GraphInterKeel.statistical.tests.MultiplePair: CompareTo method
compareTo(Object) - Method in class keel.GraphInterKeel.statistical.tests.Pair: CompareTo method
compareToIndividual(classifier, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
compareToIndividual(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier: This function returns true if this individual is better than the the individual passed as a parameter.
compareToIndividual(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier: This function returns true if this individual is better than the the individual passed as a parameter.
compareToIndividual2(classifier, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
comparison(Rule) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Rule: This function detects if one rule is already included in the Rule Set
compatibilidad(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Regla: It computes the compatibility of the rule with an input example
compatibilidad(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Regla
compatibilidad_regla(Interval[][], Vector<Vector<Float>>, Vector<partition>, int, int, int, Vector<Float>, Vector<Vector<Float>>) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.rule
compatibilidadMinimo(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Regla
compatibilidadMinimo(double[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Regla
compatibility(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Rule: It computes the compatibility of the rule with an input example
compatibility(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule: It computes the compatibility degree of an example for this rule
compatibility(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Rule: Computes the compatibility of a given example with the rule.
compatibility(double[], boolean[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Computes the compatibility degree of the rule with an input example.
compatibility(double[], boolean[]) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Computes the compatibility degree of the rule with an input example.
compatible(Node) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node: This method find out if two nodes are the same type, the same number of children or the children are compatible.
compatibleData(Node) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node: This method find out if two nodes are the same type
compatibleData(Node) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeConsequent: This method evaluate if two nodes are compatibles looking for the type and the consequent
compatibleData(Node) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeExprHold: This method evaluate if two nodes are the same type
compatibleData(Node) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeLabel: This method evaluate if a Node is teh same type as another one
compatibleData(Node) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeRule: This method evaluates if two nodes are the same type
compatibleData(Node) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeRuleBase: This method evaluates if two nodes are the same type
compatibleData(Node) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeValue: This method evaluate if two nodes are the same type
compatibleData(Node) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeVariable: This method evaluates if two nodes are the same type
competitiveReplacement() - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Cogin
Complejo - Class in keel.Algorithms.Rule_Learning.Prism: Define a complex or a rule, stores selectors
Complejo() - Constructor for class keel.Algorithms.Rule_Learning.Prism.Complejo: Default constructor.
Complejo(int) - Constructor for class keel.Algorithms.Rule_Learning.Prism.Complejo: Constructor for the complex
Complejo - Class in keel.Algorithms.Rule_Learning.UnoR: Stores conjunctions of selectors
Complejo() - Constructor for class keel.Algorithms.Rule_Learning.UnoR.Complejo: Default constructor.
Complejo(int) - Constructor for class keel.Algorithms.Rule_Learning.UnoR.Complejo: Constructor for Complex
Complejo - Class in keel.Algorithms.Subgroup_Discovery.CN2SD: Stores conjunctions of selectors
Complejo() - Constructor for class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Default constructor.
Complejo(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Constructor for Complex
complement(short[], short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Returns complement of first itemset with respect to second itemset.
complement() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask: Returns a complementary mask of this mask (the activated entries of this mask are deactivated and viceversa).
complement() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Returns a complementary mask of this mask (the activated entries of this mask are deactivated and viceversa).
complement() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask: Returns a complementary mask of this mask (the activated entries of this mask are deactivated and viceversa).
complement() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Returns a complementary mask of this mask (the activated entries of this mask are deactivated and viceversa).
complement() - Method in class keel.Algorithms.Rule_Learning.PART.Mask: Returns a complementary mask of this mask (the activated entries of this mask are deactivated and viceversa).
complement() - Method in class keel.Algorithms.Rule_Learning.Ripper.Mask: Returns a complementary mask of this mask (the activated entries of this mask are deactivated and viceversa).
complement() - Method in class keel.Algorithms.Rule_Learning.Slipper.Mask: Returns a complementary mask of this mask (the activated entries of this mask are deactivated and viceversa).
complement(short[], short[]) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Returns complement of first itemset with respect to second itemset.
complement(short[], short[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Returns complement of first itemset with respect to second itemset.
CompletaElite() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: CompletaElite Stores in elite the better individuals of temporal It is suposed here there that are at last the number of individulas that fit on elite
complete - Variable in class keel.GraphInterKeel.experiments.DataSet: indicates if all the partitions are present
completeness - Variable in class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule
completeness - Variable in class keel.Algorithms.Genetic_Rule_Learning.GIL.RuleSet
Complex - Class in keel.Algorithms.Hyperrectangles.EACH: This class stores the set of Selectors
Complex() - Constructor for class keel.Algorithms.Hyperrectangles.EACH.Complex: Default Constructor for the complex
Complex(int) - Constructor for class keel.Algorithms.Hyperrectangles.EACH.Complex: Constructor for the complex
Complex - Class in keel.Algorithms.Rule_Learning.AQ: Title: Complex Description: Structure to represent a complex of one rule
Complex() - Constructor for class keel.Algorithms.Rule_Learning.AQ.Complex: Builder.
Complex(int) - Constructor for class keel.Algorithms.Rule_Learning.AQ.Complex: Builder
Complex(Selector, int) - Constructor for class keel.Algorithms.Rule_Learning.AQ.Complex: Complete builder.
Complex(int, int, double) - Constructor for class keel.Algorithms.Rule_Learning.AQ.Complex: Builder.
Complex - Class in keel.Algorithms.Rule_Learning.CN2: Title: Complex Description: Structure to represent a complex of one rule
Complex() - Constructor for class keel.Algorithms.Rule_Learning.CN2.Complex: Builder.
Complex(int) - Constructor for class keel.Algorithms.Rule_Learning.CN2.Complex: Builder
Complex(Selector, int) - Constructor for class keel.Algorithms.Rule_Learning.CN2.Complex: Complete builder.
Complex(int, int, double) - Constructor for class keel.Algorithms.Rule_Learning.CN2.Complex: Builder.
Complex - Class in keel.Algorithms.Rule_Learning.Riona: Stores conjunctions of selectors
Complex() - Constructor for class keel.Algorithms.Rule_Learning.Riona.Complex: Default constructor.
Complex(int) - Constructor for class keel.Algorithms.Rule_Learning.Riona.Complex: Constructor for Complex
Complex - Class in keel.Algorithms.Subgroup_Discovery.SDAlgorithm: This class has the different selectors for the dataset
Complex() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Default constructor.
Complex(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Constructor
ComplexityMetrics - Class in keel.Algorithms.Complexity_Metrics: This is the main class of the ComplexityMetrics library
compose(Function, Function) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.Function: the composite of to functions
composeHyper(double[][], int[][], boolean[][], int[], Hyper[], int) - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.EHS_CHC
CompRasgos(Instance, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets: Compares the features between the instance given and the one with the index given.
compruebaReglas(myDataset) - Method in class keel.Algorithms.Rule_Learning.LEM1.BaseReglas
compruebaReglas(myDataset) - Method in class keel.Algorithms.Rule_Learning.LEM2.BaseReglas
compruebaReglas(myDataset) - Method in class keel.Algorithms.Rule_Learning.Ritio.BaseReglas
compruebaReglas(myDataset) - Method in class keel.Algorithms.Rule_Learning.Rules6.BaseReglas: Evaluates the test dataset given using the rules stored.
compruebaReglas(myDataset) - Method in class keel.Algorithms.Rule_Learning.SRI.BaseReglas: Evaluates the test dataset given using the rules stored.
compute(int[], String[], String) - Method in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Contrast: In this method, all possible post hoc statistical test between more than three algorithms results are executed, according to the configuration file
computeActivationStats(int[], int, int[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
computeAsymptoticProbability(int, double, int[]) - Static method in class keel.GraphInterKeel.statistical.tests.WilcoxonDistribution: Computes asymptotic distribution of the Wilcoxon statistic
computeBestFeatures() - Static method in class keel.Algorithms.RST_Learning.RSTData
computeBody() - Static method in class keel.GraphInterKeel.statistical.tests.Wilcoxon: Computes body of the report file (i.e. the test itself)
computeCandidates() - Static method in class keel.Algorithms.Discretizers.UCPD.FrequentItemsets: It computes the candidate itemsets from lastCandidates and saves them into array candidates
computeClass(double[]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.BTS: It computes the class for a given example
computeClassDDAG(double[]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.OVO: It computes the confidence vector for the classes using the DDAG approach
computeClassifierFitness(classifier) - Static method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifierFitness
computeClassScores(double[]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.OVO: It computes the confidence vector for the classes using the method indicated in the config file
computeClassScoresDynamic(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.OVO: It computes the confidence vector for the classes using Dynamic method and returns class as String with the maximum confidence.
computeClassScoresLVPC(double[][]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.OVO: It computes the confidence vector for the classes using LVPC (Learning valued preference for Classification) method
computeClassScoresND(double[][]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.OVO: It computes the confidence vector for the classes using the non-dominance criterion
computeClassScoresOVA(double[]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.OVO: It obtains the class scores for the OVA scheme
computeClassScoresPC(double[][]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.OVO: It computes the confidence vector for the classes using Pairwise Coupling method
computeClassScoresVote(double[][]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.OVO: It computes the confidence vector for the classes using the (classic) voting method
computeClassScoresWeighted(double[][]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.OVO: It computes the confidence vector for the classes using the Weighted voting method
computeClassScoresWeighted(double[]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.OVO: It computes the confidence vector for the classes using weighted method.
computeConsequent(myDataset) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule: Procedure to compute the best consequent for a given rule
computeConsequent(myDataset) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Rule: Procedure to compute the best consequent for a given rule
computeCriterionFuction(int[], int, int) - Method in class keel.Algorithms.Discretizers.HeterDisc.HeterDisc: It computes and returns the value of criterion function of the discretization scheme build with selectedp cutpoints
computeDatasetsLabelWidth(Vector) - Method in class keel.GraphInterKeel.experiments.SelectData: We try to compute the JLabels width in pixels, from the length of the label text in characters...
computeDefaultImpurityLevels() - Static method in class keel.Algorithms.Hyperrectangles.INNER.Rule: Computes the initial confidence intervals of the impurity levels of each class
computeDesv(int, int, int, double) - Method in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.PANDA: Computes deviation for the values attribute k whose discretized value j is equal to l.
computeDistance(double[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
computeEntropy(Vector) - Method in class keel.Algorithms.Decision_Trees.ID3.ID3: Function to compute the entropy of the set of data points.
computeEpsilon() - Static method in class keel.Algorithms.Preprocess.Missing_Values.BPCA.MachineAccuracy: Compute EPSILON from scratch
computeEpsilon() - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.util.MachineAccuracy: Compute EPSILON from scratch
computeExactProbability(int, double) - Static method in class keel.GraphInterKeel.statistical.tests.WilcoxonDistribution: Computes exact p-values for the Wilcoxon distribution, given that N<50
computeFactor(double, double) - Method in class keel.Algorithms.SVM.SMO.supportVector.Puk: computes the factor for curve-fitting (see equation (13) in paper)
computeFitness(int, myDataset, DataB) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
computeFitness(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
computeFSReduction() - Static method in class keel.Algorithms.RST_Learning.KNNClassifier: Computes reduction rates over features
computeGamma() - Static method in class keel.Algorithms.RST_Learning.RSTData
computeHeterCCPV(double[]) - Method in class keel.Algorithms.Discretizers.HeterDisc.HeterDisc: It computes the heterogeneity for a conditional class probability vector given needed for compute the heterogeneity of a discretization scheme in criterion fuction calculus
computeImpurityLevel() - Method in class keel.Algorithms.Hyperrectangles.INNER.Rule: Computes the impurity level of a rule
computeInstancesPerClass() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Counts and stores the number of instances that belong to each class.
computeInstancesPerClass() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It computes the number of instances of the dataset match to each existing class.
computeInstancesPerClass() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It computes the number of instances of the dataset match to each existing class.
computeInstancesPerClass() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It computes the number of examples per class
computeInstancesPerClass() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It computes the number of examples per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Counts and stores the number of instances that belong to each class.
computeInstancesPerClass() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It computes the number of examples per class
computeInstancesPerClass() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It computes the number of examples per class
computeInstancesPerClass() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It computes the number of examples per class
computeInstancesPerClass() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It computes the number of intances per class
computeInstancesPerClass() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It computes the number of intances per class
computeInstancesPerClass() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It computes the number of intances per class
computeInstancesPerClass() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It computes the number of intances per class
computeInstancesPerClass() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It computes the number of intances per class
computeInstancesPerClass() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It computes the number of intances per class
computeInstancesPerClass() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It computes the number of instances of the dataset match to each existing class.
computeInstancesPerClass() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It computes the number of instances of the dataset match to each existing class.
computeInstancesPerClass() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It computes the number of instances of the dataset match to each existing class.
computeInstancesPerClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It computes the number of instances of the dataset match to each existing class.
computeInstancesPerClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It computes the number of instances of the dataset match to each existing class.
computeInstancesPerClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It computes the number of intances per class
computeInstancesPerClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It computes the number of examples per class.
computeInstancesPerClass() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It computes the number of intances per class
computeInstancesPerClass() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It computes the number of intances per class
computeInstancesPerClass() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It computes the number the instances per class.
computeInstancesPerClass() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: Computes the number of examples in each class
computeInstOfAtt(int, int[], int, int[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
computeIntervalNI(int, int, int) - Method in class keel.Algorithms.Discretizers.DIBD.DIBD: It computes the number of instances with attribute value in the interval [start, end] and class class_
computeIR() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Computes the Imbalanced Rate for each class.
computeISReduction() - Static method in class keel.Algorithms.RST_Learning.KNNClassifier: Computes reduction rates over instances
computeISW() - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
computeItemsetWeights() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Changes the weights for the instances in the dataset accordingly to the data distribution
computeKNN(int, int, int, int[], int[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Distance
computeLaplace() - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Calculate the LaPlaces's value for a complex
computeLaPlace() - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Calculate the value of laplace for a complex
computeLaplacian() - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: It computes the Laplacian value for the complex
computeLarge1Itemsets(int[]) - Static method in class keel.Algorithms.Discretizers.UCPD.FrequentItemsets: It computes the large 1-itemsets from the instances array and it saves them into the array lastCandidates
computeLocalHierarchicalMeasure(myDataset, double) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Computes the local hierarchical measure for this rule, according to the matching degree given for samples
computeMatches(int[], int[], int, int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
computeMatches(int[], int[], int) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils: Returns the number of elements that match in two arrays (of integer )
computeMaximumAndMinimumSupport(int[][]) - Method in class keel.Algorithms.Discretizers.MVD.MVD: Finds the maximum and minimum supports of all groups
computeMean(int, int, int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.PANDA: Computes mean for the values attribute k whose discretized value j is equal to l.
computeMostComon() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Calculate the values most commons for each column or attribute
computeMutualInformation() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.EventCovering: Estimates the mutual information between the instances in the data set
computeNI(int, int, int) - Method in class keel.Algorithms.Discretizers.DIBD.DIBD: It computes the number of instances of class class_ (or all classes) and attribute value <= or > than value, according to option
computeNumConditions() - Method in class keel.Algorithms.PSO_Learning.REPSO.Particle
computeNumInstC() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowingGWS
computeObjetives(myDataset) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
computeObjetives(myDataset) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
computeObjetives(myDataset) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
computeOverlapping() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It computes the class-overlapping rate.
computeParameter(boolean) - Method in class keel.Algorithms.Statistical_Classifiers.Shared.DiscrAnalysis.AD: Computes the different parameters for the algorithm where the DA will be used.
ComputeParameters() - Method in class keel.Algorithms.Discretizers.UCPD.PCA: It computes all necesary parameters
computePerformance() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
computePerformance() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
computePx(Vector) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.EventCovering: Computes the conjunctive probabilities using the second order probabilities.
computeRanks(int, int) - Static method in class keel.GraphInterKeel.statistical.tests.Wilcoxon: Compute ranks and associated p-values for a giver pair of samples
computeRed() - Method in class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome: Computes and returns the reduction ratio coded in the chromosome.
computeRed() - Method in class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome: Computes and returns the reduction ratio coded in the chromosome.
computesComplementaryIndex(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.WrapperManager
computeSSEadj() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ConjGradNN: Computes the SSE statistical variable
computeStatistics() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It computes the average and standard deviation of the input attributes
computeStatistics(Classifier[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Statistics
computeStatistics(Classifier[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Statistics
computeStatisticsPerClass() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It computes the average and standard deviation of the input attributes
computeStatisticsPerClass() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It computes the average and standard deviation of the input attributes
computeStats(InstanceSet) - Method in class keel.Algorithms.SVM.SMO.SMO: Compute the mean and std. deviation of each attribute of Attributes.
computesTCs(Dataset) - Static method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.WrapperManager
computeSummary() - Static method in class keel.GraphInterKeel.statistical.tests.Wilcoxon: Computes body of the summary file
computeSVDM() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Gets a matrix where store for each nominal attribute, the distances betwen all the possible values
computesVocabulary(Dataset, int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.WrapperManager
computeTheoryLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
computeTheoryLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list
computeTheoryLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
computeTheoryLength(int[], int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveRule
computeTheoryLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
computeTheoryLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierADI
computeTheoryLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierGABIL
computeTheoryLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierUBR
computeTheoryLength(int[], int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveRule
computeTheoryLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
computeTheoryLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
computeTheoryLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
computeTree(double[][]) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.EventCovering: Computes the dependece Tree using Dijkstra algorithm
computeWeightEvidence(int[][], int[], Condition, int, int[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.RuleSet
computeWeights(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It computes the weights of a given fuzzy type
ConceptAllPossibleValues - Class in keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues: This class computes all the possible values found in the data set for a given missing value and a determined class
ConceptAllPossibleValues(String) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.ConceptAllPossibleValues: Creates a new instance of ConceptAllPossibleValues
ConceptMostCommonValue - Class in keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue: This class computes the mean (numerical) or mode (nominal) value of the attributes with missing values for each class
ConceptMostCommonValue(String) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.ConceptMostCommonValue: Creates a new instance of MostCommonValue
cond() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Matrix condition (2 norm)
cond() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.SingularValueDecomposition: Two norm condition number
Condicion - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner: Title: Condicion (Condition) Description: Contains a value for an attribute and an operator (=,>,<) to be assigned to a rule
Condicion() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Condicion: Default constructor.
Condicion(Atributo, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Condicion: Parameter constructor.
Condicion - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus: Title: Condicion (Condition) Description: Contains a value for an attribute and an operator (=,>,<) to be assigned to a rule
Condicion() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Condicion: Default constructor.
Condicion(Atributo, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Condicion: Parameter constructor.
Condicion - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus: Title: Condicion (Condition) Description: Contains a value for an attribute and an operator (=,>,<) to be assigned to a rule
Condicion() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Condicion: Default constructor.
Condicion(Atributo, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Condicion: Parameter constructor.
Condicion - Class in keel.Algorithms.Genetic_Rule_Learning.PSO_ACO: Title: Condicion (Condition) Description: Contains a value for an attribute and an operator (=,>,<) to be assigned to a rule
Condicion() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Condicion: Default constructor.
Condicion(Atributo, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Condicion: Parameter constructor.
Condicion(Condicion) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Condicion: Copy Constructor.
Condition - Class in keel.Algorithms.Genetic_Rule_Learning.DMEL
Condition() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.DMEL.Condition
Condition(int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.DMEL.Condition: Creates a condition
Condition(Condition) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.DMEL.Condition: Creates a copy of a condition
Condition - Class in keel.Algorithms.Genetic_Rule_Learning.GIL
Condition() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GIL.Condition
Condition(int, myDataset) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GIL.Condition: Creates an empty condition
Condition(int, myDataset, boolean) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GIL.Condition: Creates a random condition of an attribute
Condition(int, boolean[]) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GIL.Condition: Creates a copy of a condition
Condition - Class in keel.Algorithms.Genetic_Rule_Learning.LogenPro: Condition
Condition(double, int, String) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Condition: Constructor
Condition(double, double, String) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Condition: Constructor
Condition - Class in keel.Algorithms.Genetic_Rule_Learning.SIA: Title: Condition Description: It represents a rule condition
Condition() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.SIA.Condition: Default builder
Condition(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.SIA.Condition: Builder for the first condition type (*)
Condition(int, double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.SIA.Condition: Builder for the second condition type (enumerate attributes)
Condition(int, double, double, double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.SIA.Condition: Builder for the third type of condition (real or integer attributes)
conditionDrop() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule
conditionIntroduce(myDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule
Conditions_per_RB() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.ruleset: Returns the number of conditions per rule base.
Conditions_per_RB() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.ruleset: Returns the number of conditions per rule base.
Conditions_per_RB() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.ruleset: Returns the number of conditions per rule base.
confidence - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Command line argument for % confidence (default = 50%).
confidence - Variable in class keel.Algorithms.Decision_Trees.C45.Tree: The confidence factor for pruning.
confidence() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Rule: Returns the confidence of the rule.
confidence - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Tree: The confidence factor for pruning.
confidence - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree: The confidence factor for pruning.
confidence - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Tree: The confidence factor for pruning.
confidence - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree: The confidence factor for pruning.
confidence - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.ConfidenceInterval: Confidence of the value in the interval.
confidence - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters: Confidence value for prune purposes.
confidence - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Parameters: Confidence value for prune purposes.
confidence - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters: Confidence value for prune purposes.
confidence - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Parameters: Confidence value for prune purposes.
confidence - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Tree: The confidence factor for pruning.
confidence - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree: The confidence factor for pruning.
confidence - Variable in class keel.Algorithms.Rule_Learning.PART.Tree: The confidence factor for pruning.
confidence - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.ParametersC45: Confidence value for pruning purpose.
confidence - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: The confidence factor for pruning.
confidence - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.ParametersC45: Confidence value for pruning purpose.
confidence - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: % confidence.
confidence - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: % confidence.
confidence - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
confidence - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
confidence - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
confidence - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
confidence - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
confidence - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
ConfidenceInterval - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat: Class that implements a confidence interval.
ConfidenceInterval() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.ConfidenceInterval
confidenceThreshold - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
confidenceThreshold - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
Config - Class in keel.Algorithms.Genetic_Rule_Learning.UCS: This class contains all the configuration parameters for the XCS.
Config() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: It's de defalut constructor of the class.
Config - Class in keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser: This class contains all the configuration parameters for the XCS.
Config() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It's de defalut constructor of the class.
config_read(String) - Method in class keel.Algorithms.Preprocess.Missing_Values.BPCA.BPCA: Read the pattern file, and parse data into strings
config_read(String) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Read the pattern file, and parse data into strings
config_read(String) - Method in class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.LLSImpute: Parse the paramete file in KEEL format to obtain the parameters and working files
config_read(String) - Method in class keel.Algorithms.Preprocess.Missing_Values.SVDimpute.SVDimpute
configs - Variable in class keel.GraphInterKeel.experiments.Parameters
configuracion(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.KNN: Configures the KNN algorithm setting the k value and the outliers thershold k values as the 80% of k.
Configuration - Class in keel.GraphInterKeel.statistical: File: Configuration.java This class holds all the configuration parameters of the module
Configuration() - Constructor for class keel.GraphInterKeel.statistical.Configuration
configure(Configuration) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus
configure(Configuration) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.SoftmaxClassificationProblemEvaluator: Configuration parameters for NeuralNetEvaluator are: Problem evaluator configuration org.ayrna.jclec.problem.ProblemEvaluator error-function: complex Error function used for evaluating individuals
configure(Configuration) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Configuration parameters for NeuralNetAlgorithm class are: species: ISpecies (complex) Individual species evaluator IEvaluator (complex) Individuals evaluator population-size (int) Population size max-of-generations (int) Maximum number of generations provider: IProvider (complex) Individuals provider mutator1: IMutator (complex) Individuals mutator1 mutator2: IMutator (complex) Individuals mutator2 creation-ratio (double) Ratio "elements created"/"elements remaining" percentage-second-mutator (int) Percentage of individuals mutated with second mutator max-generations-without-improving-mean (int) Maximum number of generations without improving mean fitness max-generations-without-improving-best (int) Maximum number of generations without improving best fitness fitness-difference (double) Difference between two fitness that we consider enough to say that the fitness has improved
configure(Configuration) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArrayDataset: Configuration method Configuration parameters for ArrayDataset are:
configure(Configuration) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Configuration parameters for this data set are: [@file-name] (String) File name.
configure(Configuration) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.FileDataset: Configuration method Configuration parameters for ArrayDataset are:
configure(Configuration) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Configuration parameters for ParametricMutator are: [@selective] boolean (default=false) If this parameter is set to true only certain randomly selected nodes are parametrically mutated.
configure(Configuration) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Configuration parameters for StructuralMutator are:
configure(Configuration) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies: Configuration parameters for this species are: input-layer.number-of-inputs (int) Number of inputs.
configure(Configuration) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Configuration parameters for NeuralNetEvaluator are: train-data: complex Train data set used in individuals evaluation.
configure(Configuration) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.util.random.RanNnep: Configuration settings for Randnnep random generator are: a (int, default = 12345) b (int, default = 67890
configure(Configuration) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.problem.regression.RegressionProblemEvaluator: Configuration parameters for NeuralNetEvaluator are: Problem evaluator configuration net.sf.jclec.problem.ProblemEvaluator error-function: complex Error function used for evaluating individuals.
configureWindow(Window) - Method in class keel.GraphInterKeel.datacf.DataCFApp: This method is to initialize the specified window by injecting resources.
confMatrix - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Confusion matrix for test
confMatrix - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Test Confusion matrix.
confusionMatrix() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Returns a copy of the confusion matrix.
confusionMatrix - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PerformanceAgent
confusionMatrix - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PerformanceAgent
ConjDatos - Class in keel.Algorithms.Rule_Learning.Prism: Stores a set of data with the form: attribute attribute..class.
ConjDatos() - Constructor for class keel.Algorithms.Rule_Learning.Prism.ConjDatos: Constructor.
ConjDatos - Class in keel.Algorithms.Rule_Learning.UnoR: Stores a set of data with the form: attribute attribute..class.
ConjDatos() - Constructor for class keel.Algorithms.Rule_Learning.UnoR.ConjDatos: Constructor.
ConjDatos - Class in keel.Algorithms.Subgroup_Discovery.aprioriSD: Stores a set of data with the form: attribute attribute..class.
ConjDatos() - Constructor for class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjDatos: Constructor.
ConjDatos - Class in keel.Algorithms.Subgroup_Discovery.CN2SD: Stores a set of data with the form: attribute attribute..class.
ConjDatos() - Constructor for class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjDatos: Constructor.
ConjGradNN - Class in keel.Algorithms.Preprocess.NoiseFilters.ANR: Optimized Classificator/Model by Conjugated Gradient.
ConjGradNN(int[], double[][], double[][], Randomize) - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.ANR.ConjGradNN: Constructor for a perceptron neural network from its basic elements.
ConjGradNN - Class in keel.Algorithms.Shared.ClassicalOptim: Optimized Classificator/Model by Conjugated Gradient.
ConjGradNN(int[], double[][], double[][], Randomize) - Constructor for class keel.Algorithms.Shared.ClassicalOptim.ConjGradNN: Constructor for a perceptron neural network from its basic elements.
ConjGradQUAD - Class in keel.Algorithms.Shared.ClassicalOptim: Quadratic optimized Classificator/Model by Conjugated Gradient.
ConjGradQUAD(double[][], double[][], Randomize) - Constructor for class keel.Algorithms.Shared.ClassicalOptim.ConjGradQUAD: Constructor for a perceptron neural network from its basic elements.
ConjReglas - Class in keel.Algorithms.Rule_Learning.Prism: Set of rules.
ConjReglas() - Constructor for class keel.Algorithms.Rule_Learning.Prism.ConjReglas: Constructor
ConjReglas - Class in keel.Algorithms.Rule_Learning.UnoR: Set of rules.
ConjReglas() - Constructor for class keel.Algorithms.Rule_Learning.UnoR.ConjReglas: Constructor
ConjReglas - Class in keel.Algorithms.Subgroup_Discovery.aprioriSD: Set of rules.
ConjReglas() - Constructor for class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjReglas: Constructor
ConjReglas - Class in keel.Algorithms.Subgroup_Discovery.CN2SD: Set of rules.
ConjReglas() - Constructor for class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjReglas: Constructor
conjugatedGradient(FUN, double, double, int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ConjGradNN: Returns the mean square error of the output perceptron calculated with Conjugated Gradient training algorithm.
conjugatedGradient(FUN, double, double, int) - Method in class keel.Algorithms.Shared.ClassicalOptim.ConjGradNN: Returns the mean square error of the output perceptron calculated with Conjugated Gradient training algorithm.
ConjuntoDatos - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner: Title: ConjuntoDatos (Dataset) Description: Dataset class represents the dataset read from data files and is used by the ACO algorithm.
ConjuntoDatos() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Default constructor.
ConjuntoDatos(Vector) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Parameter constructor.
ConjuntoDatos - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus: Title: ConjuntoDatos (Dataset) Description: Dataset class represents the dataset read from data files and is used by the ACO algorithm.
ConjuntoDatos() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Default constructor.
ConjuntoDatos(Vector) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Parameter constructor.
ConjuntoDatos - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner: Title: ConjuntoDatos (Dataset) Description: Dataset class represents the dataset read from data files and is used by the ACO algorithm.
ConjuntoDatos() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ConjuntoDatos: Default constructor.
ConjuntoDatos(Vector) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ConjuntoDatos: Parameter constructor.
ConjuntoDatos - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus: Title: ConjuntoDatos (Dataset) Description: Dataset class represents the dataset read from data files and is used by the ACO algorithm.
ConjuntoDatos() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Default constructor.
ConjuntoDatos(Vector) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Parameter constructor.
ConjuntoDatos - Class in keel.Algorithms.Genetic_Rule_Learning.PSO_ACO: Title: ConjuntoDatos (Dataset) Description: Dataset class represents the dataset read from data files and is used by the PSO-ACO algorithm.
ConjuntoDatos() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Default constructor.
ConjuntoDatos(Vector) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Parameter constructor.
connectivity - Variable in class keel.Algorithms.Neural_Networks.gann.SetupParameters
ConnNetwork - Class in keel.Algorithms.Neural_Networks.gann: This is a Connectionist Network
ConnNetwork(Parameters) - Constructor for class keel.Algorithms.Neural_Networks.gann.ConnNetwork: Constructor that receives the parameters of the algorithm
conns - Variable in class keel.Algorithms.Neural_Networks.gann.ConnNetwork: Matrix containing the connections of the neural net.
consecuente - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyRule
consequent - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList.RuleNode: Consequent of AR.
consequent - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList.RuleNodeCMAR: Consequent of AR.
Consequent() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Consequent() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Returns the position of the consequent inside the list of variables
Consequent() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
Consequent() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
consequent - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.IntDouble: Consequent to be displayed.
consequent - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyRule: Consequent of the rule.
consequent - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining.RuleNode: Consequent of AR.
CONSEQUENT - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene
consequent - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining.RuleNode: Consequent of AR.
CONSEQUENT - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene
consequent - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
CONSEQUENT - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene
consequent - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
CONSEQUENT - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene
consequent - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
CONSEQUENT - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene
consistency - Variable in class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule
consistency - Variable in class keel.Algorithms.Genetic_Rule_Learning.GIL.RuleSet
consoleReporter - Static variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.KEELIRPropPlusWrapperClas: Console reporter
consoleReporter - Static variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.KEELIRPropPlusWrapperRegr: Console reporter
consoleReporter - Static variable in class keel.Algorithms.Neural_Networks.NNEP_Clas.KEELWrapperClas: Console reporter
consoleReporter - Static variable in class keel.Algorithms.Neural_Networks.NNEP_Regr.KEELWrapperRegr: Console reporter
consSupport - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
consSupport - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
consSupport - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
consSupport - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
consSupport - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
consSupport - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
construct() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.BTS: It constructs the balanced binary tree
constructor(int[], int, int, InstanceWrapper) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveAttribute
constructor(int[], int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveRule
constructor(int[], int, int, InstanceWrapper) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveAttribute
constructor(int[], int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveRule
constructWithCopy(double[][]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Construct a matrix from a copy of a 2-D array.
construyeHeuristica(myDataset, int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Regla
constType - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyRegressor: the type of constants (Crisp, Interval and Fuzzy) to manage in derived classes.
contain(Vector<fuzzy>, Vector<Vector<fuzzy>>) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling.Main
contain(Vector<fuzzy>, Vector<Vector<fuzzy>>) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.Main
contain(String, int, Node, Experiments) - Method in class keel.GraphInterKeel.experiments.Algorithm: Shows a container dialog
contain(String, int, Node, Experiments) - Method in class keel.GraphInterKeel.experiments.DataSet: Shows associated container
contain(String, int, Node, Experiments) - Method in class keel.GraphInterKeel.experiments.Jclec: Contain method
contain(String) - Method in class keel.GraphInterKeel.experiments.Joint
contain(String, int, Node, Experiments) - Method in class keel.GraphInterKeel.experiments.Multiplexor: Contain method
contain(String, int, Node, Experiments) - Method in class keel.GraphInterKeel.experiments.Node: Show the datasets introduced in the Node
contain(String, int, Node, Experiments) - Method in class keel.GraphInterKeel.experiments.Test: Contain method
contain(String, int, Node, Experiments) - Method in class keel.GraphInterKeel.experiments.UserMethod: Contain method
Container - Class in keel.GraphInterKeel.experiments
Container(Frame, boolean, String, String[], int) - Constructor for class keel.GraphInterKeel.experiments.Container: Creates new form Container
Container_Selected - Class in keel.GraphInterKeel.experiments
Container_Selected(Frame, boolean) - Constructor for class keel.GraphInterKeel.experiments.Container_Selected: Creates new form Container
Container_Selected(Frame, boolean, String, Node, Experiments) - Constructor for class keel.GraphInterKeel.experiments.Container_Selected
Container_Selected(Frame, boolean, Node, Node, Experiments) - Constructor for class keel.GraphInterKeel.experiments.Container_Selected
Container_Selected(Frame, boolean, String, Node, Node, Experiments) - Constructor for class keel.GraphInterKeel.experiments.Container_Selected
containing(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Return the prototype containing N from this set.
containing(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Return the prototype containing N from this set.
contains(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector: Checks if the given object is contained in the vector.
contains(Chromosome) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Cochromosome: Test if this cochromosome contains the specified chromosome
contains(Rule) - Method in class keel.Algorithms.Hyperrectangles.INNER.Rule: Test the rule contains a second rule
contains(Object) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector: Checks if the object given as argument is in the vector.
contains(Pair<S, F>) - Method in class keel.Algorithms.Instance_Generation.utilities.Pair
contains(float) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
contains(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
contains(float) - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
contains(float) - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
contains(float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
contains(float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
contains(float) - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
contains(double[][], IInstance) - Method in class keel.Algorithms.MIL.APR.AbstractAPR
contains(double[][], IInstance, ArrayList<Integer>) - Method in class keel.Algorithms.MIL.APR.AbstractAPR
contains(Rule) - Method in class keel.Algorithms.Rule_Learning.Slipper.Ruleset: It returns wether a rule belongs to the ruleset
contains(Pair<S, F>) - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Pair: Checks if this pair has the same elements as the one given.
contains(Object) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector: added by akibriya Checks if the object given as parameter is contained in the vector.
contains(Object) - Method in class keel.Algorithms.SVM.SMO.core.FastVector: Checks if the object given as parameter is contained in the vector.
contains(int) - Method in class keel.Algorithms.SVM.SMO.supportVector.SMOset: Checks whether an element is in the set.
containsEqualAntecedents(Rule) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Compares this rule with the specified rule to check if the rules have the same antecedents
containsSeveralClasses() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Informs if the set contains prototypes with several different classes.
containsSeveralClasses() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Informs if the set contains prototypes with several different classes.
containsSomeElementOf(Pair<S, F>) - Method in class keel.Algorithms.Instance_Generation.utilities.Pair
containsSomeElementOf(Pair<S, F>) - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Pair: Checks if this pair has at least one element equals to one of the pair given.
contenido(Organizacion) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Organizacion: Checks if one organization contains the other.
contenidoValoreInval(Integer, Double) - Method in class keel.Algorithms.Rule_Learning.Rules6.Regla: Checks if the given attribute and its value is invalid for this rule.
contenidoValoreInval(Integer, Double) - Method in class keel.Algorithms.Rule_Learning.SRI.Regla: Checks if the given attribute and its value is invalid for this rule.
content(int, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.partition
content - Variable in class keel.GraphInterKeel.datacf.help.HelpFrame
contents(Object) - Method in class keel.Algorithms.Decision_Trees.M5.Queue.QueueNode: Sets the contents of the node.
contents() - Method in class keel.Algorithms.Decision_Trees.M5.Queue.QueueNode: Returns the contents in the node.
contents(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Queue.QueueNode: Sets the contents of the node and returns them.
contents() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Queue.QueueNode: Returns the contents in the node.
contents(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue.QueueNode: Sets the contents of the node.
contents() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue.QueueNode: Returns the contents in the node.
contents(Object) - Method in class keel.Algorithms.SVM.SMO.core.Queue.QueueNode: Sets the contents of the node.
contents() - Method in class keel.Algorithms.SVM.SMO.core.Queue.QueueNode: Returns the contents in the node.
context - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.initiators.PureLayerInitiator: Execution context
contextualize(<any>) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.initiators.IInitiator: Set the system context
contextualize(<any>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.initiators.PureLayerInitiator: Set the system context
contieneAtributo(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Rule: Checks if an attribute given as a argument is considered in the rule.
contieneAtributo(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.Regla: Checks if the rule contains the given attribute in its antecedents.
contieneAtributo(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Rule: Checks if an attribute given as a argument is considered in the rule.
ContingencyTables - Class in keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest: Class implementing some statistical routines for contingency tables.
ContingencyTables() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ContingencyTables
ContingencyTables - Class in keel.Algorithms.Semi_Supervised_Learning.CLCC: Class implementing some statistical routines for contingency tables.
ContingencyTables() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.CLCC.ContingencyTables
ContingencyTables - Class in keel.Algorithms.Semi_Supervised_Learning.CoForest: Class implementing some statistical routines for contingency tables.
ContingencyTables() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.CoForest.ContingencyTables
continueExperimentGeneration() - Method in class keel.GraphInterKeel.experiments.Experiments: Continues the experiment generation, once the used has selected the initial data sets and clicked on the panel, by doing the following tasks: - Create the data set node - Loads all the trees with the methods - Sets visible the dinamicDataset panel
CONTINUOUS - Static variable in class keel.Algorithms.Decision_Trees.C45.Attribute: Continuous attribute.
CONTINUOUS - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Attribute: Continuous attribute.
CONTINUOUS - Static variable in class keel.Algorithms.Decision_Trees.ID3.Attribute: Continuous attribute.
CONTINUOUS - Static variable in class keel.Algorithms.Decision_Trees.SLIQ.Attribute: Continuous attribute.
CONTINUOUS - Static variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Continuous attribute.
CONTINUOUS - Static variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA_Attribute: Continuous attribute.
CONTINUOUS - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.MyAttribute: Continuous attribute.
CONTINUOUS - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Attribute: Continuous attribute.
CONTINUOUS - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Attribute: Continuous attribute.
CONTINUOUS - Static variable in class keel.Algorithms.Rule_Learning.ART.Attribute: Continuous attribute.
CONTINUOUS - Static variable in class keel.Algorithms.Rule_Learning.C45Rules.MyAttribute: Continuous attribute.
CONTINUOUS - Static variable in class keel.Algorithms.Rule_Learning.C45RulesSA.MyAttribute: Continuous attribute.
CONTINUOUS - Static variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Attribute: Continuous attribute.
CONTINUOUS - Static variable in class keel.Algorithms.Rule_Learning.PART.MyAttribute: Continuous attribute.
Contrast - Class in keel.Algorithms.Statistical_Tests.Shared.nonParametric: File: Contrast.java This class obtains the contrast estimation from several methods
Contrast() - Constructor for class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Contrast: Builder
Contrast - Class in keel.GraphInterKeel.statistical.tests: File: Contrast.java This class obtains the contrast estimation from several methods
Contrast() - Constructor for class keel.GraphInterKeel.statistical.tests.Contrast: Builder
ContrastC - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Classification Contrast Stat-test identifier.
ContrastR - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Regression Contrast Stat-test identifier.
Control - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist
Control() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.Control: Creates a new instance of Control
controlBloatRuleDeletion() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PerformanceAgent: This function computes the rules of an individual that will be deleted by the rule deletion operator, based on their activity during the previous fitness computation cycle
controlBloatRuleDeletion() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PerformanceAgent: This function computes the rules of an individual that will be deleted by the rule deletion operator, based on their activity during the previous fitness computation cycle
controlledReduction() - Method in class keel.Algorithms.Instance_Generation.PNN.PNNGenerator: Performs a reduction of the training data set by the PNNGenerator (aka Chang) method.
conv - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
conv - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
conv - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
conv - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
conv - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
conv - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
conversionArray - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: 2-D array used to renumber coulmns for input data in terms of frequency of single attributes (reordering will enhance performance for some ARM algorithms).
conversionArray - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: 2-D array used to renumber columns for input data in terms of frequency of single attributes (reordering will enhance performance for some ARM algorithms).
conversionArray - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: 2-D array used to renumber columns for input data in terms of frequency of single attributes (reordering will enhance performance for some ARM algorithms).
Convert(Integer) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.vectordouble: Returns a new vector containing the first "tamano" elements of the vector
convert(String, String) - Method in class keel.GraphInterKeel.datacf.exportData.ExportPanel: Exports inputFile (in KEEL format) to outputFile (in a given format).
convert_Set(int[]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal: Convert from a lisf of index to a list of Instances from m_Data
ConverterXhtml(String, String) - Method in class keel.Algorithms.Preprocess.Converter.HtmlToKeel: Method used to transform the data from the html file given as parameter to xhtml format file which will be stored in the second file given.
convertir() - Method in class keel.Algorithms.Decision_Trees.DT_GA.Individuo: Converts the chromosome (individual) representation to a valid rule of the decision tree.
convertNewLines(String) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Converts carriage returns and new lines in a string into \r and \n.
convertNewLines(String) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Converts carriage returns and new lines in a string into \r and \n.
convertNewLines(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Converts carriage returns and new lines in a string into \r and \n.
convertNewLines(String) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Converts carriage returns and new lines in a string into \r and \n.
convertNewLines(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Converts carriage returns and new lines in a string into \r and \n.
convertNewLines(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Converts carriage returns and new lines in a string into \r and \n.
convertNewLines(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Converts carriage returns and new lines in a string into \r and \n.
convertNewLines(String) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Converts carriage returns and new lines in a string into \r and \n.
convertNewLines(String) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Converts carriage returns and new lines in a string into \r and \n.
convertNominal2Binary(InstanceSet) - Method in class keel.Algorithms.Preprocess.Transformations.Nominal2Binary.Nominal2Binary: Creates a new allocated KEEL's set of Instances (i.e.
convertNominalValue(String) - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It converts a nominal value to a integer
convertNominalValue(String) - Method in class keel.Dataset.Attribute: It converts a nominal value to a integer
convertToRelativePath(File) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Converts a File's absolute path to a path relative to the user (ie start) directory.
convertToRelativePath(File) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Converts a File's absolute path to a path relative to the user (ie start) directory.
convertToRelativePath(File) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Converts a File's absolute path to a path relative to the user (ie start) directory.
convertToRelativePath(File) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Converts a File's absolute path to a path relative to the user (ie start) directory.
COPENED - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for opened squared bracket "[".
COPENED - Static variable in interface keel.Dataset.DataParserConstants
copia() - Method in class keel.Algorithms.Decision_Trees.DT_GA.Regla: Returns a copy of the rule.
copia() - Method in class keel.Algorithms.Decision_Trees.DT_GA.Selector: Returns a copy of the Selector.
copia() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.Nodo: Returns a copy of the node.
copia(Tree) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.Tree: Returns a copy of the tree with the given father set.
copia() - Method in class keel.Algorithms.Decision_Trees.Target.Nodo: Returns a copy of the node.
copia(Tree) - Method in class keel.Algorithms.Decision_Trees.Target.Tree: Returns a copy of the tree with the given father set.
copia(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseD: It makes a copy of a fuzzy label
copia(int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseD
copia(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseD
copia(Individuo) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Individuo
copia() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Individuo
copia(Regla) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Copies the rule given as argument.
copia(Regla) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Copies the rule given as argument.
copia(Regla) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Copies the rule given as argument.
copia(Regla) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Copies the rule given as argument.
copia() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Organizacion
copia(Regla) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Copies the rule given as argument.
copia(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseD
copiaC() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: It returns a copy of the values for the output (class)
copiaCabeceraTest() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: It returns a string with the file header
copiaCabeceraTest() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: It returns a string with the file header
copiaCabeceraTest() - Method in class keel.Algorithms.Rule_Learning.AQ.Dataset: It returns a string with the file header
copiaCabeceraTest() - Method in class keel.Algorithms.Rule_Learning.CN2.Dataset: It returns a string with the file header
copiaCabeceraTest() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Returns a string with the header of the file
copiaCabeceraTest() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Returns a string with the header of the file
copiaCabeceraTest() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: It returns a string with the file header
copiaCabeceraTest() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset: It returns a string with the file header
copiaCabeceraTest() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset: It returns a string with the file header
copiaConjDatos() - Method in class keel.Algorithms.Rule_Learning.Prism.ConjDatos: Copy the set of data in other one(new)
copiaConjDatos() - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjDatos: Copy the set of data in other one(new)
copiaConjDatos() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjDatos: Copy the set of data in other one(new)
copiaConjDatos() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjDatos: Copy the set of data in other one(new)
copiaConjDatos() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetData: Copy the dataset in another
copiaConjReglas() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjReglas: Do a copy of the complet set of the rules
copiaDataSet() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: Returns a copy of the Dataset read.
copiaMuestra() - Method in class keel.Algorithms.Rule_Learning.Prism.Muestra: Do a copy of the example
copiaMuestra() - Method in class keel.Algorithms.Rule_Learning.UnoR.Muestra: Do a copy of the example
copiaMuestra() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Muestra: Do a copy of the example
copiaMuestra() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Muestra: Do a copy of the example
copiaPosicionMejorPosicion() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Copies the actual position as best position of the particle.
copiar(Nodo) - Method in class keel.Algorithms.Decision_Trees.Target.Nodo: Copies the given node.
copiaRegla() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Copy the rule
Copopulation - Class in keel.Algorithms.Genetic_Rule_Learning.CORE: This class represents the co-population of cochromosomes in the cooperative-competitive scheme of the CORE algorithm
Copopulation() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.CORE.Copopulation: Default constructor.
copy(PNArray) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.PNArray: Function to copy from a PNArray to ours.
copy() - Method in class keel.Algorithms.Decision_Trees.C45.Itemset: Function to copy an itemset.
copy() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Rule: Creates a copy of a rule
copy() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Selector: Creates a copy of the Selector
copy() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset: Function to copy an itemset.
copy() - Method in class keel.Algorithms.Decision_Trees.ID3.Itemset: Function to copy an itemset.
copy() - Method in class keel.Algorithms.Decision_Trees.M5.Function: Makes a copy of a function
copy() - Method in class keel.Algorithms.Decision_Trees.M5.M5Attribute: Produces a shallow copy of this attribute.
copy() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Produces a shallow copy of this instance.
copy() - Method in class keel.Algorithms.Decision_Trees.M5.M5SparseInstance: Produces a shallow copy of this instance.
copy(M5TreeNode) - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode: Makes a copy of the tree under this node
copy() - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector: Produces a shallow copy of this vector.
copy() - Method in class keel.Algorithms.Decision_Trees.M5.Results: Makes a copy of the Errors object
copy() - Method in class keel.Algorithms.Decision_Trees.M5.SplitInfo: Makes a copy of this SplitInfo object
copy() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Itemset: Function to copy an itemset.
copy(int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.DataBase: It makes a copy of a fuzzy label
copy(int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.DataBase: It makes a copy of a fuzzy label
copy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Antd: Implements Copyable
copy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Produces a shallow copy of this attribute.
copy(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Produces a shallow copy of this attribute with a new name.
copy() - Method in interface keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Copyable: This method produces a shallow copy of an object.
copy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector: Produces a shallow copy of this vector.
copy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Produces a shallow copy of this instance.
copy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.NominalAntd: Implements Copyable
copy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.NumericAntd: Implements Copyable
copy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule: Get a shallow copy of this rule
copy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Rule: Get a shallow copy of this rule
copy() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Instance: It makes a copy of the example
copy() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
copy() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierADI
copy() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierGABIL
copy() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierUBR
copy() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Function: Makes a copy of a function
copy() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Function to copy an itemset.
copy(M5TreeNode) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode: Makes a copy of the tree under this node
copy() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask: Returns a copy of this Mask
copy() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Produces a shallow copy of this attribute.
copy() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Results: Makes a copy of the Errors object
copy() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SplitInfo: Makes a copy of this SplitInfo object
copy() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
copy() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
copy() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
copy() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset: Function to copy an itemset.
copy() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset: Function to copy an itemset.
copy() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Returns a copy of this Mask
copy() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Instance: It makes a copy of the example
copy() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset: Function to copy an itemset.
copy() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset: Function to copy an itemset.
copy() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Rule: Creates a copy of a rule
copy() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Selector: Creates a copy of the Selector
copy() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Produces a shallow copy of this attribute.
copy() - Method in interface keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Copyable: This method produces a shallow copy of an object.
copy() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector: Produces a shallow copy of this vector.
copy() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Produces a shallow copy of this instance.
copy() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance: Produces a shallow copy of this instance.
copy() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Hard-copy of the prototype set.
copy(String, String) - Static method in class keel.Algorithms.Instance_Generation.utilities.KeelFile: Copy a Keel-style file to another.
copy() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Returns a copy of the DataSet
copy() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Returns a copy of this neural net
copy() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuralNet: Returns a copy of the neural net
copy() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputLayer: Returns a copy of this input layer
copy(ILayer<? extends INeuron>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Returns a copy of this linked layer
copy(ILayer<? extends INeuron>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron: Returns a copy of this linked neuron
copy() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividual: Copies individuals
copy(Cromosoma) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.Cromosoma: it copies all gens of the cr chromosome
copy() - Method in class keel.Algorithms.Rule_Learning.AQ.Instance: It makes a copy of the example
copy() - Method in class keel.Algorithms.Rule_Learning.ART.Itemset: Function to copy an itemset.
copy() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Itemset: Function to copy an itemset.
copy() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask: Returns a copy of this Mask
copy() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset: Function to copy an itemset.
copy() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Returns a copy of this Mask
copy() - Method in class keel.Algorithms.Rule_Learning.CN2.Instance: It makes a copy of the example
copy() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset: Function to copy an itemset.
copy() - Method in class keel.Algorithms.Rule_Learning.PART.Itemset: Function to copy an itemset.
copy() - Method in class keel.Algorithms.Rule_Learning.PART.Mask: Returns a copy of this Mask
copy() - Method in class keel.Algorithms.Rule_Learning.Ripper.Mask: Returns a copy of this Mask
copy() - Method in class keel.Algorithms.Rule_Learning.Slipper.Mask: Returns a copy of this Mask
copy() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset: Function to copy an itemset.
copy() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Hard-copy of the prototype set.
copy(String, String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KeelFile: Copy a Keel-style file to another.
copy() - Method in interface keel.Algorithms.Statistical_Classifiers.Logistic.core.Copyable: This method produces a shallow copy of an object.
copy() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector: Produces a shallow copy of this vector.
copy() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Makes a deep copy of the vector
copy() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector: Makes a deep copy of the vector
copy() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Make a deep copy of a matrix
copy(QualityMeasures) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Copy the values of the quality measures
Copy(QualityMeasures, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.QualityMeasures: Copy in this object the values of qmeasures
copy() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Instance: It makes a copy of the example
copy(QualityMeasures) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.QualityMeasures: Copy the values of Sets the value of interest
copy(QualitySubgroup) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.QualitySubgroup: This function copies in this object the auxiliar
copy() - Method in interface keel.Algorithms.SVM.SMO.core.Copyable: This method produces a shallow copy of an object.
copy() - Method in class keel.Algorithms.SVM.SMO.core.FastVector: Produces a shallow copy of this vector.
copy() - Method in class keel.Algorithms.SVM.SMO.core.Instance: Produces a shallow copy of this instance.
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Item: It allows to clone correctly an item
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AssociationRule: It allows to clone correctly an association rule
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome: It allows to clone correctly a chromosome
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene: It allows to clone correctly a gene
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Chromosome
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Gene
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Chromosome
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Gene
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Chromosome
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule: It allows to clone correctly an association rule
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome: It allows to clone correctly a chromosome
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene: It allows to clone correctly a gene
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule: It allows to clone correctly an association rule
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome: It allows to clone correctly a chromosome
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene: It allows to clone correctly a gene
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule: It allows to clone correctly an association rule
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome: It allows to clone correctly a chromosome
copy() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene: It allows to clone correctly a gene
copy(String, String) - Static method in class keel.GraphInterKeel.datacf.util.FileUtils: Copy files
copy(String, String) - Static method in class keel.GraphInterKeel.experiments.FileUtils: Copy a file
copy(URL, String) - Static method in class keel.GraphInterKeel.experiments.FileUtils: Copy a file
copyA() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.PRM: Function to copy a PNArray to an auxiliar PNArray
Copyable - Interface in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: Interface implemented by classes that can produce "shallow" copies of their objects.
Copyable - Interface in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets: Interface implemented by classes that can produce "shallow" copies of their objects.
Copyable - Interface in keel.Algorithms.Statistical_Classifiers.Logistic.core: Interface implemented by classes that can produce "shallow" copies of their objects.
Copyable - Interface in keel.Algorithms.SVM.SMO.core: Interface implemented by classes that can produce "shallow" copies of their objects.
copyCenter(Instance, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.fuzzygCenter: this function initializes a center with the values of a given instance.
copyCenter(Instance, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.gCenter: this function initializes a center with the values of a given instance.
copyData(double[][], int[]) - Static method in class keel.Algorithms.Hyperrectangles.INNER.Rule: Copies the training data iniside the class
copyDataSet() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.myDataset: It copies the data-set in a new one
copyDataSet() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.myDataset: It copies the data-set in a new one
copyDataSet() - Method in class keel.Algorithms.Hyperrectangles.EACH.EachDataSet: Copy the data set in other new
copyDataSet() - Method in class keel.Algorithms.Rule_Learning.AQ.myDataset: It copies the data-set in a new one
copyDataSet() - Method in class keel.Algorithms.Rule_Learning.CN2.myDataset: It copies the data-set in a new one
copyElements() - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector: Clones the vector and shallow copies all its elements.
copyElements() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector: Clones the vector and shallow copies all its elements.
copyElements() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector: Clones the vector and shallow copies all its elements.
copyElements() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector: Clones the vector and shallow copies all its elements.
copyElements() - Method in class keel.Algorithms.SVM.SMO.core.FastVector: Clones the vector and shallow copies all its elements.
CopyFile(String, String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.INFFC_2STEPS
copyFromBegintoPoint(RuleSet, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.RuleSet: Copies the rules from the beginning of the rule set to the selected cutpoint
copyFromBegintoPoint(RuleSet, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.RuleSet: Copies the rules from the beginning of the rule set to the selected cutpoint
copyFromPointtoEnd(RuleSet, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.RuleSet: Copies the rules from cutpoint to the end of the rule set
copyFromPointtoEnd(RuleSet, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.RuleSet: Copies the rules from cutpoint to the end of the rule set
copyHeader() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: It copies the header of the dataset.
copyHeader() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It copies the header of the dataset
copyHeader() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It copies the header of the dataset
copyHeaderTest() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Returns a string with the header of the file
copyIndiv(Individual) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndCAN: Copy the indicaded individual in "this" individual
copyIndiv(Individual) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndDNF: Copy the indicaded individual in "this" individual
copyIndiv(Individual) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Copies an individual given.
copyIndiv(int, Individual) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Copy the individual otro into the individual pos of this population
copyIndiv(Individual, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndCAN: Copy the indicaded individual in "this" individual
copyIndiv(Individual, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndDNF: Copy the indicaded individual in "this" individual
copyIndiv(Individual, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Copies an individual given.
CopyIndiv(int, int, int, Individual) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Population: Copy the individual in the Individual otro
copyInstances(int, Instances, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Copies instances from one set to the end of another one.
copyInstances(int, Instances, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Copies instances from one set to the end of another one.
copyInstances(int, Instances, int) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Copies instances from one set to the end of another one.
copyItemSet(short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Makes a copy of a given itemSet.
copyItemSet(short[][]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Makes a copy of a given set of itemSets.
copyItemSet(short[]) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Makes a copy of a given itemSet.
copyItemSet(short[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Makes a copy of a given itemSet.
copyOrder(Vector<Integer>) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ruleOrderAgent
copyParameters(Parameters) - Method in class keel.GraphInterKeel.experiments.Parameters: Copy the parameters to this object
copyPN() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.PRM: Function to copy P and N values to an auxiliar structure
copyRelationalValues(Instance, Instances, AttributeLocator) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RelationalLocator: Copies relational values contained in the instance copied to a new dataset.
copyRelationalValues(Instance, boolean, Instances, AttributeLocator, Instances, AttributeLocator) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RelationalLocator: Takes relational values referenced by an Instance and copies them from a source dataset to a destination dataset.
copyRule() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Rule: It performs a copy of the rule
copyRule() - Method in class keel.Algorithms.Rule_Learning.AQ.Complex: It copies the complex
copyRule() - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: It copies the complex
copyRuleSet() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.ruleSet: It copies the rule set into a new one
copyRuleSet() - Method in class keel.Algorithms.Rule_Learning.AQ.ruleSet: It carries out a copy of the full rule-set
copyRuleSet() - Method in class keel.Algorithms.Rule_Learning.CN2.ruleSet: It carries out a copy of the full rule-set
copySample() - Method in class keel.Algorithms.Hyperrectangles.EACH.Sample: Do a copy of the example
copyStaticAttributes() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: copyStaticAttributes It copies the attributes definition statically stored in Attributes class
copyStaticAttributes() - Method in class keel.Dataset.InstanceAttributes: copyStaticAttributes It copies the attributes definition statically stored in Attributes class
copyStringValues(M5Instance, boolean, M5Instances, M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset.
copyStringValues(M5Instance, boolean, M5Instances, int[], M5Instances, int[]) - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset.
copyStringValues(M5Instance, boolean, M5Instances, M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter: Copies the values from a given dataset to other.
copyStringValues(M5Instance, boolean, M5Instances, int[], M5Instances, int[]) - Method in class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter: Copies the values from a given dataset to other.
copyStringValues(Instance, Instances, AttributeLocator) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.StringLocator: Copies string values contained in the instance copied to a new dataset.
copyStringValues(Instance, boolean, Instances, AttributeLocator, Instances, AttributeLocator) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.StringLocator: Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset.
copyTestHeader() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Returns the header of the file
copyTo(Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask: Copies this Mask into another Mask
copyTo(Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Copies this Mask into another Mask
CopyTo(node) - Method in class keel.Algorithms.Neural_Networks.gmdh.node: Copy method
copyTo(Mask) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask: Copies this Mask into another Mask
copyTo(Mask) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Copies this Mask into another Mask
copyTo(Mask) - Method in class keel.Algorithms.Rule_Learning.PART.Mask: Copies this Mask into another Mask
copyTo(Mask) - Method in class keel.Algorithms.Rule_Learning.Ripper.Mask: Copies this Mask into another Mask
copyTo(Mask) - Method in class keel.Algorithms.Rule_Learning.Slipper.Mask: Copies this Mask into another Mask
Corcoran - Class in keel.Algorithms.Genetic_Rule_Learning.Corcoran: Title: Main class of the algorithm Description: It contains the esential methods for the CN2 algorithm Created: December 11th 2004 Copyright: Copyright (c) 2004 Company: KEEL
Corcoran() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
Core - Class in keel.Algorithms.Genetic_Rule_Learning.CORE: This class contains the main body of the CORE algorithm, presented by: Tan, K.C., Yu, Q., Ang, J.H.
Core() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.CORE.Core: Default constructor, sets all structures to null.
Core(String) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.CORE.Core: Creates a new instance of Core
correc(int, int) - Static method in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Quoted from original Fortran documentation: Calculates correction for tail area of the i-th largest of n order statistics.
correct - Variable in class keel.Algorithms.Decision_Trees.C45.Algorithm: Correctly classified itemsets.
correct - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm: Correctly classified itemsets.
correct - Variable in class keel.Algorithms.Decision_Trees.ID3.Algorithm: Correctly classified itemsets.
correct() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Gets the number of instances correctly classified (that is, for which a correct prediction was made).
correct - Variable in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm: Number of correctly classified example from training dataset.
correct - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm: Correctly classified itemsets.
correct - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm: Correctly classified itemsets.
correct - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm: Correctly classified itemsets.
correct(Prototype, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.DSM.DSMGenerator: Corrects the instance using a particular method
correct(Prototype, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQ1: Corrects the instance using a particular method
correct(Prototype, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQ2: Corrects the instance using a particular method
correct(Prototype, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQ2_1: Corrects a prototype of a set.
correct(Prototype, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQ3: Corrects the instance using a particular method
correct(Prototype, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQTC: Corrects the instance using a particular method
correct(Prototype, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.VQ.VQGenerator: Corrects the instance using a particular method
correct - Variable in class keel.Algorithms.Rule_Learning.ART.Algorithm: Correctly classified itemsets.
correct - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Algorithm: Correctly classified itemsets.
correct - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Algorithm: Correctly classified itemsets.
correct - Variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm: Correctly classified itemsets.
correct - Variable in class keel.Algorithms.Rule_Learning.PART.Algorithm: Correctly classified itemsets.
correct - Variable in class keel.GraphInterKeel.experiments.SelectExp
correctlyLabeled - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters: If the instances are correctly labaled.
correlation(double[], double[], int) - Static method in class keel.Algorithms.Decision_Trees.M5.M5: Returns the correlation coefficient of two double vectors
correlation(double[], double[], int) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Returns the correlation coefficient of two double vectors.
correlation(double[], double[], int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Returns the correlation coefficient of two double vectors.
correlation(double[], double[], int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: Returns the correlation coefficient of two double vectors
correlation(double[], double[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Returns the correlation coefficient of two double vectors.
correlation(double[], double[], int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Returns the correlation coefficient of two double vectors.
correlation(double[], double[], int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Returns the correlation coefficient of two double vectors.
correlation(double[], double[], int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Returns the correlation coefficient of two double vectors.
correlation(double[], double[], int) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Returns the correlation coefficient of two double vectors.
correlation(double[], double[], int) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Returns the correlation coefficient of two double vectors.
correlationCoefficient() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Returns the correlation coefficient if the class is numeric.
Corte - Class in keel.Algorithms.Genetic_Rule_Learning.Hider
Corte() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Corte: Empty constructor
Corte(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Corte: Constructor
Corte(double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Corte: Constructor
Corte(Double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Corte: Constructor
Corte(int, double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Corte: Constructor
Corte(int, Double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Corte: Constructor
Corte(int, Double, double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Corte: Constructor
Corte(Integer, Double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Corte: Constructor
Corte(Integer, Double, double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Corte: Constructor
cost - Variable in class keel.Algorithms.Discretizers.MODL.Neighbour: cost of the operation
cost - Variable in class keel.Algorithms.Genetic_Rule_Learning.GIL.RuleSet
cost_instance - Variable in class keel.GraphInterKeel.experiments.Parameters
costeError(Node) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Computes the error cost of adding a child to the node.
costFunction(Prototype, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.MSE.MSEGenerator: Calculate the cost function.
CoTrainingAlgorithm - Class in keel.Algorithms.Semi_Supervised_Learning.CoTraining: CoTraining algorithm calling.
CoTrainingAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.CoTraining.CoTrainingAlgorithm
CoTrainingGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.CoTraining: This class implements the Co-traning wrapper.
CoTrainingGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.CoTraining.CoTrainingGenerator: Build a new CoTrainingGenerator Algorithm
CoTrainingGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.CoTraining.CoTrainingGenerator: Build a new CoTrainingGenerator Algorithm
couldComp() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Indicates if the classifier is accurate and experienced enough to not be removed from the population
couldComp() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Indicates if the classifier is accurate and experienced enough to not be removed from the population
couldReduce(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Returns if the classifier is experienced, and accurate enough to be in the reduction set.
couldReduce(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Indicates if the classifier is experienced, and accurate enough to be in the reduction set.
couldSubsume() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Returns if the classifier can subsume.
couldSubsume() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters: Indicates if the classifier can subsume.
couldSubsume() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Returns if the classifier can subsume.
couldSubsume() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Indicates if the classifier can subsume.
count - Variable in class keel.Algorithms.Decision_Trees.M5.SimpleStatistics: The number of values seen
count - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleStatistics: The number of values seen
count - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Stats: The number of values seen
Count_Labels(String, int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Count_Labels(String, int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
Count_Labels(String, int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
countAntecedents(String[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.AdministrativeStaff
countClass(int) - Method in class keel.Algorithms.Discretizers.OneR.Opt: Increases the count for the class indicated
countData() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Filter the data according to the ruleset and compute the basic stats: coverage/uncoverage, true/false positive/negatives of each rule
countData(int, Instances, double[][]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Count data from the position index in the ruleset assuming that given data are not covered by the rules in position 0...
counter - Variable in class keel.GraphInterKeel.datacf.editData.EditVariablePanel
countNumFreqSets() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Commences process of counting the number of frequent (large/supported) sets conayoned in the T-tree.
countNumFreqSets(int, TtreeNode[], int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Counts the number of supported nodes in a sub branch of the T-tree.
countNumFreqSets() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TotalSupportTree: Commences process of counting the number of frequent (large/supported sets contained in the T-tree.
countNumFreqSets(int, TtreeNode[], int) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TotalSupportTree: Counts the number of supported nodes in a sub branch of the T-tree.
countNumFreqSets() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TotalSupportTree: Commences process of counting the number of frequent (large/supported sets contained in the T-tree.
countNumFreqSets(int, TtreeNode[], int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TotalSupportTree: Counts the number of supported nodes in a sub branch of the T-tree.
countPrototypesOfEachOutput() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Count the number of prototypes of each class.
countPrototypesOfEachOutput() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Count the number of prototypes of each class.
countPrototypesWhichNearestIs(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.VQ.AVQGenerator: Count prototypes whose nearest prototype is the given.
countSingles(DataBase) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Counts number of occurances of each single attribute in the input data.
countSingles() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Counts number of occurrences of each single attribute in the input data.
countSingles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Counts number of occurrences of each single attribute in the input data.
countStatistics - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Statistics
countStatistics - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Statistics
Cover - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD: Title: Cover Description: This class contains the representation to examples covered Copyright: Copyright KEEL (c) 2007 Company: KEEL
Cover(int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Cover: Parameter constructor.
Cover - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS: Title: Cover Description: This class contains the representation to examples covered Copyright: Copyright KEEL (c) 2007 Company: KEEL
Cover(int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Cover: Parameters Constructor.
cover - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Antd: The coverage of this antecedent in the growing data
cover - Variable in class keel.Algorithms.Hyperrectangles.EHS_CHC.Cromosoma: covered percentage
cover(Instance) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Check if the complex covers to the example
coverActions(Population, Population, double[], int, boolean[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Covering: Covers the actions while, at least, theta_mna actions are covered.
coverageBreakpoint - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
CoverageDegree(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set: Returns the coverage degree for the example in position "i" of the set
coverageDegree(Instance) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule: The degree of coverage instance covered by this rule
coverageRatio - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
covered() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Rule: Returns the instances covered by the rule.
covered() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Rule: Returns the instances covered by the rule.
covered(Instance) - Method in class keel.Algorithms.Rule_Learning.AQ.Complex: It checks if the complex covers a given example
covered(Instance) - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: It checks if the complex covers a given example
covered - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: To check the number of itemsets it covers when is created
coveredSamples() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: Returns the number of samples covered.
coverExamples() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Rule: It counts how many examples are covered and also how many of them are positive
coverExamples(double[]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Rule: It checks the weights of the covered examples
coverExamples() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.RuleBase: It detects those Rule tha cover an small-disjunt
coverExamples(double[]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.RuleBase: It computes how many examples are covered, and the weights of these examples
coverExamples() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Rule: It counts how many examples are covered and also how many of them are positive
coverExamples(double[]) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Rule: It checks the weights of the covered examples
coverExamples() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.RuleBase: It detects those Rule tha cover an small-disjunt
coverExamples(double[]) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.RuleBase: It computes how many examples are covered, and the weights of these examples
covering(int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.DataBase: It returns the coverage of an specific fuzzy partition
Covering - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: This class implements the covering operator.
Covering() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Covering: Initializes the actionCovered vector (all its positions to false).
CoverInstance(double[]) - Method in class keel.Algorithms.PSO_Learning.CPSO.Particle
CoverInstance(double[]) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
CoverInstance(double[]) - Method in class keel.Algorithms.PSO_Learning.REPSO.Particle
covers(double[]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Rule: To compute whether the rule covers an example
covers(double[]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Selector: Checks if the examples is covered by the selector
covers(Instance) - Method in class keel.Algorithms.Discretizers.MVD.Interval: Checks if the provided instance is covered by this interval in the specified attribute
covers(int) - Method in class keel.Algorithms.Discretizers.MVD.Interval: Checks if the provided instance is covered by this interval in the specified attribute
covers(Instance) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Antd
covers(Instance) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.NominalAntd: Whether the instance is covered by this antecedent
covers(Instance) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.NumericAntd: The degree of coverage for the instance given that antecedent
covers(Instance) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule: Whether the instance covered by this rule
covers(Instance) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Rule: Whether the instance covered by this rule
covers(Instance) - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Chromosome: Test if the instances is covered by the rule
covers(double[]) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Rule: To compute whether the rule covers an example
covers(double[]) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Selector: Checks if the examples is covered by the selector
CPAR - Class in keel.Algorithms.Associative_Classification.ClassifierCPAR: It contains the implementation of the CPAR algorithm
CPAR() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCPAR.CPAR: Default constructor
CPAR(parseParameters) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCPAR.CPAR: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
CPM - Class in keel.Algorithms.ImbalancedClassification.Resampling.CPM: File: CPM.java The CPM algorithm is an undersampling method used to deal with the imbalanced problem.
CPM(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.CPM.CPM: Constructor of the class.
CPruner - Class in keel.Algorithms.Instance_Selection.CPruner: File: CPruner.java The CPruner algorithm.
CPruner(String) - Constructor for class keel.Algorithms.Instance_Selection.CPruner.CPruner: Builder.
CPruner - Class in keel.Algorithms.Preprocess.Instance_Selection.CPruner: File: CPruner.java The CPruner algorithm.
CPruner(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.CPruner.CPruner: Builder.
cpSelected - Variable in class keel.Algorithms.Discretizers.HeterDisc.HeterDisc.DiscretizationScheme: indeces of the selected cutpoints
CPSO - Class in keel.Algorithms.PSO_Learning.CPSO: Title: Algorithm CPSO Description: It contains the implementation of the algorithm Company: KEEL
CPSO() - Constructor for class keel.Algorithms.PSO_Learning.CPSO.CPSO: Default constructor
CPSO(parseParameters) - Constructor for class keel.Algorithms.PSO_Learning.CPSO.CPSO: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
CPW - Class in keel.Algorithms.Lazy_Learning.CPW: File: CPW.java Class and prototipe weigthed learning.
CPW(String) - Constructor for class keel.Algorithms.Lazy_Learning.CPW.CPW: The main method of the class
CR - Variable in class keel.Algorithms.Genetic_Rule_Learning.ILGA.AttributeCR: Classification rate.
CR - Variable in class keel.Algorithms.Genetic_Rule_Learning.OIGA.AttributeCR: Classification rate.
CramersV(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ContingencyTables: Computes Cramer's V for a contingency table.
CramersV(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.ContingencyTables: Computes Cramer's V for a contingency table.
CramersV(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.ContingencyTables: Computes Cramer's V for a contingency table.
cratio - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Ratio "elements created"/"elements remaining"
creaCount() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Creates a 3D array from training set, stored for each class, each attribute and each value the number of examples of the class C witch have the value V for the attribute A COUNT[C,V,A]
creaCount() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Creates a matrix training set, stored for each class, each attribute, and each value the number of examples of class C that have value V for the attribute A COUNT[C,V,A]
creaItem(Item, Item) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Item: Creates a new item (if possible) of k+1 size from two k items.
crear_nodos(DefaultMutableTreeNode) - Method in class keel.GraphInterKeel.datacf.help.HelpFrame: Init nodes for tree help
creaReglaGenerica() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.PsoAco: Creates a default rule en case that any of the generated ones by the algorithm can be applied.
create_copopulation() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Core: Creates a new copopulation, for restarting purposes.
create_file_columns(int, Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.AlgGenetic
create_file_columns(int, Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.AlgGenetic
create_file_columns(int, Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.AlgGenetic
create_file_columns(int, Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.AlgGenetic
create_file_columns(int, Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.AlgGenetic
createAndInitArray(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.UCS: Creates and initializes an integer vector of size N
createAndInitArray(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.XCS: Creates and initializes an integer vector of size N
createBall(int, double, double) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Creates a vector with the values of the attributes that d(tst[atr],trn[atr])
createClassifier() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifierFactory
createClassifier() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.populationWrapper
createCluster(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCGenerator: Creates a cluster from the centers and the complete set of prototypes
CreateConfigFileC45(String, String, String, String, String) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.MultipleClassifierSystem
CreateConfigFileKNN(String, String, String, String, String, int) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.MultipleClassifierSystem
CreateConfigFileLogistic(String, String, String, String, String) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.MultipleClassifierSystem
CreateConfigFileSVM(String, String, String, String, String) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.MultipleClassifierSystem
createCount() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Creates a matrix training set, stored for each class, each attribute, and each value the number of examples of class C that have value V for the attribute A COUNT[C,V,A]
createCP(Vector) - Method in class keel.Algorithms.Discretizers.Khiops.Khiops: Construct an array of cutpoints from the set of intervals.
createCP(Vector) - Method in class keel.Algorithms.Discretizers.MODL.MODL: Construct an array of cutpoints from the set of intervals.
createDataset(String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.ClassificationFilter: It apllies the changes to remove the noise
createDatasetDirs(DataSet, String) - Method in class keel.GraphInterKeel.experiments.Experiments: creates directories for dataset and copy training-test files selected
createDatasetDirsLQD(Joint, String) - Method in class keel.GraphInterKeel.experiments.Experiments: creates directories for dataset and copy training-test files selected
createDatasets(String, String, String, String) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ClassifierMLPerceptron: It apllies the changes to remove the noise
createDatasets(String, String, String, String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.CVCommitteesFilter: It apllies the changes to remove the noise
createDatasets(String, String, String, String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.EnsembleFilter: It apllies the changes to remove the noise
createDatasets(String, String, String, String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.INFFC_2STEPS: It apllies the changes to remove the noise
createDatasets(String, String, String, String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.IterativePartitioningFilter: It apllies the changes to remove the noise
createDatasets(String, String, String, String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.PANDA: It apllies the changes to remove the noise
createDatasets(String, String, String, String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.SaturationFilter: It apllies the changes to remove the noise
createDatasetTrain(String, String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.INFFC_2STEPS: It apllies the changes to remove the noise
createDatasetTrain(String, String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.IterativePartitioningFilter: It apllies the changes to remove the noise
createDescendants(CHC_Chromosome) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC_Chromosome: Obtains a new pair of CHC_chromosome from this chromosome and another chromosome, swapping half the differing bits at random
createFGTTFS(DataBase) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It copies the header of the dataset
createFPtree() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.FPtree: Top level method to commence the construction of the FP-Tree.
createFPtree() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.FPtree: Top level method to commence the construction of the FP-Tree.
createGenotype() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Factory method
createGenotype() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies: Creates the genotype of the individual
createIdRbf() - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils: Creates a unique identifier to be used for a RBF
createIndividual(INeuralNet) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Factory method
createIndividual() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies: Creates a new individual
createIndividual(INeuralNet) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies: Creates a new individual
CreateInform - Class in keel.GraphInterKeel.experiments
CreateInform(String, String[], int) - Constructor for class keel.GraphInterKeel.experiments.CreateInform: Builder
createLinks(LinkedLayer, ILayer<? extends INeuron>, LinkedNeuron) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.FullRandomInitiator: Create all the links of a neural net.
createLinks(LinkedLayer, ILayer<? extends INeuron>, LinkedNeuron) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.initiators.PureLayerInitiator: Create all the links of a neural net
createLinks(LinkedLayer, ILayer<? extends INeuron>, LinkedNeuron) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.initiators.RandomInitiator: Create all the links of a neural net
createMCompPopulation(Environment) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: It creates the D population defined by Wilson 2002.
createMessage(String) - Method in class keel.Algorithms.MIL.ExceptionDatasets
createNewOptionsDialog(String) - Method in class keel.GraphInterKeel.datacf.importData.ImportPanel: Creates an options dialog with the options corresponding to a type of conversion
createNext() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetCreator: Creates the next individual
createNodes(DefaultMutableTreeNode) - Method in class keel.GraphInterKeel.experiments.Experiments: Creates the trees (deprecated)
createNodes(DefaultMutableTreeNode) - Method in class keel.GraphInterKeel.help.HelpFrame: Create nodes
createNodeUncoveredExamples() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams
createNodeUncoveredExamples() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams
createObj(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Method to create the Obj array with num (+1) elements
createOutputFile(String, String) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils: Creates an output file following Keel rules: the header of the training/test file must be written into the result file.
createOutputFile(String, String) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils: Creates an output file following Keel rules: the header of the training/test file must be written into the result file.
createOutputFile(String, String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils: Creates an output file following Keel rules: the header of the training/test file must be written into the result file.
createOutputFile(String, String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils: Creates an output file following Keel rules: the header of the training/test file must be written into the result file.
createOutputFile(String, String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils: Creates an output file following Keel rules: the header of the training/test file must be written into the result file.
createOutputFile(String, String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils: Creates an output file following Keel rules: the header of the training/test file must be written into the result file.
createOutputFile(String, String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils: Creates an output file following Keel rules: the header of the training/test file must be written into the result file.
createPartitionFiles() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.PartitionScheme: It creates the files of each training and test partition
createPartitionFiles() - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.PartitionScheme: It creates the files of each training and test partition
createPartitionFiles() - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.PartitionScheme: It creates the files of each training and test partition
createPartitionFiles() - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.PartitionScheme: It creates the files of each training and test partition
createPartitionFiles() - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.PartitionScheme: It creates the files of each training and test partition
createPartitionFiles(String, String) - Method in class keel.GraphInterKeel.datacf.partitionData.PartitioningSchemes.DistanceBased_best: It creates the files of each training and test partition
createPopulationRank() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.geneticAlgorithm
createPtree() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree: Processes data set causing each row to be added to P-Tree.
createPtreeTable() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree: Creates P-tree table starting with top level in P-tree.
CreateQuantaMatrix(int, int, int[]) - Method in class keel.Algorithms.Discretizers.HeterDisc.HeterDisc: It creates the quanta matrix basis of selected cutpoints array
createRandGen() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.KEELIRPropPlusWrapperClas: Factory method.
createRandGen() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.KEELIRPropPlusWrapperRegr: Factory method.
createRandGen() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Factory method.
createRandGen() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.util.random.RanNnepFactory: Creates a random initiator
createRelativePath(File) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Converts a File's absolute path to a path relative to the user (ie start) directory.
createRelativePath(File) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Converts a File's absolute path to a path relative to the user (ie start) directory.
createRelativePath(File) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Converts a File's absolute path to a path relative to the user (ie start) directory.
createRelativePath(File) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Converts a File's absolute path to a path relative to the user (ie start) directory.
createRules(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.RuleSet: Reset the current rules, and creates a new -clean- set
createRules(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.RuleSet: Reset the current rules, and creates a new -clean- set
createRuleTestTrain(double[], int, double[], int, int, int, boolean) - Method in class keel.Algorithms.Rule_Learning.Riona.Riona: Creates a local rule
createTenthsDataSets() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Populates ten tenths data sets for use when doing Ten Cross Validation (TCV) --- test and training datasets.
createTotalSupportTree() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree: Commences process of generating a total support tree (T-tree) from a P-tree.
createTrainingAndTestDataSets() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Populates test and training datasets.
createTrainingAndTestDataSets(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Populates test and training datasets.
createTtreeLevelN() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFP_CMAR: Commences the process of determining the remaining levels in the T-tree (other than the top level), level by level in an "Apriori" manner.
createTtreeLevelN() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree: Commences process of adding support values to further levels of the T-tree (not the top level).
createTtreeTopLevel() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Generates level 1 (top) of the T-tree.
createTtreeTopLevel2() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree: Commences process to generate top level (singletons) of Ttree by looping through table level by level (row by row).
createTtreeTopLevel2() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Adds supports to level 1 (top) of the T-tree.
createTtreeTopLevel3(PartialSupportTree.PtreeRecord[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree: Processes level (row) in P-tree table to generate top level of T-tree.
createWrapperInstances(InstanceSet) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper
createWrapperInstances(InstanceSet) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper
Credits - Class in keel.GraphInterKeel.experiments
Credits(Experiments) - Constructor for class keel.GraphInterKeel.experiments.Credits: Default builder
Crisp - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyRegressor: the type for fuzzy regressors based on crisp sets (singleton fuzzy sets).
crisp - Variable in class keel.GraphInterKeel.experiments.Parameters
crisp_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Crisp button
crisp_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectData: Crisp button
crisp_lqd_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Crisp to LQD button
crisp_lqd_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectData: Crisp to LQD button
CrispEval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeAnd: This method evaluate two nodes.
CrispEval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeAssert: This abstract method evaluate a node
CrispEval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeConsequent: This method return the consequent of a node
CrispEval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeIs: This method calculates the level of membership of a value to a linguistic label
CrispEval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeLabel: This method return the membership level
CrispEval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeOr: This method evaluates two nodes and return the maximun
CrispEval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeRule: This method evaluates the weight and the consequent of a rule
CrispEval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeRuleBase: This method evaluates the node rule base
CrispEval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeVariable: This method calculates the center of mass of the fuzzy alpha cuts
crispSymRegGAP - Class in keel.Algorithms.Symbolic_Regression.crispSymRegGAP: Wrapper for symbolicRegression with crisp values and based on GAP (Genetic Algorithm Programming) paradigm.
crispSymRegGAP() - Constructor for class keel.Algorithms.Symbolic_Regression.crispSymRegGAP.crispSymRegGAP
crispSymRegSAP - Class in keel.Algorithms.Symbolic_Regression.crispSymRegSAP: Wrapper for symbolicRegression with crisp values and based on SA (Simulated Annealing) paradigm).
crispSymRegSAP() - Constructor for class keel.Algorithms.Symbolic_Regression.crispSymRegSAP.crispSymRegSAP
critchi(double, int) - Method in class keel.Algorithms.Discretizers.MVD.Chi2: This method obtains the p-value from a Chi square distribution
CromCAN - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF: Defines the structure and manage the contents of a canonical rule.
CromCAN(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.CromCAN: Creates new instance of chromosome, no initialization
CromCAN - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD: Defines the structure and manage the contents of a rule This implementation uses only integer values to store the gens.
CromCAN(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.CromCAN: Creates new instance of chromosome, no initialization
CromCAN - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA: Defines the structure and manage the contents of a canonical rule.
CromCAN(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.CromCAN: Creates new instance of chromosome, no initialization
CromDNF - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF: Defines the structure and manage the contents of a DNF rule.
CromDNF(int, TableVar) - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.CromDNF: Creates new instance of chromosome, no initialization
CromDNF - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD: Defines the structure and manage the contents of a rule This implementation uses disjunctive formal norm to store the gens.
CromDNF(int, TableVar) - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.CromDNF: Creates new instance of chromosome, no initialization
CromDNF - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA: Defines the structure and manage the contents of a DNF rule.
CromDNF(int, TableVar) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.CromDNF: Creates new instance of chromosome, no initialization
Cromosoma - Class in keel.Algorithms.Hyperrectangles.EHS_CHC: Chromosome structure for algorithm EHS_CHC.
Cromosoma(int) - Constructor for class keel.Algorithms.Hyperrectangles.EHS_CHC.Cromosoma: Construct a random chromosome of specified size
Cromosoma(int, Cromosoma) - Constructor for class keel.Algorithms.Hyperrectangles.EHS_CHC.Cromosoma: Create a copied chromosome
Cromosoma(boolean[]) - Constructor for class keel.Algorithms.Hyperrectangles.EHS_CHC.Cromosoma: Cronstruct a cromosome from a bit array
Cromosoma - Class in keel.Algorithms.Instance_Generation.SSMALVQ3: File: Cromosoma.java Auxiliriary class to represent chromosomes for Instance Generation methods
Cromosoma(int, int, double[][], double[][], double[][], int[][], boolean[][], boolean) - Constructor for class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma: Construct a random chromosome of specified size and evaluate it.
Cromosoma(int, int, Cromosoma) - Constructor for class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma: Create a copied chromosome
Cromosoma(int, Cromosoma, Cromosoma, double, int) - Constructor for class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma: Construct a chromosome throught crossover than other two parents
Cromosoma - Class in keel.Algorithms.Instance_Generation.SSMAPSO: File: Cromosoma.java Auxiliriary class to represent chromosomes for Instance Generation methods
Cromosoma(int, int, double[][], double[][], double[][], int[][], boolean[][], boolean) - Constructor for class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma: Construct a random chromosome of specified size and evaluate it.
Cromosoma(int, int, Cromosoma) - Constructor for class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma: Create a copied chromosome
Cromosoma(int, Cromosoma, Cromosoma, double, int) - Constructor for class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma: Construct a chromosome throught crossover than other two parents
Cromosoma - Class in keel.Algorithms.Instance_Generation.SSMASFLSDE: File: Cromosoma.java Auxiliriary class to represent chromosomes for Instance Generation methods
Cromosoma(int, int, double[][], double[][], double[][], int[][], boolean[][], boolean) - Constructor for class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma: Construct a random chromosome of specified size and evaluate it.
Cromosoma(int, int, Cromosoma) - Constructor for class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma: Create a copied chromosome
Cromosoma(int, Cromosoma, Cromosoma, double, int) - Constructor for class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma: Construct a chromosome throught crossover than other two parents
Cromosoma - Class in keel.Algorithms.Instance_Selection.CHC: File: Cromosoma.java Auxiliriary class to represent chromosomes for Instance selection methods
Cromosoma(int) - Constructor for class keel.Algorithms.Instance_Selection.CHC.Cromosoma: Builder.
Cromosoma(int, Cromosoma) - Constructor for class keel.Algorithms.Instance_Selection.CHC.Cromosoma: Builder.
Cromosoma(boolean[]) - Constructor for class keel.Algorithms.Instance_Selection.CHC.Cromosoma: Builder.
Cromosoma - Class in keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM
Cromosoma(int, int, double[][], double[][], int[][], boolean[][], int[], int, boolean) - Constructor for class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Construct a random chromosome of specified size and evaluate it.
Cromosoma(int) - Constructor for class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.Cromosoma
Cromosoma - Class in keel.Algorithms.Instance_Selection.GGA: File: Cromosoma.java Auxiliriary class to represent chromosomes for Instance selection methods
Cromosoma(int) - Constructor for class keel.Algorithms.Instance_Selection.GGA.Cromosoma: Builder.
Cromosoma(int, Cromosoma) - Constructor for class keel.Algorithms.Instance_Selection.GGA.Cromosoma: Builder.
Cromosoma(boolean[]) - Constructor for class keel.Algorithms.Instance_Selection.GGA.Cromosoma: Builder.
Cromosoma - Class in keel.Algorithms.Instance_Selection.IGA: File: Cromosoma.java Auxiliriary class to represent chromosomes for Instance selection methods
Cromosoma(int) - Constructor for class keel.Algorithms.Instance_Selection.IGA.Cromosoma: Builder.
Cromosoma(int, Cromosoma) - Constructor for class keel.Algorithms.Instance_Selection.IGA.Cromosoma: Builder.
Cromosoma(boolean[]) - Constructor for class keel.Algorithms.Instance_Selection.IGA.Cromosoma: Builder.
Cromosoma - Class in keel.Algorithms.Instance_Selection.PBIL: File: Cromosoma.java Auxiliriary class to represent chromosomes for Instance selection methods
Cromosoma(int) - Constructor for class keel.Algorithms.Instance_Selection.PBIL.Cromosoma: Builder.
Cromosoma(int, Cromosoma) - Constructor for class keel.Algorithms.Instance_Selection.PBIL.Cromosoma: Builder.
Cromosoma(boolean[]) - Constructor for class keel.Algorithms.Instance_Selection.PBIL.Cromosoma: Builder.
Cromosoma(int, double[]) - Constructor for class keel.Algorithms.Instance_Selection.PBIL.Cromosoma: Builder.
Cromosoma - Class in keel.Algorithms.Instance_Selection.SGA: File: Cromosoma.java Auxiliriary class to represent chromosomes for Instance selection methods
Cromosoma(int) - Constructor for class keel.Algorithms.Instance_Selection.SGA.Cromosoma: Builder.
Cromosoma(int, Cromosoma) - Constructor for class keel.Algorithms.Instance_Selection.SGA.Cromosoma: Builder.
Cromosoma(boolean[]) - Constructor for class keel.Algorithms.Instance_Selection.SGA.Cromosoma: Builder.
Cromosoma - Class in keel.Algorithms.Instance_Selection.SSMA: File: Cromosoma.java Auxiliriary class to represent chromosomes for Instance selection methods
Cromosoma(int, int, double[][], double[][], double[][], int[][], boolean[][], boolean) - Constructor for class keel.Algorithms.Instance_Selection.SSMA.Cromosoma: Builder.
Cromosoma(int, int, Cromosoma) - Constructor for class keel.Algorithms.Instance_Selection.SSMA.Cromosoma: Builder.
Cromosoma(int, Cromosoma, Cromosoma, double, int) - Constructor for class keel.Algorithms.Instance_Selection.SSMA.Cromosoma: Builder.
Cromosoma - Class in keel.Algorithms.Instance_Selection.ZhangTS: File: Cromosoma.java Auxiliriary class to represent chromosomes for Instance selection methods
Cromosoma(int) - Constructor for class keel.Algorithms.Instance_Selection.ZhangTS.Cromosoma: Builder.
Cromosoma(int, Cromosoma) - Constructor for class keel.Algorithms.Instance_Selection.ZhangTS.Cromosoma: Builder.
Cromosoma(boolean[]) - Constructor for class keel.Algorithms.Instance_Selection.ZhangTS.Cromosoma: Builder.
Cromosoma - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms: Abstract class Chromosome that defines a generalization of CromosomaBinario & CromosomaEntero
Cromosoma(int) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.Cromosoma: Creates a new instance of Cromosoma
Cromosoma - Class in keel.Algorithms.Preprocess.Instance_Selection.CHC: File: Cromosoma.java Auxiliriary class to represent chromosomes for Instance selection methods
Cromosoma(int) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.CHC.Cromosoma: Builder.
Cromosoma(int, Cromosoma) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.CHC.Cromosoma: Builder.
Cromosoma(boolean[]) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.CHC.Cromosoma: Builder.
Cromosoma - Class in keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM: File: Cromosoma.java Auxiliriary class to represent chromosomes for Instance selection methods
Cromosoma(int, int, double[][], double[][], int[][], boolean[][], int[], int, boolean) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Construct a random chromosome of specified size and evaluate it.
Cromosoma(int) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Builder.
Cromosoma - Class in keel.Algorithms.Preprocess.Instance_Selection.GGA: File: Cromosoma.java Auxiliriary class to represent chromosomes for Instance selection methods
Cromosoma(int) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.GGA.Cromosoma: Builder.
Cromosoma(int, Cromosoma) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.GGA.Cromosoma: Builder.
Cromosoma(boolean[]) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.GGA.Cromosoma: Builder.
Cromosoma - Class in keel.Algorithms.Preprocess.Instance_Selection.IGA: File: Cromosoma.java Auxiliriary class to represent chromosomes for Instance selection methods
Cromosoma(int) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.IGA.Cromosoma: Builder.
Cromosoma(int, Cromosoma) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.IGA.Cromosoma: Builder.
Cromosoma(boolean[]) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.IGA.Cromosoma: Builder.
Cromosoma - Class in keel.Algorithms.Preprocess.Instance_Selection.PBIL: File: Cromosoma.java Auxiliriary class to represent chromosomes for Instance selection methods
Cromosoma(int) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Cromosoma: Builder.
Cromosoma(int, Cromosoma) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Cromosoma: Builder.
Cromosoma(boolean[]) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Cromosoma: Builder.
Cromosoma(int, double[]) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Cromosoma: Builder.
Cromosoma - Class in keel.Algorithms.Preprocess.Instance_Selection.SGA: File: Cromosoma.java Auxiliriary class to represent chromosomes for Instance selection methods
Cromosoma(int) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.SGA.Cromosoma: Builder.
Cromosoma(int, Cromosoma) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.SGA.Cromosoma: Builder.
Cromosoma(boolean[]) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.SGA.Cromosoma: Builder.
Cromosoma - Class in keel.Algorithms.Preprocess.Instance_Selection.SSMA: File: Cromosoma.java Auxiliriary class to represent chromosomes for Instance selection methods
Cromosoma(int, int, double[][], double[][], double[][], int[][], boolean[][], boolean) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma: Builder.
Cromosoma(int, int, Cromosoma) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma: Builder.
Cromosoma(int, Cromosoma, Cromosoma, double, int) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma: Builder.
Cromosoma - Class in keel.Algorithms.Preprocess.Instance_Selection.ZhangTS: File: Cromosoma.java Auxiliriary class to represent chromosomes for Instance selection methods
Cromosoma(int) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.Cromosoma: Builder.
Cromosoma(int, Cromosoma) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.Cromosoma: Builder.
Cromosoma(boolean[]) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.Cromosoma: Builder.
Cromosoma - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs: the class which contains the characteristics of the chromosome
Cromosoma(int, int, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Cromosoma: Creates a new chromosome with the size given of the three different representations.
Cromosoma - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs: the class which contains the characteristics of the chromosome
Cromosoma(int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Cromosoma: Creates a new chromosome with the size given.
Cromosoma - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs: the class which contains the characteristics of the chromosome
Cromosoma(int, int, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Cromosoma: Creates a new chromosome with the size given of the three different representations.
Cromosoma - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs: the class which contains the characteristics of the chromosome
Cromosoma(int, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Cromosoma: Creates a new chromosome with the size given of the three different representations.
Cromosoma - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs: the class which contains the characteristics of the chromosome
Cromosoma(int, int, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Cromosoma: Creates a new chromosome with the size given of the three different representations.
Cromosoma - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs: the class which contains the characteristics of the chromosome
Cromosoma(int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Cromosoma: Creates a new chromosome with the size given.
Cromosoma - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs: the class which contains the characteristics of the chromosome
Cromosoma(int, int, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Cromosoma: Creates a new chromosome with the size given of the three different representations.
Cromosoma - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs: the class which contains the characteristics of the chromosome
Cromosoma(int, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Cromosoma: Creates a new chromosome with the size given of the three different representations.
cromosoma - Variable in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndCAN: Individual contents (Canonical cromosome).
cromosoma - Variable in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndDNF: Individual contents (DNF cromosome).
cromosoma - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndCAN: Individual contents (Canonical chromosome).
cromosoma - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndDNF: Individual contents (DNF chromosome).
cromosoma - Variable in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndCAN: Individual contents (Canonical chromosome).
cromosoma - Variable in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndDNF: Individual contents (DNF chromosome).
CromosomaBinario - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms
CromosomaBinario(int) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaBinario: Creates a new instance of CromosomaBinario
CromosomaEntero - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms
CromosomaEntero(int, int) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaEntero: Creates a new instance of CromosomaEntero
Crono - Class in keel.Algorithms.PSO_Learning.CPSO: Title: Crono Company: KEEL
Crono() - Constructor for class keel.Algorithms.PSO_Learning.CPSO.Crono: Default Constructor.
Crono - Class in keel.Algorithms.PSO_Learning.LDWPSO: Title: Crono Company: KEEL
Crono() - Constructor for class keel.Algorithms.PSO_Learning.LDWPSO.Crono: Default Constructor.
Crono - Class in keel.Algorithms.PSO_Learning.PSOLDA: Title: Crono Company: KEEL
Crono() - Constructor for class keel.Algorithms.PSO_Learning.PSOLDA.Crono: Default Constructor.
Crono - Class in keel.Algorithms.PSO_Learning.REPSO: Title: Crono Company: KEEL
Crono() - Constructor for class keel.Algorithms.PSO_Learning.REPSO.Crono: Default Constructor.
cross_prob - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
cross_validation - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters: Check if test,validation or cross validation data is going to be used
cross_validation - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters: Check if test,validation or cross validation data is going to be used
cross_validation - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters: Check if test,validation or cross validation data is going to be used
cross_validation - Variable in class keel.Algorithms.Neural_Networks.net.Parameters: Check if test,validation or cross validation data is going to be used
crossAllele(int, Classifier, Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: It crosses a real allele within two parents.
crossAllele(int, Classifier, Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: It crosses a real allele within two parents.
crossAllele(int, Representation, Representation) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: It crosses a real allele within two parents.
CrossMultipoint(TableVar, int, int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Cross operator for the genetic algorithm
crossOneParent(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.geneticAlgorithm
crossover(GeneticIndividual, GeneticIndividual, GeneticIndividual, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGAP.FuzzyGAPClassifier: This method performs the crossover genetic operation between the current object and the first parameter.
crossover(GeneticIndividual, GeneticIndividual, GeneticIndividual, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGP.FuzzyGPClassifier: This method performs the crossover genetic operation between the current object and the first parameter.
crossover(GeneticIndividual, GeneticIndividual, GeneticIndividual, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyPittsBurgh.PittsburghClassifier: This method performs the crossover genetic operation between the current object and the first parameter.
crossover(GeneticIndividual, GeneticIndividual, GeneticIndividual, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySAP.FuzzySAPClassifier: This method performs the crossover genetic operation between the current object and the first parameter.
crossover(Genotype, Genotype, Genotype, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.Genotype: abstract method for carrying out the crossover genetic operations.
crossover(Genotype, Genotype, Genotype, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGAP: The method for carrying out the crossover genetic operations.
crossover(Genotype, Genotype, Genotype, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGP: The method for carrying out the crossover genetic operations.
crossover(Genotype, Genotype, Genotype, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGPRegSym: The method for carrying out the crossover genetic operations.
crossover(Genotype, Genotype, Genotype, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypePitts: The method for carrying out the crossover genetic operations.
crossover(GeneticIndividual, GeneticIndividual, GeneticIndividual, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividual: This abstract method implement the cross operation
crossover(GeneticIndividual, GeneticIndividual, GeneticIndividual, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGAPModelIndividual: This method implement the cross operation.
crossover(GeneticIndividual, GeneticIndividual, GeneticIndividual, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPModelIndividual: This method implement the cross operation.
crossover(GeneticIndividual, GeneticIndividual, GeneticIndividual, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.PittsburghModel: This method implement the cross operation.
crossover(GeneticIndividual, GeneticIndividual, GeneticIndividual, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.RegSymFuzzyGP: This method implement the cross operation.
crossover(classifier, classifier, classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
crossover(classifier, classifier, classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list
crossover(classifier, classifier, classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
crossover() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.geneticAlgorithm
crossOver() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Core: Applies the crossover between 2 chromosomes
crossover(int[], int[], int[], int[], int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveRule
crossOver() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Ilga: It performs a one point crossover in the new poblation, using adjacent chromosomes as parents
crossOver() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.SEM: It performs a one point crossover in the new poblation, using adjacent chromosomes as parents
crossover(int[], int[], int[], int[], int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveRule
crossOver() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Oiga: It performs a one point crossover in the new poblation, using adjacent chromosomes as parents
crossOver() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.SEM: It performs a one point crossover in the new poblation, using adjacent chromosomes as parents
Crossover - Interface in keel.Algorithms.Genetic_Rule_Learning.UCS: It's the interface of crossover.
Crossover - Interface in keel.Algorithms.Genetic_Rule_Learning.XCS: It's the interface of crossover.
crossover1(RuleSet, RuleSet) - Static method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.RuleSet
crossover2(RuleSet, RuleSet) - Static method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.RuleSet
crossover_1px(classifier_hyperrect_list, classifier_hyperrect_list, classifier_hyperrect_list, classifier_hyperrect_list) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list
crossover_1px(classifier_hyperrect_list_real, classifier_hyperrect_list_real, classifier_hyperrect_list_real, classifier_hyperrect_list_real) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
crossoverClassifiers(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
crossoverClassifiers(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierADI
crossoverClassifiers(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierGABIL
crossoverClassifiers(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierUBR
crossoverClassifiers(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
crossoverClassifiers(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
crossoverClassifiers(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
crossoverProbability - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
crossoverRSW(Classifier[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
crossoverRSW(Classifier[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
crossoverRSW(Classifier[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
CROSSOVERTYPE - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
crossPMX(int[]) - Method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.Chromosome: PMX cross operator
crossPMX(int[]) - Method in class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome: Crosses this chromosome with the one given as parameter with PMX operator.
crossPMX(int[]) - Method in class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome: Crosses this chromosome with the one given as parameter with PMX operator.
crossTwoParents(int, int, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.geneticAlgorithm
crossValidateModel(M5, M5Instances, int) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
crossValidateModel(String, M5Instances, int, String[]) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
crowdingDistance - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Crowding Distance.
cruce(GenotypeBoosting) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoosting
cruce(GenotypeBoosting) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoostingMaxMin
Cruce() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion: Function which cross the population
Cruce() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion: Function which cross the population
Cruce(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion: Function which cross the population
Cruce(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion: Function which cross the population
Cruce() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion: Function which cross the population
Cruce() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion: Function which cross the population
Cruce(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion: Function which cross the population
Cruce(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion: Function which cross the population
cruce_raro(GenotypeBoosting) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoosting
CruceBasedLogical_Estacionario(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation: Stationary crossover operator
cruceCC(Cromosoma[], Cromosoma[], int, int[], int) - Method in class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.GA_MSE_CC_FSM: Computes a new generation of the GA with the clusters given.
cruceCC(Cromosoma[], Cromosoma[], int, int[], int) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.GA_MSE_CC_FSM: Computes a new generation of the GA with the clusters given.
cruceOrtogonal(Cromosoma[], Cromosoma[], int, int, int, int, int, int) - Method in class keel.Algorithms.Instance_Selection.IGA.IGA: Function that implements the uniform crossover between two selected cromosomes
cruceOrtogonal(Cromosoma[], Cromosoma[], int, int, int, int, int, int) - Method in class keel.Algorithms.Preprocess.Instance_Selection.IGA.IGA: Function that implements the uniform crossover between two selected cromosomes
crucePMX(Cromosoma[], Cromosoma[], int, int, int) - Method in class keel.Algorithms.Instance_Selection.GGA.GGA: PMX cross operator
crucePMX(Cromosoma[], Cromosoma[], int, int) - Method in class keel.Algorithms.Instance_Selection.SGA.SGA: PMX cross operator
crucePMX(Cromosoma[], Cromosoma[], int, int, int) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GGA.GGA: PMX cross operator
crucePMX(Cromosoma[], Cromosoma[], int, int) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SGA.SGA: PMX cross operator
cruza(Vector, Vector, Vector) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion
cruza(int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion: Continuous cross
cruzar(Cromosoma, Cromosoma, Cromosoma) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.Cromosoma: crossover operator.
cruzar(Cromosoma, Cromosoma, Cromosoma) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaBinario: crossover operator.
cruzar(Cromosoma, Cromosoma, Cromosoma) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaEntero: crossover operator.
cruzarHUX(Cromosoma, Cromosoma, Cromosoma, int) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.Cromosoma: uniform crossover operator (HUX) If parents are very similar, the crossover operator doesn't apply
cruzarHUX(Cromosoma, Cromosoma, Cromosoma, int) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaBinario: uniform crossover operator (HUX) If parents are very similar, the crossover operator doesn't apply
cruzarHUX(Cromosoma, Cromosoma, Cromosoma, int) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaEntero: this method can't be applied to integer chromosome.
cset - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Individuals generated
CSVFileFilter - Class in keel.GraphInterKeel.statistical: File: CSVFileFilter.java This class filter CSV methods for file dialogs in the module
CSVFileFilter() - Constructor for class keel.GraphInterKeel.statistical.CSVFileFilter
CsvToKeel - Class in keel.Algorithms.Preprocess.Converter: CsvToKeel This class extends from the Importer class.
CsvToKeel(String, String) - Constructor for class keel.Algorithms.Preprocess.Converter.CsvToKeel: CsvToKeel class Constructor.
CTANCAT - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
cTipText() - Method in class keel.Algorithms.SVM.SMO.SMO: Returns the tip text for this property
cTipText() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Returns the tip text for this property
cTipText() - Method in class keel.Algorithms.SVM.SMO.SVMreg: Returns the tip text for this property
Cua - Variable in class keel.Algorithms.Shared.ClassicalOptim.SquaresErrorQUAD: Neural network container.
cuaoutput(double[]) - Method in class keel.Algorithms.Shared.ClassicalOptim.GCQuad: Calculates the output of a perceptron with weights W for input x
cuatrain(int, int, double[][], double[][], Randomize) - Method in class keel.Algorithms.Shared.ClassicalOptim.GCQuad: trains a perceptron with Quadratic Conjugated Gradient algorithm and returns the mean square error of neural network output compared to expected output.
cubicSpline(double[], double[], double[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.Interpolation: finds the cubic spline functions that interpolates the nodes (ti,xi)
cubicSplineCoeff(double[], double[], double, double) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.Interpolation: finds the natural cubic spline function that interpolates the noeds (xi,yi)
cubierta() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Muestra: Checks if the example is covered.
cubiertos() - Method in class keel.Algorithms.Decision_Trees.DT_GA.Regla: Returns the number of covered examples.
cubiertosOK() - Method in class keel.Algorithms.Decision_Trees.DT_GA.Regla: Returns the number of correctly covered examples.
Cubo - Class in keel.Algorithms.Genetic_Rule_Learning.RMini
cubr - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Cover percentage.
cubre(double[]) - Method in class keel.Algorithms.Decision_Trees.DT_GA.Regla: Checks if an example given is covered by the rule.
cubre(double[]) - Method in class keel.Algorithms.Decision_Trees.DT_GA.Selector: Checks if an example given is covered by the selector.
cubre(double[]) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.Nodo: Checks if an example given is covered by the node.
cubre(int) - Method in class keel.Algorithms.Decision_Trees.Target.Nodo: Checks if an example with the given index is covered by the node.
cubre(double[]) - Method in class keel.Algorithms.Decision_Trees.Target.Nodo: Checks if an example given is covered by the node.
cubre(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Condicion: Checks if the attribute given is covered by the condition.
cubre(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Condicion: Checks if the attribute given is covered by the condition.
cubre(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Condicion: Checks if the attribute given is covered by the condition.
cubre(double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Regla
cubre(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Condicion: Checks if the attribute given is covered by the condition.
cubre(Muestra) - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Check if the complex gets the given data
cubre(Muestra) - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Checks if the complex covers the given sample.
cubre(Muestra) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Checks if the rule covers the sample given.
cubre(Muestra) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Checks if the complex covers the given sample.
cubre - Variable in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Stores if the invididual covers each example.
cubre - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Stores if the invididual covers each example.
cubreMinimo(Regla, Vector, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Checks if the rule covers the minimum examples to be considered.
cubreMinimo(Regla, Vector, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Checks if the rule covers the minimum examples to be considered.
cubreMinimo(Regla, Vector, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Checks if the rule covers the minimum examples to be considered.
cubreMinimo(Regla, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Checks if the rule covers the minimum examples to be considered.
cubreMuestra(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Checks if the given example is covered by the whole rule (covered by the conditions and with the same class of the rule).
cubreMuestra(Muestra, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Checks if the given example is covered by the whole rule (covered by the conditions and with the same class of the rule).
cubreMuestra(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Checks if the given example is covered by the whole rule (covered by the conditions and with the same class of the rule).
cubreMuestra(Muestra, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Checks if the given example is covered by the whole rule (covered by the conditions and with the same class of the rule).
cubreMuestra(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Checks if the given example is covered by the whole rule (covered by the conditions and with the same class of the rule).
cubreMuestra(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Checks if the given example is covered by the whole rule (covered by the conditions and with the same class of the rule).
cubreMuestra(Muestra, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Checks if the given example is covered by the whole rule (covered by the conditions and with the same class of the rule).
cubreMuestraCondiciones(Muestra, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Checks if the given example is covered by the conditions stored in the rule.
cubreMuestraCondiciones(Muestra, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Checks if the given example is covered by the conditions stored in the rule.
cubreMuestraCondiciones(Muestra, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Checks if the given example is covered by the conditions stored in the rule.
cubreMuestraTotal(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Checks if the given example is covered by the whole rule (covered by the conditions and with the same class of the rule).
cubrirEjemplos() - Method in class keel.Algorithms.Decision_Trees.DT_GA.BaseR: Detect the rules that cover a small-disjunct set of instances.
cubrirEjemplos() - Method in class keel.Algorithms.Decision_Trees.DT_GA.Regla: Computes the examples covered by the rule.
Cuentait - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.Fitness
cuentaNodosHojas(Node) - Method in class keel.Algorithms.Decision_Trees.SLIQ.SLIQ: Counts the number of nodes pending from the given node.
cumsum(DenseVector) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Cummulative sum of the elements of the vector.
cumulate() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Returns a vector that stores the cumulated values of the original vector
cumulateInPlace() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Cumulates the original vector in place
curChar - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserTokenManager
curChar - Static variable in class keel.Algorithms.Rule_Learning.Swap1.DataParserTokenManager
curChar - Static variable in class keel.Dataset.DataParserTokenManager
curDataset - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Dataset
curDataset - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: CUR dataset.
currentAccuracy - Variable in class keel.Algorithms.Instance_Generation.MCA.MCAGenerator: Algorithm Accuracy.
currentBest - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Current best fitness
currentMean - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Current mean fitness
currentRlist - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Instance of the class RuleList
currentToken - Variable in exception keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParseException: This is the last token that has been consumed successfully.
currentToken - Variable in exception keel.Algorithms.Rule_Learning.Swap1.ParseException: This is the last token that has been consumed successfully.
currentToken - Variable in exception keel.Dataset.ParseException: This is the last token that has been consumed successfully.
cursorFlux - Variable in class keel.GraphInterKeel.experiments.Experiments
cursorInstance - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset: Instance at cursor position
cursorPosition - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset: Cursor position
CUSTOM_CESAR - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividual: Operator flag (CUSTOM_CESAR).
Cut - Class in keel.Algorithms.Decision_Trees.C45: Class to implement the calculus of the cut point
Cut(int, int, double) - Constructor for class keel.Algorithms.Decision_Trees.C45.Cut: Function to initialize the cut model.
Cut(Classification) - Constructor for class keel.Algorithms.Decision_Trees.C45.Cut: Function to use when no cut is necessary.
Cut - Class in keel.Algorithms.Decision_Trees.DT_GA.C45: Class to implement the calculus of the cut point
Cut(int, int, double) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to initialize the cut model.
Cut(Classification) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to use when no cut is necessary.
Cut - Class in keel.Algorithms.Genetic_Rule_Learning.PART: Class to implement the calculus of the cut point
Cut(int, int, double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Function to initialize the cut model.
Cut(Classification) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Function to use when no cut is necessary.
Cut - Class in keel.Algorithms.ImbalancedClassification.CSMethods.C45CS: Class to implement the calculus of the cut point
Cut(int, int, double) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Function to initialize the cut model.
Cut(Classification) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Function to use when no cut is necessary.
Cut - Class in keel.Algorithms.ImbalancedClassification.Ensembles.C45: Class to implement the calculus of the cut point
Cut(int, int, double) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to initialize the cut model.
Cut(Classification) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to use when no cut is necessary.
cut(float) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
cut(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
cut(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.fuzzy
cut(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.fuzzy
cut(float) - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
cut(float) - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
cut(float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
cut(float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
cut(float) - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
Cut - Class in keel.Algorithms.Rule_Learning.C45Rules: Class to implement the calculus of the cut point
Cut(int, int, double) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.Cut: Function to initialize the cut model.
Cut(Classification) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.Cut: Function to use when no cut is necessary.
Cut - Class in keel.Algorithms.Rule_Learning.C45RulesSA: Class to implement the calculus of the cut point
Cut(int, int, double) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Function to initialize the cut model.
Cut(Classification) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Function to use when no cut is necessary.
Cut - Class in keel.Algorithms.Rule_Learning.PART: Class to implement the calculus of the cut point
Cut(int, int, double) - Constructor for class keel.Algorithms.Rule_Learning.PART.Cut: Function to initialize the cut model.
Cut(Classification) - Constructor for class keel.Algorithms.Rule_Learning.PART.Cut: Function to use when no cut is necessary.
Cut - Class in keel.Algorithms.Semi_Supervised_Learning.Basic.C45: Class to implement the calculus of the cut point
Cut(int, int, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Function to initialize the cut model.
Cut(Classification) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Function to use when no cut is necessary.
cutDataset(Dataset) - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Function to cut the dataset in subsets.
cutDataset(Dataset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to cut the dataset in subsets.
cutDataset(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Function to cut the dataset in subsets.
cutDataset(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Function to cut the dataset in subsets.
cutDataset(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to cut the dataset in subsets.
cutDataset(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Function to cut the dataset in subsets.
cutDataset(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Function to cut the dataset in subsets.
cutDataset(MyDataset) - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Function to cut the dataset in subsets.
cutDataset(Dataset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Function to cut the dataset in subsets.
cutpointEntropy(int) - Method in class keel.Algorithms.Discretizers.HellingerBD.HellingerBD: It computes the cutpoint entropy
cutPoints - Variable in class keel.Algorithms.Discretizers.Basic.Discretizer: Cut points for each attribute used to discretize them.
cutPoints - Variable in class keel.Algorithms.Discretizers.Chi2_Discretizer.Discretizer: Cut points for each attribute used to discretize them.
cutPoints - Variable in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Discretizer: Cut points for each attribute used to discretize them.
cutPoints - Variable in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Discretizer: Cut points for each attribute used to discretize them.
cutPoints - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Basic.Discretizer
cutPoints - Variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Discretizer
cutPointsTMP - Variable in class keel.Algorithms.Discretizers.Chi2_Discretizer.Discretizer: Temporal Cut points.
cutPointsTMP - Variable in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Discretizer: Temporal Cut points.
cutPointsTMP - Variable in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Discretizer: Temporal Cut points.
CVCommitteesFilter - Class in keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter: The Ensemble Filter...
CVCommitteesFilter() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.CVCommitteesFilter: It initializes the partitions from training set
CVPrintStatistics(Population, Population, int[], int, int[], int, TimeControl) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Statistic: Prints the statistics for a cross validation experiment.
CVPrintTestStatistics(Population, Population, int[], int, int[], int, TimeControl) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Statistic: It prints the statistics for a cross validation experiment, but only for the testing execution.
cvType - Variable in class keel.GraphInterKeel.experiments.Experiments
CW - Class in keel.Algorithms.Lazy_Learning.CW: File: CW.java Class weigthed learning.
CW(String) - Constructor for class keel.Algorithms.Lazy_Learning.CW.CW: The main method of the class
cwInit - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper
cwInit - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper
cycle(int, double[][], double[], int, double[][], double[], int, int, int, double, double[], int, int, long, String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Chc: Function cycle
cycle(int, double[][], double[], int, double[][], double[], int, int, int, double, double[], int, int, long, String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Chc: Function cycle
cycle(int, double[][], double[], int, double[][], double[], int, int, int, double, double[], int, int, long, String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Chc: Function cycle
cycle(int, double[][], double[], int, double[][], double[], int, int, int, double, double[], int, int, long, String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Chc: Function cycle
cycle(int, double[][], double[], int, double[][], double[], int, int, int, double, double[], int, int, long, String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Chc: Function cycle
cycle(int, double[][], double[], int, double[][], double[], int, int, int, double, double[], int, int, long, String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Chc: Function cycle
cycle(int, double[][], double[], int, double[][], double[], int, int, int, double, double[], int, int, long, String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Chc: Function main
cycles - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters: Number of cycles
cycles - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters: Number of cycles
cycles - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters: Number of cycles
cycles - Variable in class keel.Algorithms.Neural_Networks.net.Parameters: Number of cycles

D

d(Cluster) - Method in class keel.Algorithms.Instance_Generation.GMCA.Cluster: Distance between clusters.
d(Prototype, Prototype) - Static method in class keel.Algorithms.Instance_Generation.GMCA.GMCAGenerator: Computes the distances between the two given prototypes.
d(Prototype, Prototype) - Static method in class keel.Algorithms.Instance_Generation.utilities.Distance: Compute the Euclidean Distance between two prototypes.
d - Variable in class keel.Algorithms.Preprocess.Missing_Values.EM.EV: eigenvalues
D(Instance, Vector) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.EventCovering: Computes the minimum Hamming distance between the instance given and one of the instances in the set given.
d(Prototype, Prototype) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Distance: Compute the Euclidean Distance between two prototypes.
D_SKNN - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.D_SKNN: File: D_SKNN.java The D-SKNN algorithm.
D_SKNN(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.D_SKNN.D_SKNN: Main builder.
dameClases() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: It returns the name of every output values (possible classes).
dameClases() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: It returns the name of every output values (possible classes).
dameClases() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: It returns the name of every output values (possible classes).
dameClases() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: It returns the name of every output values (possible classes).
dameClases() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: It return the class values
dameClases() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: It returns the name of every output values (possible classes).
dameClases() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: It return the class values
dameClases() - Method in class keel.Algorithms.Rule_Learning.AQ.Dataset: It return the class values
dameClases() - Method in class keel.Algorithms.Rule_Learning.CN2.Dataset: It return the class values
dameClases() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Returns teh value of the classes
dameClases() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Returns teh value of the classes
dameClases() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: It return the class values
dameClases() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset: It return the class values
dameClases() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset: It return the class values
dameEtiqueta(int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseD
dameEtiqueta(int, double) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseD
dameIntervalosMax() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Individuo
dameIntervalosMin() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Individuo
dameNombres() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: It returns the name of every input attributes.
dameNombres() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: It returns the name of every input attributes.
dameNombres() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: It returns the name of every input attributes.
dameNombres() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: It returns the name of every input attributes.
dameNombres() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: It returns the name of the variables of the problem
dameNombres() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: It returns the name of every input attributes.
dameNombres() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: It returns the name of the variables of the problem
dameNombres() - Method in class keel.Algorithms.Rule_Learning.AQ.Dataset: It returns the name of the variables of the problem
dameNombres() - Method in class keel.Algorithms.Rule_Learning.CN2.Dataset: It returns the name of the variables of the problem
dameNombres() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Returns the name of the problem's variables
dameNombres() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Returns the name of the problem's variables
dameNombres() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: It returns the name of the variables of the problem
dameNombres() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset: It returns the name of the variables of the problem
dameNombres() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset: It returns the name of the variables of the problem
dameOrganizacion(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Poblacion
damePeso(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.MatrizR
dameRangos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
dameRangos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
dameRangos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
dameRangos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
dameRangos() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
dameRegla(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseR
dameRegla(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseR
dameReglas() - Method in class keel.Algorithms.Decision_Trees.DT_GA.Poblacion: Returns the rules generated by the GA and stored on the chromosomes of the population.
dameTipos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: Returns the type of each input attribute
dameTipos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: Returns the type of each input attribute
dameTipos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: Returns the type of each input attribute
dameTipos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: Returns the type of each input attribute
dameTipos() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: Returns the type of each input attribute
dameTrials() - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.GA
dameValores() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: Returns the values as String for every attribute.
dameValores() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: Returns the values as String for every attribute.
dameValores() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: Returns the values as String for every attribute.
dameValores() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: Returns the values as String for every attribute.
dameValores() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: Returns the values as String for every attribute.
dameValores() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: Returns the values as String for every attribute.
Data(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
Data(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
Data(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set: Returns the value for variable "variable" in the example in position "ejemplo" in the set of examples
Data(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set: Returns the values for all the variables in the example in position "ejemplo" in the set of examples
Data(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set
Data(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set
Data(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set
Data(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set
Data - Class in keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS: Class representing the data
Data(int, int, int, int) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Data: Constructor
Data(Parameters) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Data: Constructor that receives the parameters (NOT USED)
Data - Class in keel.Algorithms.Neural_Networks.gann: This is a class that takes the data from a file and puts it in adequate data stuctures for its processing
Data(int, int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.gann.Data: Constructor
Data(SetupParameters) - Constructor for class keel.Algorithms.Neural_Networks.gann.Data: Constructor that takes only the setup parameters (NOT USED)
Data - Class in keel.Algorithms.Neural_Networks.gmdh: Class Data
Data(int, int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.gmdh.Data: Constructor
Data(Parameters) - Constructor for class keel.Algorithms.Neural_Networks.gmdh.Data: Constructor (NOT USED)
Data - Class in keel.Algorithms.Neural_Networks.net: Class representing the data
Data(int, int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.net.Data: Constructor
Data(Parameters) - Constructor for class keel.Algorithms.Neural_Networks.net.Data: Constructor that receives the parameters (NOT USED)
DATA - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for data section.
DATA - Static variable in interface keel.Dataset.DataParserConstants
data - Variable in class keel.GraphInterKeel.datacf.editData.EditDataPanel
data - Variable in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanel: Dataset
data2string(DenseMatrix, String[][], InstanceSet) - Method in class keel.Algorithms.Preprocess.Missing_Values.BPCA.BPCA: Parse the DenseMatrix of INPUT real values to a String 2D array, ready for printing to a file.
data2string(DenseMatrix, String[][], InstanceSet) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Parse the DenseMatrix of INPUT real values to a String 2D array, ready for printing to a file.
data2string(DenseMatrix, String[][], InstanceSet) - Method in class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.LLSImpute: Parse the DenseMatrix of INPUT real values to a String 2D array, ready for printing to a file.
data2string(DenseMatrix, String[][], InstanceSet) - Method in class keel.Algorithms.Preprocess.Missing_Values.SVDimpute.SVDimpute: Parse the DenseMatrix of INPUT real values to a String 2D array, ready for printing to a file.
data_selected - Variable in class keel.GraphInterKeel.experiments.Joint
dataArray - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: 2-D aray to hold input data from data file.
dataArray - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: 2-D aray to hold input data from data file.
dataArray - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: 2-D aray to hold input data from data file.
DataB - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA
DataB() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.DataB
DataB(int, myDataset) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.DataB
DataB - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA
DataB() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.DataB
DataB(int, myDataset) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.DataB
DataBase - Class in keel.Algorithms.Associative_Classification.ClassifierCBA: Class to store the examples to work with the algorithm and some other useful information.
DataBase() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.DataBase: Default Constructor
DataBase(myDataset) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.DataBase: Parameters Constructor
DataBase - Class in keel.Algorithms.Associative_Classification.ClassifierCBA2: Class to store the examples to work with the algorithm and some other useful information
DataBase() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.DataBase: Default Constructor
DataBase(myDataset) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.DataBase: Parameters Constructor
DataBase - Class in keel.Algorithms.Associative_Classification.ClassifierCMAR: Class to store the examples to work with the algorithm and some other useful information
DataBase() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCMAR.DataBase: Default Constructor
DataBase(myDataset) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCMAR.DataBase: Parameters Constructor
DataBase - Class in keel.Algorithms.Associative_Classification.ClassifierCPAR: Class to store the examples to work with the algorithm and some other useful information
DataBase() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCPAR.DataBase: Default Constructor
DataBase(myDataset) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCPAR.DataBase: Parameters Constructor
DataBase - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR: Fuzzy Data Base
DataBase() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.DataBase: Default constructor.
DataBase(int, myDataset) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.DataBase: Parameters Constructor
DataBase - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD: Title: DataBase Description: Fuzzy Data Base Copyright: Copyright KEEL (c) 2008 Company: KEEL
DataBase() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.DataBase: Default constructor.
DataBase(int, myDataset) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.DataBase: This method builds the database, creating the initial linguistic partitions
DataBase - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA: Fuzzy Data Base
DataBase() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.DataBase: Default constructor.
DataBase(int, myDataset) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.DataBase: Parameters Constructor
DataBase - Class in keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW: This class contains the representation of a Fuzzy Data Base
DataBase() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.DataBase: Default constructor
DataBase(int, int, double[][], String[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.DataBase: Constructor with parameters.
DataBase - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD: Fuzzy Data Base
DataBase() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.DataBase: Default constructor
DataBase(int, int, double[][], String[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.DataBase: Constructor with parameters.
DataBase - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid: Title: DataBase Description: Fuzzy Data Base Copyright: Copyright (c) 2008 Company: KEEL
DataBase() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.DataBase: Default constructor
DataBase(int, double[][], String[], boolean[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.DataBase: Constructor with parameters.
DataBase - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH: Title: DataBase Description: This class contains the representation of a Fuzzy Data Base Copyright: Copyright KEEL (c) 2007 Company: KEEL
DataBase() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.DataBase: Default constructor
DataBase(int, int, double[][], String[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.DataBase: Constructor with parameters.
DataBase - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS: Title: DataBase Description: Fuzzy Data Base Copyright: Copyright KEEL (c) 2008 Company: KEEL
DataBase() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.DataBase: Default constructor.
DataBase(int, myDataset) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.DataBase: This method builds the database, creating the initial linguistic partitions
DataBase - Class in keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H: Title: DataBase Description: This class contains the representation of a Fuzzy Data Base Copyright: Copyright KEEL (c) 2007 Company: KEEL
DataBase() - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.DataBase: Default constructor
DataBase(int, int, double[][], String[], int) - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.DataBase: Constructor with parameters.
DataBase - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams: Title: DataBase Description: Fuzzy Data Base Copyright: Copyright KEEL (c) 2008 Company: KEEL
DataBase() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.DataBase: Default constructor.
DataBase(String, myDataset) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.DataBase: This method builds the database, creating the initial linguistic partitions
DataCFAboutBox - Class in keel.GraphInterKeel.datacf
DataCFAboutBox(Frame) - Constructor for class keel.GraphInterKeel.datacf.DataCFAboutBox: Constructor that receives the parent window
DataCFApp - Class in keel.GraphInterKeel.datacf
DataCFApp() - Constructor for class keel.GraphInterKeel.datacf.DataCFApp
DataCFFrame - Class in keel.GraphInterKeel.datacf
DataCFFrame() - Constructor for class keel.GraphInterKeel.datacf.DataCFFrame: Constructor that initializes the frame
dataCFFrame - Variable in class keel.GraphInterKeel.datacf.editData.EditPanel: DataCF parent frame
dataCFFrame - Variable in class keel.GraphInterKeel.datacf.importData.ImportPanel: DataCF parent frame
dataDL(double, double, double, double, double) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: The description length of data given the parameters of the data based on the ruleset.
DataException - Exception in keel.Algorithms.Fuzzy_Instance_Based_Learning: File: DataException.java A new exception type defined to be thrown when a data set is incorrect
DataException() - Constructor for exception keel.Algorithms.Fuzzy_Instance_Based_Learning.DataException: Creates a new instance of CheckException
DataException(String) - Constructor for exception keel.Algorithms.Fuzzy_Instance_Based_Learning.DataException: Creates a new instance of DataException, by using a message to define it.
DataException - Exception in keel.Algorithms.Hyperrectangles.Basic: File: DataException.java A new exception type defined to be thrown when a data set is incorrect
DataException() - Constructor for exception keel.Algorithms.Hyperrectangles.Basic.DataException: Creates a new instance of CheckException
DataException(String) - Constructor for exception keel.Algorithms.Hyperrectangles.Basic.DataException: Creates a new instance of DataException, by using a message to define it.
DataException - Exception in keel.Algorithms.Lazy_Learning: File: DataException.java A new exception type defined to be thrown when a data set is incorrect
DataException() - Constructor for exception keel.Algorithms.Lazy_Learning.DataException: Creates a new instance of CheckException
DataException(String) - Constructor for exception keel.Algorithms.Lazy_Learning.DataException: Creates a new instance of DataException, by using a message to define it.
DataException - Exception in keel.Algorithms.RST_Learning: File: DataException.java A new exception type defined to be thrown when a data set is incorrect
DataException() - Constructor for exception keel.Algorithms.RST_Learning.DataException: Creates a new instance of CheckException
DataException(String) - Constructor for exception keel.Algorithms.RST_Learning.DataException: Creates a new instance of DataException, by using a message to define it.
dataManagement_mouseEntered(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Enter in data management button
dataManagement_mouseExited(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Exit from data management button
dataManagement_mouseReleased(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Entering in Data Management module
dataMatrix - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Data matrix
dataMatrix - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Data matrix order.
dataNormalized - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: normalize data ?
DataParser - Class in keel.Algorithms.Rule_Learning.Swap1
DataParser(InputStream) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.DataParser
DataParser(InputStream, String) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.DataParser
DataParser(Reader) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.DataParser
DataParser(DataParserTokenManager) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.DataParser
DataParser - Class in keel.Dataset
DataParser(InputStream) - Constructor for class keel.Dataset.DataParser
DataParser(InputStream, String) - Constructor for class keel.Dataset.DataParser
DataParser(Reader) - Constructor for class keel.Dataset.DataParser
DataParser(DataParserTokenManager) - Constructor for class keel.Dataset.DataParser
DataParserConstants - Interface in keel.Algorithms.Rule_Learning.Swap1: Contains constants used by the data parser.
DataParserConstants - Interface in keel.Dataset
DataParserTokenManager - Class in keel.Algorithms.Rule_Learning.Swap1
DataParserTokenManager(SimpleCharStream) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.DataParserTokenManager
DataParserTokenManager(SimpleCharStream, int) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.DataParserTokenManager
DataParserTokenManager - Class in keel.Dataset
DataParserTokenManager(SimpleCharStream) - Constructor for class keel.Dataset.DataParserTokenManager
DataParserTokenManager(SimpleCharStream, int) - Constructor for class keel.Dataset.DataParserTokenManager
Dataset - Class in keel.Algorithms.Decision_Trees.C45: Class to implement the dataset
Dataset(String, boolean) - Constructor for class keel.Algorithms.Decision_Trees.C45.Dataset: Function to read the .dat file that contains the information of the dataset.
Dataset(Dataset) - Constructor for class keel.Algorithms.Decision_Trees.C45.Dataset: Constructor that copies another dataset.
Dataset(Dataset, int) - Constructor for class keel.Algorithms.Decision_Trees.C45.Dataset: Constructor to copy all the attributes of another dataset but the itemsets.
dataset - Variable in class keel.Algorithms.Decision_Trees.C45.Itemset: The dataset which the itemset has access to.
Dataset - Class in keel.Algorithms.Decision_Trees.DT_GA.C45: Class to implement the dataset
Dataset(String, boolean) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Function to read the .dat file that contains the information of the dataset.
Dataset(Dataset) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Constructor that copies another dataset.
Dataset(Dataset, int) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Constructor to copy all the attributes of another dataset but the itemsets.
dataset - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset: The dataset which the itemset has access to.
Dataset - Class in keel.Algorithms.Decision_Trees.ID3: Class to implement the dataset
Dataset(String, boolean) - Constructor for class keel.Algorithms.Decision_Trees.ID3.Dataset: Function to read the .dat file that contains the information of the dataset.
Dataset(Dataset) - Constructor for class keel.Algorithms.Decision_Trees.ID3.Dataset: Constructor that copies another dataset.
Dataset(Dataset, int) - Constructor for class keel.Algorithms.Decision_Trees.ID3.Dataset: Constructor to copy all the attributes of another dataset but the itemsets.
dataset - Variable in class keel.Algorithms.Decision_Trees.ID3.Itemset: The dataset which the itemset has access to.
dataset() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Returns the dataset this instance has access to.
Dataset - Class in keel.Algorithms.Decision_Trees.SLIQ: Class to implement the dataset
Dataset(String, boolean) - Constructor for class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Function to read the .dat file that contains the information of the dataset.
Dataset(Dataset) - Constructor for class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Constructor that copies another dataset.
Dataset(Dataset, int) - Constructor for class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Constructor to copy all the attributes of another dataset but the itemsets.
dataset - Variable in class keel.Algorithms.Decision_Trees.SLIQ.Itemset: The dataset which the itemset has access to.
dataset() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Returns the dataset this instance has access to.
Dataset - Class in keel.Algorithms.Genetic_Rule_Learning.Corcoran: Title: Data-set Description: It contains the methods for reading the training and test files
Dataset() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: Builder.
Dataset - Class in keel.Algorithms.Genetic_Rule_Learning.Hider: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
Dataset() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Init a new set of instances
dataset - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: The dataset which the itemset has access to.
Dataset - Class in keel.Algorithms.Genetic_Rule_Learning.olexGA: Class to implement the dataset
Dataset(String, boolean) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Function to read the .dat file that contains the information of the dataset.
Dataset(Dataset) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Constructor that copies another dataset.
Dataset(Dataset, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Constructor to copy all the attributes of another dataset but the itemsets.
dataset - Variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset: The dataset which the itemset has access to.
dataset - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset: The dataset which the itemset has access to.
Dataset - Class in keel.Algorithms.Genetic_Rule_Learning.SIA: Title: Data-set Description: It contains the methods for reading the training and test files
Dataset() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: Builder.
Dataset - Class in keel.Algorithms.Hyperrectangles.EACH: Class to manage data sets
Dataset() - Constructor for class keel.Algorithms.Hyperrectangles.EACH.Dataset: Constructor, creates a new set of instances
Dataset - Class in keel.Algorithms.ImbalancedClassification.CSMethods.C45CS: Class to implement the dataset
Dataset(String, boolean) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Function to read the .dat file that contains the information of the dataset.
Dataset(Dataset) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Constructor that copies another dataset.
Dataset(Dataset, int) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Constructor to copy all the attributes of another dataset but the itemsets.
dataset - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset: The dataset which the itemset has access to.
Dataset - Class in keel.Algorithms.ImbalancedClassification.Ensembles.C45: Class to implement the dataset
Dataset(String, boolean) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Function to read the .dat file that contains the information of the dataset.
Dataset(String, boolean, double[]) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Creates a new dataset.
Dataset(InstanceSet, double[]) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Create a new dataset by copying the instances of the InstanceSet given.
Dataset(Dataset) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Constructor that copies another dataset.
Dataset(Dataset, int) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Constructor to copy all the attributes of another dataset but the itemsets.
dataset - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset: The dataset which the itemset has access to.
dataset() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Returns the dataset this instance has access to.
Dataset - Class in keel.Algorithms.Rule_Learning.AQ: Title: Data-set Description: It contains the methods for reading the training and test files
Dataset() - Constructor for class keel.Algorithms.Rule_Learning.AQ.Dataset: Builder.
Dataset - Class in keel.Algorithms.Rule_Learning.ART: Class to implement the dataset
Dataset(String, boolean) - Constructor for class keel.Algorithms.Rule_Learning.ART.Dataset: Function to read the .dat file that contains the information of the dataset.
Dataset(Dataset) - Constructor for class keel.Algorithms.Rule_Learning.ART.Dataset: Constructor that copies another dataset.
Dataset(Dataset, int) - Constructor for class keel.Algorithms.Rule_Learning.ART.Dataset: Constructor to copy all the attributes of another dataset but the itemsets.
dataset - Variable in class keel.Algorithms.Rule_Learning.ART.Itemset: The dataset which the itemset has access to.
dataset - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Itemset: The dataset which the itemset has access to.
dataset - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset: The dataset which the itemset has access to.
Dataset - Class in keel.Algorithms.Rule_Learning.CN2: Title: Data-set Description: It contains the methods for reading the training and test files
Dataset() - Constructor for class keel.Algorithms.Rule_Learning.CN2.Dataset: Builder.
Dataset - Class in keel.Algorithms.Rule_Learning.DataSqueezer: Class to implement the dataset
Dataset(String, boolean) - Constructor for class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Function to read the .dat file that contains the information of the dataset.
Dataset(Dataset) - Constructor for class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Constructor that copies another dataset.
Dataset(Dataset, int) - Constructor for class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Constructor to copy all the attributes of another dataset but the itemsets.
dataset - Variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset: The dataset which the itemset has access to.
dataset - Variable in class keel.Algorithms.Rule_Learning.PART.Itemset: The dataset which the itemset has access to.
Dataset - Class in keel.Algorithms.Rule_Learning.Prism: Class to manage data sets
Dataset() - Constructor for class keel.Algorithms.Rule_Learning.Prism.Dataset: Constructor, creates a new set of instances
Dataset - Class in keel.Algorithms.Rule_Learning.Riona: Methods for reading the train & test file
Dataset() - Constructor for class keel.Algorithms.Rule_Learning.Riona.Dataset: Constructor.
Dataset - Class in keel.Algorithms.Rule_Learning.UnoR: Title: Data-set Description: It contains the methods for reading the training and test files
Dataset() - Constructor for class keel.Algorithms.Rule_Learning.UnoR.Dataset: Constructor, creates a new set of instances
Dataset - Class in keel.Algorithms.Semi_Supervised_Learning.Basic.C45: Class to implement the dataset
Dataset(String, boolean) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Function to read the .dat file that contains the information of the dataset.
Dataset(InstanceSet) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Sets the training instances set given as parameter.
Dataset(Dataset) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Constructor that copies another dataset.
Dataset(Dataset, int) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Constructor to copy all the attributes of another dataset but the itemsets.
dataset - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset: The dataset which the itemset has access to.
Dataset - Class in keel.Algorithms.Subgroup_Discovery.aprioriSD: Title: Data-set Description: It contains the methods for reading the training and test files
Dataset() - Constructor for class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: Builder.
Dataset - Class in keel.Algorithms.Subgroup_Discovery.CN2SD: Title: Data-set Description: It contains the methods for reading the training and test files
Dataset() - Constructor for class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset: Builder.
Dataset - Class in keel.Algorithms.Subgroup_Discovery.SDAlgorithm: Title: Data-set Description: It contains the methods for reading the training and test files
Dataset() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset: Builder.
dataset - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Dataset.
dataset() - Method in class keel.Algorithms.SVM.SMO.core.Instance: Returns the dataset this instance has access to.
dataset - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Instances dataset to generate the association rules.
Dataset - Class in keel.GraphInterKeel.datacf.util
Dataset(String) - Constructor for class keel.GraphInterKeel.datacf.util.Dataset: Read a DataSet
DataSet - Class in keel.GraphInterKeel.experiments
DataSet() - Constructor for class keel.GraphInterKeel.experiments.DataSet: Builder
DataSet(ExternalObjectDescription, Point, GraphPanel, Vector, int) - Constructor for class keel.GraphInterKeel.experiments.DataSet: Builder
DataSet(ExternalObjectDescription, Point, GraphPanel, Vector, boolean, int, int) - Constructor for class keel.GraphInterKeel.experiments.DataSet
dataset_used - Variable in class keel.GraphInterKeel.experiments.Parameters
DatasetChecker - Class in keel.Algorithms.Genetic_Rule_Learning.olexGA: Class to check if dataset is correct
DatasetChecker() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.olexGA.DatasetChecker
DatasetException - Exception in keel.Algorithms.Neural_Networks.NNEP_Common.data: Dataset exception.
DatasetException() - Constructor for exception keel.Algorithms.Neural_Networks.NNEP_Common.data.DatasetException: Empty Constructor
DatasetException(String) - Constructor for exception keel.Algorithms.Neural_Networks.NNEP_Common.data.DatasetException: Constructor that establishes the description
DatasetException(Throwable) - Constructor for exception keel.Algorithms.Neural_Networks.NNEP_Common.data.DatasetException: Constructor that receives a Throwable object
DatasetException(String, Throwable) - Constructor for exception keel.Algorithms.Neural_Networks.NNEP_Common.data.DatasetException: Constructor that receives a Throwable object and the description
DatasetException - Exception in keel.Algorithms.Rule_Learning.Swap1: DatasetException This class defines the exception that will be thrown if something bad happens during the dataset reading.
DatasetException() - Constructor for exception keel.Algorithms.Rule_Learning.Swap1.DatasetException: Creates a new instance of DatasetException
DatasetException(String, Vector) - Constructor for exception keel.Algorithms.Rule_Learning.Swap1.DatasetException: Does instance a new DatasetException with the message specified and the Vector with all the errors.
DatasetException - Exception in keel.Dataset: DatasetException This class defines the exception that will be thrown if something bad happens during the dataset reading.
DatasetException() - Constructor for exception keel.Dataset.DatasetException: Creates a new instance of DatasetException
DatasetException(String, Vector) - Constructor for exception keel.Dataset.DatasetException: Does instance a new DatasetException with the message specified and the Vector with all the errors.
DataSetManager - Class in keel.Algorithms.Decision_Trees.CART.dataset: This class helps managing the conversion from KeelDataset to DoubleTransposedDataset
DataSetManager() - Constructor for class keel.Algorithms.Decision_Trees.CART.dataset.DataSetManager
datasets - Variable in class keel.GraphInterKeel.datacf.exportData.ExportPanel: Dataset mode (true) or partition mode (false)
datasets - Variable in class keel.GraphInterKeel.datacf.importData.ImportPanel: Dataset mode (true) or partition mode (false)
datasetsChecksPanel - Variable in class keel.GraphInterKeel.experiments.Experiments
DatasetTable - Class in keel.GraphInterKeel.datacf.util
DatasetTable(Dataset, JPanel) - Constructor for class keel.GraphInterKeel.datacf.util.DatasetTable: Constructor
DatasetToArray(double[][], OpenDataset) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.MLPerceptronBackpropCS: Transforms the dataset into a double matrix
DatasetToArray(double[][], OpenDataset) - Static method in class keel.Algorithms.Neural_Networks.gann.Genesis: Method that transforms a dataset into a double matrix
DatasetToArray(double[][], OpenDataset) - Static method in class keel.Algorithms.Neural_Networks.gmdh.Genesis: Transform dataset into a double matrix
DatasetToArray(double[][], OpenDataset) - Static method in class keel.Algorithms.Neural_Networks.net.Genesis: Transforms the dataset into a double matrix
datasetType() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Returs the type of the dataset 0-Modelling, 1-Clasiffication, 2-Clustering
datasetType() - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Returs the type of the dataset 0-Modelling, 1-Clasiffication, 2-Clustering
datasetType() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Returs the type of the dataset 0-Modelling, 1-Clasiffication, 2-Clustering
datasetType() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Returs the type of the dataset 0-Modelling, 1-Clasiffication, 2-Clustering
datasetType() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Returs the type of the dataset 0-Modelling, 1-Clasiffication, 2-Clustering
datasetType() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Returs the type of the dataset 0-Modelling, 1-Clasiffication, 2-Clustering
datasetType() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Returs the type of the dataset 0-Modelling, 1-Clasiffication, 2-Clustering
DatasetXML - Class in keel.GraphInterKeel.experiments
DatasetXML(Element) - Constructor for class keel.GraphInterKeel.experiments.DatasetXML: Builder
DataSqueezer - Class in keel.Algorithms.Rule_Learning.DataSqueezer: A Java implementation of the DataSqueezer algorithm
DataSqueezer(String) - Constructor for class keel.Algorithms.Rule_Learning.DataSqueezer.DataSqueezer: Constructor.
dataTable1 - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Data table order 1
dataTable1 - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Data table order 1.
dataTable2 - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Data table order 2
dataTable2 - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Data table order 2.
dataTable3 - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Data table order 3
dataTable3 - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Data table order 3.
DataType(String) - Method in class keel.Algorithms.Preprocess.Converter.Importer: Returns the type of the element given as argument.
DATE - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constant set for attributes with date values.
DATE - Static variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Constant set for attributes with date values.
datos - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyDataset: Dataset.
Datos - Class in keel.Algorithms.Preprocess.Feature_Selection: Datos.java Data structure used for Feature Selection preprocessing.
Datos(String, String, int) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.Datos: Creates a new instance of Datos
datos - Variable in class keel.Algorithms.RE_SL_Methods.LEL_TSK.MiDataset
datos - Variable in class keel.Algorithms.RE_SL_Methods.MamWM.MiDataset
datos - Variable in class keel.Algorithms.RE_SL_Methods.mogulHC.MiDataset
datos - Variable in class keel.Algorithms.RE_SL_Methods.mogulIRL.MiDataset
datos - Variable in class keel.Algorithms.RE_SL_Methods.mogulSC.MiDataset
datos - Variable in class keel.Algorithms.RE_SL_Methods.TSK_IRL.MiDataset
datos - Variable in class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.MiDataset
datos - Variable in class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.MiDataset
datos - Variable in class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.MiDataset
datos - Variable in class keel.Algorithms.RE_SL_Postprocess.MamSelect.MiDataset
datos - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWSelect.MiDataset
datos - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWTuning.MiDataset
datos - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.MiDataset
datos - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKSelect.MiDataset
datosTest - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Test input data.
datosTest - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
datosTest - Variable in class keel.Algorithms.Preprocess.Basic.Metodo: Test input data.
datosTrain - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Training input data.
datosTrain - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
datosTrain - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
datosTrain - Variable in class keel.Algorithms.Preprocess.Basic.Metodo: Training input data.
DbToKeel - Class in keel.Algorithms.Preprocess.Converter: DbToKeel This class extends from the Importer class.
DbToKeel(String, String, String, String, String) - Constructor for class keel.Algorithms.Preprocess.Converter.DbToKeel: DbToKeel class Constructor.
dchisq(double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Returns the density of the Chi-squared distribution.
dchisq(double, double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Returns the density of the noncentral Chi-squared distribution.
dchisq(double, DoubleVector) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Returns the density of the noncentral Chi-squared distribution.
dchisqLog(double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Returns the log-density of the noncentral Chi-square distribution.
dchisqLog(double, double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Returns the log-density value of a noncentral Chi-square distribution.
dchisqLog(double, DoubleVector) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Returns the log-density of a set of noncentral Chi-squared distributions.
DD - Class in keel.Algorithms.MIL.Diverse_Density.DD: Diverse Density.
DD() - Constructor for class keel.Algorithms.MIL.Diverse_Density.DD.DD
DDoptimization - Class in keel.Algorithms.MIL.Diverse_Density.Optimization: DD algorithm optimization auxiliary methods
DDoptimization(DD) - Constructor for class keel.Algorithms.MIL.Diverse_Density.Optimization.DDoptimization
de(int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion: Right
de(int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion: Right
DE_TriTrainingAlgorithm - Class in keel.Algorithms.Semi_Supervised_Learning.DE_TriTraining: DE_TriTraining algorithm calling.
DE_TriTrainingAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.DE_TriTraining.DE_TriTrainingAlgorithm
DE_TriTrainingGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.DE_TriTraining: This class implements the Tri-training.
DE_TriTrainingGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.DE_TriTraining.DE_TriTrainingGenerator: Build a new DE_TriTrainingGenerator Algorithm
DE_TriTrainingGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.DE_TriTraining.DE_TriTrainingGenerator: Build a new DE_TriTrainingGenerator Algorithm
DEAlgorithm - Class in keel.Algorithms.Instance_Generation.DE: DE algorithm calling.
DEAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.DE.DEAlgorithm
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGAP.FuzzyGAPClassifier: This method performs the debug operation, which allow to analyze the behaviour of the learning process.
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGP.FuzzyGPClassifier: This method performs the debug operation, which allow to analyze the behaviour of the learning process.
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySAP.FuzzySAPClassifier: This method performs the debug operation, which allow to analyze the behaviour of the learning process.
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.Classifier: abstract method to print information useful for debugging purposes
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.FuzzyClassifier: This method prints information about the Rule Base useful for debugging purposes
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.FuzzyFGPClassifier: This method prints information about the Rule Base useful for debugging purposes
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.Genotype: abstract method for printing debug information.
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGAP: This method is intended for printing debug information.
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGP: This method is intended for printing debug information.
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGPRegSym: This method is intended for printing debug information.
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypePitts: This method is intended for printing debug information.
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividual: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForClassification: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForModels: Method for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForSymbRegr: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGAPModelIndividual: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPModel: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPModelIndividual: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPRegSymModel: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyModel: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.Model: This abstract method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.RegSymFuzzyGP: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node: This abstrac method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeAdd: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeAnd: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeConsequent: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeExp: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeExprHold: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeIs: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeLabel: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeLog: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeMinus: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeOr: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeProduct: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeRule: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeRuleBase: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeSquareRoot: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeValue: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeVariable: This method is for debug
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyRegressor: Get current debugging message setting.
debug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase: Prints the results.
Debug - Class in keel.Algorithms.Instance_Generation.utilities: Implements operations that changes the flow of the program
Debug() - Constructor for class keel.Algorithms.Instance_Generation.utilities.Debug
Debug - Class in keel.Algorithms.Semi_Supervised_Learning.utilities: Implements operations that changes the flow of the program
Debug() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.utilities.Debug
debug_fitness() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForSymbRegr: This method is for debug the fitness
debugLevel - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: It indicates the level of debug (the quantity of message that will appear in the estandard output
debugLevel - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It indicates the level of debug (the quantity of message that will appear in the estandard output
debugOutput() - Method in class keel.Algorithms.Shared.ClassicalOptim.ConjGradNN: Prints to standard output the main information about the training algorithm run: -the matrix of weights -the original input examples (not scaled) -the original obtained output (not scaled) -the original expected output (not scaled)
debugStream - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserTokenManager
debugStream - Static variable in class keel.Algorithms.Rule_Learning.Swap1.DataParserTokenManager
debugStream - Static variable in class keel.Dataset.DataParserTokenManager
debugTipText() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Classifier: Returns the tip text for this property
debugTipText() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Returns the tip text for this property
debugTipText() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Returns the tip text for this property
debugTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.Kernel: Returns the tip text for this property
DECEnvironment - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: This is the typical example for a single step problem, the multiplexer.
DECEnvironment() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.DECEnvironment: It is the constructor of the class.
decimal - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FloatingPointFormat: Decimal size.
decimal_scaling - Class in keel.Algorithms.Preprocess.Transformations.decimal_scaling: This class performs the decimal scaling transformation on the data.
decimal_scaling(String) - Constructor for class keel.Algorithms.Preprocess.Transformations.decimal_scaling.decimal_scaling: Creates a new instance of decimal_scaling
DecisionTree - Class in keel.Algorithms.Decision_Trees.CART.tree: This class represents a binary decision tree
DecisionTree() - Constructor for class keel.Algorithms.Decision_Trees.CART.tree.DecisionTree: Default Constructor
decode(double[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.DataBase: Decode the gene representation for the GA into the DataBase one based on the Triangular Membership Functions
decode(double[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.DataBase
decodificaBR(int, int, String[], String[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Individual: It decodifies the Rule Base stored in the chromosome into an string for its visualization
decomposeNode(Node) - Method in class keel.Algorithms.Decision_Trees.ID3.ID3: Function to decompose the specified node.
decrCovered(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: Function to decrease in 1 the number of examples whose output class is the given class "clas" and are covered by this rule.
decrCovered(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: Function to decrease in 1 the number of examples whose output class is the given class "clas" and are covered by this rule
decremental(double[][], double[][], int, double, double) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Uses a decremental algorithm to buid a net.
decremental(double[][], double[][], int, double, double) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbfn: Uses a decremental algorithm to buid a net.
decremental(double[][], double[][], int, double, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn: Uses a decremental algorithm to buid a net.
decremental(double[][], double[][], int, double, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn: Uses a decremental algorithm to buid a net.
decremental(double[][], double[][], int, double, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn: Uses a decremental algorithm to buid a net.
decremental(double[][], double[][], int, double, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn: Uses a decremental algorithm to buid a net.
decremental(double[][], double[][], int, double, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn: Uses a decremental algorithm to buid a net.
Deeps - Class in keel.Algorithms.Lazy_Learning.Deeps: File: Deeps.java The DeEps Algorithm.
Deeps(String) - Constructor for class keel.Algorithms.Lazy_Learning.Deeps.Deeps: The main method of the class
DeepsNN - Class in keel.Algorithms.Lazy_Learning.DeepsNN: File: DeepsNN.java The DeEpsNN Algorithm.
DeepsNN(String) - Constructor for class keel.Algorithms.Lazy_Learning.DeepsNN.DeepsNN: The main method of the class
DEFAULT - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
DEFAULT - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for default value.
DEFAULT - Static variable in interface keel.Dataset.DataParserConstants
DEFAULT_EPSILON - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQ3: Default value for the epsilon constant
DEFAULT_WINDOW_WIDTH - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQ2: Default value of the window width parameter
defaultClass - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
defaultClass - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
defaultClassInteger - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
defaultClassOption - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
defaultEditor - Variable in class keel.GraphInterKeel.datacf.util.EachRowEditor: TableCell editors
defaultRule - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
defaultValue - Variable in class keel.GraphInterKeel.experiments.Parameters
defaultValues - Variable in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelAttribute: Default values
defClass - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
defConvertArrays(int[][]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Defines conversion and reconversion arrays.
defConvertArrays(int[][]) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Defines conversion and reconversion arrays.
defConvertArrays(int[][]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Defines conversion and reconversion arrays.
DEFUZCDM - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase: Defuzzification by mass center.
DEFUZMAX - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase: Defuzzification by maximum.
defuzzify(double[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase: Defuzzifies output using method IDDEFUZZIFY.
DEGenerator - Class in keel.Algorithms.Instance_Generation.DE: DEGenerator
DEGenerator(PrototypeSet, int, int, int, int, double, double, int) - Constructor for class keel.Algorithms.Instance_Generation.DE.DEGenerator: Build a new DEGenerator Algorithm
DEGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.DE.DEGenerator: Build a new DEGenerator Algorithm
DEGLAlgorithm - Class in keel.Algorithms.Instance_Generation.DEGL: DEGL algorithm calling.
DEGLAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.DEGL.DEGLAlgorithm
DEGLGenerator - Class in keel.Algorithms.Instance_Generation.DEGL: DEGL.java DEGL prototype generator.
DEGLGenerator(PrototypeSet, int, int, int, int, double, double, int) - Constructor for class keel.Algorithms.Instance_Generation.DEGL.DEGLGenerator: Build a new DEGLGenerator Algorithm
DEGLGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.DEGL.DEGLGenerator: Build a new DEGLGenerator Algorithm
degree(DataBase, double[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Itemset: Calculate the degree of the given example inside the given data-set.
degree(DataBase, double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Itemset
degree - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
degree - Variable in class org.libsvm.svm_parameter
del(Lists) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Lists: Delete the node NODE from the list
del(Lists) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Lists: Delete the node NODE from the list
del(Lists) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Lists: Delete the node NODE from the list
delDup(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Eliminates de duplicated individuals of the population from the first individual to number max (not to the total number of individuals)
delete(int) - Method in class keel.Algorithms.Decision_Trees.C45.Dataset: Function to remove an itemset at the given position.
delete(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Function to remove an itemset at the given position.
delete(int) - Method in class keel.Algorithms.Decision_Trees.ID3.Dataset: Function to remove an itemset at the given position.
delete() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Removes all instances from the set.
delete(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Removes an instance at the given position from the set.
delete(int) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Function to remove an itemset at the given position.
delete() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.RuleBase: It deletes from the RB those rules with a negative weight
delete() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Removes all instances from the set.
delete(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Removes an instance at the given position from the set.
delete() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Removes all itemsets from the set.
delete(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Function to remove an itemset at the given position.
delete(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Function to remove an itemset at the given position.
delete(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Function to remove an itemset at the given position.
delete(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Function to remove an itemset at the given position.
delete(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Function to remove an itemset at the given position.
delete() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Removes all instances from the set.
delete(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Removes an instance at the given position from the set.
delete(int) - Method in class keel.Algorithms.Rule_Learning.ART.Dataset: Function to remove an itemset at the given position.
delete(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Function to remove an itemset at the given position.
delete(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Function to remove an itemset at the given position.
delete(int) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Function to remove an itemset at the given position.
delete(int) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Function to remove an itemset at the given position.
delete(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Function to remove an itemset at the given position.
delete() - Method in class keel.Algorithms.SVM.SMO.core.Instances: Removes all instances from the set.
delete(int) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Removes an instance at the given position from the set.
delete(int) - Method in class keel.Algorithms.SVM.SMO.supportVector.SMOset: Deletes an element from the set.
deleteAll() - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleSet: Removes the content of a set of rules
deleteAll() - Method in class keel.Algorithms.Rule_Learning.CN2.ruleSet: It truncates the rule set
deleteAll() - Method in class keel.Algorithms.Rule_Learning.Prism.ConjReglas: Removes the content of a set of rules
deleteAll() - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjReglas: Removes the content of a set of rules
deleteAll() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjReglas: Removes the content of a set of rules
deleteAll() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetRules: Delete all the content of the list
deleteAttributeAt(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteAttributeAt(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteAttributeAt(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteAttributeAt(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteAttributeAt(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteAttributeAt(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteAttributeAt(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteAttributeAt(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteAttributeAt(int) - Method in class keel.Algorithms.SVM.SMO.core.Instance: Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteAttributeAt(int) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteAttributeType(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Deletes all attributes of the given type in the dataset.
deleteAttributeType(int) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Deletes all attributes of the given type in the dataset.
deleteClassifier(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: This method means that the classifer has to be definetely removed from the population because its numerosity has decreased to 0.
deleteClassifier() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Deletes a classifier from this population.
deleteClassifier(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: This method means that the classifer has to be definetely removed from the population because its numerosity has decreased to 0.
deleteClassifier() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Deletes a classifier from this population.
deleteClFromPopulation(Population) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Deletes one classifier from the population.
deleteClFromPopulation(Population) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Deletes one classifier from the population.
deleteData(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.myDataset: It removes a data
deleteData(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.myDataset: It removes a data
deleteData(int) - Method in class keel.Algorithms.Rule_Learning.AQ.myDataset: It removes a data
deleteData(int) - Method in class keel.Algorithms.Rule_Learning.CN2.myDataset: It removes a data
deleteDato(int) - Method in class keel.Algorithms.Rule_Learning.Prism.ConjDatos: Removes a data item
deleteDato(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjDatos: Removes a data item
deleteDato(int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjDatos: Removes a data item
deleteDato(int) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjDatos: Removes a data item
deleteDato(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetData: Delete a data
deleteDescendants(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode: Removes the descendants from a identifier given of a node.
deleteDistrib() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Reset the value of the distribution
deleteDistrib() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.AssociationRule: Reset the value of the distribution
deleteDistribution() - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: It resets the distribution value for the complex
deleteEqual(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetRules: Delete rules with the same complex
deleteEqualAttributes(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetRules: Delete rules with the same attribute
deleteExecDocentWindow() - Method in class keel.GraphInterKeel.experiments.Experiments: EDUCATIONAL KEEL **********************
deleteFiles() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerCSVM: Deletes all the files generated.
deleteFiles() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerNB: Deletes all the files generated.
deleteFiles() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerSMO: Deletes all the files generated.
deleteFromXML(int) - Method in class keel.GraphInterKeel.experiments.SelectData: Delete a data set from the XML file
deleteItem - Variable in class keel.GraphInterKeel.experiments.Experiments
deleteLabel() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.FuzzyAntecedent: Deletes randomly a label from the label set of this fuzzy antecedent
deleteLabel(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Deletes a label to the fuzzy antecedent of the given variable
deleteLabel() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Deletes randomly a label from the label set of this fuzzy antecedent
deleteLabel(int, int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Deletes a label to the fuzzy antecedent of the given variable
deleteMatchedExamples(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.DECEnvironment: It deletes the examples of the database that match with the given classifier.
deleteMatchedExamples(Classifier) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Environment: It deletes the examples of the database that match with the given classifier.
deleteMatchedExamples(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MPEnvironment: It deletes the examples of the database that match with de classifier given as a parameter.
deleteMatchedExamples(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MSEnvironment: It deletes the examples of the database that match with de classifier given as a parameter.
deleteMatchedExamples(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.PAREnvironment: It deletes the examples of the database that match with the classifier given as a parameter.
deleteMatchedExamples(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.POSEnvironment: It deletes the examples of the database that match with de classifier given as a parameter.
deleteMatchedExamples(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.SSFileEnvironment: It deletes the examples of the database that match with de classifier passed.
deleteNotExpClassifiers(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: It creates a new Population with only the sufficiently experienced classifiers.
deleteNull() - Method in class keel.Algorithms.Rule_Learning.CN2.ruleSet: It deletes complexes with repeated attributes
deleteNull() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetRules: Delete rules with the same attributes
deletePartitionFiles() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.PartitionScheme: It deletes the files of each training and test partition
deletePartitionFiles() - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.PartitionScheme: It deletes the files of each training and test partition
deletePartitionFiles() - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.PartitionScheme: It deletes the files of each training and test partition
deletePartitionFiles() - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.PartitionScheme: It deletes the files of each training and test partition
deletePartitionFiles() - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.PartitionScheme: It deletes the files of each training and test partition
deletePartitionFiles() - Method in class keel.GraphInterKeel.datacf.partitionData.PartitioningSchemes.DistanceBased_best: It deletes the files of each training and test partition
deletePath(File) - Static method in class keel.GraphInterKeel.datacf.util.FileUtils: Utilities for Files.
deleter(Rbfn) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation: Performs the DELETER mutator operator: modifies C_DELETER% of the Radius of the net
deleteRegla(int) - Method in class keel.Algorithms.Rule_Learning.Prism.ConjReglas: Removes a rule from the list
deleteRegla(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjReglas: Removes a rule from the list
deleteRegla(int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjReglas: Removes a rule from the list
deleteRegla(int) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjReglas: Removes a rule from the list
deleteRegla(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetRules: Delete a rule of the list
deleteRow(int) - Method in class keel.GraphInterKeel.datacf.util.AttributeTable: Deletes a row in the table
deleteRule(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.ruleSet: It removes a rule in the i-th position
deleteRule(int) - Method in class keel.Algorithms.Rule_Learning.AQ.ruleSet: It removes one rule of the list
deleteRule(int) - Method in class keel.Algorithms.Rule_Learning.CN2.ruleSet: It removes one rule of the list
deleteRules(int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
deleteRules(int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierADI
deleteRules(int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierGABIL
deleteRules(int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierUBR
deleteRules(int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
deleteRules(int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
deleteRules(int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
deleteRulesLowSupport(int, float) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetRules: Delete rules with low support
deleteStringAttributes() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Deletes all string attributes in the dataset.
deleteStringAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Deletes all string attributes in the dataset.
deleteStringAttributes() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Deletes all string attributes in the dataset.
deleteStringAttributes() - Method in class keel.Algorithms.SVM.SMO.core.Instances: Deletes all string attributes in the dataset.
deleteSubsumed(int) - Method in class keel.Algorithms.Rule_Learning.AQ.ruleSet: It removes rules that are semantically equal
deleteSubsumed(int) - Method in class keel.Algorithms.Rule_Learning.CN2.ruleSet: It removes rules that are semantically equal
deleteTrailingZerosAndDot(StringBuffer) - Static method in class keel.Algorithms.Decision_Trees.M5.M5: Deletes the trailing zeros and decimal point in a stringBuffer
deleteTrailingZerosAndDot(StringBuffer) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: Deletes the trailing zeros and decimal point in a stringBuffer
deleteVar() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Deletes a variable from the fuzzy antecedent set of this rule
deleteVar(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Deletes a variable from the fuzzy antecedent set of this rule
DeleteVector(int[], int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Arrays
deleteWithMissing(int) - Method in class keel.Algorithms.Decision_Trees.C45.Dataset: Function to remove all the attributes with missing value in the given attribute.
deleteWithMissing(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Function to remove all the attributes with missing value in the given attribute.
deleteWithMissing(int) - Method in class keel.Algorithms.Decision_Trees.ID3.Dataset: Function to remove all the attributes with missing value in the given attribute.
deleteWithMissing(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissing(M5Attribute) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissing(int) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Function to remove all the attributes with missing value in the given attribute.
deleteWithMissing(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissing(AttributeWeka) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissing(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Function to remove all the attributes with missing value in the given attribute.
deleteWithMissing(MyAttribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Removes all itemsets with missing values for a particular attribute from the dataset.
deleteWithMissing(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Function to remove all the attributes with missing value in the given attribute.
deleteWithMissing(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Function to remove all the attributes with missing value in the given attribute.
deleteWithMissing(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Function to remove all the attributes with missing value in the given attribute.
deleteWithMissing(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Function to remove all the attributes with missing value in the given attribute.
deleteWithMissing(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissing(Attribute) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissing(int) - Method in class keel.Algorithms.Rule_Learning.ART.Dataset: Function to remove all the attributes with missing value in the given attribute.
deleteWithMissing(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Function to remove all the attributes with missing value in the given attribute.
deleteWithMissing(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Function to remove all the attributes with missing value in the given attribute.
deleteWithMissing(int) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Function to remove all the attributes with missing value in the given attribute.
deleteWithMissing(int) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Function to remove all the attributes with missing value in the given attribute.
deleteWithMissing(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Function to remove all the attributes with missing value in the given attribute.
deleteWithMissing(int) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissing(Attribute) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissingClass() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Removes all instances with a missing class value from the dataset.
deleteWithMissingClass() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Removes all instances with a missing class value from the dataset.
deleteWithMissingClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Removes all itemsets with a missing class value from the dataset.
deleteWithMissingClass() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Removes all instances with a missing class value from the dataset.
deleteWithMissingClass() - Method in class keel.Algorithms.SVM.SMO.core.Instances: Removes all instances with a missing class value from the dataset.
deletionVote(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Returns the probability of a classifier to be deleted.
deletionVote(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters: Returns the probability of a classifier to be deleted.
deletionVote(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Returns the probability of a classifier to be deleted.
deletionVote(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Returns the probability of a classifier to be deleted.
delta - Variable in class keel.Algorithms.Discretizers.Khiops.DeltaValue: the cost derived from merging the two intervals (erase the boundary), that is, the Chi2 variation
delta - Variable in class keel.Algorithms.Discretizers.MODL.DeltaValue: the cost derived from merging the two intervals (erase the boundary)
delta - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Value of the fraction used in the second deletion method.
delta - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Value of the fraction used in the second deletion method.
DELTA - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
delta - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network: Delta weights
delta - Variable in class keel.Algorithms.Neural_Networks.gann.Network: Delta weights
delta - Variable in class keel.Algorithms.Neural_Networks.gmdh.Network: Delta weights
delta - Variable in class keel.Algorithms.Neural_Networks.net.Network: Delta weights
deltaE - Variable in class keel.Algorithms.Instance_Generation.HYB.HYBGenerator: Increment of epsilon in each step.
DeltaValue - Class in keel.Algorithms.Discretizers.Khiops: This class represents the cost variation associated with merging two adjacent intervals
DeltaValue() - Constructor for class keel.Algorithms.Discretizers.Khiops.DeltaValue
DeltaValue - Class in keel.Algorithms.Discretizers.MODL: This class represents the cost variation associated with merging two adjacent intervals
DeltaValue() - Constructor for class keel.Algorithms.Discretizers.MODL.DeltaValue
deltaW - Variable in class keel.Algorithms.Instance_Generation.HYB.HYBGenerator: Increment of window width in each step.
DemocraticAlgorithm - Class in keel.Algorithms.Semi_Supervised_Learning.Democratic: Democratic algorithm calling.
DemocraticAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Democratic.DemocraticAlgorithm
DemocraticGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.Democratic: This class implements the Co-traning wrapper.
DemocraticGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Democratic.DemocraticGenerator: Build a new DemocraticGenerator Algorithm
DemocraticGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Democratic.DemocraticGenerator: Build a new DemocraticGenerator Algorithm
denormalize() - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Denormalize the values of the inputs and outputs.
denormalize() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Denormalize the values of the inputs and outputs.
depth() - Method in class keel.Algorithms.Decision_Trees.CART.tree.DecisionTree: It returns the depth of the tree
depth() - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode
Depur - Class in keel.Algorithms.Instance_Generation.Depur: Depur algorithm
Depur(String) - Constructor for class keel.Algorithms.Instance_Generation.Depur.Depur: Constructor.
Depur(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.DE_TriTraining.DE_TriTrainingGenerator: Filtration algorithm.
DerivingNewData(double[][]) - Method in class keel.Algorithms.Discretizers.UCPD.PCA: It computes the final data
desc - Variable in class keel.GraphInterKeel.datacf.importData.ImportPanel: Description of the format selected by the user
descentGradient(FUN, double, double, int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ConjGradNN: Returns the mean square error of the output perceptron calculated with Descendent Gradient training algorithm.
descentGradient(FUN, double, double, int) - Method in class keel.Algorithms.Shared.ClassicalOptim.ConjGradNN: Returns the mean square error of the output perceptron calculated with Descendent Gradient training algorithm.
descodificaC(int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Codificacion: Decode column number
descodificaD(int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Codificacion: Decode discrete attributes
descodificaF(int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Codificacion: Decode row number
descompone(int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Codificacion: If any bit is '0', its exponent will be '-1'
description() - Method in class keel.Algorithms.Decision_Trees.M5.Information: Returns the option's description.
description() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Option: Returns the option's description.
description() - Method in class keel.Algorithms.SVM.SMO.core.Option: Returns the option's description.
Description_algorithm - Class in keel.GraphInterKeel.experiments
Description_algorithm(Frame, boolean, ExternalObjectDescription) - Constructor for class keel.GraphInterKeel.experiments.Description_algorithm: Creates new form Description_algorithm
descriptions - Variable in class keel.GraphInterKeel.experiments.Parameters
deseado - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
deselectAll() - Method in class keel.GraphInterKeel.experiments.SelectData: UnSelect all
desiredFinalSize - Variable in class keel.Algorithms.Instance_Generation.HYB.HYBGenerator: Desired final size of the generated prototype set.
desiredProbabilities(Prototype, PrototypeSet, int) - Method in class keel.Algorithms.Instance_Generation.MSE.MSEGenerator: Desired Probabilities.
Desnormalizar(Double, int, double) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: Denormalizes a value given with the attribute and the fraction given
Desnormalizar(Double, int, double) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: Denormalizes a value given with the attribute and the fraction given
desordenar_vector(int[]) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Shuffles the values of the vector given as parameter.
desordenar_vector(int[]) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Shuffles the vector given.
desordenar_vector_sin(int[]) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Shuffles the values of the vector given as parameter, maintaining the last element value.
desordenar_vector_sin(int[]) - Method in class keel.Algorithms.Instance_Generation.DROP3SFLSDE.DROP3SFLSDE
desordenar_vector_sin(int[]) - Method in class keel.Algorithms.Instance_Generation.ICFSFLSDE.ICFSFLSDE
desordenar_vector_sin(int[]) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE
desordenar_vector_sin(int[]) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Cuando quitas uno, con el inic vector, el desordenar no puede coger el ultimo..
destroyClassifier(classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.populationWrapper
det() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.LUDecomposition: Determinant
det() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Matrix determinant
DetectNoise(Instance[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.INFFC_2STEPS
deteleteAttributes1() - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelCharts2D: Detetes all attributes from the first combo box
deteleteAttributes2() - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelCharts2D: Deletes all attributes from the second combo box
determinaForg() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Organizacion: Computes the fixed attributes set.
determinante(double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
determinante(double[][]) - Static method in class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
devolverFeaturesVector() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.Cromosoma: returns a boolean array needed for Leaving One Out, Cross Validation and other methods used in Feature Selection Algorithm
devolverFeaturesVector() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaBinario: returns a boolean array needed for Leaving One Out, Cross Validation and other methods used in Feature Selection Algorithm
devolverFeaturesVector() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaEntero: returns a boolean array needed for Leaving One Out, Cross Validation and other methods used in Feature Selection Algorithm
devolverGen(int) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.Cromosoma: return the ith gen of chromosome
devolverGen(int) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaBinario: return the ith gene of chromosome
devolverGen(int) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaEntero: return the ith gen of chromosome
devolverTamCromosoma() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.Cromosoma: return the length of the chromosome
devuelveBR() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Individuo
devuelveRangos() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: Returns the minimum and maximum values of every attributes as a matrix.
devuelveRangos() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
diag(DenseMatrix) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Returns the diagonal of the matrix
diagonalHessian(MultivariateFunction, double[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.util.NumericalDerivative: determine diagonal of Hessian
dialog - Variable in class keel.GraphInterKeel.experiments.Node
DialogDataset - Class in keel.GraphInterKeel.experiments
DialogDataset(Experiments, String, boolean, DataSet, int) - Constructor for class keel.GraphInterKeel.experiments.DialogDataset: Builder
DialogDataset() - Constructor for class keel.GraphInterKeel.experiments.DialogDataset: Default builder
DialogDataset2 - Class in keel.GraphInterKeel.experiments
DialogDataset2(Experiments, String, boolean, DataSet, int) - Constructor for class keel.GraphInterKeel.experiments.DialogDataset2: Builder
DialogDataset2() - Constructor for class keel.GraphInterKeel.experiments.DialogDataset2: Default builder
DialogSeed - Class in keel.GraphInterKeel.experiments
DialogSeed(Experiments, String, boolean) - Constructor for class keel.GraphInterKeel.experiments.DialogSeed: Builder
DialogSeed() - Constructor for class keel.GraphInterKeel.experiments.DialogSeed: Default builder
DialogUser - Class in keel.GraphInterKeel.experiments
DialogUser(Frame, String, boolean, UserMethod) - Constructor for class keel.GraphInterKeel.experiments.DialogUser: Buider
DialogUser() - Constructor for class keel.GraphInterKeel.experiments.DialogUser: Default builder
DIBD - Class in keel.Algorithms.Discretizers.DIBD: This class implements the DIBD
DIBD() - Constructor for class keel.Algorithms.Discretizers.DIBD.DIBD: Constructor of the class
die(PrototypeSet, double[]) - Method in class keel.Algorithms.Instance_Generation.ENPC.ENPCGenerator: Die operator.
Dietterich5x2cvC - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Classification Dietterich 5x2cv Stat-test identifier.
Dietterich5x2cvR - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Regression Dietterich 5x2cv Stat-test identifier.
Diferencia(int[], int[]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets
Diferencias(int[][], int[][]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets
diff(int, int, int) - Method in class keel.Algorithms.Preprocess.Feature_Selection.Datos: data must be discretized. it is used by the RELIEF method. returns 1 if the feature value passed as argument is equal in both instances (also passed as arguments),0 in other case.
differenceAtOne(Cromosoma) - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Cromosoma: Test if two chromosome differ in only one gene
differenceAtOne(Cromosoma) - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma: Test if two chromosome differ in only one gene
differenceAtOne(Cromosoma) - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma: Test if two chromosome differ in only one gene
differenceAtOne(Cromosoma) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma: Test if two chromosome differ in only one gene
differenceAtOne(Cromosoma) - Method in class keel.Algorithms.Instance_Selection.CHC.Cromosoma: Test if two chromosome differ in only one gene
differenceAtOne(Cromosoma) - Method in class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Test if two chromosome differ in only one gene.
differenceAtOne(Cromosoma) - Method in class keel.Algorithms.Instance_Selection.GGA.Cromosoma: Test if two chromosome differ in only one gene
differenceAtOne(Cromosoma) - Method in class keel.Algorithms.Instance_Selection.PBIL.Cromosoma: Test if two chromosome differ in only one gene
differenceAtOne(Cromosoma) - Method in class keel.Algorithms.Instance_Selection.SGA.Cromosoma: Test if two chromosome differ in only one gene
differenceAtOne(Cromosoma) - Method in class keel.Algorithms.Instance_Selection.SSMA.Cromosoma: Test if two chromosome differ in only one gene
differenceAtOne(Cromosoma) - Method in class keel.Algorithms.Instance_Selection.ZhangTS.Cromosoma: Function that informs about if a cromosome is different only in a bit, and obtains the position of this bit.
differenceAtOne(Cromosoma) - Method in class keel.Algorithms.Preprocess.Instance_Selection.CHC.Cromosoma: Test if two chromosome differ in only one gene
differenceAtOne(Cromosoma) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Test if two chromosome differ in only one gene.
differenceAtOne(Cromosoma) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GGA.Cromosoma: Test if two chromosome differ in only one gene
differenceAtOne(Cromosoma) - Method in class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Cromosoma: Test if two chromosome differ in only one gene
differenceAtOne(Cromosoma) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SGA.Cromosoma: Test if two chromosome differ in only one gene
differenceAtOne(Cromosoma) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma: Test if two chromosome differ in only one gene
differenceAtOne(Cromosoma) - Method in class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.Cromosoma: Function that informs about if a cromosome is different only in a bit, and obtains the position of this bit.
differentClass(int, int, double[][], double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.KNN: Returns the number of neighbours of the example given that differ in the class value.
DifToKeel - Class in keel.Algorithms.Preprocess.Converter: DifToKeel This class extends from the Importer class.
DifToKeel(String) - Constructor for class keel.Algorithms.Preprocess.Converter.DifToKeel: DifToKeel class Constructor.
Difuso - Class in keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted: Title: Difuso Description: Contains the definition of a fuzzy value Copyright: Copyright (c) 2009 Company: KEEL
Difuso() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Difuso: Default constructor
Difuso - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec: Title: Description: Copyright: Copyright (c) 2007 Company:
Difuso() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Difuso
Difuso - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF: Title: Description: Copyright: Copyright (c) 2007 Company:
Difuso() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Difuso
Difuso - Class in keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM: Title: Description: Copyright: Copyright (c) 2007 Company:
Difuso() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Difuso
Difuso - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs: Class which defines the fuzzy number
Difuso() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Difuso
Difuso(double, double, double, double, double, double) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Difuso: Constructor of the class
Difuso - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs: Class which defines the fuzzy number
Difuso() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Difuso
Difuso(double, double, double, double, double, double) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Difuso: Constructor of the class
Difuso - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs: Class which defines the fuzzy number
Difuso() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Difuso
Difuso(double, double, double, double, double, double) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Difuso: Constructor of the class
Difuso - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs: Class which defines the fuzzy number
Difuso() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Difuso
Difuso(double, double, double, double, double, double) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Difuso: Constructor of the class
Difuso - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs: Class which defines the fuzzy number
Difuso() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Difuso
Difuso(double, double, double, double, double, double) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Difuso: Constructor of the class
Difuso - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs: Class which defines the fuzzy number
Difuso() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Difuso
Difuso(double, double, double, double, double, double) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Difuso: Constructor of the class
Difuso - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs: Class which defines the fuzzy number
Difuso() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Difuso
Difuso(double, double, double, double, double, double) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Difuso: Constructor of the class
Difuso - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs: Class which defines the fuzzy number
Difuso() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Difuso
Difuso(double, double, double, double, double, double) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Difuso: Constructor of the class
DIGIT - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
DIGIT - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for a digit.
DIGIT - Static variable in interface keel.Dataset.DataParserConstants
digits - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.ExponentialFormat: Number of digits of the base.
dim - Variable in class keel.Algorithms.PSO_Learning.CPSO.Particle
dim - Variable in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
dim - Variable in class keel.Algorithms.PSO_Learning.PSOLDA.Particle
dim - Variable in class keel.Algorithms.PSO_Learning.REPSO.Particle
dimensions() - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Hyper
DinamicDataset - Class in keel.GraphInterKeel.experiments
DinamicDataset() - Constructor for class keel.GraphInterKeel.experiments.DinamicDataset: Builder
DinamicDataset(Experiments) - Constructor for class keel.GraphInterKeel.experiments.DinamicDataset: Builder
DinamicParameter - Class in keel.GraphInterKeel.experiments
DinamicParameter() - Constructor for class keel.GraphInterKeel.experiments.DinamicParameter
dinDatasets - Variable in class keel.GraphInterKeel.experiments.Experiments
dinDatasetsPanel - Variable in class keel.GraphInterKeel.experiments.Experiments
dinDatasetsScrollPane - Variable in class keel.GraphInterKeel.experiments.Experiments
DIR_NOT_DEF - Static variable in class keel.Algorithms.Rule_Learning.Swap1.Attribute: Label to identify attributes that hasn't been defined neither as input or output
DIR_NOT_DEF - Static variable in class keel.Dataset.Attribute: Label to identify attributes that hasn't been defined neither as input or output
disable_tracing() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
disable_tracing() - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
disable_tracing() - Static method in class keel.Dataset.DataParser
DISABLED - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
DISABLED - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_DefaultC
DISABLED - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_DefaultC
DISCRET - Static variable in class keel.Algorithms.Decision_Trees.C45.Attribute: Discret attribute.
DISCRET - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Attribute: Discret attribute.
DISCRET - Static variable in class keel.Algorithms.Decision_Trees.ID3.Attribute: Discret attribute.
DISCRET - Static variable in class keel.Algorithms.Decision_Trees.SLIQ.Attribute: Discret attribute.
DISCRET - Static variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Discret attribute.
DISCRET - Static variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA_Attribute: Discret attribute.
DISCRET - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.MyAttribute: Discret attribute.
DISCRET - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Attribute: Discret attribute.
DISCRET - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Attribute: Discret attribute.
DISCRET - Static variable in class keel.Algorithms.Rule_Learning.ART.Attribute: Discret attribute.
DISCRET - Static variable in class keel.Algorithms.Rule_Learning.C45Rules.MyAttribute: Discret attribute.
DISCRET - Static variable in class keel.Algorithms.Rule_Learning.C45RulesSA.MyAttribute: Discret attribute.
DISCRET - Static variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Attribute: Discret attribute.
DISCRET - Static variable in class keel.Algorithms.Rule_Learning.PART.MyAttribute: Discret attribute.
DiscreteDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA
DiscreteDataset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.DiscreteDataset: Default constructor
DiscreteDataset(int, myDataset) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.DiscreteDataset: It sets a discrete dataset by setting up its properties
discretiza() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Discretizacion: Makes discretization in database
Discretizacion - Class in keel.Algorithms.Genetic_Rule_Learning.Hider
Discretizacion(Vector) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Discretizacion: Parameter constructor.
Discretizacion(BaseDatos) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Discretizacion: Parameter constructor.
Discretizacion(BaseDatos, Vector) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Discretizacion: Parameter constructor.
DiscretizationManager - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist
DiscretizationManager() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.DiscretizationManager
DiscretizationManager - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS
DiscretizationManager() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.DiscretizationManager
DiscretizationScheme(int[], int, double) - Constructor for class keel.Algorithms.Discretizers.HeterDisc.HeterDisc.DiscretizationScheme: Parameter constructor.
discretize(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: Uniform width discretization
discretize(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Discretizacion en anchura uniforme
discretize(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: Uniform width discretization
discretize(int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: Uniform width discretization
discretize(int, double) - Method in class keel.Algorithms.Discretizers.Basic.Discretizer: Discretizes the given value of the given attribute.
discretize(int, double) - Method in class keel.Algorithms.Discretizers.Chi2_Discretizer.Discretizer: Discretizes the given value of the given attribute.
discretize(int, double) - Method in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Discretizer: Discretizes the given value of the given attribute.
discretize(int, double) - Method in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Discretizer: Discretizes the given value of the given attribute.
discretize(int, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Basic.Discretizer
discretize(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: Uniform width discretization
discretize(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Uniform width discretization
discretize(int, double) - Method in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Discretizer
discretize(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.Rel_RASCO.Rel_RASCOGenerator: It return a discretized prototypeset in 10 equal sized bins placed between the minimum and the maximum values
discretizeAllAttributes() - Method in class keel.Algorithms.Discretizers.MVD.MVD: Computes the cutpoints for each continuous variable
discretizeAllAttributes() - Method in class keel.Algorithms.Discretizers.UCPD.UCPD: It computes the cutpoints for each continuous variable
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.Ameva_Discretizer.AmevaDiscretizer: Selects, for a given attribute, the real values that best discretize the attribute according to the Ameva discretizer
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.Basic.Discretizer: This abstract method creates the cut points of the attribute given using its values for each instances given.
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.Bayesian_Discretizer.BayesianDiscretizer: Selects, for a given attribute, the real values that best discretize the attribute according to the bayesian discretizer
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.CACC.CACC: Returns a vector with the discretized values
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.CADD_Discretizer.CADDDiscretizer
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.CAIM_Discretizer.CAIMDiscretizer
discretizeAttribute(int, int[], Vector<Interval>, double) - Method in class keel.Algorithms.Discretizers.Chi2_Discretizer.Chi2Discretizer
discretizeAttribute(int, int[], Vector<Interval>, double) - Method in class keel.Algorithms.Discretizers.Chi2_Discretizer.Discretizer: This abstract method creates the cut points of the attribute given using its values for each instances given.
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.ChiMerge_Discretizer.ChiMergeDiscretizer
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.Cluster_Analysis.Cluster_Analysis: Returns a vector with the discretized values.
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.DIBD.DIBD: Returns a vector with the discretized values
discretizeAttribute(int, int[], Vector<Interval>, double) - Method in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Discretizer: This abstract method creates the cut points of the attribute given using its values for each instances given.
discretizeAttribute(int, int[], Vector<Interval>, double) - Method in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.ExtendedChi2Discretizer
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.Fayyad_Discretizer.FayyadDiscretizer: It returns a vector with the discretized values.
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.FixedFrequency_Discretizer.FixedFrequencyDiscretizer: It returns a vector with the discretized values
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.FUSINTER.FUSINTER
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.HDD.HDD
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.HellingerBD.HellingerBD: It returns a vector with the discretized values
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.HeterDisc.HeterDisc: Returns a vector with the discretized values
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.Id3_Discretizer.Id3Discretizer
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.IDD.IDD: Returns a vector with the discretized values
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.Khiops.Khiops
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.MantarasDist_Discretizer.MantarasDistDiscretizer: Selects, for a given attribute, the real values that best discretize the attribute according to the Distance-Based discretizer by Mantaras
discretizeAttribute(int, int[], Vector<Interval>, double) - Method in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Discretizer: This abstract method creates the cut points of the attribute given using its values for each instances given.
discretizeAttribute(int, int[], Vector<Interval>, double) - Method in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.ModifiedChi2Discretizer
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.MODL.MODL
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.MVD.MVD: Returns a vector with the discretized values
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.OneR.OneR
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.Proportional_Discretizer.ProportionalDiscretizer: Returns a vector with the discretized values.
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.Random_Discretizer.RandomDiscretizer
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.UCPD.UCPD: It returns a vector with the discretized values
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.UniformFrequency_Discretizer.UniformFrequencyDiscretizer: Returns a vector with the discretized values.
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.UniformWidth_Discretizer.UniformWidthDiscretizer: Returns a vector with the discretized values.
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.USD_Discretizer.USDDiscretizer
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.Zeta_Discretizer.ZetaDiscretizer: Selects, for a given attribute, the real values that best discretize the attribute according to the Zeta based discretizer
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Basic.Discretizer
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.ChiMerge_Discretizer.ChiMergeDiscretizer
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Fayyad_Discretizer.FayyadDiscretizer
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Id3_Discretizer.Id3Discretizer
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.UniformFrequency_Discretizer.UniformFrequencyDiscretizer
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.UniformWidth_Discretizer.UniformWidthDiscretizer
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.USD_Discretizer.USDDiscretizer
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Discretizer
discretizeAttribute(int, int[], int, int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.UniformFrequencyDiscretizer: Returns a vector with the discretized values.
discretizeAttributePreliminary(int, int[], Vector<Interval>) - Method in class keel.Algorithms.Discretizers.Chi2_Discretizer.Chi2Discretizer
discretizeAttributePreliminary(int, int[], Vector<Interval>) - Method in class keel.Algorithms.Discretizers.Chi2_Discretizer.Discretizer: This abstract method creates a preliminary cut points of the attribute given using its values for each instances given without sigma level.
discretizeAttributePreliminary(int, int[], Vector<Interval>) - Method in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Discretizer: This abstract method creates a preliminary cut points of the attribute given using its values for each instances given without sigma level.
discretizeAttributePreliminary(int, int[], Vector<Interval>) - Method in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.ExtendedChi2Discretizer
discretizeAttributePreliminary(int, int[], Vector<Interval>) - Method in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Discretizer: This abstract method creates a preliminary cut points of the attribute given using its values for each instances given without sigma level.
discretizeAttributePreliminary(int, int[], Vector<Interval>) - Method in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.ModifiedChi2Discretizer
Discretizer - Class in keel.Algorithms.Discretizers.Basic: File: Discretizer.java A general framework for Discretizers.
Discretizer() - Constructor for class keel.Algorithms.Discretizers.Basic.Discretizer
Discretizer - Class in keel.Algorithms.Discretizers.Chi2_Discretizer: File: Discretizer.java A general framework for Chi Discretizers.
Discretizer() - Constructor for class keel.Algorithms.Discretizers.Chi2_Discretizer.Discretizer
Discretizer - Class in keel.Algorithms.Discretizers.ExtendedChi2_Discretizer: File: Discretizer.java A general framework for Chi Discretizers.
Discretizer() - Constructor for class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Discretizer
Discretizer - Class in keel.Algorithms.Discretizers.ModifiedChi2_Discretizer: File: Discretizer.java A general framework for Chi Discretizers.
Discretizer() - Constructor for class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Discretizer
Discretizer - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Basic
Discretizer() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Basic.Discretizer
Discretizer - Class in keel.Algorithms.Preprocess.NoiseFilters.PANDA
Discretizer() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Discretizer
discretizer1 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
discretizer1 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
discretizer10 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
discretizer10 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
discretizer2 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
discretizer2 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
discretizer3 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
discretizer3 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
discretizer4 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
discretizer4 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
discretizer5 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
discretizer5 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
discretizer6 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
discretizer6 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
discretizer7 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
discretizer7 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
discretizer8 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
discretizer8 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
discretizer9 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
discretizer9 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
discretizeTMP(int, double) - Method in class keel.Algorithms.Discretizers.Chi2_Discretizer.Discretizer: Discretizes the given value of the given attribute with the temporal cut points.
discretizeTMP(int, double) - Method in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Discretizer: Discretizes the given value of the given attribute with the temporal cut points.
discretizeTMP(int, double) - Method in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Discretizer: Discretizes the given value of the given attribute with the temporal cut points.
discretToBinary() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Transforms the discret attribute into numValues()-1 synthetic binary attributes.
discriminatingFeatures(double[][], int) - Method in class keel.Algorithms.MIL.APR.AbstractAPR
discriminatingFeatures(double[][], int) - Method in class keel.Algorithms.MIL.APR.IteratedDiscrimination.IteratedDiscrimination
displayB() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Debuggage function Compute and display bLow, lUp and so on...
displayStat(int, int) - Method in class keel.Algorithms.SVM.SMO.SMOreg: Debuggage function.
displayURL(String) - Static method in class keel.GraphInterKeel.menu.BrowserControl: Display a file in the system browser.
dist() - Method in class keel.Algorithms.Hyperrectangles.INNER.Pair: Returns the distance between rules
dist - Variable in class keel.Algorithms.Instance_Selection.MNV.ReferenciaMNV: Distance.
dist - Variable in class keel.Algorithms.Preprocess.Instance_Selection.MNV.ReferenciaMNV: Distance.
dist(Instance, Instance) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.EventCovering: Computes the Hamming distance between 2 instances
distance(double[], double[]) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.KNN: Calculates the Euclidean distance between two instances
distance(double[]) - Method in class keel.Algorithms.Hyperrectangles.BNGE.Rule: Computes the distance between a given instance and the rule.
distance(double[], double[]) - Method in class keel.Algorithms.Hyperrectangles.EACH.Hyperrectangle: Calculates the distance between hyperrectangle and the example(parameter)
distance(double[]) - Method in class keel.Algorithms.Hyperrectangles.EACH.HyperrectangleSet: Calculates the two lowest distances of the example to two hyperrectangles
distance(double[]) - Method in class keel.Algorithms.Hyperrectangles.INNER.Rule: Computes the distance between a rule and an instance
distance(double[]) - Method in class keel.Algorithms.Hyperrectangles.RISE.Rule: Computes the distance between a given instance and the rule.
distance(double[], double[]) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.KNN: Calculates the Euclidean distance between two instances
distance(double[], double[]) - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm: Calculates the Euclidean distance between two instances
distance(double[], double[]) - Static method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE: Calculates the Euclidean distance between two instances
Distance - Class in keel.Algorithms.Instance_Generation.utilities: Distance measurer between prototypes.
Distance(Prototype) - Constructor for class keel.Algorithms.Instance_Generation.utilities.Distance: Construct a new Distance object.
distance(Prototype, Prototype) - Static method in class keel.Algorithms.Instance_Generation.utilities.Distance: Compute the Euclidean Distance between two prototypes.
distance(Vector<Vector<fuzzy>>, int) - Static method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.Main
distance(double[], double[]) - Method in class keel.Algorithms.MIL.Nearest_Neighbour.AbstractNearestNeighbour
distance(IIndividual) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividual: Hamming distance
distance(double[], double[]) - Static method in class keel.Algorithms.Preprocess.Basic.KNN: Calculates the Euclidean distance between two instances
distance(Instance, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.fuzzygCenter: Computes the distance between a instances (without previous normalization) and one clusters (i.e. its centroid).
distance(Instance, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.gCenter: Computes the distance between a instances (without previous normalization) and one clusters (i.e. its centroid).
Distance - Class in keel.Algorithms.Preprocess.NoiseFilters.INFFC
Distance(String) - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Distance
distance(Complex, double[], int, int, double) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Calculates the distance betwen one rule and an example/instance
distance(int, double[], double) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Calculates the distance betwen two examples
Distance - Class in keel.Algorithms.Semi_Supervised_Learning.utilities: Distance measurer between prototypes.
Distance(Prototype) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.utilities.Distance: Construct a new Distance object.
distance(Prototype, Prototype) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Distance: Compute the Euclidean Distance between two prototypes.
DistanceBased_best - Class in keel.GraphInterKeel.datacf.partitionData.PartitioningSchemes: This class implements a stratified scheme (equal number of examples of each class in each partition) to partition a dataset
DistanceBased_best(String, int) - Constructor for class keel.GraphInterKeel.datacf.partitionData.PartitioningSchemes.DistanceBased_best: It reads the training set and creates the partitions
distanceEu - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Flag type of distance (true = euclidean, false = HVDM).
distanceEu - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
distanceEu - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
distanceEu - Variable in class keel.Algorithms.Preprocess.Basic.Metodo: True if euclidean distance is used, false if HVDM is used.
DistanceFunction - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
DistanceMatrixByClass - Class in keel.Algorithms.Instance_Generation.MCA
DistanceMatrixByClass(PrototypeSet) - Constructor for class keel.Algorithms.Instance_Generation.MCA.DistanceMatrixByClass
distanceRule(Rule) - Method in class keel.Algorithms.Hyperrectangles.BNGE.Rule: Computes the distance between two rules.
distances(double[]) - Method in class keel.Algorithms.Statistical_Classifiers.Shared.DiscrAnalysis.AD: Computes the distances for the values given.
distanceType - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters: Distance used.
distanceType - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters: Distance used.
distanceWeighting(double[], double[], double[]) - Static method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE: Calculates the Euclidean distance between two instances
distancia(double[], double[]) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.KNN: Calculates the Euclidean distance between two instances
distancia(double[], double[], int[], boolean[], double[], double[], int[], boolean[], boolean) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.KNN: Calculates the HVDM distance between two instances
distancia(double[], double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.KNN: Computes the Euclidean distance between the two examples given.
distancia(GenotypeBoosting) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoosting
distancia(GenotypeBoosting) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoostingMaxMin
distancia(double[], int[], boolean[], double[], int[], boolean[]) - Static method in class keel.Algorithms.Hyperrectangles.EHS_CHC.EHS_CHC
distancia(Hyper, double[], int[], boolean[]) - Static method in class keel.Algorithms.Hyperrectangles.EHS_CHC.EHS_CHC
distancia(double[], double[]) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.KNN
distancia(double[], double[], int[], boolean[], double[], double[], int[], boolean[], boolean) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.KNN
distancia(double[], double[]) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.KNN: Calculates the Euclidean distance between two instances
distancia(double[], double[], int[], boolean[], double[], double[], int[], boolean[], boolean) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.KNN: Calculates the HVDM distance between two instances
distancia - Variable in class keel.Algorithms.Instance_Selection.CPruner.Trio: Distance.
distancia(double[][], double[][], int[][], boolean[][], int[], boolean) - Method in class keel.Algorithms.Instance_Selection.ZhangTS.Cromosoma: Function that calculates the distance between the train set and the cromosome
distancia(double[], double[]) - Static method in class keel.Algorithms.Preprocess.Basic.KNN: Calculates the Euclidean distance between two instances
distancia(double[], double[], int[], boolean[], double[], double[], int[], boolean[], boolean) - Static method in class keel.Algorithms.Preprocess.Basic.KNN: Calculates the HVDM distance between two instances
distancia - Variable in class keel.Algorithms.Preprocess.Instance_Selection.CPruner.Trio: Distance.
distancia(double[][], double[][], int[][], boolean[][], int[], boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.Cromosoma: Function that calculates the distance between the train set and the cromosome
distancia(int, int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Distance
distancia(int, int) - Method in class keel.GraphInterKeel.datacf.partitionData.PartitioningSchemes.DistanceBased_best: Calculates the HVDM distance between two instances
distancia2(double[], double[]) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.KNN: Calculates the unsquared Euclidean distance between two instances
distancia2(double[], double[], int[], boolean[], double[], double[], int[], boolean[], boolean) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.KNN: Calculates the unsquared HVDM distance between two instances
distancia2(double[], double[]) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.KNN
distancia2(double[], double[], int[], boolean[], double[], double[], int[], boolean[], boolean) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.KNN
distancia2(double[], double[]) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.KNN: Calculates the unsquared Euclidean distance between two instances
distancia2(double[], double[], int[], boolean[], double[], double[], int[], boolean[], boolean) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.KNN: Calculates the unsquared HVDM distance between two instances
distancia2(double[], double[]) - Static method in class keel.Algorithms.Preprocess.Basic.KNN: Calculates the unsquared Euclidean distance between two instances
distancia2(double[], double[], int[], boolean[], double[], double[], int[], boolean[], boolean) - Static method in class keel.Algorithms.Preprocess.Basic.KNN: Calculates the unsquared HVDM distance between two instances
distancias(double[]) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.AD: Computes the distances for the values given.
distHamming(Individual, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual: Computes the Hamming distance with the Individual given as a argument.
distHamming(Individual, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual
distinctCount - Variable in class keel.Algorithms.Decision_Trees.M5.M5AttrStats: The number of distinct values
distinctCount - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.AttributeStats: The number of distinct values
DISTINTO - Static variable in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Condition: Tag (NOT EQUAL).
distorsion(Prototype) - Method in class keel.Algorithms.Instance_Generation.VQ.Cluster: Distorsion of the cluster (sum of distances of the prototypes to the center).
distorsion(Pair<Prototype, Prototype>, Pair<Cluster, Cluster>) - Static method in class keel.Algorithms.Instance_Generation.VQ.Cluster: Distorsion of two sets, given two centers.
distribution(double[][], double[][][], int, PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Computes class distribution for an attribute.
distribution(double[][], double[][][], int, PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Computes class distribution for an attribute.
distribution(double[][], double[][][], int, PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Computes class distribution for an attribute.
Distribution2KeyTable - Class in keel.GraphInterKeel.statistical.tests: File: Distribution2KeyTable.java.
Distribution2KeyTable(int, int) - Constructor for class keel.GraphInterKeel.statistical.tests.Distribution2KeyTable: Builder
distributionForInstance(Instance) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Classifier: Predicts the class memberships for a given instance.
distributionForInstance(Instance) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Classify the test instance with the rule learner and provide the class distributions
distributionForInstance(Instance) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.PDFC: Estimates class probabilities for given instance.
distributionForInstance(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Computes class distribution of an instance using the decision tree.
distributionForInstance(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Computes class distribution of an instance using the decision tree.
distributionForInstance(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Computes class distribution of an instance using the decision tree.
distributionForInstance(Instance) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Computes the distribution for a given instance
distributionForInstance(double[]) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Computes the distribution for a given instance
distributionForInstance(Instance) - Method in class keel.Algorithms.SVM.SMO.SMO: Estimates class probabilities for given instance.
Distributions - Class in keel.Algorithms.Decision_Trees.M5: Class implementing some distributions, tests, etc.
Distributions() - Constructor for class keel.Algorithms.Decision_Trees.M5.Distributions
div(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
div(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
div(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
div(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
div(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
div(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
div(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
div(Function, Function) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.Function: the quaotient of two functions
divDif(double[], double[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.Interpolation: finds the divided differences, i.e. the coefrficients i Newton's interpolation polynomial intyerpolating the nodes (xi,yi)
divergeCHC(double, Cromosoma, double) - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Cromosoma: Reinitializes the chromosome by using CHC diverge procedure
divergeCHC(double, Chromosome, double) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.Chromosome: Function that does the CHC diverge
divergeCHC(double, Cromosoma, double) - Method in class keel.Algorithms.Instance_Selection.CHC.Cromosoma: Reinitializes the chromosome by using CHC diverge procedure
divergeCHC(double, Cromosoma, double) - Method in class keel.Algorithms.Instance_Selection.GGA.Cromosoma: Reinitializes the chromosome by using CHC diverge procedure
divergeCHC(double, Cromosoma, double) - Method in class keel.Algorithms.Instance_Selection.PBIL.Cromosoma: Reinitializes the chromosome by using CHC diverge procedure
divergeCHC(double, Cromosoma, double) - Method in class keel.Algorithms.Instance_Selection.SGA.Cromosoma: Reinitializes the chromosome by using CHC diverge procedure
divergeCHC(double, Cromosoma, double) - Method in class keel.Algorithms.Preprocess.Instance_Selection.CHC.Cromosoma: Reinitializes the chromosome by using CHC diverge procedure
divergeCHC(double, Cromosoma, double) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GGA.Cromosoma: Reinitializes the chromosome by using CHC diverge procedure
divergeCHC(double, Cromosoma, double) - Method in class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Cromosoma: Reinitializes the chromosome by using CHC diverge procedure
divergeCHC(double, Cromosoma, double) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SGA.Cromosoma: Reinitializes the chromosome by using CHC diverge procedure
divide() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Divides the nodes into its two children.
divide(double, double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Discretizacion: Divides two number.
dividedBy(DoubleVector) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Divided by another DoubleVector element by element
dividedByEquals(DoubleVector) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Divided by another DoubleVector element by element in place
divideFeaturesRandomly() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: This method divides the PrototypeSet into two different prototypesets where the original attributes sets are randomly partititoned, with similar sizes.
divideFeaturesRandomly(int, int, int[][]) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: This method divides the datasets into X subspaces of dimension ''dimension''.
divideFeaturesRandomly(int[][]) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: This method divides the features based on the indexes parameters
DivideInstancesByClass() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
DivideInstancesByClass() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal
dividir(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.Interval
dividir(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.Interval
dividir(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.Interval
division(double[], double[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule
divnum(fuzzy, float) - Static method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
divnum(fuzzy, float) - Static method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
divnum(fuzzy, float) - Static method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
divnum(fuzzy, float) - Static method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
divnum(fuzzy, float) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
divnum(fuzzy, float) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
divnum(fuzzy, float) - Static method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
DixonReduction - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: This class implements the reduction Interface.
DixonReduction() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.DixonReduction: Constructs an object of the class.
DMEL - Class in keel.Algorithms.Genetic_Rule_Learning.DMEL: Title: Algorithm Description: It contains the implementation of the algorithm Company: KEEL
DMEL() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.DMEL.DMEL: Default constructor
DMEL(parseParameters) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.DMEL.DMEL: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
dnorm(double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Returns the density of the standard normal.
dnorm(double, double, double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Returns the density value of a standard normal.
dnorm(double, DoubleVector, double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Returns the density values of a set of normal distributions with different means.
dnormLog(double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Returns the log-density of the standard normal.
dnormLog(double, double, double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Returns the log-density value of a standard normal.
dnormLog(double, DoubleVector, double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Returns the log-density values of a set of normal distributions with different means.
doActionSetSubsumption() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: This method applies the action set subsumption
doASSubsumption - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It indicates if the subsumption in the action set is required.
DOASSUBSUMPTION - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
doClustering() - Method in class keel.Algorithms.Lazy_Learning.NSC.NSC: The MVC clustering algorithm
doContrast(double[][], String[]) - Static method in class keel.GraphInterKeel.statistical.tests.Contrast: In this method, all possible post hoc statistical test between more than three algorithms results are executed, according to the configuration file
doControl() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Check if evolution is finished.
doElitism() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.geneticAlgorithm
doEpoche(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQTC
doesSubsume(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Returns if the classifier of the class subsumes the classifier passed as a parameter.
doesSubsume(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Returns if the classifier of the class subsumes the classifier passed as a parameter.
doEvaluation(Classifier[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper
doEvaluation(Classifier[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper
doEvRBF_Cl - Class in keel.Algorithms.Neural_Networks.EvRBF_CL: This class allows the building of an Evolutionary Algorithm to generate RBF Neural Networks.
doEvRBF_Cl() - Constructor for class keel.Algorithms.Neural_Networks.EvRBF_CL.doEvRBF_Cl: Does nothing.
doFitnessComputations() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.geneticAlgorithm
doFriedman(double[][], String[]) - Static method in class keel.GraphInterKeel.statistical.tests.Friedman: In this method, all possible post hoc statistical test between more than three algorithms results are executed, according to the configuration file
doFriedmanAligned(double[][], String[]) - Static method in class keel.GraphInterKeel.statistical.tests.Friedman: In this method, all possible post hoc statistical test between more than three algorithms results are executed, according to the configuration file
doGASubsumption - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: It indicates if the GA subsumption is required.
doGASubsumption(Population, Classifier, Classifier, Classifier, Classifier, boolean, boolean, double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.GA: It applies the GA subsumption.
doGASubsumption(Population, Classifier, Classifier, Classifier, Classifier, boolean, boolean, double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.GA: It applies the GA subsumption.
doGASubsumption - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It indicates if the GA subsumption is required.
DOGASUBSUMPTION - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
doGeneration() - Method in class keel.Algorithms.Instance_Selection.CoCoIS.Subpopulation: Performs a new generation of the subpopulation
doGeneration() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Implementation for the Generation task IMPORTANT NOTE: Parametric and structural mutators work directly with the individuals instead of returning a mutated copy of them.
doGeneration() - Method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.Subpopulation: Performs a new generation of the subpopulation
doHeader() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It prints the header of a dataset into KEEL format
doHierarchicalSelection - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
doHierarchicalSelection - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
doInBackground() - Method in class keel.GraphInterKeel.experiments.PartitionCreator: Do partitions
doInit() - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.algorithm.classification.CCRElitistNeuralNetAlgorithm: Create individuals in population, evaluating before start rest of evolution
doInit() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Create individuals in population, evaluating before start rest of evolution
doIterate() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Iteration of the algorithm
doIterate() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: This method run a partition and creates the report when all partitions of the experiment are finished
doIterationReport(NeuralNetAlgorithm<NeuralNetIndividual>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.listener.NeuralNetReporterClas: Outputs the information of an iteration to System.out
doIterationReport(NeuralNetAlgorithm<NeuralNetIndividual>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.listener.NeuralNetReporterRegr: Outputs the information of an iteration to System.out
doIterations(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.geneticAlgorithm
doLearning(Dataset) - Static method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.WrapperManager
doLocalSearch() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
doLocalSearch() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
doLocalSearch() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
Domain() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
Domain() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
Domain(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Domain() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Returns an domain_t object with the domain
Domain() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t: Returns a domain_t object with the definition of the variable's domain
Domain(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Creates a new domain_t object containing the domain of the variable in position "var" of the list
Domain() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
Domain() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
Domain(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
Domain() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
Domain() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
Domain(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
domain - Variable in class keel.GraphInterKeel.experiments.Parameters
domain_t - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV: It contains the methods for handling the domain of the variables
domain_t - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE
domain_t() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Default Constructor
domain_t(int, double, double, boolean, boolean) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Constructor
domain_t(domain_t) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Creates a domain_t object as a copy of "x"
domain_t - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2
domain_t - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0
doMatch(Instance) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
doMatch(Instance) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list
doMatch(Instance) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
doMatch(int[], int, InstanceWrapper) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveRule
doMatch(InstanceWrapper) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
doMatch(InstanceWrapper) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierADI: This function classifies input instances.
doMatch(InstanceWrapper) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierGABIL: This function classifies input instances.
doMatch(InstanceWrapper) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierUBR
doMatch(int[], int, InstanceWrapper) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveRule
doMatch(InstanceWrapper) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
doMatch(InstanceWrapper) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI: This function classifies input instances.
doMatch(InstanceWrapper) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL: This function classifies input instances.
doMatchNominal(int[], int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveAttribute
doMatchNominal(int[], int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveAttribute
doMatchReal(int[], int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveAttribute
doMatchReal(int[], int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveAttribute
doMerge(int[], int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveAttribute
doMerge(int[], int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveRule
doMerge() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierADI
doMerge(int[], int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveAttribute
doMerge(int[], int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveRule
doMerge() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
dominado - Variable in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Individual dominated or not flag.
Dominance - Class in keel.Algorithms.LQD.methods.FGFS_costInstances: File: Dominance.java Obtain the rule with more compatibility between the fitness of two rule expressed by a interval-value.
Dominance() - Constructor for class keel.Algorithms.LQD.methods.FGFS_costInstances.Dominance
Dominance - Class in keel.Algorithms.LQD.methods.FGFS_Minimum_Risk: File: Dominance.java Obtain the rule with more compatibility between the fitness of two rule expressed by a interval-value.
Dominance() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.Dominance
Dominance - Class in keel.Algorithms.LQD.methods.FGFS_Original: File: Dominance.java Obtain the rule with more compatibility between the fitness of two rule expressed by a interval-value.
Dominance() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Original.Dominance
Dominance - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight: File: Dominance.java Obtain the rule with more compatibility between the fitness of two rule expressed by a interval-value.
Dominance() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.Dominance
Dominance - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty: File: Dominance.java Obtain the rule with more compatibility between the fitness of two rule expressed by a interval-value.
Dominance() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.Dominance
dominate(Individual) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Calculates if "this" individual dominates "other" NOTE: this function can not be used before the "original support" measures (if used) is computed
dominated(Individual) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Computes if this individual is dominated by other NOTE: this function can not be used before the "original support" measures (if used) is computed
doMultiple(double[][], String[]) - Static method in class keel.GraphInterKeel.statistical.tests.Multiple: In this method, all possible post hoc statistical test between more than three algorithms results are executed, according to the configuration file
doMutation() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
doMutation() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierADI
doMutation() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierGABIL
doMutation() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierUBR
doMutation(Classifier[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.GA
doMutation() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
doMutation() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
doMutation() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
doMutation(Classifier[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.GA
Done() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
Done() - Static method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
Done() - Static method in class keel.Dataset.SimpleCharStream
dontCareSymbol - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Represents the don't care symbol for problem that use a character representation or a mixed representation.
dontCareSymbol - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Represents the don't care symbol for problem that use a character representation or a mixed representation.
doOneReductionExecution(Environment) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.XCS: Runs a reduction of the population and returns the reduced population.
doOneSingleStepExploit(double[], int, int, int[], boolean, int, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.UCS: Performs one single step exploit.
doOneSingleStepExploit(Environment, double[], int, int[], double[], boolean, int, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.XCS: Performs one single step exploit.
doOneSingleStepExplore(double[], int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.UCS: Performs one explore iteration.
doOneSingleStepExplore(double[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.XCS: Performs one explore iteration.
doOneTestExperiment(Environment, int, int[], boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.UCS: Performs a test experiment.
doOneTestExperiment(Environment, int, int[], boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.XCS: Performs a test experiment.
doOneTrainExperiment(int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.UCS: Runs one train UCS experiment.
doOneTrainExperiment(int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.XCS: Runs one train XCS experiment.
doParse(String) - Static method in class keel.Algorithms.Discretizers.Basic.ParserParameters: Creates a new instance of ParserParameters
doParse(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters: It parses the parameters of the algorithm
doParse(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ParserParameters: Creates a new instance of ParserParameters
doParse(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Basic.ParserParameters: Creates a new instance of ParserParameters
doParse(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ParserParameters: Creates a new instance of ParserParameters
doParse(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
doParse(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser: Main function It receives as parameters: .
doParse(String) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters: It parses the parameters of the algorithm
doParse(String) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Parameters: It parses the parameters of the algorithm
doParse(String) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters: It parses the parameters of the algorithm
doParse(String) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters: It parses the parameters of the algorithm
doParse(String) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Parameters: It parses the parameters of the algorithm
doParse(String) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters: It parses the parameters of the algorithm
doParse(String) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Parameters: It parses the parameters of the algorithm
doParse(String) - Static method in class keel.Algorithms.Rule_Learning.Swap1.Parameters: It parses the parameters of the algorithm
doQuade(double[][], String[]) - Static method in class keel.GraphInterKeel.statistical.tests.Friedman: In this method, all possible post hoc statistical test between more than three algorithms results are executed, according to the configuration file
doRbfn - Class in keel.Algorithms.Neural_Networks.RBFN: This class allows the building of RBF neural networks with a decremental algorithm.
doRbfn() - Constructor for class keel.Algorithms.Neural_Networks.RBFN.doRbfn: Does nothing.
doRbfnCl - Class in keel.Algorithms.Neural_Networks.RBFN_CL: This class allows the building of RBF neural networks with a decremental algorithm This class contains a MAIN function that reads parameters, builds the net, and produces the results yielded by the net when is applied to the test data set.
doRbfnCl() - Constructor for class keel.Algorithms.Neural_Networks.RBFN_CL.doRbfnCl: Does nothing.
doRbfnDec - Class in keel.Algorithms.Neural_Networks.RBFN_decremental: This class allows the building of RBF neural networks with a decremental algorithm.
doRbfnDec() - Constructor for class keel.Algorithms.Neural_Networks.RBFN_decremental.doRbfnDec: Does nothing.
doRbfnDecCl - Class in keel.Algorithms.Neural_Networks.RBFN_decremental_CL: This class allows the building of RBF neural networks with a decremental algorithm This class contains a MAIN function that reads parameters, builds the net, and produces the results yielded by the net when is applied to the test data set.
doRbfnDecCl() - Constructor for class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.doRbfnDecCl: Does nothing.
doRbfnInc - Class in keel.Algorithms.Neural_Networks.RBFN_incremental: This class allows the building of RBF neural networks with a decremental algorithm This class contains a MAIN function that reads parameters, builds the net, and produces the results yielded by the net when is applied to the test data set.
doRbfnInc() - Constructor for class keel.Algorithms.Neural_Networks.RBFN_incremental.doRbfnInc: Does nothing.
doRbfnIncCl - Class in keel.Algorithms.Neural_Networks.RBFN_incremental_CL: This class allows the building of RBF neural networks with a decremental algorithm This class contains a MAIN function that reads parameters, builds the net, and produces the results yielded by the net when is applied to the test data set.
doRbfnIncCl() - Constructor for class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.doRbfnIncCl: Does nothing.
doReduction - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Indicates if reduction has to be made
DOREDUCTION - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
doReinitialize(int[], int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveAttribute
doReinitialize(int[], int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveRule
doReinitialize() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierADI
doReinitialize(int[], int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveAttribute
doReinitialize(int[], int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveRule
doReinitialize() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
doReplacement() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Implementation for the Replacement task
doRuleCleaning - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
doRuleDeletion - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
doRuleDeletion - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
doRuleGeneralizing - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
doRuleSplitting - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
doScreenStatistics(int, int, int[], int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.UCS: It computes the statistics
doScreenStatistics(int, int, double[], int[], int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.XCS: It computes the statistics
doSelection() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Implementation for the Selection task
doSort() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Sort individuals in bset
doSpecialStage(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
doSpecialStage(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list
doSpecialStage(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
doSpecialStage(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
doSpecialStage(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierADI
doSpecialStage(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierGABIL
doSpecialStage(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierUBR
doSpecialStage(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
doSpecialStage(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
doSpecialStage(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
doSpecialStages(Classifier[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.GA
doSpecialStages(Classifier[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.GA
doSpecify - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It indicates if the specify operator has to be applied.
DOSPECIFY - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
doSplit(int[], int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveAttribute
doSplit(int[], int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveRule
doSplit() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierADI
doSplit(int[], int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveAttribute
doSplit(int[], int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveRule
doSplit() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
doStatistics - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: It indicates if the user wants to get some statistics in a file.
doStatistics - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It indicates if the user wants to get some statistics in a file.
DOSTATISTICS - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
doSVMSelection() - Method in class keel.Algorithms.Instance_Generation.HYB.SVMSEL: Executes the SVM prototype selection.
dot(double[], double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
dot(double[], double[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
doTest - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: This parameter indicates if test has to be made between the train execution.
doTest - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: This parameter indicates if test has to be made between the train execution.
DOTEST - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
doTests() - Method in class keel.Algorithms.SVM.SMO.core.Check: Begin the tests, reporting results to System.out
doTestScreenStatistics(int, int[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.UCS: It does test screen statistics
doTestScreenStatistics(int, int[], double[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.XCS: It does test screen statistics
doTournamentSelection(Classifier[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.GA: Does Tournament Selection without replacement.
doTournamentSelection(Classifier[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.GA: Does Tournament Selection without replacement.
dotProd(Instance, Instance) - Method in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: Calculates a dot product between two instances
doTrain - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: It indicates if a train has to be made
doTrain - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It indicates if a train has to be made
DOUBLE - Static variable in class keel.Algorithms.Instance_Generation.Basic.Prototype: Double type of attributes.
DOUBLE - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Double type of attributes.
Double_t - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV: Double wrapper.
Double_t - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2: Double wrapper.
Double_t - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0: Double wrapper.
DoubleFunc - Interface in keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat: Double Function interface
doubleToprototypeSet(double[][], int[]) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: PrototypeSet from double values.
doubleToprototypeSet(double[][], int) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: PrototypeSet from double values.
doubleToprototypeSet(double[][], int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: PrototypeSet to double.
doubleToString(double, int) - Static method in class keel.Algorithms.Decision_Trees.C45.Cut: Function to round a double and converts it into String.
doubleToString(double, int, int) - Static method in class keel.Algorithms.Decision_Trees.C45.Cut: Function to round a double and converts it into String.
doubleToString(double, int) - Static method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to round a double and converts it into String.
doubleToString(double, int, int) - Static method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to round a double and converts it into String.
doubleToString(double, int) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Rounds a double and converts it into String.
doubleToString(double, int, int) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Rounds a double and converts it into a formatted decimal-justified String.
doubleToString(double, int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Rounds a double and converts it into String.
doubleToString(double, int, int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Rounds a double and converts it into a formatted decimal-justified String.
doubleToString(double, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Rounds a double and converts it into String.
doubleToString(double, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Rounds a double and converts it into a formatted decimal-justified String.
doubleToString(double, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Function to round a double and converts it into String.
doubleToString(double, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Function to round a double and converts it into String.
doubleToString(double, int) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Function to round a double and converts it into String.
doubleToString(double, int, int) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Function to round a double and converts it into String.
doubleToString(double, int) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to round a double and converts it into String.
doubleToString(double, int, int) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to round a double and converts it into String.
doubleToString(double, int) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Rounds a double and converts it into String.
doubleToString(double, int, int) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Rounds a double and converts it into a formatted decimal-justified String.
doubleToString(double, int) - Static method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Function to round a double and converts it into String.
doubleToString(double, int, int) - Static method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Function to round a double and converts it into String.
doubleToString(double, int) - Static method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Function to round a double and converts it into String.
doubleToString(double, int, int) - Static method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Function to round a double and converts it into String.
doubleToString(double, int) - Static method in class keel.Algorithms.Rule_Learning.PART.Cut: Function to round a double and converts it into String.
doubleToString(double, int, int) - Static method in class keel.Algorithms.Rule_Learning.PART.Cut: Function to round a double and converts it into String.
doubleToString(double, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Rounds a double and converts it into String.
doubleToString(double, int, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Rounds a double and converts it into a formatted decimal-justified String.
doubleToString(double, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Function to round a double and converts it into String.
doubleToString(double, int, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Function to round a double and converts it into String.
doubleToString(double, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Rounds a double and converts it into String.
doubleToString(double, int, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Rounds a double and converts it into a formatted decimal-justified String.
doubleToString(double, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Rounds a double and converts it into String.
doubleToString(double, int, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Rounds a double and converts it into a formatted decimal-justified String.
doubleToString(double, int) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Rounds a double and converts it into String.
doubleToString(double, int, int) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Rounds a double and converts it into a formatted decimal-justified String.
doubleToString(double, int) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Rounds a double and converts it into String.
doubleToString(double, int, int) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Rounds a double and converts it into a formatted decimal-justified String.
doubleToStringF(double, int, int) - Static method in class keel.Algorithms.Decision_Trees.M5.M5: Rounds a double and converts it into a formatted right-justified String.
doubleToStringF(double, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: Rounds a double and converts it into a formatted right-justified String.
doubleToStringG(double, int, int) - Static method in class keel.Algorithms.Decision_Trees.M5.M5: Rounds a double and converts it into a formatted right-justified String.
doubleToStringG(double, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: Rounds a double and converts it into a formatted right-justified String.
DoubleTransposedDataSet - Class in keel.Algorithms.Neural_Networks.NNEP_Common.data: Set of data of a problem (Transposed).
DoubleTransposedDataSet() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Empty constructor
doubleValues - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.CategoricalAttribute: Categories list (internal values)
DoubleVector - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix: A vector specialized on doubles.
DoubleVector() - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Constructs a null vector.
DoubleVector(int) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Constructs an n-vector of zeros.
DoubleVector(int, double) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Constructs a constant n-vector.
DoubleVector(double[]) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Constructs a vector directly from a double array
doUniform(Dataset) - Method in class keel.Algorithms.Hyperrectangles.EACH.EachDataSet: Adapt the examples to the [0,1] interval
doUpdate() - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.algorithm.classification.CCRElitistNeuralNetAlgorithm: Implementation for the Update task
doUpdate() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Implementation for the Update task
doValidation(Itemset, Configuration, String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.WrapperManager
doWilcoxon(double[][], String[]) - Static method in class keel.GraphInterKeel.statistical.tests.Wilcoxon: In this method, all possible pairwise Wilcoxon comparisons are performed
draw(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: It draws the population to a file.
draw(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: It draws the population to a file.
draw(Graphics2D, boolean) - Method in class keel.GraphInterKeel.experiments.Algorithm: Draws this component
draw(Graphics2D, Point, Point, boolean) - Method in class keel.GraphInterKeel.experiments.Arc: Draws this component
draw(Graphics2D, boolean) - Method in class keel.GraphInterKeel.experiments.DataSet: Draws the node
draw(Graphics2D, boolean) - Method in class keel.GraphInterKeel.experiments.Jclec: Draw method
draw(Graphics2D, boolean) - Method in class keel.GraphInterKeel.experiments.Multiplexor
draw(Graphics2D, boolean) - Method in class keel.GraphInterKeel.experiments.Node: Draws the node in a 2D component
draw(Graphics2D, boolean) - Method in class keel.GraphInterKeel.experiments.Test: Drawing component
draw(Graphics2D, boolean) - Method in class keel.GraphInterKeel.experiments.UserMethod: Drawing component
drawPopulation(Population) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Statistic: It draws the population to a file.
drawPopulationToFile(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: It draws the population to a file.
drop(int) - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster: Drop an element from the cluster
DROP1 - Class in keel.Algorithms.Instance_Selection.DROP1: File: DROP1.java The DROP1 algorithm.
DROP1(String) - Constructor for class keel.Algorithms.Instance_Selection.DROP1.DROP1: Builder.
DROP1 - Class in keel.Algorithms.Preprocess.Instance_Selection.DROP1: File: DROP1.java The DROP1 algorithm.
DROP1(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.DROP1.DROP1: Builder.
DROP2 - Class in keel.Algorithms.Instance_Selection.DROP2: File: DROP2.java The DROP2 algorithm.
DROP2(String) - Constructor for class keel.Algorithms.Instance_Selection.DROP2.DROP2: Builder.
DROP2 - Class in keel.Algorithms.Preprocess.Instance_Selection.DROP2: File: DROP2.java The DROP2 algorithm.
DROP2(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.DROP2.DROP2: Builder.
DROP3 - Class in keel.Algorithms.Instance_Selection.DROP3: File: DROP3.java The DROP3 Instance Selection algorithm.
DROP3(String) - Constructor for class keel.Algorithms.Instance_Selection.DROP3.DROP3: Builder.
DROP3 - Class in keel.Algorithms.Preprocess.Instance_Selection.DROP3: File: DROP3.java The DROP3 Instance Selection algorithm.
DROP3(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.DROP3.DROP3: Builder.
DROP3LVQ3 - Class in keel.Algorithms.Instance_Generation.DROP3LVQ3: DROP3LVQ3
DROP3LVQ3(String) - Constructor for class keel.Algorithms.Instance_Generation.DROP3LVQ3.DROP3LVQ3
DROP3PSO - Class in keel.Algorithms.Instance_Generation.DROP3PSO: Hybridization of DROP3 with PSO.
DROP3PSO(String) - Constructor for class keel.Algorithms.Instance_Generation.DROP3PSO.DROP3PSO: Builder.
DROP3SFLSDE - Class in keel.Algorithms.Instance_Generation.DROP3SFLSDE
DROP3SFLSDE(String) - Constructor for class keel.Algorithms.Instance_Generation.DROP3SFLSDE.DROP3SFLSDE
dropAndInsertArc(int, Arc) - Method in class keel.GraphInterKeel.experiments.Graph: Deletes the indexed arc, then insert a new one
dropArc(int) - Method in class keel.GraphInterKeel.experiments.Graph: Deletes an arc from the graph
dropArcLQD(int) - Method in class keel.GraphInterKeel.experiments.Graph: Drop arc
dropByContent(int) - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster: Drop an element from the cluster
dropiRule(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.RuleSet
dropNode(int) - Method in class keel.GraphInterKeel.experiments.Graph: Deletes a node from the graph
dropNodeLQD_move(int) - Method in class keel.GraphInterKeel.experiments.Graph: Drop node
dsc - Variable in class keel.GraphInterKeel.experiments.Node
DSMAlgorithm - Class in keel.Algorithms.Instance_Generation.DSM: DSM algorithm calling.
DSMAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.DSM.DSMAlgorithm
DSMGenerator - Class in keel.Algorithms.Instance_Generation.DSM: Decision Surface Mapping algorithm (DSMGenerator) for prototype reduction.
DSMGenerator(PrototypeSet, int, int, double) - Constructor for class keel.Algorithms.Instance_Generation.DSM.DSMGenerator: DSMGenerator constructor.
DSMGenerator(PrototypeSet, int, double, double) - Constructor for class keel.Algorithms.Instance_Generation.DSM.DSMGenerator: DSMGenerator constructor.
DSMGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.DSM.DSMGenerator: DSMGenerator constructor.
dSquared(Prototype, Prototype) - Static method in class keel.Algorithms.Instance_Generation.utilities.Distance: Compute the Squared euclidean distance between two prototypes.
dSquared(Prototype, Prototype) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Distance: Compute the Squared euclidean distance between two prototypes.
DT_GA - Class in keel.Algorithms.Decision_Trees.DT_GA: Description: It contains the implementation of the algorithm DT_GA.
DT_GA() - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.DT_GA: Default constructor
DT_GA(parseParameters) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.DT_GA: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
DT_oblicuo - Class in keel.Algorithms.Decision_Trees.DT_oblicuo: It contains the implementation of the algorithm Company: KEEL
DT_oblicuo() - Constructor for class keel.Algorithms.Decision_Trees.DT_oblicuo.DT_oblicuo: Default constructor
DT_oblicuo(parseParameters) - Constructor for class keel.Algorithms.Decision_Trees.DT_oblicuo.DT_oblicuo: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
dTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: Returns the tip text for this property
dumpPhenotype() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
dumpPhenotype() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_aggregated
dumpPhenotype() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list
dumpPhenotype() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
dumpPhenotype(int[], int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveAttribute
dumpPhenotype(int[], int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveRule
dumpPhenotype(int[], int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveAttribute
dumpPhenotype(int[], int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveRule
dumpStats() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.timersManagement
dumpStats(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PerformanceAgent: This function dumps the test statistics
dumpStats(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PerformanceAgent: This function dumps the test statistics
duplica() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoosting
duplica() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoostingMaxMin
duplicate(boolean[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.FUN: The protected method duplicate a set of boolean values
duplicate(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.FUN: The protected method duplicate a set of double values
duplicate(double[]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.ConjGradQUAD: Creates and returns a copy of vector x
duplicate(double[][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.ConjGradQUAD: Creates and returns a copy of vector x
duplicate(double[][][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.ConjGradQUAD: Creates and returns a copy of vector x
duration - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Time to generate P-tree.
dVectorCopy(double[], int) - Static method in class keel.Algorithms.Decision_Trees.M5.Function: Copy the first n elements of the array a.
dVectorCopy(double[], int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Function

E

e1 - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Pair: First element of the pair.
e2 - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Pair: Second element of the pair.
EACH - Class in keel.Algorithms.Hyperrectangles.EACH: Main methods of the EACHsd algorithm
EACH() - Constructor for class keel.Algorithms.Hyperrectangles.EACH.EACH: Default constructor.
EACH(String, String, String, String, String, long, double, int) - Constructor for class keel.Algorithms.Hyperrectangles.EACH.EACH: Constructor
EachDataSet - Class in keel.Algorithms.Hyperrectangles.EACH: Stores a set of data with the form: attribute, attribute, , class
EachDataSet() - Constructor for class keel.Algorithms.Hyperrectangles.EACH.EachDataSet: Constructor.
EachRowEditor - Class in keel.GraphInterKeel.datacf.util
EachRowEditor(JTable) - Constructor for class keel.GraphInterKeel.datacf.util.EachRowEditor: Constructs a EachRowEditor. create default editor
EARMGA - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA: Title: Algorithm Description: It contains the implementation of the EARMGA algorithm Company: KEEL
EARMGA() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.EARMGA: Default constructor
EARMGA(parseParameters) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.EARMGA: It reads the data from the input files and parse all the parameters from the parameters array.
EARMGAProcess - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA: It provides the implementation of the EARMGA algorithm to be run in a process
EARMGAProcess(myDataset, DataB, int, int, int, double, double, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.EARMGAProcess
Eclat - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat: It gathers all the parameters, launches the algorithm, and prints out the results
Eclat() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.Eclat: Default constructor
Eclat(parseParameters) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.Eclat: It reads the data from the input files and parse all the parameters from the parameters array.
EclatProcess - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat: It provides the implementation of the Eclat algorithm to be run in a process
EclatProcess(myDataset, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.EclatProcess: It creates a new process for the algorithm by setting up its parameters
Ecm - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs: The class that contains the functions to do the mean square error(MSE)
Ecm(int, double[][], double[], int, double[][], double[], int, int, int, double, double[]) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Ecm: Constructor of the class
Ecm - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs: The class that contains the functions to do the mean square error(MSE)
Ecm(int, double[][], double[], int, double[][], double[], int, int, int, double, double[]) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Ecm: Constructor of the class
Ecm - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs: The class that contains the functions to do the mean square error(MSE)
Ecm(int, double[][], double[], int, double[][], double[], int, int, int, double, double[]) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Ecm: Constructor of the class
Ecm - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs: The class that contains the functions to do the mean square error(MSE)
Ecm(int, double[][], double[], int, double[][], double[], int, int, int, double, double[]) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Ecm: Constructor of the class
Ecm - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs: The class that contains the functions to do the mean square error(MSE)
Ecm(int, double[][], double[], int, double[][], double[], int, int, int, double, double[]) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Ecm: Constructor of the class
Ecm - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs: The class that contains the functions to do the mean square error(MSE)
Ecm(int, double[][], double[], int, double[][], double[], int, int, int, double, double[]) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Ecm: Constructor of the class
Ecm - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs: The class that contains the functions to do the mean square error(MSE)
Ecm(int, double[][], double[], int, double[][], double[], int, int, int, double, double[]) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Ecm: Constructor of the class
Ecm - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs: The class that contains the functions to do the mean square error(MSE)
Ecm(int, double[][], double[], int, double[][], double[], int, int, int, double, double[]) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Ecm: Constructor of the class
ECM_tra(double[], double[], char[], int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Ecm: It calculates the mean square error(MSE) of the training data
ECM_tra(double[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Ecm: It calculates the mean square error(MSE) of the training data
ECM_tra(double[], double[], char[], int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Ecm: It calculates the mean square error(MSE) of the training data
ECM_tra(double[], char[], int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Ecm: It calculates the mean square error(MSE) of the training data
ECM_tra(double[], double[], char[], int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Ecm: It calculates the mean square error(MSE) of the training data
ECM_tra(double[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Ecm: It calculates the mean square error(MSE)
ECM_tra(double[], double[], int[], int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Ecm: It calculates the mean square error(MSE) of the training data
ECM_tra(double[], char[], int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Ecm: It calculates the mean square error(MSE) of the training data
ECM_tst(double[], double[], char[], int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Ecm: It calculates the mean square error(MSE) of the training data
ECM_tst(double[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Ecm: It calculates the mean square error(MSE) of the test data
ECM_tst(double[], double[], char[], int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Ecm: It calculates the mean square error(MSE) of the test data
ECM_tst(double[], char[], int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Ecm: It calculates the mean square error(MSE) of the test data
ECM_tst(double[], double[], char[], int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Ecm: It calculates the mean square error(MSE) of the test data
ECM_tst(double[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Ecm: It calculates the mean square error(MSE) of the test data
ECM_tst(double[], double[], int[], int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Ecm: It calculates the mean square error(MSE) of the test data
ECM_tst(double[], char[], int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Ecm: It calculates the mean square error(MSE) of the training data
Ecom(int, int) - Method in class keel.Algorithms.Discretizers.DIBD.DIBD: It computes the compound distributional index needed to compute the compound decrement of a cutpoint
Ectra() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Chc
Ectst() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Chc
Ed(int, int) - Method in class keel.Algorithms.Discretizers.DIBD.DIBD: It computes the decision distributional index needed to compute the compound distributional index (Ecom)
edit3RS() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff: all new instances are in upper approximation and they are in boundary to because in this case the list have not lower approximation instances
EditDataPanel - Class in keel.GraphInterKeel.datacf.editData
EditDataPanel() - Constructor for class keel.GraphInterKeel.datacf.editData.EditDataPanel: Constructor that initializes the panel
editDataPanel - Variable in class keel.GraphInterKeel.datacf.editData.EditVariablePanel
editor - Variable in class keel.GraphInterKeel.datacf.util.EachRowEditor: TableCell editors
editors - Variable in class keel.GraphInterKeel.datacf.util.EachRowEditor: Editors
EditPanel - Class in keel.GraphInterKeel.datacf.editData
EditPanel() - Constructor for class keel.GraphInterKeel.datacf.editData.EditPanel: Constructor that initializes the panel
editRule(int[], matchProfileAgent, ClassifierGABIL.splittedRule) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
edits - Variable in class keel.GraphInterKeel.experiments.DinamicDataset
editVariablePanel - Variable in class keel.GraphInterKeel.datacf.editData.EditDataPanel: Edit Variable Panel
EditVariablePanel - Class in keel.GraphInterKeel.datacf.editData
EditVariablePanel() - Constructor for class keel.GraphInterKeel.datacf.editData.EditVariablePanel: Constructor that initializes the panel
EducationalDiscretizerReport - Class in keel.GraphInterKeel.experiments
EducationalDiscretizerReport(ArrayList<Element>, int) - Constructor for class keel.GraphInterKeel.experiments.EducationalDiscretizerReport: Constructor
EducationalFSReport - Class in keel.GraphInterKeel.experiments
EducationalFSReport(ArrayList<Element>, int) - Constructor for class keel.GraphInterKeel.experiments.EducationalFSReport: Constructor
EducationalISReport - Class in keel.GraphInterKeel.experiments
EducationalISReport(ArrayList<Element>, int) - Constructor for class keel.GraphInterKeel.experiments.EducationalISReport: Constructor
EducationalMethodReport - Class in keel.GraphInterKeel.experiments
EducationalMethodReport(ArrayList<Element>, int) - Constructor for class keel.GraphInterKeel.experiments.EducationalMethodReport: Constructor
EducationalPartitionsRun - Class in keel.GraphInterKeel.experiments
EducationalPartitionsRun(EducationalRunKeelTxt, IEducationalRunkeelListener<EducationalRunKeelTxt>) - Constructor for class keel.GraphInterKeel.experiments.EducationalPartitionsRun
EducationalReport - Class in keel.GraphInterKeel.experiments
EducationalReport(ArrayList<Element>, int) - Constructor for class keel.GraphInterKeel.experiments.EducationalReport: Constructor
EducationalRun - Class in keel.GraphInterKeel.experiments
EducationalRun() - Constructor for class keel.GraphInterKeel.experiments.EducationalRun: Constructor Creates new form EducationalRun
EducationalRun(IEducationalRunListener<EducationalRun>) - Constructor for class keel.GraphInterKeel.experiments.EducationalRun: Constructor Interaction with Experiments.java
educationalRun - Variable in class keel.GraphInterKeel.experiments.EducationalRunEvent
EducationalRunEvent<A extends EducationalRun> - Class in keel.GraphInterKeel.experiments
EducationalRunEvent(A) - Constructor for class keel.GraphInterKeel.experiments.EducationalRunEvent
EducationalRunEvent(A, Exception) - Constructor for class keel.GraphInterKeel.experiments.EducationalRunEvent
EducationalRunkeelEvent<A extends EducationalRunKeelTxt> - Class in keel.GraphInterKeel.experiments
EducationalRunkeelEvent(A) - Constructor for class keel.GraphInterKeel.experiments.EducationalRunkeelEvent: Constructor
EducationalRunkeelEvent(A, Exception) - Constructor for class keel.GraphInterKeel.experiments.EducationalRunkeelEvent: Constructor
EducationalRunKeelTxt - Class in keel.RunKeelTxtDocente: This class run a iteration of a experiment when the method doIterate() is invoqued. successive invocations finalize the experiment.
EducationalRunKeelTxt(int) - Constructor for class keel.RunKeelTxtDocente.EducationalRunKeelTxt: Constructor.
EFS_RPS - Class in keel.Algorithms.RST_Learning.EFS_RPS: File: EFS_RPS.java The EFS_RPS Algorithm.
EFS_RPS(String) - Constructor for class keel.Algorithms.RST_Learning.EFS_RPS.EFS_RPS: Main builder.
EHS_CHC - Class in keel.Algorithms.Hyperrectangles.EHS_CHC: EHS_CHC algorithm.
EHS_CHC(String) - Constructor for class keel.Algorithms.Hyperrectangles.EHS_CHC.EHS_CHC
eig() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Eigenvalue Decomposition
eigenValue - Variable in class keel.Algorithms.Preprocess.Missing_Values.EM.EVpair: the eigenvalue
EigenvalueDecomposition - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix: Eigenvalues and eigenvectors of a real matrix.
eigenvalueDecomposition(double[][], double[]) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Performs Eigenvalue Decomposition using Householder QR Factorization Matrix must be symmetrical.
EigenvalueDecomposition(Matrix) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.EigenvalueDecomposition: Check for symmetry, then construct the eigenvalue decomposition
EIS_RFS - Class in keel.Algorithms.RST_Learning.EIS_RFS: File: EIS_RFS.java The EIS_RFS Algorithm.
EIS_RFS(String) - Constructor for class keel.Algorithms.RST_Learning.EIS_RFS.EIS_RFS: Main builder.
ej_cubiertos - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Population: Covered examples of the population
ejecutar() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.ENN: Executes the algorithm
ejecutar(int[], int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.KNN: Executes the KNN algorithm on the training dataset to obtain the outliers during the classification.
ejecutar() - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.EHS_CHC
ejecutar() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.SMOTE.MSMOTE
ejecutar() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.SMOTE.SMOTE
ejecutar() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.SPIDER.SPIDER
ejecutar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.SMOTE_RSB
ejecutar() - Method in class keel.Algorithms.Instance_Generation.Depur.Depur: Execute the algorithm Depur.
ejecutar() - Method in class keel.Algorithms.Instance_Generation.DROP3LVQ3.DROP3LVQ3
ejecutar() - Method in class keel.Algorithms.Instance_Generation.DROP3PSO.DROP3PSO: Executes the algorithms combining them.
ejecutar() - Method in class keel.Algorithms.Instance_Generation.DROP3SFLSDE.DROP3SFLSDE
ejecutar() - Method in class keel.Algorithms.Instance_Generation.ICFLVQ3.ICFLVQ3
ejecutar() - Method in class keel.Algorithms.Instance_Generation.ICFPSO.ICFPSO: Executes the algorithms combining them.
ejecutar() - Method in class keel.Algorithms.Instance_Generation.ICFSFLSDE.ICFSFLSDE
ejecutar() - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.SSMALVQ3
ejecutar() - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.SSMAPSO: Executes the algorithms combining them.
ejecutar() - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE
ejecutar() - Method in class keel.Algorithms.Instance_Selection.AllKNN.AllKNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.BSE.BSE: execute the method.
ejecutar() - Method in class keel.Algorithms.Instance_Selection.CCIS.CCIS: execute the method.
ejecutar() - Method in class keel.Algorithms.Instance_Selection.CHC.CHC: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.CNN.CNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.CoCoIS.CoCoIS: Executes CCIS
ejecutar() - Method in class keel.Algorithms.Instance_Selection.CPruner.CPruner: Executes the CPruner algorithm.
ejecutar() - Method in class keel.Algorithms.Instance_Selection.DROP1.DROP1: Executes the DROP1 algorithm.
ejecutar() - Method in class keel.Algorithms.Instance_Selection.DROP2.DROP2: Executes the DROP2 algorithm.
ejecutar() - Method in class keel.Algorithms.Instance_Selection.DROP3.DROP3: Executes the DROP3 algorithm.
ejecutar() - Method in class keel.Algorithms.Instance_Selection.ENN.ENN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.ENNRS.ENNRS: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.ENNTh.ENNTh: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.ENRBF.ENRBF: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.Explore.Explore: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.FCNN.FCNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.GA_MSE_CC_FSM: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.GCNN.GCNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.GG.GG: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.GGA.GGA: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.HMNEI.HMNEI: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.IB2.IB2: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.IB3.IB3: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.ICF.ICF: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.IGA.IGA: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.IKNN.IKNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.MCNN.MCNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.MCS.MCS: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.MENN.MENN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.MNV.MNV: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.ModelCS.ModelCS: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.MSS.MSS: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.Multiedit.Multiedit: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.NCNEdit.NCNEdit: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.NRMCS.NRMCS: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.PBIL.PBIL: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.POP.POP: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.PSC.PSC: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.PSRCG.PSRCG: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.Reconsistent.Reconsistent: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.RENN.RENN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.RMHC.RMHC: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.RNG.RNG: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.RNN.RNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.SGA.SGA: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.Shrink.Shrink: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.SNN.SNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.SSMA.SSMA: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.SVBPS.SVBPS: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.TCNN.TCNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.TRKNN.TRKNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.VSM.VSM: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Instance_Selection.ZhangTS.ZhangTS: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Neural_Networks.LVQ.LVQ: Executes the LVQ algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CHC.filter.CHCBinaryIncon: method interface for CHC algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CHC.wrapper.CHCBinaryLVO: method inteface for CHC algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_BinCod.filter.GGABinaryIncon: method inteface for CHC algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_BinCod.wrapper.GGABinaryLVO: method inteface for CHC algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_IntCod.filter.GGAIntegerIncon: method inteface for CHC algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_IntCod.wrapper.GGAIntegerLVO: method inteface for CHC algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_BinCod.filter.SSGABinaryIncon: method inteface for CHC algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_BinCod.wrapper.SSGABinaryLVO: method inteface for CHC algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_IntCod.filter.SSGAIntegerIncon: method inteface for CHC algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_IntCod.wrapper.SSGAIntegerLVO: method inteface for CHC algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_IEP.ABB: Method interface for Automatic Branch and Bound
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_LIU.ABB: Method interface for Automatic Branch and Bound
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_MI.ABB: Method interface for Automatic Branch and Bound
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.BACKWARD.filter.BackwardIncon: method interface for backward algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.BACKWARD.wrapper.BackwardLVO: method interface for backward algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FOCUS.FocusIncon: Method interface for FOCUS algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FORWARD.filter.ForwardIncon: method interface for forward algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FORWARD.wrapper.ForwardLVO: method interface for forward algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_IEP.Full: Method interface for Full Exploration algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_LIU.Full: Method interface for Full Exploration algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_MI.Full: Method interface for Full Exploration algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.filter.im.GraspMI: method interface for GRASP algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.filter.inconsistency.GraspIncon: method interface for GRASP algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.wrapper.GraspLVO: method interface for GRASP algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVF.LVFIncon: Method interface for LVF algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVF_IEP.LVFIncon: Method interface for LVF algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVW.LVWLVO: Method interface for LVF algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.RELIEF.ReliefDiff: Method interface for Relief Algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.RELIEF_F.Relieff: Method interface for Relief Algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_IEP_FS.SA: Method interface for SA algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_LIU_FS.SA: Method interface for SA algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_MI_FS.SA: Method interface for SA algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_IEP_FS.SBS: Method interface for SBS algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_LIU_FS.SBS: Method interface for SBS algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_MI_FS.SBS: Method interface for SBS algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_IEP_FS.SFS: Method interface for SFS algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_LIU_FS.SFS: Method interface for SFS algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_MI_FS.SFS: Method interface for SFS algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.AllKNN.AllKNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.BSE.BSE: execute the method.
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.CCIS.CCIS: execute the method.
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.CHC.CHC: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.CNN.CNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.CoCoIS: Executes CCIS
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.CPruner.CPruner: Executes the CPruner algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.DROP1.DROP1: Executes the DROP1 algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.DROP2.DROP2: Executes the DROP2 algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.DROP3.DROP3: Executes the DROP3 algorithm.
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.ENN.ENN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.ENNRS.ENNRS: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.ENNTh.ENNTh: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.ENRBF.ENRBF: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.Explore.Explore: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.FCNN.FCNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.GA_MSE_CC_FSM: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.GCNN.GCNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.GG.GG: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.GGA.GGA: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.HMNEI.HMNEI: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.IB2.IB2: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.IB3.IB3: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.ICF.ICF: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.IGA.IGA: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.IKNN.IKNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.MCNN.MCNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.MCS.MCS: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.MENN.MENN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.MNV.MNV: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.ModelCS.ModelCS: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.MSS.MSS: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.Multiedit.Multiedit: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.NCNEdit.NCNEdit: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.NRMCS.NRMCS: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.PBIL.PBIL: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.POP.POP: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.PSC.PSC: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.PSRCG.PSRCG: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.Reconsistent.Reconsistent: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.RENN.RENN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.RMHC.RMHC: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.RNG.RNG: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.RNN.RNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.SGA.SGA: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.Shrink.Shrink: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.SNN.SNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.SSMA: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.SVBPS.SVBPS: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.TCNN.TCNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.TRKNN.TRKNN: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.VSM.VSM: Executes the algorithm
ejecutar() - Method in class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.ZhangTS: Executes the algorithm
ejecutar(double[][], int[], double[][], int[], int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerNB: Executes the algorithms with the datasets given.
ejecutar() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.aprioriSD: Executes the aprioriSD algorithm.
ejecutar() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.CN2SD: Executes the algorithm
ejecutarSMOTE() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.SMOTE_RSB
ejemplo - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Ttabla: Example vector.
ejemplo - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Ttabla: Example vector.
ejemplo - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Ttabla: Example vector.
ejemplo - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Ttabla: Example vector.
ejemplo - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Ttabla: Example vector.
ejemplo - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Ttabla: Example vector.
ejemplo - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Ttabla: Example vector.
ejemplo - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Ttabla: Example vector.
ejemplos - Static variable in class keel.Algorithms.Decision_Trees.DT_oblicuo.Tree: Number of examples for each leaf/class
ejemplos - Static variable in class keel.Algorithms.Decision_Trees.Target.Tree: Number of examples for each leaf/class
ejemplos - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
Eleft(int) - Method in class keel.Algorithms.Discretizers.DIBD.DIBD: It computes the Left decision distributional index needed to compute the entropy of a cutpoint
ElegirEjemplosAEliminarDistancia_3(String, Vector) - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.ChooseExamples
elem1SumFreq(String) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqListPair: Sums the frequencies for the first elements which are equal to the provided one
elem2SumFreq(String) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqListPair: Sums the frequencies for the second elements which are equal to the provided one
elementAt(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector: Returns the element at the given position.
elementAt(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector: Returns the element at the given position.
elementAt(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector: Returns the element at the given position.
elementAt(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.FreqList: The element at position indicated (i.e. the not-stored element inserted in position i)
elementAt(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.FreqList: The element at position indicated (i.e. the not-stored element inserted in position i)
elementAt(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.FreqList: The element at position indicated (i.e. the not-stored element inserted in position i)
elementAt(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqList: The element at position indicated (i.e. the not-stored element inserted in position i)
elementAt(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqListPair: Returns the element at position indicated
elementAt(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.FreqList: The element at position indicated (i.e. the not-stored element inserted in position i)
elementAt(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.FreqList: The element at position indicated (i.e. the not-stored element inserted in position i)
elementAt(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.FreqList: The element at position indicated (i.e. the not-stored element inserted in position i)
elementAt(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.FreqList: The element at position indicated (i.e. the not-stored element inserted in position i)
elementAt(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.FreqList: The element at position indicated (i.e. the not-stored element inserted in position i)
elementAt(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.FreqList: The element at position indicated (i.e. the not-stored element inserted in position i)
elementAt(int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector: Returns the element at the given position.
elementAt(int) - Method in class keel.Algorithms.SVM.SMO.core.FastVector: Returns the element at the given position.
elements() - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector: Returns an enumeration of this vector.
elements(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector: Returns an enumeration of this vector, skipping the element with the given index.
elements() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector: Returns an enumeration of this vector.
elements(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector: Returns an enumeration of this vector, skipping the element with the given index.
elements() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector: Returns an enumeration of this vector.
elements(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector: Returns an enumeration of this vector, skipping the element with the given index.
elements() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector: Returns an enumeration of this vector.
elements(int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector: Returns an enumeration of this vector, skipping the element with the given index.
elements() - Method in class keel.Algorithms.SVM.SMO.core.FastVector: Returns an enumeration of this vector.
elements(int) - Method in class keel.Algorithms.SVM.SMO.core.FastVector: Returns an enumeration of this vector, skipping the element with the given index.
elementsInCommon(byte[], byte[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams
elemsList - Variable in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.VAList: Value-attribute elements list.
elemsList - Variable in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.VAList: Value-attribute elements list.
elemsList - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.VAList: Value-attribute elements list.
elemsList - Variable in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.VAList: Value-attribute elements list.
elemsList - Variable in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.VAList: Value-attribute elements list.
elemsList - Variable in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.VAList: Value-attribute elements list.
elemsList - Variable in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.VAList: Value-attribute elements list.
elemsList - Variable in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.VAList: Value-attribute elements list.
elijeNodo() - Method in class keel.Algorithms.Decision_Trees.Target.Tree: Chooses randomly a node.
eliminaCondicion(Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Removes the condition passed as parameter.
eliminaCondicion(int, Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Removes the condition with the index given and adds a new one in that position.
eliminaCondicion(Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Removes the condition passed as parameter.
eliminaCondicion(int, Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Removes the condition with the index given and adds a new one in that position.
eliminaCondicion(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Removes the condition passed as parameter.
eliminaCondicion(Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Removes the condition passed as parameter.
eliminaCondicion(int, Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Removes the condition with the index given and adds a new one in that position.
eliminaCondicionContinuos(Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Removes the continuous condition passed as parameter.
eliminaCondicionContinuos(int, Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Removes the continuous condition with the index given and adds a new one in that position.
eliminaCondicionesNominales() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Removes all nominal conditions.
eliminaCondicionNominal(Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Removes the nominal condition passed as parameter.
eliminaCondicionNominal(int, Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Removes the nominal condition with the index given and adds a new one in that position.
eliminaMuestra(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Removes an example with the given position.
eliminaMuestra(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Removes the given example from the dataset.
eliminaMuestra(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Removes an example with the given position.
eliminaMuestra(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Removes the given example from the dataset.
eliminaMuestra(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ConjuntoDatos: Removes an example with the given position.
eliminaMuestra(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ConjuntoDatos: Removes the given example from the dataset.
eliminaMuestra(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Removes an example with the given position.
eliminaMuestra(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Removes the given example from the dataset.
eliminaMuestra(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Removes an example with the given position.
eliminaMuestra(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Removes the given example from the dataset.
eliminaMuestrasClase(Regla) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Removes from the set the covered cases which have the same class as the rule given.
eliminaMuestrasClase(Regla) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Removes from the set the covered cases which have the same class as the rule given.
eliminaMuestrasClase(Regla) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Removes from the set the covered cases which have the same class as the rule given.
eliminaMuestrasClase(Regla) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Removes from the set the covered cases which have the same class as the rule given.
eliminaMuestrasCubiertas(Regla, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Removes from dataset the covered cases by the given rule.
eliminaMuestrasCubiertas(Regla, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Removes from dataset the covered cases by the given rule.
eliminaMuestrasCubiertas(Regla) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ConjuntoDatos: Removes from dataset the covered cases by the given rule.
eliminaMuestrasCubiertas(Regla, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Removes from dataset the covered cases by the given rule.
eliminaMuestrasCubiertas(Regla) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Removes from dataset the covered cases by the given rule.
eliminaNulos() - Method in class keel.Algorithms.Rule_Learning.Prism.ConjReglas: Remove complex with repetitive attributes
eliminaNulos() - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjReglas: Remove complex with repetitive attributes
eliminaNulos() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjReglas: Remove complex with repetitive attributes
eliminaRegla(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseR: It removes a given rule from the rule set
eliminaRegla(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseR
eliminaRepetidos(int) - Method in class keel.Algorithms.Rule_Learning.CN2.ruleSet: It removes those complexes which are repeated (at1 = 0 ^ at2 = 0 -- at2 = 0 ^ at1 = 0)
eliminaRepetidos(int) - Method in class keel.Algorithms.Rule_Learning.Prism.ConjReglas: Remove repetitive complex(at1 = 0 ^ at2 = 0 -- at2 = 0 ^ at1 = 0)
eliminaRepetidos(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjReglas: Remove repetitive complex(at1 = 0 ^ at2 = 0 -- at2 = 0 ^ at1 = 0)
eliminaRepetidos(int) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjReglas: Remove repetitive complex(at1 = 0 ^ at2 = 0 -- at2 = 0 ^ at1 = 0)
EliminarInstanciasClasificadas(Particle) - Method in class keel.Algorithms.PSO_Learning.CPSO.CPSO: Removes from the dataset the instances that are correctly classified by the rule contained in the particle given as argument.
EliminarInstanciasClasificadas(Particle) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.LDWPSO: Removes from the dataset the instances that are correctly classified by the rule contained in the particle given as argument.
EliminarInstanciasClasificadas(Particle) - Method in class keel.Algorithms.PSO_Learning.REPSO.REPSO: Removes from the dataset the instances that are correctly classified by the rule contained in the particle given as argument.
eliminarNoUtiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Poblacion: eliminar organizaciones sin atributos útiles
eliminarSubsumidas() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.BaseR: Removes those rules whose examples have been already covered by other with a higher level (relative support).
eliminaSubsumidos(int) - Method in class keel.Algorithms.Rule_Learning.Prism.ConjReglas: Remove rules are the same ina semantic way(At = 1, At <> 0, At = [0,1])
eliminaSubsumidos(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjReglas: Remove rules are the same ina semantic way(At = 1, At <> 0, At = [0,1])
eliminaSubsumidos(int) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjReglas: Remove rules are the same ina semantic way(At = 1, At <> 0, At = [0,1])
eliminaSubsumidos(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetRules: Delete rules with equal semantics
eliminaUltimaCondicion() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Removes the last condition.
eliminaUltimaCondicion() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Removes the last condition.
eliminaUltimaCondicion() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Removes the last condition.
eliminaUltimaCondicionContinua() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Removes the last continuous condition.
eliminaUltimaCondicionNominal() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Removes the last nominal condition.
Elite() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
Elite() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
Elite() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
elite - Variable in class keel.Algorithms.Neural_Networks.gann.SetupParameters
elitism() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Core: Ensures the elitism, keeping the best rule.
elitism() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Ilga: Copy the survivorsPercent proportion of the old poblation into the bottom half of the new one
elitism() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.SEM: Copy the survivorsPercent proportion of the old poblation into the bottom half of the new one
elitism() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Oiga: Copy the survivorsPercent proportion of the old poblation into the bottom half of the new one
elitism() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.SEM: Copy the survivorsPercent proportion of the old poblation into the bottom half of the new one
ELITISM_PROPORTION - Static variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
elitismEnabled - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
elitismProportionTipText() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
elmentsToString() - Method in class keel.Algorithms.SVM.SMO.supportVector.SMOset: Prints all the current elements in the set.
EM - Class in keel.Algorithms.Preprocess.Missing_Values.EM: This class implements the Regularized Expectation-Maximization imputation for Missing Values
EM() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Default constructor.
EM(int, double, int, int, double, double, double, int, boolean) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Parametrized constructor
EM(String) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Constructor for KEEL parameter file
EMDD - Class in keel.Algorithms.MIL.Diverse_Density.EMDD: MIEMDD Qi Zhang, Sally A.
EMDD() - Constructor for class keel.Algorithms.MIL.Diverse_Density.EMDD.EMDD
EMDDoptimization - Class in keel.Algorithms.MIL.Diverse_Density.Optimization: EMDD algorithm optimization auxiliary methods
EMDDoptimization(EMDD) - Constructor for class keel.Algorithms.MIL.Diverse_Density.Optimization.EMDDoptimization
empty() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: To compute whether the dataset is empty
empty() - Method in class keel.Algorithms.Decision_Trees.M5.Queue: Checks if queue is empty.
empty() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Queue: Checks if queue is empty.
empty() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Condition
empty() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue: Checks if queue is empty.
empty() - Method in class keel.Algorithms.SVM.SMO.core.Queue: Checks if queue is empty.
EMstep(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.MixtGauss.MixtGaussGenerator: Expectation-Maximisation Algorithm
enable_tracing() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
enable_tracing() - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
enable_tracing() - Static method in class keel.Dataset.DataParser
enabledRanges(boolean) - Method in class keel.GraphInterKeel.datacf.editData.EditVariablePanel: Set if the range button is enabled or disabled
enabledTable(boolean) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelAttribute: Enables tha Attribute Table
enablePDF(boolean) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelCharts2D: Enables pdf format
enablePNG(boolean) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelCharts2D: Enables png format
Encode(int[], int[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Encode(ArrayList<Integer>) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Obtains the number of active antecedent variables and consequent variables in the list
Encode(int[], int[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
Encode(int[], int[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
EncodingLength - Class in keel.Algorithms.Instance_Selection.Explore: File: EncodingLength.java This is the enconding length of the Explore algorithm.
EncodingLength() - Constructor for class keel.Algorithms.Instance_Selection.Explore.EncodingLength
EncodingLength - Class in keel.Algorithms.Preprocess.Instance_Selection.Explore: File: EncodingLength.java This is the enconding length of the Explore algorithm.
EncodingLength() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.Explore.EncodingLength
EncuentraMinimo(int, int, Fitness, int, Randomize) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GeneticAlgorithmForBoosting
EncuentraMinimoPositivo(Randomize) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.LinearSearchBrent
EncuentraMinimoSA(Fitness, int, double, double, Randomize) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GeneticAlgorithmForBoosting
EncuentraMinimoSimple() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.LinearSearchBrent
END - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
endColumn - Variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Token: beginLine and beginColumn describe the position of the first character of this token; endLine and endColumn describe the position of the last character of this token.
endColumn - Variable in class keel.Algorithms.Rule_Learning.Swap1.Token: beginLine and beginColumn describe the position of the first character of this token; endLine and endColumn describe the position of the last character of this token.
endColumn - Variable in class keel.Dataset.Token: beginLine and beginColumn describe the position of the first character of this token; endLine and endColumn describe the position of the last character of this token.
endElement(String, String, String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.FuncionEvaluacionBeanHandler
endElement(String, String, String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.OperacionHandler
endLine - Variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Token: beginLine and beginColumn describe the position of the first character of this token; endLine and endColumn describe the position of the last character of this token.
endLine - Variable in class keel.Algorithms.Rule_Learning.Swap1.Token: beginLine and beginColumn describe the position of the first character of this token; endLine and endColumn describe the position of the last character of this token.
endLine - Variable in class keel.Dataset.Token: beginLine and beginColumn describe the position of the first character of this token; endLine and endColumn describe the position of the last character of this token.
endsIf(boolean, String) - Static method in class keel.Algorithms.Instance_Generation.utilities.Debug: Ends the program if the condition occurs.
endsIf(boolean, String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Debug: Ends the program if the condition occurs.
endsIfNull(Object, String) - Static method in class keel.Algorithms.Instance_Generation.utilities.Debug: Ends the program if the condition occurs.
endsIfNull(Object, String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Debug: Ends the program if the condition occurs.
enlargeBounds(double) - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It does enlarge the attribute bounds
enlargeBounds(double) - Method in class keel.Dataset.Attribute: It does enlarge the attribute bounds
enlargeInterval(int) - Method in class keel.Algorithms.Discretizers.Chi2_Discretizer.Interval: Enlarge the interval using a new "end"
enlargeInterval(int) - Method in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Interval: Enlarge the interval using a new "end"
enlargeInterval(int) - Method in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Interval: Enlarge the interval using a new "end"
ENN - Class in keel.Algorithms.Decision_Trees.C45_Binarization: File: ENN.java The ENN Instance Selection algorithm.
ENN(String) - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.ENN: Default builder.
ENN(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.ICPL.ICPLGenerator: Edited nearest neighbor of T.
ENN(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.RSP.RSPGenerator: Edited nearest neighbor of T.
ENN - Class in keel.Algorithms.Instance_Selection.ENN: File: ENN.java The ENN Instance Selection algorithm.
ENN(String) - Constructor for class keel.Algorithms.Instance_Selection.ENN.ENN: Default constructor.
ENN - Class in keel.Algorithms.Preprocess.Instance_Selection.ENN: File: ENN.java The ENN Instance Selection algorithm.
ENN(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.ENN.ENN: Default builder.
ENNRS - Class in keel.Algorithms.Instance_Selection.ENNRS: File: ENNRS.java The ENN Instance Selection algorithm.
ENNRS(String) - Constructor for class keel.Algorithms.Instance_Selection.ENNRS.ENNRS: Default builder.
ENNRS - Class in keel.Algorithms.Preprocess.Instance_Selection.ENNRS: File: ENNRS.java The ENN Instance Selection algorithm.
ENNRS(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.ENNRS.ENNRS: Default builder.
ENNTh - Class in keel.Algorithms.Instance_Selection.ENNTh: File: ENNTh.java The ENNTh Instance Selection algorithm.
ENNTh(String) - Constructor for class keel.Algorithms.Instance_Selection.ENNTh.ENNTh: Default builder.
ENNTh - Class in keel.Algorithms.Preprocess.Instance_Selection.ENNTh: File: ENNTh.java The ENNTh Instance Selection algorithm.
ENNTh(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.ENNTh.ENNTh: Default builder.
ENPCAlgorithm - Class in keel.Algorithms.Instance_Generation.ENPC: PSO algorithm calling.
ENPCAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.ENPC.ENPCAlgorithm
ENPCGenerator - Class in keel.Algorithms.Instance_Generation.ENPC: ENPC prototype generator.
ENPCGenerator(PrototypeSet, int, int) - Constructor for class keel.Algorithms.Instance_Generation.ENPC.ENPCGenerator: Build a new ENPCGenerator Algorithm
ENPCGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.ENPC.ENPCGenerator: Build a new RSPGenerator Algorithm
ENRBF - Class in keel.Algorithms.Instance_Selection.ENRBF: File: ENRBF.java The ENRBF Instance Selection algorithm.
ENRBF(String) - Constructor for class keel.Algorithms.Instance_Selection.ENRBF.ENRBF: Default builder.
ENRBF - Class in keel.Algorithms.Preprocess.Instance_Selection.ENRBF: File: ENRBF.java The ENRBF Instance Selection algorithm.
ENRBF(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.ENRBF.ENRBF: Default builder.
Ensemble - Class in keel.Algorithms.Neural_Networks.ensemble: Class representing an ensemble
Ensemble(EnsembleParameters) - Constructor for class keel.Algorithms.Neural_Networks.ensemble.Ensemble: Constructor
EnsembleFilter - Class in keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter: The Ensemble Filter...
EnsembleFilter() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.EnsembleFilter: It initializes the partitions from training set
EnsembleLearn(PrototypeSet[], PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoBC.CoBCGenerator: Builds the ensemble with the labeled dataset and classifies the test instances.
EnsembleNetwork - Class in keel.Algorithms.Neural_Networks.ensemble: Class representing an ensemble of neural networks
EnsembleNetwork() - Constructor for class keel.Algorithms.Neural_Networks.ensemble.EnsembleNetwork: Empty constructor
EnsembleNetwork(EnsembleParameters) - Constructor for class keel.Algorithms.Neural_Networks.ensemble.EnsembleNetwork: Constructor.
EnsembleOutput(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.ensemble.Ensemble: Output of every network
EnsembleParameters - Class in keel.Algorithms.Neural_Networks.ensemble: Class representing the parameters of an ensemble
EnsembleParameters() - Constructor for class keel.Algorithms.Neural_Networks.ensemble.EnsembleParameters: Empty constructor
ENTERO - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: Number to represent type of variable integer.
ENTERO - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: Number to represent type of variable integer.
ENTERO - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: Number to represent type of variable integer.
ENTERO - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: Number to represent type of variable integer.
ENTERO - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: Number to represent type of variable integer.
ENTERO - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: Number to represent type of variable integer.
Entero - Class in keel.Algorithms.Genetic_Rule_Learning.Hider
Entero(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Entero
entero - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Referencia
entero - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Referencia: Integer value.
entero - Variable in class keel.Algorithms.Instance_Selection.CCIS.Pareja: Integer element of the pair.
entero - Variable in class keel.Algorithms.Instance_Selection.MNV.ReferenciaMNV: Integer value.
entero - Variable in class keel.Algorithms.Preprocess.Basic.Referencia: Reference value (integer).
entero - Variable in class keel.Algorithms.Preprocess.Instance_Selection.CCIS.Pareja: Integer element of the pair.
entero - Variable in class keel.Algorithms.Preprocess.Instance_Selection.MNV.ReferenciaMNV: Integer value.
ENTERO - Static variable in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: Number to represent type of variable integer.
ENTERO - Static variable in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: Number to represent type of variable integer.
entra - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoostingMaxMin
entradas - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Input attributes.
entradas - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
entradas - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
entradas - Variable in class keel.Algorithms.Preprocess.Basic.Metodo: Inputs attributes
entrado() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Cromosoma: Returns the flag value that carry the information about if this chromosome has been selected to be crossed or not.
entrado() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Cromosoma: Returns the flag value that carry the information about if this chromosome has been selected to be crossed or not.
entrado() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Cromosoma: Returns the flag value that carry the information about if this chromosome has been selected to be crossed or not.
entrado() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Cromosoma: Returns the flag value that carry the information about if this chromosome has been selected to be crossed or not.
entrado() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Cromosoma: Returns the flag value that carry the information about if this chromosome has been selected to be crossed or not.
entrado() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Cromosoma: Returns the flag value that carry the information about if this chromosome has been selected to be crossed or not.
entrado() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Cromosoma: Returns the flag value that carry the information about if this chromosome has been selected to be crossed or not.
entrado() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Cromosoma: Returns the flag value that carry the information about if this chromosome has been selected to be crossed or not.
ENTRE - Static variable in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Condition: Tag (BETWEEN).
entropy(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ContingencyTables: Computes the entropy of the given array.
entropy(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.ContingencyTables: Computes the entropy of the given array.
entropy(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.ContingencyTables: Computes the entropy of the given array.
entropyConditionedOnColumns(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ContingencyTables: Computes conditional entropy of the rows given the columns.
entropyConditionedOnColumns(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.ContingencyTables: Computes conditional entropy of the rows given the columns.
entropyConditionedOnColumns(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.ContingencyTables: Computes conditional entropy of the rows given the columns.
entropyConditionedOnRows(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ContingencyTables: Computes conditional entropy of the columns given the rows.
entropyConditionedOnRows(double[][], double[][], double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ContingencyTables: Computes conditional entropy of the columns given the rows of the test matrix with respect to the train matrix.
entropyConditionedOnRows(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.ContingencyTables: Computes conditional entropy of the columns given the rows.
entropyConditionedOnRows(double[][], double[][], double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.ContingencyTables: Computes conditional entropy of the columns given the rows of the test matrix with respect to the train matrix.
entropyConditionedOnRows(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.ContingencyTables: Computes conditional entropy of the columns given the rows.
entropyConditionedOnRows(double[][], double[][], double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.ContingencyTables: Computes conditional entropy of the columns given the rows of the test matrix with respect to the train matrix.
entropyOverColumns(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ContingencyTables: Computes the columns' entropy for the given contingency table.
entropyOverColumns(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.ContingencyTables: Computes the columns' entropy for the given contingency table.
entropyOverColumns(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.ContingencyTables: Computes the columns' entropy for the given contingency table.
entropyOverRows(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ContingencyTables: Computes the rows' entropy for the given contingency table.
entropyOverRows(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.ContingencyTables: Computes the rows' entropy for the given contingency table.
entropyOverRows(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.ContingencyTables: Computes the rows' entropy for the given contingency table.
enumConv - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
enumConv - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
enumerate() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Sets the attribute as synthetizied from an enumerate one.
enumerateAttributes() - Method in class keel.Algorithms.Decision_Trees.C45.Dataset: Enumerates all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Enumerates all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.Decision_Trees.ID3.Dataset: Enumerates all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Returns an enumeration of all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Returns an enumeration of all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Enumerates all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Returns an enumeration of all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns an enumeration of all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Returns an enumeration of all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Enumerates all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Enumerates all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Enumerates all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Enumerates all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Enumerates all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Returns an enumeration of all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns an enumeration of all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.Rule_Learning.ART.Dataset: Enumerates all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Enumerates all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Enumerates all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Enumerates all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Enumerates all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Enumerates all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.SVM.SMO.core.Instance: Returns an enumeration of all the attributes.
enumerateAttributes() - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns an enumeration of all the attributes.
enumerateInstances() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Returns an enumeration of all instances in the dataset.
enumerateInstances() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns an enumeration of all instances in the dataset.
enumerateInstances() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns an enumeration of all instances in the dataset.
enumerateInstances() - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns an enumeration of all instances in the dataset.
enumerateItemsets() - Method in class keel.Algorithms.Decision_Trees.C45.Dataset: Enumerates all the itemsets.
enumerateItemsets() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Enumerates all the itemsets.
enumerateItemsets() - Method in class keel.Algorithms.Decision_Trees.ID3.Dataset: Enumerates all the itemsets.
enumerateItemsets() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Enumerates all the itemsets.
enumerateItemsets() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Enumerates all the itemsets.
enumerateItemsets() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Enumerates all the itemsets.
enumerateItemsets() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Enumerates all the itemsets.
enumerateItemsets() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Enumerates all the itemsets.
enumerateItemsets() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Enumerates all the itemsets.
enumerateItemsets() - Method in class keel.Algorithms.Rule_Learning.ART.Dataset: Enumerates all the itemsets.
enumerateItemsets() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Enumerates all the itemsets.
enumerateItemsets() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Enumerates all the itemsets.
enumerateItemsets() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Enumerates all the itemsets.
enumerateItemsets() - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Enumerates all the itemsets.
enumerateItemsets() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Enumerates all the itemsets.
enumerateMeasures() - Method in class keel.Algorithms.Decision_Trees.M5.M5: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in interface keel.Algorithms.SVM.SMO.core.AdditionalMeasureProducer: Returns an enumeration of the measure names.
enumerateMeasures() - Method in class keel.Algorithms.SVM.SMO.SVMreg: Returns an enumeration of the measure names.
enumerateNames() - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription: Gets a string with all the names from all the layers
enumerateValues() - Method in class keel.Algorithms.Decision_Trees.M5.M5Attribute: Returns an enumeration of all the attribute's values if the attribute is nominal or a string, null otherwise.
enumerateValues() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Returns an enumeration of all the attribute's values if the attribute is nominal, string, or relation-valued, null otherwise.
enumerateValues() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Returns an enumeration of all the attribute's values if the attribute is nominal, string, or relation-valued, null otherwise.
Environment - Interface in keel.Algorithms.Genetic_Rule_Learning.UCS: This interface is the environment interface.
Environment - Interface in keel.Algorithms.Genetic_Rule_Learning.XCS: This interface is the environment interface.
EOF - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
EOF - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for EOF.
EOF - Static variable in interface keel.Dataset.DataParserConstants
eol - Variable in exception keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParseException: The end of line string for this machine.
eol - Variable in exception keel.Algorithms.Rule_Learning.Swap1.ParseException: The end of line string for this machine.
eol - Variable in exception keel.Dataset.ParseException: The end of line string for this machine.
epochs - Variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus: Value of stop condition
eps - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
eps - Variable in class org.libsvm.svm_parameter
epsilon - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQ3: Epsilon constant (multiplier of the window width)
epsilon - Variable in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
EPSILON - Static variable in class keel.Algorithms.Preprocess.Missing_Values.BPCA.MachineAccuracy: Machine accuracy constant
EPSILON - Static variable in class keel.Algorithms.Preprocess.Missing_Values.EM.util.MachineAccuracy: Machine accuracy constant
epsilon_0 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Parameter of the accuracy function (Is the error threshold under which the accuracy of the classifier is set to 1.
EPSILON_0 - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
epsilon_reduct - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Parameter of the Dixon Reduction.
EPSILON_REDUCT - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
EPSILON_SVR - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
EPSILON_SVR - Static variable in class org.libsvm.svm_parameter
epsilonParameterTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: Returns the tip text for this property
epsilonTimesAlpha_0 - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQ3: Epsilon times alpha constant
epsilonTipText() - Method in class keel.Algorithms.SVM.SMO.SMO: Returns the tip text for this property
epsilonTipText() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Returns the tip text for this property
epsilonTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMO: Returns the tip text for this property
epsTipText() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Returns the tip text for this property
eq(double, double) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Tests if a is equal to b.
eq(double, double) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Tests if a is equal to b.
eq(double, double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Tests if a is equal to b.
eq(double, double) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Tests if a is equal to b.
eq(double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Tests if a is equal to b.
eq(double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Tests if a is equal to b.
eq(double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Tests if a is equal to b.
eq(double, double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Tests if a is equal to b.
eq(double, double) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Tests if a is equal to b.
eqDouble(double, double) - Static method in class keel.Algorithms.Decision_Trees.M5.M5: Tests if two double values are equal to each other
eqDouble(double, double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: Tests if two double values are equal to each other
EQUAL - Static variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Selector: Number to represent equality.
EQUAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule: number to indentify the operator =.
EQUAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleRule: number to indentify the operator =.
EQUAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule: number to indentify operator =.
EQUAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.SimpleRule: number to indentify the operator =.
EQUAL - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Selector: Number to represent equality.
equal(fuzzy) - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
equal(fuzzy) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
equal(Vector<Vector<fuzzy>>, Vector<Vector<fuzzy>>) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling.Main
equal(fuzzy) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
equal(Vector<Vector<fuzzy>>, Vector<Vector<fuzzy>>) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.Main
EQUAL - Static variable in class keel.Algorithms.Rule_Learning.C45Rules.Rule: Flag for equal operator
EQUAL - Static variable in class keel.Algorithms.Rule_Learning.C45Rules.SimpleRule: Flag for equal operator
EQUAL - Static variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule: Flag for equal operator
EQUAL - Static variable in class keel.Algorithms.Rule_Learning.C45RulesSA.SimpleRule: Flag for equal operator
EQUAL - Static variable in class keel.Algorithms.Rule_Learning.PART.Rule: Flag for equal operator
EQUAL - Static variable in class keel.Algorithms.Rule_Learning.PART.SimpleRule: Flag for equal operator
EQUAL - Static variable in class keel.Algorithms.Rule_Learning.Ripper.Rule: Flag for equal operator
EQUAL - Static variable in class keel.Algorithms.Rule_Learning.Ripper.SimpleRule: Flag for equal operator
EQUAL - Static variable in class keel.Algorithms.Rule_Learning.Slipper.Rule: Flag for equal operator
EQUAL - Static variable in class keel.Algorithms.Rule_Learning.Slipper.SimpleRule: Flag for equal operator
EQUAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene
equalHeaders(M5Instance) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Tests if the headers of two instances are equivalent.
equalHeaders(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Checks if two headers are equivalent.
equalHeaders(Instance) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Tests if the headers of two instances are equivalent.
equalHeaders(Instances) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Checks if two headers are equivalent.
equalHeaders(Itemset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Tests if the headers of two instances are equivalent.
equalHeaders(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Checks if two headers are equivalent.
equalHeaders(Instance) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Tests if the headers of two instances are equivalent.
equalHeaders(Instances) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Checks if two headers are equivalent.
equalHeaders(Instance) - Method in class keel.Algorithms.SVM.SMO.core.Instance: Tests if the headers of two instances are equivalent.
equalHeaders(Instances) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Checks if two headers are equivalent.
equals(Object) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute: Checks if an attribute is the same attribute as another object
equals(Object) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset: Check if a dataset is the same dataset as another object
equals(Object) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.Split: Checks if a split is the same split as another object
equals(Object) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Checks if a tree node is the same tree node as another object
equals(Object) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Tests whether the current evaluation object is equal to another evaluation object
equals(Object) - Method in class keel.Algorithms.Decision_Trees.M5.M5Attribute: Tests if given attribute is equal to this attribute.
equals(Object) - Method in class keel.Algorithms.Decision_Trees.M5.SelectedAssociation: Returns true if this SelectedAssociation equals another object.
equals(Object) - Method in class keel.Algorithms.Decision_Trees.M5.SerializedObject: Compares this object with another for equality.
equals(Object) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute: Checks if an attribute is the same attribute as another object
equals(Object) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset: Check if a dataset is the same dataset as another object
equals(Object) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Node: Checks if a node is the same node as another object
equals(Object) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Register: Checks if a register is the same register as another object
equals(Object) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Split: Checks if a split is the same split as another object
equals(Object) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode: Checks if a node is the same node as another object
equals(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Tests if given attribute is equal to this attribute.
equals(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.SerializedObject
equals(Fuzzy) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.Fuzzy: Indicates whether some other object is "equal to" this one.
equals(Fuzzy) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut: Indicates whether some other object is "equal to" this one.
equals(Fuzzy) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyInterval: Indicates whether some other object is "equal to" this one.
equals(Fuzzy) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyNumberTRIANG: Indicates whether some other object is "equal to" this one.
equals(Fuzzy) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyNumberTRLEFT: Indicates whether some other object is "equal to" this one.
equals(Fuzzy) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzySingleton: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Atributo: Checks if this attribute is equal to the one given.
equals(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Atributo: Checks if this attribute is equal to the one given.
equals(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Atributo: Checks if this attribute is equal to the one given.
equals(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Atributo: Checks if this attribute is equal to the one given.
equals(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.Condition
equals(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.Rule
equals(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.RuleSet
equals(RuleSet) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.RuleSet: Test if the fitness of the rule sets are equal
equals(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Tests if given attribute is equal to this attribute.
equals(RuleSet) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.RuleSet: Test if the fitness of the rule sets are equal
equals(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Atributo: Checks if this attribute is equal to the one given.
equals(Attribute) - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Attribute: It checks if two attributes are equal
equals(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Returns if the classifier of the class is equal to the classifier given as a parameter.
equals(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep: Returns true if the allele matches with the environment
equals(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep: Returns true if the allele is equal to the allele given as a parameter.
equals(Attribute) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute: It checks if two attributes are equal
equals(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Returns if the classifier of the class is equal to the classifier given as a parameter.
equals(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: Return true if the allele matches with the environment
EQUALS - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
equals(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: Returns true if the allele matches with the environment
equals(Representation) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: Indicates if the classifier of the class is equal to the classifier passed.
equals(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep: Returns true if the allele is equal to the allele given as a parameter.
equals(Object) - Method in class keel.Algorithms.Hyperrectangles.BNGE.Rule: Equals method
equals(Object) - Method in class keel.Algorithms.Hyperrectangles.INNER.Rule: Equals method
equals(Object) - Method in class keel.Algorithms.Hyperrectangles.RISE.Rule: Equals method
equals(Object) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Fuzzy: Compares this fuzzy label with another fuzzy label to check if both fuzzy labels are equal
equals(Object) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Compares this FuzzyAntecedent with another FuzzyAntecedent to check if both FuzzyAntecedents are equal
equals(Object) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Compares this rule with another rule to check if both rules are equal
equals(Object) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Tests if given attribute is equal to this attribute.
equals(Object) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SerializedObject
equals(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Test if two prototypes are equals
equals(Pair<S, F>) - Method in class keel.Algorithms.Instance_Generation.utilities.Pair
equals(Object) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Compare this algorithm to another
equals(INeuralNet) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Checks if this neural net is equal to another
equals(ILayer<N>) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.ILayer: Checks if this layer is equal to another
equals(INeuralNet) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuralNet: Checks if this neural net is equal to another
equals(INeuron) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuron: Checks if this neuron is equal to another
equals(ILayer<InputNeuron>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputLayer: Checks if this layer is equal to another
equals(INeuron) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputNeuron: Checks if this link is equal to another
equals(Link) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.Link: Checks if this link is equal to another
equals(ILayer<LinkedNeuron>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Checks if this layer is equal to another
equals(INeuron) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron: Checks if this neuron is equal to another
equals(Object) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetCreator: Compares creators
equals(Object) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividual: Compares individuals
equals(Object) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies: Compare two individuals
equals(Object) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.util.random.RanNnep: Compares if two random generators are equal
equals(Object) - Method in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.ValueFreq: Test it the provided object is equal in value to this object Overrides Object.equals()
equals(Object) - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.ValueFreq: Test it the provided object is equal in value to this object Overrides Object.equals()
equals(Object) - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.ValueFreq: Test it the provided object is equal in value to this object Overrides Object.equals()
equals(Object) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValueFreq: Test it the provided object is equal in value to this object Overrides Object.equals()
equals(Object) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValuesFreq: Test it the provided object is equal in values to this object Overrides Object.equals()
equals(Object) - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.ValueFreq: Test it the provided object is equal in value to this object Overrides Object.equals()
equals(Object) - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.ValueFreq: Test it the provided object is equal in value to this object Overrides Object.equals()
equals(Object) - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.ValueFreq: Test it the provided object is equal in value to this object Overrides Object.equals()
equals(Object) - Method in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.ValueFreq: Test it the provided object is equal in value to this object Overrides Object.equals()
equals(Object) - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.ValueFreq: Test it the provided object is equal in value to this object Overrides Object.equals()
equals(Object) - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.ValueFreq: Test it the provided object is equal in value to this object Overrides Object.equals()
equals(Atributo_valor) - Method in class keel.Algorithms.Rule_Learning.LEM2.Atributo_valor
equals(Atributo_valor) - Method in class keel.Algorithms.Rule_Learning.Rules6.Atributo_valor: Compares two objects, checking if they are equals.
equals(Regla) - Method in class keel.Algorithms.Rule_Learning.Rules6.Regla: Checks if the rule is equal to the given one.
equals(Atributo_valor) - Method in class keel.Algorithms.Rule_Learning.SRI.Atributo_valor: Compares two objects, checking if they are equals.
equals(Regla) - Method in class keel.Algorithms.Rule_Learning.SRI.Regla: Checks if the rule is equal to the given one.
equals(Attribute) - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It compares two attributes.
equals(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Test if two prototypes are equals
equals(Pair<S, F>) - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Pair: Checks if this pair is equals to the other given.
equals(Object) - Method in class keel.Algorithms.SVM.SMO.core.ClassDiscovery.StringCompare: Indicates whether some other object is "equal to" this Comparator.
equals(Object) - Method in class keel.Algorithms.SVM.SMO.core.SelectedTag: Returns true if this SelectedTag equals another object
equals(Object) - Method in class keel.Algorithms.SVM.SMO.core.SerializedObject
equals(Object) - Method in class keel.Algorithms.SVM.SMO.supportVector.KernelEvaluation: Tests whether the current evaluation object is equal to another evaluation object
equals(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Chromosome: It indicates whether some other chromosome is "equal to" this one
equals(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Gene: It indicates whether some other gene is "equal to" this one
equals(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Item: It indicates whether some other item is "equal to" this one
equals(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Itemset: It indicates whether some other itemset is "equal to" this one
equals(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Item: It indicates whether some other item is "equal to" this one
equals(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Itemset: It indicates whether some other itemset is "equal to" this one
equals(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Item: It indicates whether some other item is "equal to" this one
equals(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Itemset: It indicates whether some other itemset is "equal to" this one
equals(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Item: It indicates whether some other item is "equal to" this one
equals(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Itemset: It indicates whether some other itemset is "equal to" this one
equals(Chromosome) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome: It indicates whether some other chromosome is "equal to" this one
equals(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene: It indicates whether some other gene is "equal to" this one
equals(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.Item: It indicates whether some other item is "equal to" this one
equals(Chromosome) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
equals(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
equals(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.Item: It indicates whether some other item is "equal to" this one
equals(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Chromosome
equals(Object) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene: It indicates whether some other gene is "equal to" this one
equals(Chromosome) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome: It indicates whether some other chromosome is "equal to" this one
equals(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene
equals(Chromosome) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome: It indicates whether some other chromosome is "equal to" this one
equals(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene: It indicates whether some other gene is "equal to" this one
equals(Chromosome) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome: It indicates whether some other chromosome is "equal to" this one
equals(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene: It indicates whether some other gene is "equal to" this one
equals(Attribute) - Method in class keel.Dataset.Attribute: It compares two attributes.
equalsInputs(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Test if two prototypes have the same inputs
equalsInputs(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Test if two prototypes have the same inputs
equalsTo(Itemset) - Method in class keel.Algorithms.Discretizers.UCPD.Itemset: It checks if the itemset given is equals to this itemset
equalTo(Hyper) - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Hyper
EqualTo(Individual) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual: Returns if the indicated individual is equal to another individual
equalTo(Individual) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndCAN: Returns if the indicated individual is equal to "this" Used to know if two individuals describe the same rule
equalTo(Individual) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndDNF: Returns if the indicated individual is equal to "this" Used to know if two individuals describe the same rule
equalTo(Individual) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Checks if this individual is equal to other given.
EqualTo(Individual) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual: Returns if the indicated individual is equal to another individual
EqualTo(Individual) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual: Returns if the indicated individual is equal to another individual
equivalence_sets - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
equivalence_sets - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal
EquivalenceClasses() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff: Compute the equivalence classes
EquivalenceClasses() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal: Compute the equivalence classes
equivalents(Object) - Method in class keel.Algorithms.Rule_Learning.Riona.Selector: Returns 0 if the selectors are equivalents
EquivClasses_Instance(Instance) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff: Compute the equivalence classes to an instance.
EquivClasses_Instance(Instance) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal: Compute the equivalence classes to an instance.
erase(int) - Method in class keel.GraphInterKeel.statistical.tests.Distribution2KeyTable: Erase a row of the table
erf(double) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.StatFunc: Error function.
Eright(int) - Method in class keel.Algorithms.Discretizers.DIBD.DIBD: It computes the Right decision distributional index needed to compute the entropy of a cutpoint
ERR_LEX - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
ERR_LEX - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for lex error.
ERR_LEX - Static variable in interface keel.Dataset.DataParserConstants
errms(StreamTokenizer, String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Throws error message with line number and last token read.
error - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN.Triplet: Error obtained.
Error(myDataset, int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.GA
error - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters
error - Variable in class keel.Algorithms.Neural_Networks.gmdh.SetupParameters
error - Variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus: Output errors
ErrorDimension - Exception in keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs: Implements a Exception used just for dimension errors for matrices.
errorFlag - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Error flag used when checking command line aruments (default = true).
ErrorInfo - Class in keel.Algorithms.Rule_Learning.Swap1: ErrorInfo This class conatins the information about an error apperaed during the dataset read.
ErrorInfo() - Constructor for class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo: Creates a new instance of ErrorInfo
ErrorInfo(int, int, int, int, int, boolean, String) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo: Creates a new instance with the parameters passed.
ErrorInfo(String) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo: It creates a new Error info with the message passed
ErrorInfo - Class in keel.Dataset: ErrorInfo This class conatins the information about an error apperaed during the dataset read.
ErrorInfo() - Constructor for class keel.Dataset.ErrorInfo: Creates a new instance of ErrorInfo
ErrorInfo(int, int, int, int, int, boolean, String) - Constructor for class keel.Dataset.ErrorInfo: Creates a new instance with the parameters passed.
ErrorInfo(String) - Constructor for class keel.Dataset.ErrorInfo: It creates a new Error info with the message passed
errorInTrain - Variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo: It stores if the error has been in the train dataset.
errorInTrain - Variable in class keel.Dataset.ErrorInfo: It stores if the error has been in the train dataset.
errorln(String) - Static method in class keel.Algorithms.Instance_Generation.utilities.Debug: Prints a message in the error console.
errorln(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Debug: Prints a message in the error console.
errorMsg(String) - Static method in class keel.Algorithms.Decision_Trees.M5.M5: Prints error message and exits
errorMsg(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: Prints error message and exits
errorRate() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Returns the estimated error rate or the root mean squared error (if the class is numeric).
errorRbfn(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Computes the difference between the ouput of the net and desired output
errorRbfn(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbfn: Computes the difference between the ouput of the net and desired outpunt
errorRbfn(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn: Computes the difference between the ouput of the net and desired outpunt
errorRbfn(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn: Computes the difference between the ouput of the net and desired outpunt
errorRbfn(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn: Computes the difference between the ouput of the net and desired outpunt
errorRbfn(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn: Computes the difference between the ouput of the net and desired outpunt
errorRbfn(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn: Computes the difference between the ouput of the net and desired outpunt
errors(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.Function: Evaluates a function
errors(M5Instances, boolean) - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode: Evaluates a tree
errors(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Function: Evaluates a function
errors(MyDataset, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode: Evaluates a tree
ErrorSingular - Exception in keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs: Implements a Exception used just for singular errors for matrices.
es_crisp() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
es_crisp() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
es_crisp() - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
es_crisp() - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
es_crisp() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
es_crisp() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
es_crisp() - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
es_rect() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
es_rect() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
es_rect() - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
es_rect() - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
es_rect() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
es_rect() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
es_rect() - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
es_trian() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
es_trian() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
es_trian() - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
es_trian() - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
es_trian() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
es_trian() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
es_trian() - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
escero() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.Interval
escero() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.Interval
escero() - Method in class keel.Algorithms.LQD.preprocess.Expert.interval
escero() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.interval
escero() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.interval
escogerEjemplos(boolean[]) - Method in class keel.Algorithms.Decision_Trees.DT_GA.Poblacion: Chooses the examples to be used as training whoses boolean value is true.
EscribeBCLing - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs: The class that writest the data base
EscribeBCLing() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.EscribeBCLing
EscribeBCLing - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs: The class that writest the data base
EscribeBCLing() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.EscribeBCLing
EscribeBCLing - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs: The class that writest the data base
EscribeBCLing() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.EscribeBCLing
EscribeBCLing - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs: The class that writest the data base
EscribeBCLing() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.EscribeBCLing
EscribeBCLing - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs: The class that writest the data base
EscribeBCLing() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.EscribeBCLing
EscribeBCLing - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs: The class that writest the data base
EscribeBCLing() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.EscribeBCLing
EscribeBCLing - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs: The class that writest the data base
EscribeBCLing() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.EscribeBCLing
EscribeBCLing - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs: The class that writest the data base
EscribeBCLing() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.EscribeBCLing
escribeFichero(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseD: It writes the Data Base into an output file
escribeFichero(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseR: It prints the rule base into a File
escribeFichero(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseD
escribeFichero(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseR
escribeFichero(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Poblacion
escribeFichero(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseD
escribeFichero(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseR
escribeFichero(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Individuo
escribeFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Methods.LEL_TSK.Fichero: Function for writing a String Object in a file
escribeFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Methods.MamWM.Fichero: Function for writing a String Object in a file
escribeFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Methods.mogulIRL.Fichero: Function for writing a String Object in a file
escribeFichero(String) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseD
escribeFichero(String) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseR
escribeFichero(String) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Individuo
escribeFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.Fichero: Function for writing a String Object in a file
escribeFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.Fichero: Function for writing a String Object in a file
escribeFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.Fichero: Function for writing a String Object in a file
escribeFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamSelect.Fichero: Function for writing a String Object in a file
escribeFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamWSelect.Fichero: Function for writing a String Object in a file
escribeFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamWTuning.Fichero: Function for writing a String Object in a file
escribeFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.Fichero: Function for writing a String Object in a file
escribeFichero(String, String) - Static method in class keel.Algorithms.RE_SL_Postprocess.TSKSelect.Fichero: Function for writing a String Object in a file
escribeFichero(String, String) - Static method in class org.core.Fichero
escribeFicherosSalida(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider: Write output files
escribeSalida(String, int[][], int[][], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Output
escribeSalida(String, String[][], String[][], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Output
escribeSalida(String, double[][], int[], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.OutputIS
escribeSalida(String, InstanceSet, Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.OutputIS
escribeSalida(String, double[][], int[][], boolean[][], int[], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.OutputIS
escribeSalida(String, int[][], int[][], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Output: Writes results
escribeSalida(String, String[][], String[][], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Output: Writes results
escribeSalida(String, double[][], int[], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.OutputIS: Writes results
escribeSalida(String, InstanceSet, Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.OutputIS: Writes results
escribeSalida(String, double[][], int[][], boolean[][], int[], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.OutputIS: Writes results (Required for HVDM distance)
escribeSalida(String, InstanceSet, Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.SMOTE: Writes results for the test file
escribeSalida(String, InstanceSet, Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE.SMOTE: Writes results for the test file
escribeSalida(String, String[], String[], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.Neural_Networks.LVQ.LVQ: Prints on the given file the output values given as arguments.
escribeSalida(String, int[][], int[][], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.Preprocess.Basic.Output: Writes results
escribeSalida(String, String[][], String[][], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.Preprocess.Basic.Output: Writes results
escribeSalida(String, double[][], int[], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.Preprocess.Basic.OutputIS: Writes results
escribeSalida(String, InstanceSet, Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.Preprocess.Basic.OutputIS: Writes results
escribeSalida(String, double[][], int[][], boolean[][], int[], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.Preprocess.Basic.OutputIS: Writes results (Required for HVDM distance)
escribeSalidaAux(String, double, double, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.OutputIS: Writes auxiliary results file
escribeSalidaAux(String, double, double, String) - Static method in class keel.Algorithms.Preprocess.Basic.OutputIS: Writes auxiliary results file
escribeSalidaNumber(String, double[][], double[][], Attribute[], Attribute[], int, int, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.OutputIS
escribeSalidaNumber(String, double[][], double[][], Attribute[], Attribute[], int, int, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.OutputIS: Writes numeric results
escribeSalidaNumber(String, double[][], double[][], Attribute[], Attribute[], int, int, String) - Static method in class keel.Algorithms.Preprocess.Basic.OutputIS: Writes numeric results
escribeSalidaRanging(String, double[][], double[][], Attribute[], Attribute[], int, int, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.OutputIS
escribeSalidaRanging(String, double[][], double[][], Attribute[], Attribute[], int, int, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.OutputIS: Writes results and ranges
escribeSalidaRanging(String, double[][], double[][], Attribute[], Attribute[], int, int, String) - Static method in class keel.Algorithms.Preprocess.Basic.OutputIS: Writes results and ranges
escribeSalidas(double, double) - Method in class keel.Algorithms.Decision_Trees.DT_GA.DT_GA: Writes all the output files, including statistics and the generated rules for the model.
escribeSalidaSinNull(String, InstanceSet) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.OutputIS
escribeSalidaSinNull(String, InstanceSet) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.OutputIS: Writes results avoiding null values
escribeSalidaSinNull(String, InstanceSet) - Static method in class keel.Algorithms.Preprocess.Basic.OutputIS: Writes results avoiding null values
esHoja() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Checks if the node is a leaf.
esIgual(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Atributo: Checks if this attribute is equal to the one given.
esIgual(Regla) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Checks if the rule is equal to the given one.
esIgual(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Atributo: Checks if this attribute is equal to the one given.
esIgual(Regla) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Checks if the rule is equal to the given one.
esIgual(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Atributo: Checks if this attribute is equal to the one given.
esIgual(Regla) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Checks if the rule is equal to the given one.
esIgual(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Atributo: Checks if this attribute is equal to the one given.
esIgual(Regla) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Checks if the rule is equal to the given one.
esIgual(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Atributo: Checks if this attribute is equal to the one given.
esIgual(Cubo) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.Cubo
esIgual(Complejo) - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Check is two complex are equals
esIgual(Complejo) - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Check if two complex are equals (represent the same)
esIgual(Complejo) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Check if two complex are equals (represent the same)
esNominal(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: This function checks if the attribute value is nominal
esNominal(int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: This function checks if the attribute value is nominal
estaAtributo(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Muestra: Checks if the given attribute is already in the example.
estaAtributo(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Muestra: Checks if the given attribute is already in the example.
estaAtributo(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Muestra: Checks if the given attribute is already in the example.
estaAtributo(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Checks if the given attribute/condition is already in the example.
estaAtributo(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Muestra: Checks if the given attribute is already in the example.
estaAtributo(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Muestra: Checks if the given attribute is already in the example.
establishTrain(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE
estaClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Muestra: Checks if the class of the example is the same as the one passed as parameter.
estaClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Muestra: Checks if the class of the example is the same as the one passed as parameter.
estaClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Muestra: Checks if the class of the example is the same as the one passed as parameter.
estaClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Muestra: Checks if the class of the example is the same as the one passed as parameter.
estaClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Muestra: Checks if the class of the example is the same as the one passed as parameter.
estaCondicion(Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Checks if the given condition is already in the example.
estaCondicion(Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Checks if the given condition is already in the example.
estaCondicion(Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Checks if the given condition is already in the example.
estaCubierta() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Muestra: Checks if the example is covered.
estaCubierta() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Muestra: Checks if the example is covered.
estaCubierta() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Muestra: Checks if the example is covered.
estaCubierta() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Muestra: Checks if the example is covered.
estaEvaluado() - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Cromosoma: Tests if the chromosome is already evaluated
estaEvaluado() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.Chromosome
estaEvaluado() - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma: Tests if the chromosome is already evaluated
estaEvaluado() - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma: Tests if the chromosome is already evaluated
estaEvaluado() - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma: Tests if the chromosome is already evaluated
estaEvaluado() - Method in class keel.Algorithms.Instance_Selection.CHC.Cromosoma: Tests if the chromosome is already evaluated
estaEvaluado() - Method in class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Tests if the chromosome is already evaluated
estaEvaluado() - Method in class keel.Algorithms.Instance_Selection.GGA.Cromosoma: Tests if the chromosome is already evaluated
estaEvaluado() - Method in class keel.Algorithms.Instance_Selection.PBIL.Cromosoma: Tests if the chromosome is already evaluated
estaEvaluado() - Method in class keel.Algorithms.Instance_Selection.SGA.Cromosoma: Tests if the chromosome is already evaluated
estaEvaluado() - Method in class keel.Algorithms.Instance_Selection.SSMA.Cromosoma: Tests if the chromosome is already evaluated
estaEvaluado() - Method in class keel.Algorithms.Preprocess.Instance_Selection.CHC.Cromosoma: Tests if the chromosome is already evaluated
estaEvaluado() - Method in class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Tests if the chromosome is already evaluated
estaEvaluado() - Method in class keel.Algorithms.Preprocess.Instance_Selection.GGA.Cromosoma: Tests if the chromosome is already evaluated
estaEvaluado() - Method in class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Cromosoma: Tests if the chromosome is already evaluated
estaEvaluado() - Method in class keel.Algorithms.Preprocess.Instance_Selection.SGA.Cromosoma: Tests if the chromosome is already evaluated
estaEvaluado() - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma: Tests if the chromosome is already evaluated
estanAtributosEn(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Checks if in the sample, all the rule conditions/attributes are found.
estanCondicionesEn(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Checks if in the sample, all the rule conditions are found.
estanCondicionesEn(Muestra, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Checks if in the sample, all the rule conditions are found.
estanCondicionesEn(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Checks if in the sample, all the rule conditions are found.
estanCondicionesEn(Muestra, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Checks if in the sample, all the rule conditions are found.
estanCondicionesEn(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Checks if in the sample, all the rule conditions are found.
estanCondicionesEn(Muestra, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Checks if in the sample, all the rule conditions are found.
estanCondicionesEn(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Checks if in the sample, all the rule conditions are found.
estaYdistinto(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Organizacion
EstimaParticiones(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
estimatedNoiseLevel - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters: Estimated Noise level.
esValido() - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Cromosoma: Tests if the chromosome is valid
esValido() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.Chromosome
esValido() - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma: Tests if the chromosome is valid
esValido() - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma: Tests if the chromosome is valid
esValido() - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma: Tests if the chromosome is valid
esValido() - Method in class keel.Algorithms.Instance_Selection.CHC.Cromosoma: Tests if the chromosome is valid
esValido() - Method in class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.Cromosoma
esValido() - Method in class keel.Algorithms.Instance_Selection.GGA.Cromosoma: Tests if the chromosome is valid
esValido() - Method in class keel.Algorithms.Instance_Selection.IGA.Cromosoma: Tests if the chromosome is valid
esValido() - Method in class keel.Algorithms.Instance_Selection.PBIL.Cromosoma: Tests if the chromosome is valid
esValido() - Method in class keel.Algorithms.Instance_Selection.SGA.Cromosoma: Tests if the chromosome is valid
esValido() - Method in class keel.Algorithms.Instance_Selection.SSMA.Cromosoma: Tests if the chromosome is valid
esValido() - Method in class keel.Algorithms.Preprocess.Instance_Selection.CHC.Cromosoma: Tests if the chromosome is valid
esValido() - Method in class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Tests if the chromosome is valid
esValido() - Method in class keel.Algorithms.Preprocess.Instance_Selection.GGA.Cromosoma: Tests if the chromosome is valid
esValido() - Method in class keel.Algorithms.Preprocess.Instance_Selection.IGA.Cromosoma: Tests if the chromosome is valid
esValido() - Method in class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Cromosoma: Tests if the chromosome is valid
esValido() - Method in class keel.Algorithms.Preprocess.Instance_Selection.SGA.Cromosoma: Tests if the chromosome is valid
esValido() - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma: Tests if the chromosome is valid
eta - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters: Problem coefficients
eta - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters: Problem coefficients
eta - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters: Problem coefficients
eta - Variable in class keel.Algorithms.Neural_Networks.net.Parameters: Problem coefficients
Etiquetas - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
euclideaDist(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbf: Computes the euclidean distance between a neuron and a vector
euclideaDist(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbf: Computes the euclidean distance between a neuron and a vector
euclideaDist(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbf: Computes the euclidean distance between a neuron and a vector
euclideaDist(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf: Computes the euclidean distance between a neuron and a vector
euclideaDist(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbf: Computes the euclidean distance between a neuron and a vector
euclideaDist(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf: Computes the euclidean distance between a neuron and a vector
euclidean(double[], double[]) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils: Computes the euclidean distance between two vector of doubles with equal size.
euclidean(double[], double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils: Computes the euclidean distance between two vector of doubles with equal size.
euclidean(double[], double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils: Computes the euclidean distance between two vector of doubles with equal size.
euclidean(double[], double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils: Computes the euclidean distance between two vector of doubles with equal size.
euclidean(double[], double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils: Computes the euclidean distance between two vector of doubles with equal size.
euclidean(double[], double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils: Computes the euclidean distance between two vector of doubles with equal size.
euclidean(double[], double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils: Computes the euclidean distance between two vector of doubles with equal size.
euclideanDist(double[]) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbf: Computes the euclidean distance between a neuron and a vector
euclideanDist(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbfn: Computes the euclidean distance between 2 vectors
euclideanDist(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn: Computes the euclidean distance between 2 vectors
euclideanDist(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn: Computes the euclidean distance between 2 vectors
euclideanDist(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn: Computes the euclidean distance between 2 vectors
euclideanDist(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn: Computes the euclidean distance between 2 vectors
euclideanDist(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn: Computes the euclidean distance between 2 vectors
euclideanDistance(double[], double[]) - Method in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Calculates the Euclidean distance between two instances
euclideanDistance(double[], double[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.Util: Computes the Euclidean distance between two instances (L2 norm)
euclideanDistance(double[], double[]) - Method in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Calculates the Euclidean distance between two instances
euclideanDistance(Prototype, Prototype) - Static method in class keel.Algorithms.Instance_Generation.utilities.Distance: Compute the Euclidean Distance between two prototypes.
euclideanDistance(double[], double[]) - Method in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Calculates the Euclidean distance between two instances
euclideanDistance(double[], double[]) - Method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm: Calculates the Euclidean distance between two instances
euclideanDistance(double[], double[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: Calculates the Euclidean distance between two instances
euclideanDistance(double[], double[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: Calculates the Euclidean distance between two instances
euclideanDistance(double[], double[]) - Static method in class keel.Algorithms.RST_Learning.Util: Calculates the Euclidean distance between two instances
euclideanDistance(Prototype, Prototype) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Distance: Compute the Euclidean Distance between two prototypes.
euclideanDistanceNS(double[], double[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.Util: Computes the unsquared Euclidean distance between two instances (L1 norm)
euclideanDistanceNS(double[], double[]) - Static method in class keel.Algorithms.RST_Learning.Util: Calculates the unsquared Euclidean distance between two instances
EUSCHCQstat - Class in keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat: The auxiliary class for the Qstat computation (diversity of the chrosomomes)
EUSCHCQstat(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.EUSCHCQstat: Builder with a script file (configuration file)
Ev(int, int) - Method in class keel.Algorithms.Discretizers.DIBD.DIBD: It computes the value distributional index needed to compute the compound distributional index (Ecom)
EV - Class in keel.Algorithms.Preprocess.Missing_Values.EM: This class stores a set of eigenvalues and eigenvectors
EV(DenseMatrix, double[]) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EM.EV: Copy constructor (soft)
eval(Individual) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.Algorithm: It evaluate the RB encoded in the individual "indiv"
eval(myDataset, Individual) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.Algorithm: It evaluates an individual
eval(int, int, Instance) - Method in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: Implements the abstract function of Kernel using the cache.
eval(int, int, Instance) - Method in class keel.Algorithms.SVM.SMO.supportVector.Kernel: Computes the result of the kernel function for two instances.
eval(int, int, Instance) - Method in class keel.Algorithms.SVM.SMO.supportVector.NormalizedPolyKernel: Computes the result of the kernel function for two instances.
eval(int, int, Instance) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Computes the result of the kernel function for two instances.
eval_EC(double[], double[], char[], int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Ecm: It evaluates by the mean square error(MSE)
eval_EC(double[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Ecm: It evaluates by the mean square error(MSE)
eval_EC(double[], double[], char[], int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Ecm: It evaluates by MSE
eval_EC(double[], char[], int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Ecm: It evaluates by MSE
eval_EC(double[], double[], char[], int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Ecm: It calculates the mean square error(MSE) of the training data
eval_EC(double[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Ecm: It evaluates by MSE
eval_EC(double[], double[], int[], int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Ecm: It evaluates the mean square error(MSE)
eval_EC(double[], char[], int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Ecm: It calculates the mean square error(MSE) of the training data
eval_test(Individual) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.Algorithm: It evaluate the RB encoded in the individual "indiv"
evalData - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
evalEliteIndiv(int, Genetic, TableVar, TableDat) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Evaluates an individual of the elite population
evalInd(Genetic, TableVar, TableDat) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndCAN: Evaluate a individual.
evalInd(Genetic, TableVar, TableDat) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndDNF: Evaluate a individual.
evalInd(Genetic, TableVar, TableDat) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Evaluate a individual.
evalInd(Genetic, TableVar, TableDat) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndCAN: Evaluate a individual.
evalInd(Genetic, TableVar, TableDat) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndDNF: Evaluate a individual.
evalInd(Genetic, TableVar, TableDat) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Evaluate a individual.
evalInd(Genetic, TableVar, TableDat, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndCAN: Evaluate a individual.
evalInd(Genetic, TableVar, TableDat, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndDNF: Evaluate a individual.
evalInd(Genetic, TableVar, TableDat, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual: Evaluate a individual.
evalIndiv(int, Genetic, TableVar, TableDat) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Evaluates an individual of the main population
evalIndiv(int, Genetic, TableVar, TableDat) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Evaluates an individual of the population
evalIndiv(int, Genetic, TableVar, TableDat, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Evaluates an individual of the main population
evalIndiv(int, Genetic, TableVar, TableDat, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Population: Evaluates an individual of the population
evalPop(Genetic, int, TableVar, TableDat) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Evaluates the non-evaluates individuals of the population Evaluates individuals from 0 to this.num_used (not all the individuals) and computes "original support"
evalPop(Genetic, TableVar, TableDat) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Population: Evaluates non-evaluated individuals
evalPop(Genetic, TableVar, TableDat, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Population: Evaluates the population
evalRbf(double[]) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbf: Computes the ouput of a RBF
evalRbfn(double[]) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Passes an input to the net obtaining its output
evalua() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.RuleBase: Function to evaluate the whole rule base by using the training dataset
evalua() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseR
evalua(GenotypeBoosting) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.Fitness
evalua(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.Fun
evalua(int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.GA
evalua(double[][], int[][], boolean[][], int[], Hyper[], double[][], double, int, double) - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Cromosoma: Evaluates a chromosome
evalua(double[][], double[][], int[][], boolean[][], int[], double[][], double[][], int[][], boolean[][], int[], String, int, String, boolean, boolean, double, int, int, boolean, Attribute[], boolean[][], boolean[][]) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.Chromosome: Function that evaluates a cromosome
evalua(double[][], double[][], int[][], boolean[][], int[], double, int, int, boolean) - Method in class keel.Algorithms.Instance_Selection.CHC.Cromosoma: Evaluates a chromosome
evalua(int[], int, int) - Method in class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Function that evaluates a chromosome
evalua(double[][], double[][], int[][], boolean[][], int[], double, int, int, boolean) - Method in class keel.Algorithms.Instance_Selection.GGA.Cromosoma: Evaluates a chromosome
evalua(double[][], double[][], int[][], boolean[][], int[], double, int, int, boolean) - Method in class keel.Algorithms.Instance_Selection.IGA.Cromosoma: Evaluates a chromosome
evalua(double[][], double[][], int[][], boolean[][], int[], double, int, int, boolean) - Method in class keel.Algorithms.Instance_Selection.PBIL.Cromosoma: Evaluates a chromosome
evalua(double[][], double[][], int[][], boolean[][], int[], double, int, int, boolean) - Method in class keel.Algorithms.Instance_Selection.SGA.Cromosoma: Evaluates a chromosome
evalua(double[][], double[][], int[][], boolean[][], int[], double, int, int, boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.CHC.Cromosoma: Evaluates a chromosome
evalua(int[], int, int) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Function that evaluates a chromosome
evalua(double[][], double[][], int[][], boolean[][], int[], double, int, int, boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GGA.Cromosoma: Evaluates a chromosome
evalua(double[][], double[][], int[][], boolean[][], int[], double, int, int, boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.IGA.Cromosoma: Evaluates a chromosome
evalua(double[][], double[][], int[][], boolean[][], int[], double, int, int, boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Cromosoma: Evaluates a chromosome
evalua(double[][], double[][], int[][], boolean[][], int[], double, int, int, boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SGA.Cromosoma: Evaluates a chromosome
evalua_poblation() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.AlgGenetic
evalua_poblation_test(int, float[][], Vector<Vector<Float>>) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.AlgGenetic
evalua_poblation_test(int, float[][], Vector<Vector<Float>>) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.AlgGenetic
evalua_poblation_test(int, int) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.AlgGenetic
evalua_poblation_test(int, float[][], Vector<Vector<Float>>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.AlgGenetic
evalua_poblation_test(int, float[][], Vector<Vector<Float>>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.AlgGenetic
EvaluaCalidadReglas - Class in keel.Algorithms.Rule_Learning.Prism: Get the statistical data from the algorithm.
EvaluaCalidadReglas(ConjReglas, ConjDatos, ConjDatos, int[], int[], String[]) - Constructor for class keel.Algorithms.Rule_Learning.Prism.EvaluaCalidadReglas: Calculates the final statisticals for a set of rules and a set of data
EvaluaCalidadReglas - Class in keel.Algorithms.Rule_Learning.UnoR: Get the statistical data from the algorithm.
EvaluaCalidadReglas(ConjReglas, ConjDatos, ConjDatos, int[], int[], String[], String[]) - Constructor for class keel.Algorithms.Rule_Learning.UnoR.EvaluaCalidadReglas: Calculates the final statisticals for a set of rules and a set of data
EvaluaCalidadReglas - Class in keel.Algorithms.Subgroup_Discovery.aprioriSD: Get the statistical data from the algorithm.
EvaluaCalidadReglas(ConjReglas, ConjDatos, ConjDatos, int[], int[], String[]) - Constructor for class keel.Algorithms.Subgroup_Discovery.aprioriSD.EvaluaCalidadReglas: Calculates the final statisticals for a set of rules and a set of data
EvaluaCalidadReglas - Class in keel.Algorithms.Subgroup_Discovery.CN2SD: Get the statistical data from the algorithm.
EvaluaCalidadReglas(ConjReglas, ConjDatos, ConjDatos, int[], int[], int, double, String[]) - Constructor for class keel.Algorithms.Subgroup_Discovery.CN2SD.EvaluaCalidadReglas: Calculates the final statisticals for a set of rules and a set of data
evaluacion(Vector, Vector, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion
evaluacionCompleta(int, int, int[]) - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma: Function that evaluates a cromosome completely
evaluacionCompleta(int, int, int[]) - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma: Function that evaluates a cromosome completely
evaluacionCompleta(int, int, int[]) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma: Function that evaluates a cromosome completely
evaluacionCompleta(int, int, int[]) - Method in class keel.Algorithms.Instance_Selection.SSMA.Cromosoma: Performs a full evaluation of a chromosome
evaluacionCompleta(int, int, int[]) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma: Performs a full evaluation of a chromosome
evaluacionKNN(int, double[][], int[], double[], int) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.KNN: Executes KNN
evaluacionKNN(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.KNN: Executes KNN
evaluacionKNN(int, double[][], int[], double[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.KNN
evaluacionKNN(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.KNN
evaluacionKNN(int, double[][], int[], double[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN(int, double[][], int[], double[], int) - Static method in class keel.Algorithms.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean) - Static method in class keel.Algorithms.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN2(int, double[][], int[], double[], int) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.KNN: Executes KNN
evaluacionKNN2(int, double[][], int[], double[], int, Referencia) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.KNN: Executes KNN
evaluacionKNN2(int, double[][], int[], double[], int, int[]) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.KNN: Executes KNN
evaluacionKNN2(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.KNN: Executes KNN
evaluacionKNN2(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, Referencia) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.KNN: Executes KNN
evaluacionKNN2(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[]) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.KNN: Executes KNN
evaluacionKNN2(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[], double[], int[]) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.KNN: Executes KNN
evaluacionKNN2(int, double[][], int[], double[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.KNN
evaluacionKNN2(int, double[][], int[], double[], int, Referencia) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.KNN
evaluacionKNN2(int, double[][], int[], double[], int, int[]) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.KNN
evaluacionKNN2(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.KNN
evaluacionKNN2(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, Referencia) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.KNN
evaluacionKNN2(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[]) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.KNN
evaluacionKNN2(int, double[][], int[], double[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN2(int, double[][], int[], double[], int, Referencia) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN2(int, double[][], int[], double[], int, int[]) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN2(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN2(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, Referencia) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN2(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[]) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN2(int, double[][], int[], double[], int) - Static method in class keel.Algorithms.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN2(int, double[][], int[], double[], int, Referencia) - Static method in class keel.Algorithms.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN2(int, double[][], int[], double[], int, int[]) - Static method in class keel.Algorithms.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN2(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean) - Static method in class keel.Algorithms.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN2(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, Referencia) - Static method in class keel.Algorithms.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN2(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[]) - Static method in class keel.Algorithms.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN3(int, double[][], int[], double[], int) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.KNN: Executes KNN
evaluacionKNN3(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.KNN: Executes KNN
evaluacionKNN3(int, double[][], int[], double[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.KNN: To implement Depur Algorithm, we need the neighboor's vector, to decide what we must make
evaluacionKNN3(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.KNN: To implement Depur Algorithm, we need the neighboor's vector, to decide what we must make
evaluacionKNN3(int, double[][], int[], double[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN3(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN3(int, double[][], int[], double[], int) - Static method in class keel.Algorithms.Preprocess.Basic.KNN: Executes KNN
evaluacionKNN3(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean) - Static method in class keel.Algorithms.Preprocess.Basic.KNN: Executes KNN
evaluacionKNNClass(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.EUSCHCQstat: Knn evaluation for classification
evaluacionKNNClass(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.SMOTE.MSMOTE
evaluacionKNNClass(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.SMOTE.SMOTE
evaluacionKNNClass(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.SMOTE_RSB
evaluacionKNNHyper(Hyper[], double[], int[], boolean[], int) - Static method in class keel.Algorithms.Hyperrectangles.EHS_CHC.EHS_CHC
evaluacionKNNHyper(Hyper[], double[], int[], boolean[], int, boolean[]) - Static method in class keel.Algorithms.Hyperrectangles.EHS_CHC.EHS_CHC
evaluacionParcial(int, int, int[], int, double[][], double, double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma: Partial evaluation.
evaluacionParcial(int, int, int[], int, double[][], double, double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma: Partial evaluation.
evaluacionParcial(int, int, int[], int, double[][], double, double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma: Partial evaluation.
evaluacionParcial(int, int, int[], int, double[][], double, double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Instance_Selection.SSMA.Cromosoma: Performs ta partial evaluation
evaluacionParcial(int, int, int[], int, double[][], double, double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma: Performs ta partial evaluation
evaluado() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Individuo
evaluado - Variable in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Evaluated flag.
evaluado - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Evaluated flag.
evaluado - Variable in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual: Evaluated flag.
evaluaEL(double[][], double[][], int[][], boolean[][], int[], double[][], double[][], int[][], boolean[][], int[], int, int, int, boolean) - Static method in class keel.Algorithms.Instance_Selection.Explore.EncodingLength: Function that return the Encoding Length value of a S set
evaluaEL(double[][], double[][], int[][], boolean[][], int[], double[][], double[][], int[][], boolean[][], int[], int, int, int, boolean) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.Explore.EncodingLength: Function that return the Encoding Length value of a S set
evaluaError(double[][], double[][], int[][], boolean[][], int[], boolean) - Method in class keel.Algorithms.Instance_Selection.ZhangTS.Cromosoma: Function that calculates the error threshold of a cromosome
evaluaError(double[][], double[][], int[][], boolean[][], int[], boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.Cromosoma: Function that calculates the error threshold of a cromosome
evaluaKNN(int, int[], double[], int[], boolean, boolean) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.ENN: Executes KNN to predict the class of the instance i.
evaluar() - Method in class keel.Algorithms.Decision_Trees.Target.Tree: Computes the fitness for the tree.
evaluar2() - Method in class keel.Algorithms.Decision_Trees.Target.Tree: Computes the fitness for the tree.
evaluate() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.RuleBase: Function to evaluate the whole rule base by using the training dataset
evaluate() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.RuleBase: Function to evaluate the whole rule base by using the training dataset
evaluate() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.RuleBase: Function to evaluate the whole rule base by using the training dataset
evaluate() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual: Evaluate this individual (fitness function)
evaluate() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: Function to evaluate the whole rule base by using the training dataset.
evaluate(double[], int[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: Function to evaluate the selected rules by using the training dataset and the fuzzy functions stored in the gene given.
evaluate(Apriori, double, double, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Individual: Evaluates this individual
evaluate() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.RuleBase: Function to evaluate the whole rule base by using the training dataset
evaluate(double[], double[][], int, int[], int) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.KNN: Evaluates a instance to predict its class.
evaluate(double[], boolean) - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode: Given a instance, returns its class or regression value.
evaluate(double[]) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Classifies a given item with the information stored in the node and its descendants, making a call to the specific classifiers at the leaves
evaluate(double[], ArrayList<myAttribute>) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode: Classifies a given item with the information stored in the node and its descendants
evaluate() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase: It evaluates the rule set
evaluate() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.RuleBase: Evaluation funtion It counts the number of examples correctly classified
evaluate(myDataset, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Evaluates this rule, computing the raw_fitness of the rule and the weight.
evaluate() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual: Evaluate this individual (fitness function)
evaluate() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
evaluate(double[], int[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
evaluate(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.Classifier: abstract method for evaluating the classifier for a given input example.
evaluate(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.FuzzyClassifier: This method evaluates the classifier for a given input example.
evaluate(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.FuzzyFGPClassifier: This method evaluates the classifier for a given input example.
evaluate(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.FUN: The abstract method evaluate the fitness of the rule set
evaluate(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.FUNGPRS: The public method evaluate the fitness of the rule set
evaluate() - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Cogin
evaluate(double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome: Evaluates an input example (array of doubles as extracted from KEEL API)
evaluate(InstanceSet) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Cochromosome: Evaluates this cochromosome with the provided data set
evaluate() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Core: Evaluates the current population (set of rules) with the training data set
evaluate(InstanceSet) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Population: Evaluates all this population with a given data set.
evaluate() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Ilga: Its evaluate the NEW poblation, with the train data
evaluate(double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Rule: Evaluates the inputs and output the class if covered, -1 if not
evaluate() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.SEM: Its evaluate the NEW poblation, using a metric which summarizes the train CR and test CR
evaluate(myDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual: Evaluates (calculate the fitness of) the individual
evaluate() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Oiga: Its evaluate the NEW poblation, with the train data
evaluate(double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Rule: Evaluates the inputs and output the class if covered, -1 if not
evaluate() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.SEM: Its evaluate the NEW poblation, using a metric which summarizes the train CR and test CR
evaluate(double[]) - Method in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.Hyperrectangles.BNGE.BNGE: Classifies an instance using the ruleset
evaluate(double[]) - Method in class keel.Algorithms.Hyperrectangles.INNER.INNER: Classifies an instance using the ruleset
evaluate(double[]) - Method in class keel.Algorithms.Hyperrectangles.RISE.RISE: Classifies an instance using the ruleset
evaluate(double[], double[][], int, int[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.KNN: Evaluates a instance to predict its class.
evaluate(myDataset, ArrayList<Rule>, GP_COACH_H, boolean, boolean, int, int, double) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC_Chromosome: Evaluates this chromosome, computing the fitness of the rule and the weight.
evaluate(myDataset, double) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Evaluates this rule, computing the raw_fitness of the rule and the weight.
evaluate(double[], double[][], int, int[], int) - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm: Evaluates a instance to predict its class.
evaluate(double[], double[][], int, int[], int) - Static method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.Lazy_Learning.CamNN.CamNN: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.Lazy_Learning.CenterNN.CenterNN: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.Lazy_Learning.CPW.CPW: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.Lazy_Learning.CW.CW: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.Lazy_Learning.Deeps.Deeps: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.Lazy_Learning.DeepsNN.DeepsNN: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.Lazy_Learning.IDIBL.IDIBL: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.Lazy_Learning.KNN.KNN: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.Lazy_Learning.KNNAdaptive.KNNAdaptive: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.Lazy_Learning.KSNN.KSNN: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.Lazy_Learning.KStar.KStar: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.Lazy_Learning.LazyDT.LazyDT: Classifies a given item with the information stored using the LazyDT algorithm
evaluate(double[]) - Method in class keel.Algorithms.Lazy_Learning.LBR.LBR: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.Lazy_Learning.NM.NM: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.Lazy_Learning.NSC.NSC: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.Lazy_Learning.PW.PW: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.MIL.Diverse_Density.Optimization.DDoptimization
evaluate(double[]) - Method in class keel.Algorithms.MIL.Diverse_Density.Optimization.EMDDoptimization
evaluate(double[]) - Method in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
evaluate(<any>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.SoftmaxClassificationProblemEvaluator: Evaluates a individual
evaluate(I) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Evaluates a individual
evaluate(<any>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.problem.regression.RegressionProblemEvaluator: Evaluate a individual
evaluate(double[], double[][], int, int[], int) - Static method in class keel.Algorithms.Preprocess.Basic.KNN: Evaluates a instance to predict its class.
evaluate() - Method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.Chromosome: Fitness function.
evaluate(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.Gcvfctn: Evaluates the GCV function
evaluate(double[]) - Method in interface keel.Algorithms.Preprocess.Missing_Values.EM.util.MultivariateFunction: compute function value
evaluate(double) - Method in interface keel.Algorithms.Preprocess.Missing_Values.EM.util.UnivariateFunction: compute function value
evaluate(double[][][]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.FUN: Returns the training mean square error for a perceptron with weights x
evaluate(double[][][]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.SquaresErrorNN: Returns the training mean square error for a perceptron with weights x
evaluate(double[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: Evaluates a instance to predict its class.
evaluate(double[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: Evaluates a instance to predict its class.
Evaluate(Instance[], int, String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.MultipleClassifierSystem
Evaluate(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion: Function which evaluates the population
Evaluate(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion: Function which evaluates the population
Evaluate(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion: Function which evaluates the population
Evaluate(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion: Function which evaluates the population
Evaluate(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion: Function which evaluates the population
Evaluate(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion: Function which evaluates the population
Evaluate(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion: Function which evaluates the population
Evaluate(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion: Function which evaluates the population
evaluate() - Method in class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome: Function that evaluates a chromosome.
evaluate() - Method in class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome: Function that evaluates a chromosome.
evaluate(double[]) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.NumericalNaiveBayes: Evaluates a instance to predict its class.
evaluate(double[][][]) - Method in class keel.Algorithms.Shared.ClassicalOptim.FUN: Returns the training mean square error for a perceptron with weights x
evaluate(double[][][]) - Method in class keel.Algorithms.Shared.ClassicalOptim.SquaresErrorNN: Returns the training mean square error for a perceptron with weights x
evaluate(double[][][]) - Method in class keel.Algorithms.Shared.ClassicalOptim.SquaresErrorQUAD: Returns the training mean square error for a perceptron with weights x
evaluate(int, int, Instance) - Method in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: This method is overridden in subclasses to implement specific kernels.
evaluate(Kernel, Instances) - Method in class keel.Algorithms.SVM.SMO.supportVector.KernelEvaluation: Evaluates the Kernel with the given commandline options and returns the evaluation string.
evaluate(int, int, Instance) - Method in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel
evaluate(int, int, Instance) - Method in class keel.Algorithms.SVM.SMO.supportVector.PolyKernel
evaluate(int, int, Instance) - Method in class keel.Algorithms.SVM.SMO.supportVector.Puk: returns the dot product
evaluate(int, int, Instance) - Method in class keel.Algorithms.SVM.SMO.supportVector.RBFKernel
evaluate2(double[]) - Method in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Evaluates a instance to predict its class probabilities.
evaluate2(double[]) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.NumericalNaiveBayes: Evaluates a instance to predict its class.
Evaluate_best_fuzzy_system_in_test() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.Algorithm: It Evaluates the performance of the best evolved fuzzy system on test data.
evaluate_copopulation() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Core: Evaluates the current copopulation
Evaluate_fuzzy_system() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.Algorithm: It Evaluates the performance of the fuzzy system.
evaluate_poblation() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.AlgGenetic
evaluate_poblation() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.AlgGenetic
evaluate_poblation() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.AlgGenetic
evaluate_poblation() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.AlgGenetic
evaluateAllSplits(int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Evaluates all splits and returns the best split found.
evaluateAllSplits() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Node: Evaluates all splits and returns the best split found.
evaluateClassifier(Classifier) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper
evaluateClassifier(Classifier) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper
evaluateDeeps(double[]) - Method in class keel.Algorithms.Lazy_Learning.DeepsNN.DeepsNN: Evaluates a instance to predict its class, using Deeps algorithm.
evaluateGradient(double[]) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization: Subclass should implement this procedure to evaluate gradient of the objective function
evaluateHessian(double[], int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization: Subclass is recommended to override this procedure to evaluate second-order gradient of the objective function.
evaluateItem(double[]) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.FunctionalTrees: Classifies a given item with the information stored in the tree
evaluateItem(double[], ArrayList<myAttribute>) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.PUBLIC: Classifies a given item with the information stored in the tree
evaluateItemset(Itemset) - Method in class keel.Algorithms.Decision_Trees.C45.C45: Function to evaluate the class which the itemset must have according to the classification of the tree.
evaluateItemset(Itemset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.C45: Function to evaluate the class which the itemset must have according to the classification of the tree.
evaluateItemset(Itemset, Node) - Method in class keel.Algorithms.Decision_Trees.ID3.ID3: Function to evaluate the class which the itemset must have according to the classification of the tree.
evaluateItemset(Itemset, Node) - Method in class keel.Algorithms.Decision_Trees.SLIQ.SLIQ: Returns the predicted class of the given example obtained from the given node.
evaluateItemset(Itemset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.C45: Function to evaluate the class which the itemset must have according to the classification of the tree.
evaluateItemset(Itemset, int, boolean) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.C45CS: Function to evaluate the class which the itemset must have according to the classification of the tree.
evaluateItemset(Itemset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.C45: Function to evaluate the class which the itemset must have according to the classification of the tree.
evaluateItemset(Itemset) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.C45: Function to evaluate the class which the itemset must have according to the classification of the tree.
evaluateItemset(Itemset) - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.C45: Function to evaluate the class which the itemset must have according to the classification of the tree.
evaluateItemset(Itemset) - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.C45: Function to evaluate the class which the itemset must have according to the classification of the tree.
evaluateItemset(Itemset) - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.C45: Function to evaluate the class which the itemset must have according to the classification of the tree.
evaluateItemset(Itemset, Node) - Method in class keel.Algorithms.Rule_Learning.ART.ART: Function to evaluate the class which the itemset must have according to the classification of the tree.
evaluateItemset(Itemset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.C45: Function to evaluate the class which the itemset must have according to the classification of the tree.
evaluateItemset(Itemset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.C45: Function to evaluate the class which the itemset must have according to the classification of the tree.
evaluateItemset(Itemset) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.DataSqueezer: Function to evaluate the class which the itemset must have according to the classification of the rules.
evaluateItemset(Itemset) - Method in class keel.Algorithms.Rule_Learning.PART.C45: Function to evaluate the class which the itemset must have according to the classification of the tree.
evaluateItemset(Itemset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45: Function to evaluate the class which the itemset must have according to the classification of the tree.
evaluateMembership(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.Fuzzy: Returns the membership level for the individual x.
evaluateMembership(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut: Returns the membership level for the individual x.
evaluateMembership(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyInterval: Returns the membership level for the individual x.
evaluateMembership(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyNumberTRIANG: Returns the membership level for the individual x.
evaluateMembership(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyNumberTRLEFT: Returns the membership level for the individual x.
evaluateMembership(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzySingleton: Returns the membership level for the individual x.
evaluateMembership(long, double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase: Returns Grade of Membership of x to rule r antecedent.
EvaluateModel - Class in keel.Algorithms.Decision_Trees.M5: Class for evaluating machine learning models.
EvaluateModel(M5Instances) - Constructor for class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Initializes all the counters for the evaluation and also takes a cost matrix as parameter.
evaluateModel(String, String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Evaluate the model M5 by executing the function evaluateModel(M5 classifier, String[] options).
evaluateModel(M5, String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Evaluates a classifier with the options given in an array of strings.
evaluateModel(M5, M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Evaluates the classifier on a given set of instances.
evaluateModelOnce(M5, M5Instance) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Evaluates the classifier on a single instance.
evaluateModelOnce(double[], M5Instance) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Evaluates the supplied distribution on a single instance.
evaluateModelOnce(double, M5Instance) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Evaluates the supplied prediction on a single instance.
evaluatePRDF(double[]) - Method in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: Evaluates a vector of doubles (an instance) with the Positive Definite Functions associated
evaluateRuleCX(int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
evaluateRuleCX(int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
evaluateRuleQuality - Class in keel.Algorithms.Genetic_Rule_Learning.SIA: Title: Evaluation of the quality of the rules Description: This class computes the final statistics
evaluateRuleQuality(ruleSet, int, double[][], int[], int[], String[]) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.SIA.evaluateRuleQuality: Class Builder
evaluateRuleQuality - Class in keel.Algorithms.Rule_Learning.AQ: Title: Evaluation of the quality of the rules Description: This class computes the final statistics
evaluateRuleQuality(ruleSet, myDataset, myDataset, int[], int[], String[]) - Constructor for class keel.Algorithms.Rule_Learning.AQ.evaluateRuleQuality: It computes the final statistics for a rule set and a data-set
evaluateRuleQuality - Class in keel.Algorithms.Rule_Learning.CN2: Title: Evaluation of the quality of the rules Description: This class computes the final statistics
evaluateRuleQuality(ruleSet, myDataset, myDataset, int[], int[], String[]) - Constructor for class keel.Algorithms.Rule_Learning.CN2.evaluateRuleQuality: It computes the final statistics for a rule set and a data-set
EvaluateRules - Class in keel.Algorithms.Subgroup_Discovery.SDAlgorithm: Evaluate the rules obtained by the algorithm
EvaluateRules(SetRules, SetData, SetData, int[], int[], String[], String) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.EvaluateRules: Calculate the quality measures of the rules obtained by the algorithm
evaluateTest() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
evaluation(myDataset) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule: Evaluates the rule for computing its fitness
evaluation() - Method in class keel.Algorithms.PSO_Learning.CPSO.Particle
evaluation() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
evaluation() - Method in class keel.Algorithms.PSO_Learning.REPSO.Particle
evaluationKNN_SPIDER2(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[], boolean[]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SPIDER2.SPIDER2: Computes the k nearest neighbors SPIDER of a given item.
evaluationKNNClass(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[], int) - Static method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.SMOTE: Computes the k nearest neighbors of a given item belonging to a fixed class.
evaluationKNNClass(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.ADASYN.ADASYN: Computes the k nearest neighbors of a given item belonging to a fixed class.
evaluationKNNClass(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.ADOMS.ADOMS: Computes the k nearest neighbors of a given item belonging to a fixed class.
evaluationKNNClass(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.Borderline_SMOTE.Borderline_SMOTE: Computes the k nearest neighbors of a given item belonging to a fixed class.
evaluationKNNClass(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.Safe_Level_SMOTE.Safe_Level_SMOTE: Computes the k nearest neighbors of a given item belonging to a fixed class.
evaluationKNNClass(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE.SMOTE: Computes the k nearest neighbors of a given item belonging to a fixed class.
evaluationKNNClass(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_ENN.SMOTE_ENN: Computes the k nearest neighbors of a given item belonging to a fixed class.
evaluationKNNClass(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_TomekLinks.SMOTE_TomekLinks: Computes the k nearest neighbors of a given item belonging to a fixed class.
evaluationKNNClass(int, double[][], double[][], int[][], boolean[][], int[], double[], double[], int[], boolean[], int, boolean, int[], int, boolean[]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SPIDER2.SPIDER2: Computes the k nearest neighbors of a given item belonging to a fixed class.
evaluationKNNClass(int, int, int, boolean, int[], int, int[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Distance
evaluationKNNClass(int, int, int, boolean, int[], int) - Method in class keel.GraphInterKeel.datacf.partitionData.PartitioningSchemes.DistanceBased_best: Computes the k nearest neighbors of a given item belonging to a fixed class.
evaluationRbf(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbf: Computes the ouput of a RBF
evaluationRbf(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbf: Computes the ouput of a RBF
evaluationRbf(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbf: Computes the ouput of a RBF
evaluationRbf(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf: Computes the ouput of a RBF
evaluationRbf(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbf: Computes the ouput of a RBF
evaluationRbf(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf: Computes the ouput of a RBF
evaluationRbfn(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbfn: Passes an input to the net obtaining its output
evaluationRbfn(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn: Passes an input to the net obtaining its output
evaluationRbfn(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn: Passes an input to the net obtaining its output
evaluationRbfn(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn: Passes an input to the net obtaining its output
evaluationRbfn(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn: Passes an input to the net obtaining its output
evaluationRbfn(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn: Passes an input to the net obtaining its output
evaluator - Static variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.KEELIRPropPlusWrapperClas: Individuals evaluator
evaluator - Static variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.KEELIRPropPlusWrapperRegr: Individuals evaluator
evaluator - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Individuals evaluator
EventCovering - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering: Based on the work of Wong et al., a mixed-mode probability model is approximated by a discrete one.
EventCovering(String) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.EventCovering: Creates a new instance of EventCovering
everythingOK() - Method in class keel.Algorithms.Rule_Learning.AQ.AQ: It checks if some of the preconditions are not satisfied: There are any continuous value or there was a problem while reading the data files
everythingOK() - Method in class keel.Algorithms.Rule_Learning.CN2.CN2: It checks if some of the preconditions are not satisfied: There are any continuous value or there was a problem while reading the data files
evIndex - Variable in class keel.Algorithms.Preprocess.Missing_Values.EM.EVpair: the index that this element has in the original structure
Evolucion - Class in keel.Algorithms.Genetic_Rule_Learning.Hider
Evolucion(Vector, double, String) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion: Constructor
evolucion() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.AlgGenetic
evolucion() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.AlgGenetic
evolution() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.AlgGenetic
evolution() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.AlgGenetic
evolution() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.AlgGenetic
evolution() - Method in class keel.Algorithms.RST_Learning.EFS_RPS.EFS_RPS: Performs the evolution process
evolution() - Method in class keel.Algorithms.RST_Learning.EIS_RFS.EIS_RFS: Performs the evolution process
evolve(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms.AlgorithmGAPGen: this method is intended for evolving the generational GAP algorithm for the given number of iterations with an generational GAP algorithm.
evolve(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms.AlgorithmGAPNiches: this method is intended for evolving the algorithm for a given number of iterations with an steady with niches GAP algorithm.
evolve(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms.AlgorithmGAPSteady: this method is intended for evolving the algorithm for a given number of iterations with an steady with niches GAP algorithm.
evolve(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms.GeneticAlgorithm: abstract method for evolving the algorithm for a given number of iterations.
evolve(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms.GeneticAlgorithmGenerational: this method is intended for evolving the algorithm for a given number of iterations with an generational GA algorithm.
evolve(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms.GeneticAlgorithmSteady: this method is intended for evolving the algorithm for a given number of iterations with an steady GA algorithm.
evolve(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms.SimulatedAnnealing: this method is intended for evolving the algorithm for a given number of iterations with an SAP algorithm.
evolve_copopulation() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Core: Evaluates the copopulation, mutating and evaluating it for a established number of iterations.
EvolvePopulation(SetupParameters, Data) - Method in class keel.Algorithms.Neural_Networks.gann.Population: Method that evolves the population until the maximum number of generations is reached.
EVpair - Class in keel.Algorithms.Preprocess.Missing_Values.EM: This class implements a pair of eigenvalues and their index, in order to be possible to sort them, when inserted in a Collections structure
EVpair(double, int) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EM.EVpair: Copy constructor
ex_classes - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
examineExample(int) - Method in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: Examines instance.
examineExample(int) - Method in class keel.Algorithms.SVM.SMO.SMOreg: Examines instance.
examineExample(int) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMO: examineExample method from pseudocode.
examineExample(int) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: parameters correspond to pseudocode from paper.
example_set - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV
example_set - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE: Encodes a set of examples (including information about if the example is covered by a rule, the coverage degree, ...).
example_set - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2
example_set - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0
Examples_per_Class(int, int, int, int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set: For each class, it counts the number of examples
Examples_per_Class(int, int, int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set: For each class, it counts the number of examples
Examples_per_Class(int, int, int, int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set: Calculates the number of examples per class in the problem
Examples_per_Class(int, int, int, int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set: For each class, it counts the number of examples
Examples_per_Class(int, int, int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set: For each class, it counts the number of examples
Examples_per_Class(int, int, int, int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set: For each class, it counts the number of examples
Examples_per_Class(int, int, int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set: For each class, it counts the number of examples
ExamplesClass(int) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Utils: Counts the number of examples of the DataSet belonging to the number of the class indicated
ExamplesClass(int) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Utils: Counts the number of examples of the DataSet belonging to the number of the class indicated
ExamplesClass(int) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Utils: Counts the number of examples of the DataSet belonging to the number of the class indicated
examplesCoverPopulation(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Population: This function marks the examples covered by the actual population.
ExampleWeight - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD: Title: ExampleWeight Description: The objects of this class contain the weights of a patterns Copyright: Copyright KEEL (c) 2007 Company: KEEL
ExampleWeight(int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.ExampleWeight: Builder
ExampleWeight - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS: Title: ExampleWeight Description: The objects of this class contain the weights of a patterns Copyright: Copyright KEEL (c) 2007 Company: KEEL
ExampleWeight(int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.ExampleWeight: Builder
ExcelAdapter - Class in keel.GraphInterKeel.statistical: ExcelAdapter enables Copy-Paste Clipboard functionality on JTables.
ExcelAdapter(JTable) - Constructor for class keel.GraphInterKeel.statistical.ExcelAdapter: The Excel Adapter is constructed with a JTable on which it enables Copy-Paste and acts as a Clipboard listener.
ExcelToKeel - Class in keel.Algorithms.Preprocess.Converter: ExcelToKeel This class extends from the Importer class.
ExcelToKeel(String) - Constructor for class keel.Algorithms.Preprocess.Converter.ExcelToKeel: ExcelToKeel class Constructor.
exception - Variable in class keel.GraphInterKeel.experiments.EducationalRunEvent
exception - Variable in class keel.GraphInterKeel.experiments.EducationalRunkeelEvent
exception - Variable in class keel.GraphInterKeel.experiments.RunkeelEvent
ExceptionDatasets - Class in keel.Algorithms.MIL
ExceptionDatasets(String) - Constructor for class keel.Algorithms.MIL.ExceptionDatasets: initializes the capability with the given flags
exceptionsLength - Variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
exceptionsLength - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
Exchange() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.Algorithm: It exchange the old and the new population
exchangeAntecedentLabel(int, FuzzyAntecedent, DataBase) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Changes the antecedent labels for one condition in the antecedent to another labels in the antecedent
exchangeAntecedentLabel(int, FuzzyAntecedent, DataBase, int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Changes the antecedent labels for one condition in the antecedent to another labels in the antecedent
exchangeVariables(boolean[], ArrayList<FuzzyAntecedent>) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Changes the antecedent labels for one condition in the antecedent to another labels in the antecedent
exchangeVariables(boolean[], ArrayList<FuzzyAntecedent>, int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Changes the antecedent labels for one condition in the antecedent to another labels in the antecedent
EXCHANGING - Static variable in class keel.Algorithms.Genetic_Rule_Learning.OCEC.OCEC: Number to indentify the different types of scheme (EXCHANGING).
execute() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.CBA: It launches the algorithm
execute() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.CBA2: It launches the algorithm
execute() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.CMAR: It launches the algorithm
execute() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.CPAR: It launches the algorithm
execute() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.CFAR: It launches the algorithm
execute() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Farchd: It launches the algorithm
execute() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.FCRA: It launches the algorithm
execute() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Multiclassifier: It launches the algorithm
execute() - Method in class keel.Algorithms.Decision_Trees.DT_GA.DT_GA: It launches the algorithm
execute() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.DT_oblicuo: It launches the algorithm
execute() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.FunctionalTrees: This method performs the classification for all the instances: the train and the test sets
execute() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.PUBLIC: This method performs the classification for all the instances: the train and the test sets
execute() - Method in class keel.Algorithms.Decision_Trees.Target.Target: It launches the algorithm
execute() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Fuzzy_Chi: It launches the algorithm
execute() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Fuzzy_Ish: It launches the algorithm
execute() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.SGERD: It launches the algorithm
execute() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.Algorithm: It launches the algorithm
execute(parseParameters) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.main_c: It launches the algorithm
execute() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.Algorithm: It launches the algorithm
execute() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.Algorithm: It launches the algorithm
execute(parseParameters) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.main_c: It launches the algorithm
execute(parseParameters) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.main_c: It launches the algorithm
execute() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Fuzzy_Ish: It launches the algorithm
execute() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Fuzzy_Ish: It launches the algorithm
execute() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.GFS_RB_MF: It launches the algorithm
execute() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.GP_COACH: It launches the algorithm
execute() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.IVTURS: It launches the algorithm
execute() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Thrift: It launches the algorithm
execute() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: It launches the FURIA algorithm
execute() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.DMEL: It launches the algorithm
execute() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.GIL: It launches the algorithm
execute() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Algorithm: It launches the algorithm
execute() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.OCEC: It launches the algorithm
execute() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.RMini: It launches the algorithm
execute() - Method in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Executes the classification of train and test data sets
execute() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.multi_C45: It launches the algorithm
execute() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.GP_COACH_H: It launches the algorithm
execute(String[]) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm: Execute the algorithm given.
execute() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Execute the reduction of the data set.
execute() - Method in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Executes the classification of train and test data sets
execute() - Method in class keel.Algorithms.MIL.AbstractMIAlgorithm
execute() - Method in class keel.Algorithms.MIL.APR.GFS_AllPositive_APR.GFS_AllPositive_APR
execute() - Method in class keel.Algorithms.MIL.APR.GFS_ElimCount_APR.GFS_ElimCount_APR
execute() - Method in class keel.Algorithms.MIL.APR.GFS_Kde_APR.GFS_Kde_APR
execute() - Method in class keel.Algorithms.MIL.APR.IteratedDiscrimination.IteratedDiscrimination
execute() - Method in class keel.Algorithms.MIL.Diverse_Density.DD.DD
execute() - Method in class keel.Algorithms.MIL.Diverse_Density.EMDD.EMDD
execute() - Method in class keel.Algorithms.MIL.Nearest_Neighbour.CKNN.CKNN
execute() - Method in class keel.Algorithms.MIL.Nearest_Neighbour.KNN.KNN
execute() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GGA.GGA: Executes the GGA
execute() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: Executes the classification of train and test data sets
execute() - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: Executes the classification of train and test data sets
execute() - Method in class keel.Algorithms.PSO_Learning.CPSO.CPSO: It launches the algorithm
execute() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.LDWPSO: It launches the algorithm
execute() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.PSOLDA: It launches the algorithm
execute() - Method in class keel.Algorithms.PSO_Learning.REPSO.REPSO: It launches the algorithm
execute() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.Algorithm: It launches the algorithm
execute() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.Algorithm: It launches the algorithm
execute() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Fuzzy_GB_NFRM: It launches the algorithm
execute(Chc, String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Algorithm: It launches the algorithm
execute(Chc, String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Algorithm: It launches the algorithm
execute(Chc, String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Algorithm: It launches the algorithm
execute(Chc, String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Algorithm: It launches the algorithm
execute(Chc, String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Algorithm: It launches the algorithm
execute(Chc, String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Algorithm: It launches the algorithm
execute(Chc, String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Algorithm: It launches the algorithm
execute(Chc, String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Algorithm: It launches the algorithm
execute() - Method in class keel.Algorithms.Rule_Learning.AQ.AQ: We execute here the AQ algorithm and we create the necessary output data
execute() - Method in class keel.Algorithms.Rule_Learning.CN2.CN2: We execute here the CN2 algorithm and we create the necessary output data
execute() - Method in class keel.Algorithms.Rule_Learning.LEM1.Algorithm: It launches the algorithm
execute() - Method in class keel.Algorithms.Rule_Learning.LEM2.Algorithm: It launches the algorithm
execute(String) - Method in class keel.Algorithms.Rule_Learning.LEM2.Main: It launches the algorithm
execute() - Method in class keel.Algorithms.Rule_Learning.Ripper.Ripper: It launches the algorithm.
execute() - Method in class keel.Algorithms.Rule_Learning.Ritio.Algorithm: It launches the algorithm
execute(String) - Method in class keel.Algorithms.Rule_Learning.Ritio.Main: It launches the algorithm
execute() - Method in class keel.Algorithms.Rule_Learning.Rules6.Algorithm: It launches the algorithm
execute(String) - Method in class keel.Algorithms.Rule_Learning.Rules6.Main: It launches the algorithm
execute() - Method in class keel.Algorithms.Rule_Learning.Slipper.Slipper: It launches the algorithm.
execute() - Method in class keel.Algorithms.Rule_Learning.SRI.Algorithm: It launches the algorithm
execute(String) - Method in class keel.Algorithms.Rule_Learning.SRI.Main: It launches the algorithm
execute(String[]) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm: Execute the algorithm given.
execute() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Execute the reduction of the data set.
execute() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.Algorithm: It launches the algorithm
execute() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SD: Execute the algorithm SD
execute() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.FPgrowth: It launches the algorithm
execute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Alcalaetal: It launches the algorithm
execute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FingramsKEEL: It launches the algorithm
execute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyApriori: It launches the algorithm
execute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.GeneticFuzzyApriori: It launches the algorithm
execute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.GeneticFuzzyAprioriDC: It launches the algorithm
execute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Alatasetal: It launches the algorithm
execute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.Apriori: It launches the algorithm
execute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.ARMMGA: It launches the algorithm
execute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.EARMGA: It launches the algorithm
execute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.Eclat: It launches the algorithm
execute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.FPgrowth: It launches the algorithm
execute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.GAR: It launches the algorithm
execute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.GENAR: It launches the algorithm
execute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENAR: It launches the algorithm
execute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_Ghosh: It launches the algorithm
execute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_Gosh: It launches the algorithm
execute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.MOPNAR: It launches the algorithm
execute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.QAR_CIP_NSGAII: It launches the algorithm
execute() - Method in class keel.GraphInterKeel.experiments.CreateInform: This method has to invoque for to create the report.
execute_nesting(int[]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Multiclassifier: It executes the algorithm, but only for those instances which were ties in the previous OVO
executeCommand() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.SystemCommandExecutor
executeReference() - Method in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Executes the classification of reference and test data sets
executeReference() - Method in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Executes the classification of reference and test data sets
executeReference() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: Executes the classification of reference and test data sets
executeReference() - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: Executes the classification of reference and test data sets
ExecutionOptions - Class in keel.GraphInterKeel.experiments
ExecutionOptions(Frame, boolean) - Constructor for class keel.GraphInterKeel.experiments.ExecutionOptions: Builder
ExecutionOptions(Frame, String, boolean) - Constructor for class keel.GraphInterKeel.experiments.ExecutionOptions
exh_test - Variable in class keel.GraphInterKeel.experiments.DatasetXML
exhaustiveMerge(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.MODL.MODL: Performs an exhaustive bottom-up merge of all unitary intervals to a unique interval.
existContinousAttributes() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Checks if in the class the is any in-put of real type or continous
existingSelectors() - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Return an array of size numAttributes.
existInstanceOfClassC(int) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Check if in the set of the instances the are instances of a determined class
existMore() - Method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Informs if there are more arguments.
existMore() - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Informs if there are more arguments.
exists(TechnicalInformation.Field) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation: returns TRUE if the field is stored and has a value different from the empty string.
exists(TechnicalInformation.Field) - Method in class keel.Algorithms.SVM.SMO.core.TechnicalInformation: returns TRUE if the field is stored and has a value different from the empty string.
existsAnyMissingValue() - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: It returns if there is any missing value.
existsAnyMissingValue() - Method in class keel.Dataset.Instance: It returns if there is any missing value.
existsInputMissingValues() - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: It informs about the existence of missing values in the inputs
existsInputMissingValues() - Method in class keel.Dataset.Instance: It informs about the existence of missing values in the inputs
existsOutputMissingValues() - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: It informs about the existence of missing values in the outputs.
existsOutputMissingValues() - Method in class keel.Dataset.Instance: It informs about the existence of missing values in the outputs.
exit - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.EscribeBCLing: Exit value.
exit - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.LeerWm: Exit value.
exit - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.EscribeBCLing: Exit value.
exit - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.LeerWm: Exit value.
exit - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.EscribeBCLing: Exit value.
exit - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.LeerWm: Exit value.
exit - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.EscribeBCLing: Exit value.
exit - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.LeerWm: Exit value.
exit - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.EscribeBCLing: Exit value.
exit - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.LeerWm: Exit value.
exit - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.EscribeBCLing: Exit value.
exit - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.LeerWm: Exit value.
exit - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.EscribeBCLing: Exit value.
exit - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.LeerWm: Exit value.
exit - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.EscribeBCLing: Exit value.
exit - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.LeerWm: Exit value.
exitResult(SetData) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.EvaluateRules: Generate a string with the classification of the total examples for a data set
ExNotNominalAttr - Exception in keel.Algorithms.Rule_Learning.Swap1
ExNotNominalAttr() - Constructor for exception keel.Algorithms.Rule_Learning.Swap1.ExNotNominalAttr: Creates a new instance of ExNotNominalAttr without detail message.
ExNotNominalAttr(String) - Constructor for exception keel.Algorithms.Rule_Learning.Swap1.ExNotNominalAttr: Constructs an instance of ExNotNominalAttr with the specified detail message.
exp() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut: Returns the exponential of the present FuzzyInterval.
EXP - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for output line.
exp - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.ExponentialFormat: Exponent size.
EXP - Static variable in interface keel.Dataset.DataParserConstants
exp - Variable in class keel.GraphInterKeel.experiments.Container_Selected
expan(ActionEvent, int, Joint, Node) - Method in class keel.GraphInterKeel.experiments.Container_Selected
ExpandBuff(boolean) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
ExpandBuff(boolean) - Static method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
ExpandBuff(boolean) - Static method in class keel.Dataset.SimpleCharStream
expectation(double, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.ENPC.ENPCGenerator: Function expectation of class sj in the S prototypeSet., see equation (3).
expectedRuleSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
expectedTokenSequences - Variable in exception keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParseException: Each entry in this array is an array of integers.
expectedTokenSequences - Variable in exception keel.Algorithms.Rule_Learning.Swap1.ParseException: Each entry in this array is an array of integers.
expectedTokenSequences - Variable in exception keel.Dataset.ParseException: Each entry in this array is an array of integers.
expectedValue(int[][], int, int) - Method in class keel.Algorithms.Discretizers.MVD.MVD: Computes the expected value from the contingency table of this node
experimentName - Variable in class keel.GraphInterKeel.experiments.EducationalReport
Experiments - Class in keel.GraphInterKeel.experiments
Experiments() - Constructor for class keel.GraphInterKeel.experiments.Experiments: Builder
Experiments(Frame, int) - Constructor for class keel.GraphInterKeel.experiments.Experiments: Creates a new form, assigning a parent frame (so the experiment windows can be disposed, and the father set visible again on closing)
experiments_mouseEntered(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Enter in experiments button
experiments_mouseExited(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Exit from experiments button
experiments_mouseReleased(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Entering in Experiments module
experimentsLQD_mouseEntered(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Enter in LQD button
experimentsLQD_mouseExited(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Exit from LQD button
experimentsLQD_mouseReleased(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Entering in LQD module
experimentType - Variable in class keel.GraphInterKeel.experiments.EducationalReport
experimentType() - Method in class keel.GraphInterKeel.experiments.Experiments: EDUCATIONAL KEEL **********************
ExpLayer - Class in keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet: Represents a neural net layer with all the neurons of ExpNeuron type
ExpLayer() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.ExpLayer: Empty constructor
Explore - Class in keel.Algorithms.Instance_Selection.Explore: File: Explore.java The Explore Instance Selection algorithm.
Explore(String) - Constructor for class keel.Algorithms.Instance_Selection.Explore.Explore: Default builder.
Explore - Class in keel.Algorithms.Preprocess.Instance_Selection.Explore: File: Explore.java The Explore Instance Selection algorithm.
Explore(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.Explore.Explore: Default builder.
exploresBetweenExploits - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Indicates the number of explore experiments that have to be made before doing an exploit experiment
exploresBetweenExploits - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Indicates the number of explore experiments that have to be made before doing an exploit experiment
EXPLORESBETWEENEXPLOITS - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
ExpNeuron - Class in keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet: Represents a exponential neuron (exp transformated Product Unit) of a neural net
ExpNeuron() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.ExpNeuron: Default constructor.
ExpNeuronParametricMutator - Class in keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric: Parametric Mutator of Exponential Neurons
ExpNeuronParametricMutator() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ExpNeuronParametricMutator: Empty constructor
ExpNeuronStructuralMutator - Class in keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural: Structural Mutator of Product Unit Neurons.
ExpNeuronStructuralMutator() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.ExpNeuronStructuralMutator: Empty constructor
exponent() - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
exponent() - Static method in class keel.Dataset.DataParser
ExponentialFormat - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix: ExponentialFormat is a concrete subclass of DecimalFormat that formats exponential numbers.
ExponentialFormat() - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.ExponentialFormat: Default constructor.
ExponentialFormat(int) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.ExponentialFormat: Constructor.
ExponentialFormat(int, boolean) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.ExponentialFormat: Constructor.
ExponentialFormat(int, int, boolean, boolean) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.ExponentialFormat: Constructor.
exponentTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.PolyKernel: Returns the tip text for this property
Exporter - Class in keel.Algorithms.Preprocess.Converter: Exporter Abstract class that contains the methods to export a file with KEEL format to other different formats.
Exporter() - Constructor for class keel.Algorithms.Preprocess.Converter.Exporter
exportOptionsDialog - Variable in class keel.GraphInterKeel.datacf.exportData.ExportPanel: An option dialog for obtaining the options of the export proccess
ExportPanel - Class in keel.GraphInterKeel.datacf.exportData
ExportPanel() - Constructor for class keel.GraphInterKeel.datacf.exportData.ExportPanel: Constructor that initializes the panel
expType - Variable in class keel.GraphInterKeel.experiments.Experiments
ext - Variable in class keel.GraphInterKeel.datacf.importData.ImportPanel: Extension of the format selected by the user
ExtendedChi2Discretizer - Class in keel.Algorithms.Discretizers.ExtendedChi2_Discretizer: This class implements the Chi2 discretizer.
ExtendedChi2Discretizer() - Constructor for class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.ExtendedChi2Discretizer: Default constructor.
ExternalObjectDescription - Class in keel.GraphInterKeel.experiments
ExternalObjectDescription() - Constructor for class keel.GraphInterKeel.experiments.ExternalObjectDescription: Default builder
ExternalObjectDescription(ExternalObjectDescription) - Constructor for class keel.GraphInterKeel.experiments.ExternalObjectDescription: Copy builder
ExternalObjectDescription(ExternalObjectDescription, boolean) - Constructor for class keel.GraphInterKeel.experiments.ExternalObjectDescription: Copy builder.
ExternalObjectDescription(String, String, int) - Constructor for class keel.GraphInterKeel.experiments.ExternalObjectDescription: Builder
ExternalObjectDescription(String, String, int, String) - Constructor for class keel.GraphInterKeel.experiments.ExternalObjectDescription: Builder
Extract(int, Double_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.ruleset: Removes the rule "i" from the ruleset
extract(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQ1: Extracts a instance using a particular method
Extract_Test_Set(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
Extract_Test_Set(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set: Returns the subset of examples belonging to the partition "particion"
Extract_Test_Set(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set
Extract_Test_Set(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set
Extract_Training_Set(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
Extract_Training_Set(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set: Returns the subset of examples belonging to the partition "particion"
Extract_Training_Set(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set
Extract_Training_Set(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set
extremos - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyDataset: Variables intervals.
extremos - Variable in class keel.Algorithms.RE_SL_Methods.LEL_TSK.MiDataset
extremos - Variable in class keel.Algorithms.RE_SL_Methods.MamWM.MiDataset
extremos - Variable in class keel.Algorithms.RE_SL_Methods.mogulHC.MiDataset
extremos - Variable in class keel.Algorithms.RE_SL_Methods.mogulIRL.MiDataset
extremos - Variable in class keel.Algorithms.RE_SL_Methods.mogulSC.MiDataset
extremos - Variable in class keel.Algorithms.RE_SL_Methods.TSK_IRL.MiDataset
extremos - Variable in class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.MiDataset
extremos - Variable in class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.MiDataset
extremos - Variable in class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.MiDataset
extremos - Variable in class keel.Algorithms.RE_SL_Postprocess.MamSelect.MiDataset
extremos - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWSelect.MiDataset
extremos - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWTuning.MiDataset
extremos - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.MiDataset
extremos - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKSelect.MiDataset

F

F(int, int) - Static method in class keel.Algorithms.Instance_Selection.Explore.EncodingLength: Function that calculates the F function of Encoding Length cost
F(int, int) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.Explore.EncodingLength: Function that calculates the F function of Encoding Length cost
F(double) - Method in interface keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.DoubleFunc: Function that receive a double as input and returns a double.
f(double[][][]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ConjGradNN: Returns the mean square error of a perceptron with weights x for all the examples Input
f(double[][][]) - Method in class keel.Algorithms.Shared.ClassicalOptim.ConjGradNN: Returns the mean square error of a perceptron with weights x for all the examples Input
f2minx - Variable in class keel.Algorithms.Preprocess.Missing_Values.EM.util.UnivariateMinimum: curvature at minimum
f_denormalized(double[][][], double[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ConjGradNN: Returns the denormalized mean square error of a perceptron with weights x for all the examples Input
f_denormalized(double[][][], double[]) - Method in class keel.Algorithms.Shared.ClassicalOptim.ConjGradNN: Returns the denormalized mean square error of a perceptron with weights x for all the examples Input
f_x(double, double, double) - Method in class keel.Algorithms.Instance_Generation.MixtGauss.MixtGaussGenerator: F(x) = N(x^t_k)
factDivision(int, int[], int[][]) - Method in class keel.Algorithms.Discretizers.MODL.MODL: Computes the division of factorials of the form (ni[i]!
factor(int, int, double) - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode: Calculates a multiplication factor used at this node
factor(int, int, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode: Calculates a multiplication factor used at this node
factorial(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.SETRED.SETREDGenerator: Computes the factorial number of the given one.
factorialLog(int) - Static method in class keel.Algorithms.Discretizers.MODL.MODL: Returns the natural logarithm of n!.
Factory - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist
Factory() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.Factory
Factory - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS
Factory() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Factory
fadaboostinc(int, int, double[], double[], double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FB
fadaboostincmaxmin(int, int, double[], double[], double[], double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FB
fagre - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.T_FRM: F agreement.
falseNegativeRate(int) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Calculate the false negative rate with respect to a particular class.
falsePositiveRate(int) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Calculate the false positive rate with respect to a particular class.
family - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
Farchd - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD: Title: Farchd Description: It contains the implementation of the Farchd algorithm Company: KEEL
Farchd() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Farchd: Default constructor
Farchd(parseParameters) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Farchd: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
FARFingrams - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams: Implements Fuzzy Association Rules Learning Fuzzy Inference-grams
FARFingrams() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams: Default Constructor.
FARFingrams(int, String) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams: Parameter Constructor.
farthestPrototypes() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Returns the two nearest to each other prototypes
farthestPrototypes() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Returns the two nearest to each other prototypes
farthestTo(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Return the farthest prototype to another in the set.
farthestTo(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Return the farthest prototype to another in the set.
fastPathfinder(List<List<Double>>, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.AdministrativeStaff
fastSqrt(float) - Static method in class keel.Algorithms.Instance_Generation.utilities.ApproximateSqrt
fastSqrt(float) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.ApproximateSqrt: Fast way to compute the sqrt of the value given.
FastVector - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: Implements a fast vector class without synchronized methods.
FastVector() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector: Constructs an empty vector with initial capacity zero.
FastVector(int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector: Constructs a vector with the given capacity.
FastVector - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets: Implements a fast vector class without synchronized methods.
FastVector() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector: Constructs an empty vector with initial capacity zero.
FastVector(int) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector: Constructs a vector with the given capacity.
FastVector - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core: Implements a fast vector class without synchronized methods.
FastVector() - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector: Constructs an empty vector with initial capacity zero.
FastVector(int) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector: Constructs a vector with the given capacity.
FastVector - Class in keel.Algorithms.SVM.SMO.core: Implements a fast vector class without synchronized methods.
FastVector() - Constructor for class keel.Algorithms.SVM.SMO.core.FastVector: Constructs an empty vector with initial capacity zero.
FastVector(int) - Constructor for class keel.Algorithms.SVM.SMO.core.FastVector: Constructs a vector with the given capacity.
FastVector.FastVectorEnumeration - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: Class for enumerating the vector's elements.
FastVector.FastVectorEnumeration - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets: Class for enumerating the vector's elements.
FastVector.FastVectorEnumeration - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core: Class for enumerating the vector's elements.
FastVector.FastVectorEnumeration - Class in keel.Algorithms.SVM.SMO.core: Class for enumerating the vector's elements.
FastVectorEnumeration(M5Vector) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Vector.FastVectorEnumeration: Constructs an enumeration.
FastVectorEnumeration(M5Vector, int) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Vector.FastVectorEnumeration: Constructs an enumeration with a special element.
FastVectorEnumeration(FastVector) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector.FastVectorEnumeration: Constructs an enumeration.
FastVectorEnumeration(FastVector, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector.FastVectorEnumeration: Constructs an enumeration with a special element.
FastVectorEnumeration(FastVector) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector.FastVectorEnumeration: Constructs an enumeration.
FastVectorEnumeration(FastVector, int) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector.FastVectorEnumeration: Constructs an enumeration with a special element.
FastVectorEnumeration(FastVector) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector.FastVectorEnumeration: Constructs an enumeration.
FastVectorEnumeration(FastVector, int) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector.FastVectorEnumeration: Constructs an enumeration with a special element.
FastVectorEnumeration(FastVector) - Constructor for class keel.Algorithms.SVM.SMO.core.FastVector.FastVectorEnumeration: Constructs an enumeration.
FastVectorEnumeration(FastVector, int) - Constructor for class keel.Algorithms.SVM.SMO.core.FastVector.FastVectorEnumeration: Constructs an enumeration with a special element.
FayyadDiscretizer - Class in keel.Algorithms.Discretizers.Fayyad_Discretizer: This class implements the Fayyad discretizer.
FayyadDiscretizer() - Constructor for class keel.Algorithms.Discretizers.Fayyad_Discretizer.FayyadDiscretizer
FayyadDiscretizer - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Fayyad_Discretizer
FayyadDiscretizer() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Fayyad_Discretizer.FayyadDiscretizer
FB - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting
FB() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FB
fC - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Classification F Stat-test identifier.
FCMKNN - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.FCMKNN: File: FCMKNN.java The FCMKNN algorithm.
FCMKNN(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.FCMKNN.FCMKNN: Main builder.
FCNN - Class in keel.Algorithms.Instance_Selection.FCNN: File: FCNN.java The FCNN Instance Selection algorithm.
FCNN(String) - Constructor for class keel.Algorithms.Instance_Selection.FCNN.FCNN: Default builder.
FCNN - Class in keel.Algorithms.Preprocess.Instance_Selection.FCNN: File: FCNN.java The FCNN Instance Selection algorithm.
FCNN(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.FCNN.FCNN: Default builder.
FCRA - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA: It contains the implementation of the FCRA algorithm
FCRA() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.FCRA: Default constructor
FCRA(parseParameters) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.FCRA: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
FCriticalValue(double, int, int) - Static method in class keel.Algorithms.Decision_Trees.M5.Distributions: Critical value for given probability of F-distribution.
fDrawFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
fDrawFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
FeaturesSet1 - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Features set 1.
FeaturesSet2 - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Features set 2.
FENN - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.FENN: File: FENN.java The FENN algorithm.
FENN(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.FENN.FENN: Main builder.
ffsqrt(float) - Static method in class keel.Algorithms.Instance_Generation.utilities.ApproximateSqrt
ffsqrt(float) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.ApproximateSqrt: Faster way to compute the sqrt of the value given.
fichero - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyDataset: Input filename.
fichero(fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
fichero(fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
fichero(fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
fichero(fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
Fichero - Class in keel.Algorithms.RE_SL_Methods.LEL_TSK: File class.
Fichero() - Constructor for class keel.Algorithms.RE_SL_Methods.LEL_TSK.Fichero
fichero - Variable in class keel.Algorithms.RE_SL_Methods.LEL_TSK.MiDataset
Fichero - Class in keel.Algorithms.RE_SL_Methods.MamWM: File class.
Fichero() - Constructor for class keel.Algorithms.RE_SL_Methods.MamWM.Fichero
fichero - Variable in class keel.Algorithms.RE_SL_Methods.MamWM.MiDataset
fichero - Variable in class keel.Algorithms.RE_SL_Methods.mogulHC.MiDataset
Fichero - Class in keel.Algorithms.RE_SL_Methods.mogulIRL: File class.
Fichero() - Constructor for class keel.Algorithms.RE_SL_Methods.mogulIRL.Fichero
fichero - Variable in class keel.Algorithms.RE_SL_Methods.mogulIRL.MiDataset
fichero - Variable in class keel.Algorithms.RE_SL_Methods.mogulSC.MiDataset
fichero - Variable in class keel.Algorithms.RE_SL_Methods.TSK_IRL.MiDataset
Fichero - Class in keel.Algorithms.RE_SL_Postprocess.Mam2TSK: File class.
Fichero() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.Fichero
fichero - Variable in class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.MiDataset
Fichero - Class in keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB: File class.
Fichero() - Constructor for class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.Fichero
fichero - Variable in class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.MiDataset
Fichero - Class in keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules: File class.
Fichero() - Constructor for class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.Fichero
fichero - Variable in class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.MiDataset
Fichero - Class in keel.Algorithms.RE_SL_Postprocess.MamSelect: File class.
Fichero() - Constructor for class keel.Algorithms.RE_SL_Postprocess.MamSelect.Fichero
fichero - Variable in class keel.Algorithms.RE_SL_Postprocess.MamSelect.MiDataset
Fichero - Class in keel.Algorithms.RE_SL_Postprocess.MamWSelect: File class.
Fichero() - Constructor for class keel.Algorithms.RE_SL_Postprocess.MamWSelect.Fichero
fichero - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWSelect.MiDataset
Fichero - Class in keel.Algorithms.RE_SL_Postprocess.MamWTuning: File class.
Fichero() - Constructor for class keel.Algorithms.RE_SL_Postprocess.MamWTuning.Fichero
fichero - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWTuning.MiDataset
Fichero - Class in keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules: File class.
Fichero() - Constructor for class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.Fichero
fichero - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.MiDataset
Fichero - Class in keel.Algorithms.RE_SL_Postprocess.TSKSelect: File class.
Fichero() - Constructor for class keel.Algorithms.RE_SL_Postprocess.TSKSelect.Fichero
fichero - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKSelect.MiDataset
Fichero - Class in org.core
Fichero() - Constructor for class org.core.Fichero
ficheroReglas(String, String) - Method in class keel.Algorithms.Rule_Learning.LEM1.BaseReglas
ficheroReglas(String, String) - Method in class keel.Algorithms.Rule_Learning.LEM2.BaseReglas
ficheroReglas(String, String) - Method in class keel.Algorithms.Rule_Learning.Ritio.BaseReglas
ficheroReglas(String) - Method in class keel.Algorithms.Rule_Learning.Rules6.BaseReglas: Generates a file with the stored rules and their statistics.
ficheroReglas(String) - Method in class keel.Algorithms.Rule_Learning.SRI.BaseReglas: Generates a file with the stored rules and their statistics.
ficheroSalida - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Output files names.
ficheroSalida - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
ficheroSalida - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
ficheroSalida - Variable in class keel.Algorithms.Preprocess.Basic.Metodo: Output files names
ficheroTest - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Test file name.
ficheroTest - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
ficheroTest - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
ficheroTest - Variable in class keel.Algorithms.Preprocess.Basic.Metodo: Test file name
ficheroTraining - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Training file name.
ficheroTraining - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
ficheroTraining - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
ficheroTraining - Variable in class keel.Algorithms.Preprocess.Basic.Metodo: Train file name
ficheroValidation - Variable in class keel.Algorithms.Preprocess.Basic.Metodo: Reference file name
field - Variable in class keel.GraphInterKeel.experiments.DatasetXML
fields() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation: returns an enumeration over all the stored fields
fields() - Method in class keel.Algorithms.SVM.SMO.core.TechnicalInformation: returns an enumeration over all the stored fields
fight(PrototypeSet, PrototypeSet[][], double[]) - Method in class keel.Algorithms.Instance_Generation.ENPC.ENPCGenerator
figure - Variable in class keel.GraphInterKeel.experiments.Node
fila(double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
fila(double[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
FILE_EXTENSION - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: The filename extension that should be used for arff files
FILE_EXTENSION - Static variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: The filename extension that should be used for arff files
FILE_EXTENSION - Static variable in class keel.Algorithms.SVM.SMO.core.Instances: The filename extension that should be used for arff files
FileBrowserPanel - Class in keel.GraphInterKeel.datacf.util
FileBrowserPanel() - Constructor for class keel.GraphInterKeel.datacf.util.FileBrowserPanel: Constructor of the FileBrowser panel
FileDataset - Class in keel.Algorithms.Neural_Networks.NNEP_Common.data: File dataset
FileDataset() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.data.FileDataset: Empty constructor
FileDataset(String) - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.data.FileDataset: Constructor that receives the name of the file to be opened
fileInput - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: The input stream.
fileLineNum - Variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo: It stores the file number where the error has appeared.
fileLineNum - Variable in class keel.Dataset.ErrorInfo: It stores the file number where the error has appeared.
fileLocation - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: File location.
FileManagement - Class in keel.Algorithms.Genetic_Rule_Learning.Globals: FileManagement.java.
FileManagement() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Globals.FileManagement: Creates a new instance of FileManagement
FileManagement - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals
FileManagement() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.FileManagement: Creates a new instance of FileManagement
fileName - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Command line argument for data file name.
fileName - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.FileDataset: Data file name
filePath - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: The file path
fileProcess(String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: method that process the configuration file nfconfig.
fileProcess(String) - Method in class keel.Algorithms.Shared.Parsing.ProcessConfig: method that process the configuration file nfconfig.
fileReader - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.FileDataset: Data file reader
files - Variable in class keel.GraphInterKeel.experiments.DatasetXML
Files - Class in keel.GraphInterKeel.experiments
Files() - Constructor for class keel.GraphInterKeel.experiments.Files
Files - Class in keel.GraphInterKeel.statistical: File: Files.java.
Files() - Constructor for class keel.GraphInterKeel.statistical.Files
Files - Class in org.core: Implements methods to manage data files
Files() - Constructor for class org.core.Files
FileUtils - Class in keel.GraphInterKeel.datacf.util
FileUtils() - Constructor for class keel.GraphInterKeel.datacf.util.FileUtils
FileUtils - Class in keel.GraphInterKeel.experiments
FileUtils() - Constructor for class keel.GraphInterKeel.experiments.FileUtils
FillBuff() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
FillBuff() - Static method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
FillBuff() - Static method in class keel.Dataset.SimpleCharStream
filter(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.FUN: The protected method filter the rules to select the used one
filter(Mask, int, double, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: It filters the instances covered by a simple rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, SimpleRule) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: It filters the instances covered by a simple rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: It filters the instances covered by a rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, Ruleset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: It filters the instances covered by a set of rule from this dataset; i.e., it deactivates the instances not covered by that ruleset.
filter(Mask, Ruleset, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: It filters the instances covered by a set of rule from this dataset; i.e., it deactivates the instances not covered by that ruleset.
filter(Mask, int, double, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: It filters the instances covered by a simple rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, SimpleRule) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: It filters the instances covered by a simple rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: It filters the instances covered by a rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, Ruleset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: It filters the instances covered by a set of rule from this dataset; i.e., it deactivates the instances not covered by that ruleset.
filter(Mask, Ruleset, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: It filters the instances covered by a set of rule from this dataset; i.e., it deactivates the instances not covered by that ruleset.
filter(Mask, int, double, int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: It filters the instances covered by a simple rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, SimpleRule) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: It filters the instances covered by a simple rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, Rule) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: It filters the instances covered by a rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, Ruleset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: It filters the instances covered by a set of rule from this dataset; i.e., it deactivates the instances not covered by that ruleset.
filter(Mask, Ruleset, int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: It filters the instances covered by a set of rule from this dataset; i.e., it deactivates the instances not covered by that ruleset.
filter(Mask, int, double, int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: It filters the instances covered by a simple rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, SimpleRule) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: It filters the instances covered by a simple rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, Rule) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: It filters the instances covered by a rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, Ruleset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: It filters the instances covered by a set of rule from this dataset; i.e., it deactivates the instances not covered by that ruleset.
filter(Mask, Ruleset, int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: It filters the instances covered by a set of rule from this dataset; i.e., it deactivates the instances not covered by that ruleset.
filter(Mask, int, double, int) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: It filters the instances covered by a simple rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, SimpleRule) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: It filters the instances covered by a simple rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, Rule) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: It filters the instances covered by a rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, Ruleset) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: It filters the instances covered by a set of rule from this dataset; i.e., it deactivates the instances not covered by that ruleset.
filter(Mask, Ruleset, int) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: It filters the instances covered by a set of rule from this dataset; i.e., it deactivates the instances not covered by that ruleset.
filter(Mask, int, double, int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It filters the instances covered by a simple rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, SimpleRule) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It filters the instances covered by a simple rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, Rule) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It filters the instances covered by a rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, Ruleset) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It filters the instances covered by a set of rule from this dataset; i.e., it deactivates the instances not covered by that ruleset.
filter(Mask, int, double, int) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It filters the instances covered by a simple rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, SimpleRule) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It filters the instances covered by a simple rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, Rule) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It filters the instances covered by a rule from this dataset; i.e., it deactivates the instances not covered by that rule.
filter(Mask, Ruleset) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It filters the instances covered by a set of rule from this dataset; i.e., it deactivates the instances not covered by that ruleset.
FILTER_NONE - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: filter: No normalization/standardization
FILTER_NONE - Static variable in class keel.Algorithms.SVM.SMO.SMO: filter: No normalization/standardization
FILTER_NONE - Static variable in class keel.Algorithms.SVM.SMO.SMOreg: no filtering
FILTER_NONE - Static variable in class keel.Algorithms.SVM.SMO.SVMreg: The filter to apply to the training data: None
FILTER_NORMALIZE - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: filter: Normalize training data
FILTER_NORMALIZE - Static variable in class keel.Algorithms.SVM.SMO.SMO: filter: Normalize training data
FILTER_NORMALIZE - Static variable in class keel.Algorithms.SVM.SMO.SMOreg: normalize data
FILTER_NORMALIZE - Static variable in class keel.Algorithms.SVM.SMO.SVMreg: The filter to apply to the training data: Normalzie
FILTER_STANDARDIZE - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: filter: Standardize training data
FILTER_STANDARDIZE - Static variable in class keel.Algorithms.SVM.SMO.SMO: filter: Standardize training data
FILTER_STANDARDIZE - Static variable in class keel.Algorithms.SVM.SMO.SMOreg: standardize data
FILTER_STANDARDIZE - Static variable in class keel.Algorithms.SVM.SMO.SVMreg: The filter to apply to the training data: Standardize
filterByClass(Mask, String) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: It filters the instances of a given class from this dataset; i.e., it deactivates the instances from the other class.
filterByClass(Mask, String) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: It filters the instances of a given class from this dataset; i.e., it deactivates the instances from the other class.
filterByClass(Mask, String) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: It filters the instances of a given class from this dataset; i.e., it deactivates the instances from the other class.
filterByClass(Mask, String) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: It filters the instances of a given class from this dataset; i.e., it deactivates the instances from the other class.
filterByClass(Mask, String) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: It filters the instances of a given class from this dataset; i.e., it deactivates the instances from the other class.
filterByClass(Mask, String) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It filters the instances of a given class from this dataset; i.e., it deactivates the instances from the other class.
filterByClass(Mask, String) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It filters the instances of a given class from this dataset; i.e., it deactivates the instances from the other class.
filterFile(NominalToBinaryFilter, String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Method for testing filters.
filtering - Variable in class keel.Algorithms.Hyperrectangles.EHS_CHC.EHS_CHC
filtering() - Method in class keel.Algorithms.Instance_Generation.ICPL.ICPLGenerator: Method to call the appropriate method
filterSmartCrossover - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
filterType - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Parameters: Filter type.
filterType - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters: Filter type.
filterType - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters: Filter used.
filterType - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Parameters: Filter used.
filterTypeTipText() - Method in class keel.Algorithms.SVM.SMO.SMO: Returns the tip text for this property
filterTypeTipText() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Returns the tip text for this property
filterTypeTipText() - Method in class keel.Algorithms.SVM.SMO.SVMreg: Returns the tip text for this property
fin() - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.FileManagement: Indicates if we've achieved to the end of file.
fin() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.FileManagement: Indicates if we've achieved to the end of file.
fin() - Method in class keel.Algorithms.PSO_Learning.CPSO.Crono: Stops the chronometer and computes the time consumed.
fin() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.Crono: Stops the chronometer and computes the time consumed.
fin() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.Crono: Stops the chronometer and computes the time consumed.
fin() - Method in class keel.Algorithms.PSO_Learning.REPSO.Crono: Stops the chronometer and computes the time consumed.
finalizado - Variable in class keel.GraphInterKeel.experiments.EducationalPartitionsRun
fIncFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
fIncFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
find(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.vectordouble: Returns the position in the vector for a value "x" or -1 if the value is not found in the vector
find(boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: returns the indices of the searched-for attributes (if TRUE) or the indices of AttributeLocator objects (if FALSE)
find(double[], double[], double[], double, boolean[], double[][], ArrayList<Integer>) - Method in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
find(String, String[]) - Static method in class keel.Algorithms.SVM.SMO.core.ClassDiscovery: Checks the given packages for classes that inherited from the given class, in case it's a class, or implement this class, in case it's an interface.
find(String, String) - Static method in class keel.Algorithms.SVM.SMO.core.ClassDiscovery: Checks the given package for classes that inherited from the given class, in case it's a class, or implement this class, in case it's an interface.
find(Class, String[]) - Static method in class keel.Algorithms.SVM.SMO.core.ClassDiscovery: Checks the given packages for classes that inherited from the given class, in case it's a class, or implement this class, in case it's an interface.
find(Class, String) - Static method in class keel.Algorithms.SVM.SMO.core.ClassDiscovery: Checks the given package for classes that inherited from the given class, in case it's a class, or implement this class, in case it's an interface.
findAndSetSupportBoundForKnownAntecedents(Instances, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule: Finds and sets the support bound for the known antecedents.
findArgmin(double[], double[][]) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization: Main algorithm.
findElement(LinkedList<Atributo_valor>) - Method in class keel.Algorithms.Rule_Learning.LEM2.Atributo_valor
findElement2(LinkedList<Atributo_valor>) - Method in class keel.Algorithms.Rule_Learning.LEM2.Atributo_valor
finding_poc_nn(PrototypeSet, double, double) - Method in class keel.Algorithms.Instance_Generation.POC.POCGenerator: S is a training set of n pattern composed of TWO subsets, S1 y S2, with n1,n2 sizes,
findItemSetInTtree(short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Commences process of determining if an itemset exists in a T-tree.
findKey(double) - Method in class keel.Algorithms.Rule_Learning.Ripper.Score: Returns the vector's position of a given value
findKey(double) - Method in class keel.Algorithms.Rule_Learning.Slipper.Score: Returns the vector's position of a given value
findMinimum(double, UnivariateFunction) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.util.UnivariateMinimum: Find minimum (first estimate given)
findMinimum(double, UnivariateFunction, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.util.UnivariateMinimum: Find minimum (first estimate given, desired number of fractional digits specified)
findMinimum(UnivariateFunction) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.util.UnivariateMinimum: Find minimum (no first estimate given)
findMinimum(UnivariateFunction, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.util.UnivariateMinimum: Find minimum (no first estimate given, desired number of fractional digits specified)
findNearestHit(int) - Method in class keel.Algorithms.Preprocess.Feature_Selection.Datos: returns the nearest instance according to the instance passed as an argument.
findNearestMiss(int) - Method in class keel.Algorithms.Preprocess.Feature_Selection.Datos: returns the nearest instance according to the instance passed as an argument.
findNeigbours() - Method in class keel.Algorithms.Lazy_Learning.IDIBL.IDIBL: Finds MAXK Nearest Neighbors for every reference instance
findNominalValue(int, double) - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Returns the nominal value of the double value of the attribute
findNominalValue(int, double) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Return the nominal value of the attribute
findNominalValue(int, double) - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Returns the nominal value of the double value of the attribute
findOptimalK(int) - Method in class keel.Algorithms.Rule_Learning.Riona.Riona: Calculates the optimum size of the neighborhood for the training set
findOptimalPointOnLine(int, double, double, double, int, double, double, double, double, double, double) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMO: Finds optimal point on line constrained by first (i1) and second (i2) candidate.
findOrder(Vector<matchProfileAgent>, Vector<int[]>, Vector<Integer>) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
findOrder(Vector<matchProfileAgent>, Vector<int[]>, Vector<Integer>) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
findPackages() - Static method in class keel.Algorithms.SVM.SMO.core.ClassDiscovery: Lists all packages it can find in the classpath.
findParameters(int) - Method in class keel.Algorithms.Lazy_Learning.IDIBL.IDIBL: Finds best parameters for drop 4 algorithm
FindParent(Element, String) - Method in class keel.Algorithms.Preprocess.Converter.HtmlToKeel: Searchs in the whole xml tree to find the parent of the node or xaml tag whose name is equals to the one given as parameter.
FindParent(Element, String) - Method in class keel.Algorithms.Preprocess.Converter.PropertyListToKeel: Searchs in the whole xml tree to find the parent of the node or xaml tag whose name is equals to the one given as parameter.
FindParent(Element, String) - Method in class keel.Algorithms.Preprocess.Converter.XmlToKeel: Searchs in the whole xml tree to find the parent of the node or xaml tag whose name is equals to the one given as parameter.
findProbabilities() - Method in class keel.Algorithms.Lazy_Learning.IDIBL.IDIBL: Find probabilities to calculate IVDM distances
Fingrams - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams: Implements Fuzzy Inference-grams abstract class for algorithm based on this scheme.
Fingrams() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Default Constructor.
Fingrams(int, String) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Parameter Constructor.
FingramsKEEL - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams: It gathers all the parameters, launches the algorithm, and prints out the results
FingramsKEEL() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FingramsKEEL: Default constructor.
FingramsKEEL(parseParameters) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FingramsKEEL: It reads the data from the input files and parse all the parameters from the parameters array.
FingramsProcess - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams: It provides the implementation of the algorithm to be run in a process
FingramsProcess(myDataset, DataBase) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FingramsProcess: It creates a new process for the algorithm by setting up its parameters
finishUCS(long) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.UCS: It makes the last things to do after closing the program.
finishXCS(long) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.XCS: It makes the last things to do after closing the program.
Finner - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Apply Finner flag.
fireIterationCompleted() - Method in class keel.GraphInterKeel.experiments.EducationalPartitionsRun: This method is invoqued when a partition is finished
fireRunkeelFinished() - Method in class keel.GraphInterKeel.experiments.EducationalPartitionsRun
first - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.pair_fi
first - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.pair_gf
first - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.pair_gg
first - Variable in class keel.Algorithms.Instance_Generation.utilities.Pair: First element of the pair.
first() - Method in class keel.Algorithms.Instance_Generation.utilities.Pair: Get first element of the pair.
first - Variable in class keel.Algorithms.Semi_Supervised_Learning.utilities.Pair: First element of the pair.
first() - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Pair: Get first element of the pair.
firstDerivative(UnivariateFunction, double) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.util.NumericalDerivative: determine first derivative
firstElement() - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector: Returns the first element of the vector.
firstElement() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector: Returns the first element of the vector.
firstElement() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector: Returns the first element of the vector.
firstElement() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector: Returns the first element of the vector.
firstElement() - Method in class keel.Algorithms.SVM.SMO.core.FastVector: Returns the first element of the vector.
firstInstance() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Returns the first instance in the set.
firstInstance() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns the first instance in the set.
firstInstance() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns the first itemset in the set.
firstInstance() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns the first instance in the set.
firstInstance() - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns the first instance in the set.
firstOutput() - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Returns the first output of the protoype.
firstOutput() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Returns the first output of the protoype.
fitAndSetCoreBound(Instances) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule: This function fits the rule to the data which it overlaps.
fitDif - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Difference between two fitness that we consider enoung to say that the fitness has improved
fitDif - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Difference between two fitnesses that we consider enoung to say that the fitness has improved
fitLogistic(Instances, int, int, int, Random) - Method in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: Fits logistic regression model to SVM outputs analogue to John Platt's method.
fitness - Variable in class keel.Algorithms.Discretizers.HeterDisc.HeterDisc.DiscretizationScheme: fitness of the discretization
Fitness - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting
Fitness() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.Fitness
fitness() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividual: This abstract method calculates the classification error
fitness() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForClassification: This method calculate the classification error using the examples set
fitness() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForModels: This method calculate the model error using the examples set
fitness() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForSymbRegr: This method calculates the fitness based in the ECM
fitness - Variable in class keel.Algorithms.Genetic_Rule_Learning.DMEL.RuleSet
fitness - Variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
fitness - Variable in class keel.Algorithms.Genetic_Rule_Learning.GIL.RuleSet
fitness - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
fitness(PrototypeSet, int) - Method in class keel.Algorithms.Instance_Generation.AMPSO.AMPSOGenerator: Return the local-fitness of the particle.
fitness() - Method in class keel.Algorithms.Instance_Generation.VQ.Cluster: Returns the Medium Squared Error of the cluster.
fitness - Variable in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.Cromosoma: chromosome fitness
fitness_regla(int) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.AlgGenetic
fitnessComputation() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
fitnessType - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividual: The fitness type.
fitReduction - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: The factor by which the fitness is reduced when a new classifier is generated in the AG.
fitReduction - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: The factor by which the fitness is reduced when a new classifier is generated in the AG.
FITREDUCTION - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
fixAttributePresence() - Method in class keel.Algorithms.PSO_Learning.CPSO.Particle
fixAttributePresence() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
FixedFrequencyDiscretizer - Class in keel.Algorithms.Discretizers.FixedFrequency_Discretizer: This class implements the Fixed Frequency discretizer.
FixedFrequencyDiscretizer(int) - Constructor for class keel.Algorithms.Discretizers.FixedFrequency_Discretizer.FixedFrequencyDiscretizer: Constructor of the class, initializes the numInt attribute
fkmeans - Class in keel.Algorithms.Preprocess.Missing_Values.fkmeans: This class imputes the missing values by means of the Fuzzy K-means clustering algorithm.
fkmeans(String) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.fkmeans.fkmeans: Creates a new instance of fkmeans
FLC(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseR: Returns the ouput of the controller
FlexibleDecimalFormat - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix: FlexibleDecimalFormat is a concrete subclass of DecimalFormat that formats decimal numbers in a more flexible way.
FlexibleDecimalFormat() - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FlexibleDecimalFormat: Default constructor.
FlexibleDecimalFormat(int) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FlexibleDecimalFormat: Constructor.
FlexibleDecimalFormat(int, boolean) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FlexibleDecimalFormat: Constructor.
FlexibleDecimalFormat(int, boolean, boolean, boolean) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FlexibleDecimalFormat: Constructor.
FlexibleDecimalFormat(double) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FlexibleDecimalFormat: Constructor.
FloatingPointFormat - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix: Class for the format of floating point numbers
FloatingPointFormat() - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FloatingPointFormat: Default constructor.
FloatingPointFormat(int) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FloatingPointFormat: Constructor.
FloatingPointFormat(int, int) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FloatingPointFormat: Constructor.
FloatingPointFormat(int, int, boolean) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FloatingPointFormat: Constructor.
flogitboostinc(int, int, double[], double[], double[], boolean) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FB
floorDouble(double) - Static method in class keel.Algorithms.Decision_Trees.M5.M5: Returns the largest (closest to positive infinity) long integer value that is not greater than the argument.
floorDouble(double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: Returns the largest (closest to positive infinity) long integer value that is not greater than the argument.
flushInput() - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: This will remove all buffered instances from the inputformat dataset.
flushInput() - Method in class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter: Flushed the inputs (cleans the input formats)
fM - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Regression F Stat-test identifier.
fMeasure(int) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Calculate the F-Measure with respect to a particular class.
fminx - Variable in class keel.Algorithms.Preprocess.Missing_Values.EM.util.UnivariateMinimum: function value at minimum
FN - Variable in class keel.Algorithms.PSO_Learning.CPSO.Particle
FN - Variable in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
FN - Variable in class keel.Algorithms.PSO_Learning.REPSO.Particle
FocusIncon - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FOCUS: Main class of focus method for feature selection using inconsistency ratio as evaluation measure.
FocusIncon(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FOCUS.FocusIncon: Creates a new instance of FocusIncon
foldsTipText() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Returns the tip text for this property
force(boolean, String) - Static method in class keel.Algorithms.Instance_Generation.utilities.Debug: Force a condition.
force(boolean, String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Debug: Force a condition.
forceConsistency() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome: It checks whether a chromosome always contains at least one antecedent gene as well as at least one consequent gene.
forceConsistency() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome: It checks whether a chromosome always contains at least one antecedent gene as well as at least one consequent gene.
forceConsistency() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome: It checks whether a chromosome always contains at least one antecedent gene as well as at least one consequent gene.
forceConsistency() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome: It checks whether a chromosome always contains at least one antecedent gene as well as at least one consequent gene.
forceUndo() - Method in class keel.GraphInterKeel.experiments.Experiments: Forces the undo action
format(double, StringBuffer, FieldPosition) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.ExponentialFormat
format(double, StringBuffer, FieldPosition) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FlexibleDecimalFormat
format(double, StringBuffer, FieldPosition) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FloatingPointFormat
format() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.AdministrativeStaff
formatDate(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Returns the given amount of milliseconds formatted according to the current Date format.
formatDate(double) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute
formatear() - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Resets the prototype.
formatear(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Adds the elements of a reset set given.
formatear() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: This function is for NOminal adaptation...
formatear(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet
FormatErrorKeeper - Class in keel.Algorithms.Rule_Learning.Swap1: FormatErrorKeeper This class is a warehouse of format dataset errors.
FormatErrorKeeper() - Constructor for class keel.Algorithms.Rule_Learning.Swap1.FormatErrorKeeper: Creates a new instance of FormatErrorKeeper
FormatErrorKeeper - Class in keel.Dataset: FormatErrorKeeper This class is a warehouse of format dataset errors.
FormatErrorKeeper() - Constructor for class keel.Dataset.FormatErrorKeeper: Creates a new instance of FormatErrorKeeper
formatString(String) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FlexibleDecimalFormat: Formats a double to produce a string.
formulaeToString(boolean) - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode: Converts all the linear models at the leaves under the node to a string
formulaeToString(boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode: Converts all the linear models at the leaves under the node to a string
forName(String, String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5: Allows to construct the M5 classifier calling it by its name and options.
forName(Class, String, String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Creates a new instance of an object given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Classifier: Creates a new instance of a classifier given it's class name and (optional) arguments to pass to it's setOptions method.
forName(Class, String, String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Creates a new instance of an object given it's class name and (optional) arguments to pass to it's setOptions method.
forName(Class, String, String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Creates a new instance of an object given it's class name and (optional) arguments to pass to it's setOptions method.
forName(Class, String, String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Creates a new instance of an object given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, Class, String, String[]) - Method in class keel.Algorithms.SVM.SMO.core.Check: Tries to instantiate a new instance of the given class and checks whether it is an instance of the specified class.
forName(Class, String, String[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Creates a new instance of an object given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class keel.Algorithms.SVM.SMO.supportVector.Kernel: Creates a new instance of a kernel given it's class name and (optional) arguments to pass to it's setOptions method.
ForwardIncon - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FORWARD.filter: MAIN CLASS OF FORWARD FEATURE SELECTION ALGORITHM USING INCONSISTENCY RATIO
ForwardIncon(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FORWARD.filter.ForwardIncon: Creates a new instance of ForwardIncon
ForwardLVO - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FORWARD.wrapper: MAIN CLASS OF FORWARD FEATURE SELECTION ALGORITHM USING LVO AS WRAPPER METHOD
ForwardLVO(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FORWARD.wrapper.ForwardLVO: Creates a new instance of ForwardLVO
fOTestFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
fOTestFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
fOTrainFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
fOTrainFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
FP - Variable in class keel.Algorithms.PSO_Learning.CPSO.Particle
FP - Variable in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
FP - Variable in class keel.Algorithms.PSO_Learning.REPSO.Particle
FPgrowth - Class in keel.Algorithms.Subgroup_Discovery.SDMap.SDMap: It gathers all the parameters, launches the algorithm, and prints out the results
FPgrowth() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.FPgrowth: Default constructor
FPgrowth(parseParameters) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.FPgrowth: It reads the data from the input files and parse all the parameters from the parameters array.
FPgrowth - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth: It gathers all the parameters, launches the FPgrowth algorithm, and prints out the results
FPgrowth() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.FPgrowth: Default constructor
FPgrowth(parseParameters) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.FPgrowth: It reads the data from the input files and parse all the parameters from the parameters array.
FPgrowthHeaderTable(short) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.FPtree.FPgrowthHeaderTable: Constructor.
FPgrowthHeaderTable(short) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.FPtree.FPgrowthHeaderTable: Parameters constructor.
FPgrowthProcess - Class in keel.Algorithms.Subgroup_Discovery.SDMap.SDMap: It provides the implementation of the algorithm to be run in a process
FPgrowthProcess(myDataset, double, double) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.FPgrowthProcess: It creates a new process for the algorithm by setting up its parameters
FPgrowthProcess - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth: It provides the implementation of the algorithm to be run in a process
FPgrowthProcess(myDataset, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.FPgrowthProcess: It creates a new process for the algorithm by setting up its parameters
fPopFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
fPopFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
fPopNormFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
fPopNormFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
FProbability(double, int, int) - Static method in class keel.Algorithms.Decision_Trees.M5.Distributions: Computes probability of F-ratio.
FProbability(double, int, int) - Static method in class keel.Algorithms.Lazy_Learning.Statistics: Computes probability of F-ratio.
FProbability(double, int, int) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics: Computes probability of F-ratio.
FPtree - Class in keel.Algorithms.Subgroup_Discovery.SDMap.FPTree: Implementation of Han's FP-growth ARM algorithm
FPtree(myDataset, double, double) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.FPtree: Constructor to process dataset and parameters.
FPtree - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD
FPtree(myDataset, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.FPtree: Constructor to process dataset and parameters.
FPtree.FPgrowthHeaderTable - Class in keel.Algorithms.Subgroup_Discovery.SDMap.FPTree: Header table.
FPtree.FPgrowthHeaderTable - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD: Header table.
FPtree.FPtreeNode - Class in keel.Algorithms.Subgroup_Discovery.SDMap.FPTree: FP-tree node structure comprising a FPgrowthItemPrefixSubtreeNode in which to store counts and a reference to a child branch.
FPtree.FPtreeNode - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD: FP-tree node structure comprising a FPgrowthItemPrefixSubtreeNode in which to store counts and a reference to a child branch.
FPtreeNode() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.FPtree.FPtreeNode: Default constructor.
FPtreeNode(FPtree.FPgrowthItemPrefixSubtreeNode) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.FPtree.FPtreeNode: Single argument constructor.
FPtreeNode() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.FPtree.FPtreeNode: Default constructor.
FPtreeNode(FPtree.FPgrowthItemPrefixSubtreeNode) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.FPtree.FPtreeNode: Single argument constructor.
fr - Variable in class keel.GraphInterKeel.experiments.EducationalReport
fr_classes - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
Frame - Class in keel.GraphInterKeel.menu: Title: Keel Description: Initial screen
Frame() - Constructor for class keel.GraphInterKeel.menu.Frame: Frame builder
Frame1 - Class in keel.RunKeelGraph: File: Frame1.java Frame of the application to process the execution of a experiment.
Frame1() - Constructor for class keel.RunKeelGraph.Frame1: Default builder
FrameModules - Class in keel.GraphInterKeel.menu: Title: Keel Description: Modules screen
FrameModules() - Constructor for class keel.GraphInterKeel.menu.FrameModules: Builder
frandom(double, double) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.MLPerceptronBackpropCS: Generates a random number between min and max
frandom(double, double) - Static method in class keel.Algorithms.Neural_Networks.ensemble.Genesis: Generate random double between min and max
frandom(double, double) - Static method in class keel.Algorithms.Neural_Networks.gann.Genesis: Generate random number between min and max
frandom(double, double) - Static method in class keel.Algorithms.Neural_Networks.gann.Rand: Method that returns a random double value between min and max values
frandom(double, double) - Static method in class keel.Algorithms.Neural_Networks.gmdh.Genesis: Generate random number between min and max
frandom(double, double) - Static method in class keel.Algorithms.Neural_Networks.net.Genesis: Generates a random number between min and max
Frecuence_each_Variables(int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.ruleset: Returns in "frec" the times that each variable appears in the rule base.
Frecuence_each_Variables(int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.ruleset: Returns in "frec" the times that each variable appears in the rule base.
Frecuence_each_Variables(int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.ruleset: Returns in "frec" the times that each variable appears in the rule base.
frecuentClass(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It returns the frequency of a class
frecuentClass(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It returns the ratio of instances of the given class in the dataset
frecuentClass(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It returns the frequency of a class
freq(Vector<Float>, Vector<Float>) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling.Main
freq(Vector<Float>, Vector<Float>) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.Main
Freq - Variable in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.ValueFreq: Frequecy of the value.
Freq - Variable in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.ValueFreq: Frequecy of the value.
Freq - Variable in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.ValueFreq: Frequecy of the value.
Freq - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValueFreq: Frequecy of the value.
Freq - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValuesFreq: Frequecy of the value.
Freq - Variable in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.ValueFreq: Frequecy of the value.
Freq - Variable in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.ValueFreq: Frequecy of the value.
Freq - Variable in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.ValueFreq: Frequecy of the value.
Freq - Variable in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.ValueFreq: Frequecy of the value.
Freq - Variable in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.ValueFreq: Frequecy of the value.
Freq - Variable in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.ValueFreq: Frequecy of the value.
freqConstrMet - Variable in class keel.Algorithms.Discretizers.Khiops.DeltaValue: any of the intervals implied in this merge does not meet the frequency constraints
FreqList - Class in keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues: This class represents a list of frequencies of Strings
FreqList() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.FreqList: Default constructor.
FreqList - Class in keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues: This class represents a list of frequencies of Strings
FreqList() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.FreqList: Default constructor.
FreqList - Class in keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue: This class represents a list of frequencies of Strings
FreqList() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.FreqList: Default constructor.
FreqList - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering: This class represents a list of frequencies of Strings
FreqList() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqList: Default constructor.
FreqList - Class in keel.Algorithms.Preprocess.Missing_Values.fkmeans: This class represents a list of frequencies of Strings
FreqList() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.fkmeans.FreqList: Default constructor.
FreqList - Class in keel.Algorithms.Preprocess.Missing_Values.kmeansImpute: This class represents a list of frequencies of Strings
FreqList() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.FreqList: Default constructor.
FreqList - Class in keel.Algorithms.Preprocess.Missing_Values.knnImpute: This class represents a list of frequencies of Strings
FreqList() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.knnImpute.FreqList: Default constructor.
FreqList - Class in keel.Algorithms.Preprocess.Missing_Values.MostCommonValue: This class represents a list of frequencies of Strings
FreqList() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.FreqList: Default constructor.
FreqList - Class in keel.Algorithms.Preprocess.Missing_Values.svmImpute: This class represents a list of frequencies of Strings
FreqList() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.svmImpute.FreqList: Default constructor.
FreqList - Class in keel.Algorithms.Preprocess.Missing_Values.wknnImpute: This class represents a list of frequencies of Strings
FreqList() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.FreqList: Default constructor.
FreqListPair - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering: This class represents a list of pairs frequency (i.e. the frequency associated to a determined pair of strings).
FreqListPair() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqListPair: Creates a new object, with fresh allocated memory
freqs - Variable in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.FreqList: Frequencies vector of strings.
freqs - Variable in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.FreqList: Frequencies vector of strings.
freqs - Variable in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.FreqList: Frequencies vector of strings.
freqs - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqList: Frequencies vector of strings.
freqs - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqListPair: Frequencies vector of strings.
freqs - Variable in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.FreqList: Frequencies vector of strings.
freqs - Variable in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.FreqList: Frequencies vector of strings.
freqs - Variable in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.FreqList: Frequencies vector of strings.
freqs - Variable in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.FreqList: Frequencies vector of strings.
freqs - Variable in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.FreqList: Frequencies vector of strings.
freqs - Variable in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.FreqList: Frequencies vector of strings.
frequencySize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
frequentItemsetForInterval(Vector<Itemset>, int[], int) - Method in class keel.Algorithms.Discretizers.UCPD.UCPD: It computes the frequent itemsets of the given instances
FrequentItemsets - Class in keel.Algorithms.Discretizers.UCPD: This class implements the algorithm to find the large itemsets of a dataset
FrequentItemsets() - Constructor for class keel.Algorithms.Discretizers.UCPD.FrequentItemsets
freshAttributeInfo() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Replaces the attribute information by a clone of itself.
freshAttributeInfo() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Replaces the attribute information by a clone of itself.
freshAttributeInfo() - Method in class keel.Algorithms.SVM.SMO.core.Instances: Replaces the attribute information by a clone of itself.
Friedman - Class in keel.Algorithms.Statistical_Tests.Shared.nonParametric: File: Friedman.java This class performs several statistical comparisons between 1xN methods
Friedman() - Constructor for class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Friedman: Builder
Friedman - Class in keel.GraphInterKeel.statistical.tests: File: Multiple.java This class performs several statistical comparisons between 1xN methods
Friedman() - Constructor for class keel.GraphInterKeel.statistical.tests.Friedman: Builder
FriedmanAlignedC - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Classification Aligned Friedman Stat-test identifier.
FriedmanAlignedR - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Regression Aligned Friedman Stat-test identifier.
FriedmanC - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Classification Friedman Stat-test identifier.
FriedmanI - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Imbalanced Friedman Stat-test identifier.
FriedmanR - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Regression Friedman Stat-test identifier.
FRKNNA - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.FRKNNA: File: FRKNNA.java The FRKNNA algorithm.
FRKNNA(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRKNNA.FRKNNA: Main builder.
FRM(int[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.RuleBase: It returns the class which better fits to the given example
FRM(int[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.RuleBase: It returns the class which better fits to the given example
FRM(int[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.RuleBase: It returns the class which better fits to the given example
FRM(double[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.RuleBase: It returns the class which better fits to the given example
FRM(double[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: Predicts the class value for a given example, using the rules and type of inference stored on the RuleBase.
FRM(double[], int[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: Predicts the class value for a given example, using the selected rules and type of inference stored on the RuleBase.
FRM(double[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.RuleBase: It returns the class which better fits to the given example
FRM(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.RuleBase: Fuzzy Reasoning Method
FRM(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseR: Fuzzy Reasoning Method
FRM(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase: Fuzzy Reasoning Method
FRM(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.RuleBase: Fuzzy Reasoning Method
FRM(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseR
FRM(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
FRM(double[], int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
FRM(double[], MatrizR) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseR
FRM_NS(ArrayList<Rule>, double[], boolean[], ArrayList<Integer>) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.GP_COACH_H: Normalized Sum FRM (also known as Additive Combination)
FRM_WR(ArrayList<Rule>, double[], boolean[], ArrayList<Integer>) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.GP_COACH_H: Winning Rule FRM
FRNN - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN: File: FRNN.java The FRNN algorithm.
FRNN(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN.FRNN: Main builder.
FRNN_FRS - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_FRS: File: FRNN_FRS.java The FRNN_FRS algorithm.
FRNN_FRS(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_FRS.FRNN_FRS: Main builder.
FRNN_VQRS - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_VQRS: File: FRNN_VQRS.java The FRNN_VQRS algorithm.
FRNN_VQRS(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_VQRS.FRNN_VQRS: Main builder.
fromBitString(boolean[], myDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule
FSAlgorithm - Class in keel.Algorithms.Preprocess.Feature_Selection.Shared: File: FSAlgorithm.java A general framework for FS Algorithms.
FSAlgorithm() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm
fsqrt(float) - Static method in class keel.Algorithms.Instance_Generation.utilities.ApproximateSqrt
fsqrt(float) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.ApproximateSqrt: Fast way to compute the sqrt of the value given.
FSS98 - Class in keel.Algorithms.Fuzzy_Rule_Learning.Random_Sets.FSS98: ModelFuzzySAP is intended to generate a Fuzzy Rule Based System (FRBS) regression model using the fuzzy random sets regression algorithm.
FSS98() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Random_Sets.FSS98.FSS98
fTestFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
fTestFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
fTimeFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
fTimeFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
fTrainFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
fTrainFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
Full - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_IEP: Simulated Annealing Algorithm
Full(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_IEP.Full: Creates a new instance of SA
Full - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_LIU: Simulated Annealing Algorithm
Full(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_LIU.Full: Creates a new instance of SA
Full - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_MI: Simulated Annealing Algorithm
Full(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_MI.Full: Creates a new instance of SA
FullRandomInitiator - Class in keel.Algorithms.Neural_Networks.IRPropPlus_Clas
FullRandomInitiator() - Constructor for class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.FullRandomInitiator: Empty constructor
Fun - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting
Fun() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.Fun
FUN - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal: Class for duplicate the initial ana used rles and fro filter the used ones.
FUN - Class in keel.Algorithms.Preprocess.NoiseFilters.ANR: Abstract class that represent a error function for a neural network.
FUN() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.ANR.FUN
FUN - Class in keel.Algorithms.Shared.ClassicalOptim: Abstract class that represent a error function for a neural network.
FUN() - Constructor for class keel.Algorithms.Shared.ClassicalOptim.FUN
fun_aux - Class in keel.Algorithms.LQD.methods.FGFS_costInstances: File: fun_aux.java Obtain a fuzzy number from one number.
fun_aux() - Constructor for class keel.Algorithms.LQD.methods.FGFS_costInstances.fun_aux
fun_aux - Class in keel.Algorithms.LQD.methods.FGFS_Minimum_Risk: File: fun_aux.java Obtain a fuzzy number from one number.
fun_aux() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fun_aux
fun_aux - Class in keel.Algorithms.LQD.methods.FGFS_Original: File: fun_aux.java Obtain a fuzzy number from one number.
fun_aux() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Original.fun_aux
fun_aux - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight: File: fun_aux.java Obtain a fuzzy number from one number.
fun_aux() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.fun_aux
fun_aux - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty: File: fun_aux.java Obtain a fuzzy number from one number.
fun_aux() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.fun_aux
fun_aux - Class in keel.Algorithms.LQD.preprocess.Expert: File: fun_aux.java Obtain a fuzzy number from one number.
fun_aux() - Constructor for class keel.Algorithms.LQD.preprocess.Expert.fun_aux
fun_aux - Class in keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE: File: fun_aux.java Obtain a fuzzy number from one number.
fun_aux() - Constructor for class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fun_aux
fun_aux - Class in keel.Algorithms.LQD.preprocess.Prelabelling: File: fun_aux.java Obtain a fuzzy number from one number.
fun_aux() - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling.fun_aux
fun_aux - Class in keel.Algorithms.LQD.preprocess.Prelabelling_Expert: File: fun_aux.java Obtain a fuzzy number from one number.
fun_aux() - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fun_aux
fun_aux - Class in keel.Algorithms.LQD.tests.IntermediateBoost: File: fun_aux.java
fun_aux() - Constructor for class keel.Algorithms.LQD.tests.IntermediateBoost.fun_aux
Funciones - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs: It contains the functions to convert the data in other type or change
Funciones() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Funciones
Funciones - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs: It contains the functions to convert the data in other type or change
Funciones() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Funciones
Funciones - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs: It contains the functions to convert the data in other type or change
Funciones() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Funciones
Funciones - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs: It contains the functions to convert the data in other type or change
Funciones() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Funciones
Funciones - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs: It contains the functions to convert the data in other type or change
Funciones() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Funciones
Funciones - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs: It contains the functions to convert the data in other type or change
Funciones() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Funciones
Funciones - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs: It contains the functions to convert the data in other type or change
Funciones() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Funciones
Funciones - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs: It contains the functions to convert the data in other type or change
Funciones() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Funciones
FuncionEvaluacionBean - Class in keel.Algorithms.Genetic_Rule_Learning.Hider
FuncionEvaluacionBeanHandler - Class in keel.Algorithms.Genetic_Rule_Learning.Hider
FuncionEvaluacionBeanHandler(XMLReader) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.FuncionEvaluacionBeanHandler: Constructor
Function - Class in keel.Algorithms.Decision_Trees.M5: Class for handling a linear function.
Function() - Constructor for class keel.Algorithms.Decision_Trees.M5.Function: Constructs a function of constant value
Function(M5Instances) - Constructor for class keel.Algorithms.Decision_Trees.M5.Function: Constucts a function with all attributes except the class in the inst
Function(int) - Constructor for class keel.Algorithms.Decision_Trees.M5.Function: Constructs a function with one attribute
function() - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode: Finds the appropriate order of the unsmoothed linear model at this node
Function - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules: Class for handling a linear function.
Function() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Function: Constructs a function of constant value
Function(MyDataset) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Function: Constucts a function with all attributes except the class in the inst
Function(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Function: Constructs a function with one attribute
function() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode: Finds the appropriate order of the unsmoothed linear model at this node
Function - Class in keel.Algorithms.Preprocess.Missing_Values.EM: Abstract class that contains all the neeaded methods to implement function of several variables
Function() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EM.Function
function(double[], double[]) - Method in interface keel.Algorithms.Preprocess.Missing_Values.SVDimpute.RegressionFunction: To implement non-linear the regression funciont
function - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters: Function used.
FunctionalTrees - Class in keel.Algorithms.Decision_Trees.FunctionalTrees: The Functional Trees algorithm builds a decision tree model integrating in only one model a decision tree and another classifier.
FunctionalTrees(String) - Constructor for class keel.Algorithms.Decision_Trees.FunctionalTrees.FunctionalTrees: Creates a FunctionalTrees instance by reading the script file that contains all the information needed for running the algorithm
FUNGPRS - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal: Class for evaluating the fitness of a rule set.
FUNGPRS(RegSymFuzzyGP, boolean[], double[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.FUNGPRS: Constructor
FURIA - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA: This class implements the FURIA algorithm proposed by Hühn and Hüllermeier 2009

The FURIA algorithm is a fuzzy rule learner based on the JRip implementation (RIPRER).
FURIA(parseParameters) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Constructor.
FURIAException - Exception in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: Class for Weka-specific exceptions.
FURIAException() - Constructor for exception keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FURIAException: Creates a new WekaException with no message.
FURIAException(String) - Constructor for exception keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FURIAException: Creates a new WekaException.
FUSINTER - Class in keel.Algorithms.Discretizers.FUSINTER: This class implements the FUSINTER discretizer.
FUSINTER(double, double) - Constructor for class keel.Algorithms.Discretizers.FUSINTER.FUSINTER: Builder
Fuzzifica(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Fuzzy: If fuzzyfies a crisp value
Fuzzifica(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Fuzzy: If fuzzyfies a crisp value
Fuzzifica(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Fuzzy: If fuzzyfies a crisp value
Fuzzifica(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Difuso: If fuzzyfies a crisp value
Fuzzifica(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Difuso
Fuzzifica(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Difuso
Fuzzifica(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Fuzzy: If fuzzyfies a crisp value
Fuzzifica(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Difuso
Fuzzifica(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Difuso: The fuzzy interface
Fuzzifica(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Difuso: The fuzzy interface
Fuzzifica(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Difuso: The fuzzy interface
Fuzzifica(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Difuso: The fuzzy interface
Fuzzifica(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Difuso: The fuzzy interface
Fuzzifica(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Difuso: The fuzzy interface
Fuzzifica(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Difuso: The fuzzy interface
Fuzzifica(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Difuso: The fuzzy interface
Fuzzifica(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fuzzy: If fuzzyfies a crisp value
Fuzzification(double, FuzzySet) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Sel: Fuzzification Interface
Fuzzification(double, FuzzySet) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Tun: Fuzzification Interface
Fuzzify(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Fuzzy: If fuzzyfies a crisp value
Fuzzify(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Fuzzy: If fuzzyfies a crisp value
Fuzzify(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Fuzzy: If fuzzyfies a crisp value
Fuzzify(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Fuzzy: If fuzzyfies a crisp value
Fuzzify(double) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Fuzzy: If fuzzyfies a crisp value
Fuzzy - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR: This class contains the representation of a fuzzy value
Fuzzy() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Fuzzy: Default constructor.
Fuzzy - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD: Title: Fuzzy Description: This class contains the representation of a fuzzy value Copyright: Copyright KEEL (c) 2007 Company: KEEL
Fuzzy() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Fuzzy: Default constructor.
Fuzzy - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA: This class contains the representation of a fuzzy value
Fuzzy() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Fuzzy: Default Constructor.
Fuzzy - Class in keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW: This class contains the representation of a fuzzy value
Fuzzy() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Fuzzy: Default constructor
Fuzzy - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD: This class contains the representation of a fuzzy value
Fuzzy() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Fuzzy: Default constructor.
Fuzzy - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid: Title: Fuzzy Description: This class contains the representation of a fuzzy value Copyright: Copyright KEEL (c) 2007 Company: KEEL
Fuzzy() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Fuzzy: Default constructor.
Fuzzy - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH: Title: Fuzzy Description: This class contains the representation of a fuzzy value Copyright: Copyright KEEL (c) 2007 Company: KEEL
Fuzzy() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Fuzzy: Default constructor.
Fuzzy - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS: Title: Fuzzy Description: This class contains the representation of a fuzzy value Copyright: Copyright KEEL (c) 2007 Company: KEEL
Fuzzy() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Fuzzy: Default constructor.
Fuzzy - Class in keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy: It is the abstract class for the remaining basic classes related with Fuzzy Logic..
Fuzzy() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.Fuzzy: A constructor by default.
Fuzzy - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyRegressor: the type for fuzzy regressors based on fuzzy sets (triangular fuzzy sets).
Fuzzy - Class in keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H: Title: Fuzzy Description: This class contains the representation of a fuzzy value Copyright: Copyright KEEL (c) 2007 Company: KEEL
Fuzzy() - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Fuzzy: Default constructor.
fuzzy - Class in keel.Algorithms.LQD.methods.FGFS_costInstances: File: fuzzy.java Properties and functions of fuzzy number
fuzzy() - Constructor for class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
fuzzy(float) - Constructor for class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
fuzzy - Class in keel.Algorithms.LQD.methods.FGFS_Minimum_Risk: File: fuzzy.java Properties and functions of fuzzy number
fuzzy() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
fuzzy(float) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
fuzzy - Class in keel.Algorithms.LQD.methods.FGFS_Original: File: partitions.java Properties and functions of fuzzy partitions
fuzzy() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Original.fuzzy
fuzzy - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight: File: partitions.java Properties and functions of fuzzy partitions
fuzzy() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.fuzzy
fuzzy - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty: File: fuzzy.java Properties and functions of fuzzy number
fuzzy() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.fuzzy
fuzzy - Class in keel.Algorithms.LQD.preprocess.Expert: File: fuzzy.java Properties and functions of fuzzy number
fuzzy() - Constructor for class keel.Algorithms.LQD.preprocess.Expert.fuzzy
fuzzy(float) - Constructor for class keel.Algorithms.LQD.preprocess.Expert.fuzzy
fuzzy - Class in keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE: File: fuzzy.java Properties and functions of fuzzy number
fuzzy() - Constructor for class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
fuzzy(float) - Constructor for class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
fuzzy - Class in keel.Algorithms.LQD.preprocess.Prelabelling: File: fuzzy.java Properties and functions of fuzzy number
fuzzy() - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
fuzzy(float) - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
fuzzy - Class in keel.Algorithms.LQD.preprocess.Prelabelling_Expert: File: fuzzy.java Properties and functions of fuzzy number
fuzzy() - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
fuzzy(float) - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
fuzzy - Class in keel.Algorithms.LQD.tests.IntermediateBoost
fuzzy() - Constructor for class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
fuzzy(float) - Constructor for class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
Fuzzy - Class in keel.Algorithms.RE_SL_Methods.P_FCS1
Fuzzy() - Constructor for class keel.Algorithms.RE_SL_Methods.P_FCS1.Fuzzy: Default constructor
Fuzzy(Fuzzy) - Constructor for class keel.Algorithms.RE_SL_Methods.P_FCS1.Fuzzy: Creates a gaussian fuzzy set as a copy of another gaussian fuzzy set
Fuzzy - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate: Class for fuzzy set definition.
Fuzzy() - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Fuzzy: Creates a new instance of Fuzzy
Fuzzy(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Fuzzy: This function fuzzy a value
Fuzzy - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF: Class for fuzzy set definition
Fuzzy() - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Fuzzy: Creates a new instance of Fuzzy
Fuzzy(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Fuzzy: Returns the belonging degree
Fuzzy(int, int, float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Return the belonging of a value
Fuzzy - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate: Class for fuzzy set definition
Fuzzy() - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Fuzzy: Creates a new instance of Fuzzy
Fuzzy(float) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Fuzzy: This function fuzzy a value
Fuzzy - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD: Class for a fuzzy set definition
Fuzzy() - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Fuzzy: Creates a new instance of Fuzzy
Fuzzy(float) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Fuzzy: Returns the belonging degree
Fuzzy(int, int, float) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Return the belonging of a value
Fuzzy - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate: Class for a fuzzy set definition
Fuzzy() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Fuzzy: Creates a new instance of Fuzzy
Fuzzy(float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Fuzzy: This function fuzzy a value
Fuzzy - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA: Class for a fuzzy set definition.
Fuzzy() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Fuzzy: Creates a new instance of Fuzzy
Fuzzy(float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Fuzzy: Returns the belonging degree
Fuzzy(int, int, float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Return the belonging of a value
Fuzzy - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams: Title: Fuzzy Description: This class contains the representation of a fuzzy value Copyright: Copyright KEEL (c) 2007 Company: KEEL
Fuzzy() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fuzzy: Default constructor.
fuzzy - Variable in class keel.GraphInterKeel.experiments.DatasetXML
fuzzy - Variable in class keel.GraphInterKeel.experiments.Parameters
Fuzzy_Chi - Class in keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW: It contains the implementation of the Chi algorithm
Fuzzy_Chi() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Fuzzy_Chi: Default constructor
Fuzzy_Chi(parseParameters) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Fuzzy_Chi: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Fuzzy_GB_NFRM - Class in keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM: Title: Fuzzy_GB_NFRM Description: It contains the implementation of the Fuzzy_GB_NFRM Company: KEEL
Fuzzy_GB_NFRM() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Fuzzy_GB_NFRM: Default constructor
Fuzzy_GB_NFRM(parseParameters) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Fuzzy_GB_NFRM: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Fuzzy_Ish - Class in keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted: It contains the implementation of the algorithm
Fuzzy_Ish() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Fuzzy_Ish: Default constructor
Fuzzy_Ish(parseParameters) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Fuzzy_Ish: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Fuzzy_Ish - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid: Title: Fuzzy_Ish Description: It contains the implementation of the FH-GBML algorithm Company: KEEL
Fuzzy_Ish() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Fuzzy_Ish: Default constructor
Fuzzy_Ish(parseParameters) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Fuzzy_Ish: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Fuzzy_Ish - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec: Title: Fuzzy_Ish Description: It contains the implementation of the Fuzzy Ish algorithm Company: KEEL
Fuzzy_Ish() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Fuzzy_Ish: Default constructor
Fuzzy_Ish(parseParameters) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Fuzzy_Ish: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
fuzzy_t - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV
fuzzy_t - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE
fuzzy_t() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.fuzzy_t: Default Constructor
fuzzy_t(double, double, double, double, String) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.fuzzy_t: Constructor [a,b,c,d] represents a trapezoidal label and "name" is the name of the label.
fuzzy_t(double, double, double, double, String, boolean, boolean) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.fuzzy_t: Constructor [a,b,c,d] represents a trapezoidal label and "name" is the name of the label.
fuzzy_t(fuzzy_t) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.fuzzy_t: Creates a fuzzy_t object as a copy of "x"
fuzzy_t - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2
fuzzy_t - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0
FuzzyAlphaCut - Class in keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy: Represents an alpha-cut for any type of fuzzy number (triangular, trapezoidal and singleton).
FuzzyAlphaCut(FuzzyNumberTRIANG) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut: A copy constructor specialized for Triangular Fuzzy Numbers (FuzzyNumberTRIANG).
FuzzyAlphaCut(FuzzyInterval) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut: A copy constructor specialized for Fuzzy Intervals (FuzzyInterval).
FuzzyAlphaCut(FuzzySingleton) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut: A copy constructor specialized for Fuzzy Singleton Sets(FuzzySingleton).
FuzzyAlphaCut(FuzzyAlphaCut) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut: A copy constructor specialized for Fuzzy Alpha Cuts(FuzzyAlphaCut).
FuzzyAntecedent - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH: Title: Fuzzy Antecedent Description: Fuzzy Antecedent for a variable in the GP-COACH algorithm Company: KEEL
FuzzyAntecedent() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.FuzzyAntecedent: Default constructor
FuzzyAntecedent(DataBase, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.FuzzyAntecedent: Constructor with parameters.
FuzzyAntecedent(Fuzzy, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.FuzzyAntecedent: Constructor with parameters.
FuzzyAntecedent(FuzzyAntecedent) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.FuzzyAntecedent: Copy constructor for a FuzzyAntecedent from another FuzzyAntecedent
FuzzyAntecedent - Class in keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H: Title: Fuzzy Antecedent Description: Fuzzy Antecedent for a variable in the GP-COACH algorithm Company: KEEL
FuzzyAntecedent() - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Default constructor
FuzzyAntecedent(DataBase, int) - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Constructor with parameters.
FuzzyAntecedent(Fuzzy, int, int) - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Constructor with parameters.
FuzzyAntecedent(FuzzyAntecedent) - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Copy constructor for a FuzzyAntecedent from another FuzzyAntecedent
FuzzyAntecedent(FuzzyAntecedent, double[][]) - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Constructor for a FuzzyAntecedent that builds it from another FuzzyAntecedent applying a lateral tuning
FuzzyApriori - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori: It gathers all the parameters, launches the algorithm, and prints out the results
FuzzyApriori() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyApriori: Default constructor
FuzzyApriori(parseParameters) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyApriori: It reads the data from the input files and parse all the parameters from the parameters array.
FuzzyAprioriProcess - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori: It provides the implementation of the algorithm to be run in a process
FuzzyAprioriProcess(myDataset, boolean, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyAprioriProcess: It creates a new process for the algorithm by setting up its parameters
FuzzyAttribute - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal
FuzzyAttribute(int, FuzzyRegion[]) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyAttribute: It creates a new fuzzy attribute by setting up its properties
FuzzyAttribute - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori
FuzzyAttribute(int, FuzzyRegion[]) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyAttribute: It creates a new fuzzy attribute by setting up its properties
FuzzyAttribute - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC
FuzzyAttribute(int, FuzzyRegion[]) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyAttribute: It creates a new fuzzy attribute by setting up its properties
fuzzyclasifica(double[], int, double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FB
fuzzyclasificamaxmin(double[], int, double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FB
FuzzyClassifier - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting
FuzzyClassifier(double[][], double[][]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
FuzzyClassifier - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier: FuzzyClassifier is designed to allow a Fuzzy Classifier evolve by means of an Genetic Algorithm (GA).
FuzzyClassifier(FuzzyPartition[], FuzzyPartition, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.FuzzyClassifier: Class constructor using the following parameters:
FuzzyClassifier(FuzzyClassifier) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.FuzzyClassifier: Copy constructor of this class, which clones its components
fuzzycreavacio(int, int, int, double[], double[], double[], Randomize) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FB
FuzzyDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal: It represents a fuzzy dataset which is based on the original dataset and handles fuzzy transactions
FuzzyDataset(myDataset, ArrayList<FuzzyAttribute>) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyDataset: It creates a new fuzzy dataset by setting up its properties
FuzzyDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori
FuzzyDataset(myDataset, ArrayList<FuzzyAttribute>) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyDataset: It creates a new fuzzy dataset by setting up its properties
FuzzyDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC: It represents a fuzzy dataset which is based on the original dataset and handles fuzzy transactions.
FuzzyDataset(myDataset, ArrayList<FuzzyAttribute>) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyDataset: It creates a new fuzzy dataset by setting up its properties
FuzzyFGPClassifier - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier: FuzzyFGPClassifier is designed to allow a Fuzzy Classifier evolve by means of an Genetic Programming (GP).
FuzzyFGPClassifier(NodeRuleBase, FuzzyPartition) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.FuzzyFGPClassifier: Class constructor using the following parameters:
FuzzyFGPClassifier(FuzzyFGPClassifier) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.FuzzyFGPClassifier: Copy constructor of this class, which clones its components
fuzzyFSSmodeling(boolean, ProcessConfig) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Random_Sets.FSS98.FSS98: This private static method extracts the dataset and the method's parameters from the KEEL environment, carries out with the partitioning of the input and output spaces, learn the FRBS model --which is a RSFSS instance-- using the random sets regression algorithm and print out the results with the validation dataset
FuzzyGAPClassifier - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGAP: FuzzyGAPClassifier is designed to allow a Fuzzy Classifier evolve by means of an Genetic Algorithm and Programming (GAP).
FuzzyGAPClassifier(FuzzyPartition[], FuzzyPartition, int, int, Randomize) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGAP.FuzzyGAPClassifier: A constructor of the class specifying the input and class variables partitions, the maximum height of the tree, the fitness type and the Randomize object to use.
FuzzyGAPClassifier(FuzzyGAPClassifier) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGAP.FuzzyGAPClassifier: The copy constructor for this class.
FuzzyGAPModelIndividual - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model: Class for management fuzzy individuals in GAP models
FuzzyGAPModelIndividual(FuzzyPartition[], FuzzyPartition, int, int, Randomize, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGAPModelIndividual: Constructor.
FuzzyGAPModelIndividual(FuzzyGAPModelIndividual) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGAPModelIndividual: Constructor.
FuzzyGAPModelIndividualoClona() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGAPModelIndividual: This method generate a fuzzy individual for GAP model from another one
fuzzygCenter - Class in keel.Algorithms.Preprocess.Missing_Values.fkmeans: This class represents a group of centers (centroids) of a set of fuzzy clusters
fuzzygCenter() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.fkmeans.fuzzygCenter: Creates a new instance of gCenter
fuzzygCenter(int, int, int, double) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.fkmeans.fuzzygCenter: Creates a new instance of gCenter with provided number of centers, number of instances of the data set and number of attributes
FuzzyGPClassifier - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGP: FuzzyGPClassifier is designed to allow a Fuzzy Classifier evolve by means of an Genetic Programming (GP).
FuzzyGPClassifier(FuzzyPartition[], FuzzyPartition, int, int, Randomize) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGP.FuzzyGPClassifier: A constructor of the class specifying the input and class variables partitions, the maximum height of the tree, the fitness type and the Randomize object to use.
FuzzyGPClassifier(FuzzyGPClassifier) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGP.FuzzyGPClassifier: The copy constructor for this class.
FuzzyGPModel - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model: Class for management fuzzy models in GP.
FuzzyGPModel(NodeRuleBase, FuzzyPartition, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPModel: Constructor.
FuzzyGPModel(FuzzyGPModel) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPModel: Constructor.
FuzzyGPModelIndividual - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model: Class for management fuzzy individuals in GP models
FuzzyGPModelIndividual(FuzzyPartition[], FuzzyPartition, int, int, Randomize, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPModelIndividual: Constructor.
FuzzyGPModelIndividual(FuzzyGPModelIndividual) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPModelIndividual: Constructor.
FuzzyGPModelIndividualoClona() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPModelIndividual: This method generate a fuzzy individual for GP model from another one
FuzzyGPRegresionSimbolicaClona() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.RegSymFuzzyGP: This method generate a genetic individual from a fuzzy system of symbolic regession
FuzzyGPRegSymModel - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model: Fuzzy GP regresion model.
FuzzyGPRegSymModel(NodeExprHold, double, double, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPRegSymModel: Constructor.
FuzzyGPRegSymModel(FuzzyGPRegSymModel) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPRegSymModel: Constructor.
FuzzyIBLAlgorithm - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning: File: FuzzyIBLAlgorithm.java Main class for FuzzyIBL methods.
FuzzyIBLAlgorithm() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm
FuzzyInterval - Class in keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy: Represents an interval fuzzy set.
FuzzyInterval(double, double) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyInterval: A constructor for an interval fuzzy set, given the extremes.
FuzzyInterval(FuzzyInterval) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyInterval: A copy constructor for an interval fuzzy set, given other interval fuzzy set.
FuzzyKNN - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyKNN: File: FuzzyKNN.java The FuzzyKNN algorithm.
FuzzyKNN(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyKNN.FuzzyKNN: Main builder.
FuzzyLabel(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
FuzzyLabel() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.fuzzy_t
FuzzyLabel(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
FuzzyLabel(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
FuzzyLabel(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Returns the fuzzy label number i in the domain
FuzzyLabel() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.fuzzy_t: Returns a fuzzy_t object with the label
FuzzyLabel(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t: Returns a fuzzy_t object with the definition of the label number "i" in the variable's domain.
FuzzyLabel(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Returns a fuzzy_t object with the definition of the label number "lab" in the variable in position "var" of the list.
FuzzyLabel(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
FuzzyLabel() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.fuzzy_t
FuzzyLabel(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
FuzzyLabel(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
FuzzyLabel(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
FuzzyLabel() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.fuzzy_t
FuzzyLabel(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
FuzzyLabel(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
FuzzyModel - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model: Fuzzy model.
FuzzyModel(FuzzyPartition[], FuzzyPartition, int, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyModel: Constructor.
FuzzyModel(FuzzyModel) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyModel: Construxtor.
FuzzyNPC - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyNPC: File: FuzzyNPC.java The FuzzyNPC algorithm.
FuzzyNPC(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyNPC.FuzzyNPC: Main builder.
FuzzyNumberTRIANG - Class in keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy: Represents a triangular fuzzy number.
FuzzyNumberTRIANG(double, double, double) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyNumberTRIANG: A constructor for a triangle fuzzy set, given the extremes.
FuzzyNumberTRIANG(FuzzyNumberTRIANG) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyNumberTRIANG: A copy constructor for a triangular fuzzy number, given other triangular fuzzy number.
FuzzyNumberTRLEFT - Class in keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy: Represents a right-angled triangular fuzzy number with the right-angle in left side.
FuzzyNumberTRLEFT(double, double) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyNumberTRLEFT: A constructor for a left right-angled triangle fuzzy number, given the extremes.
FuzzyNumberTRLEFT(FuzzyNumberTRLEFT) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyNumberTRLEFT: A copy constructor for a left right-angled triangle fuzzy number, given other left right-angled triangle fuzzy number.
FuzzyPartition - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting
FuzzyPartition(double[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyPartition
FuzzyPartition - Class in keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy: Represents a partition of fuzzy sets.
FuzzyPartition(double, double, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyPartition: A constructor for a fuzzy partition of n fuzzy sets whose support is uniformely defined between min and max.
FuzzyPartition(double[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyPartition: A constructor for a fuzzy partition of n fuzzy sets whose support is given in a vector.
FuzzyPartition(int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyPartition: A constructor for a fuzzy partition of n singleton fuzzy sets with values i.
FuzzyPartition(FuzzyPartition) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyPartition: A copy constructor for fuzy partition, given other fuzzy partition.
fuzzyPartition - Class in keel.Algorithms.LQD.methods.FGFS_costInstances: File: fuzzy.java Properties and functions of fuzzy partitions
fuzzyPartition() - Constructor for class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyPartition
fuzzyPartition(float, float, int) - Constructor for class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyPartition
fuzzyPartition - Class in keel.Algorithms.LQD.methods.FGFS_Minimum_Risk: File: fuzzy.java Properties and functions of fuzzy partitions
fuzzyPartition() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyPartition
fuzzyPartition(float, float, int) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyPartition
fuzzypartition - Class in keel.Algorithms.LQD.preprocess.Prelabelling: File: fuzzy.java Properties and functions of fuzzy partitions
fuzzypartition() - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzypartition
fuzzypartition(float, float, int) - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzypartition
fuzzypartition - Class in keel.Algorithms.LQD.preprocess.Prelabelling_Expert: File: fuzzy.java Properties and functions of fuzzy partitions
fuzzypartition() - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzypartition
fuzzypartition(float, float, int) - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzypartition
FuzzyRegion - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal
FuzzyRegion() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyRegion: Default constructor
FuzzyRegion - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori
FuzzyRegion() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyRegion: Default constructor
FuzzyRegion - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori
FuzzyRegion() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyRegion: Default constructor
FuzzyRegion - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC
FuzzyRegion() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyRegion: Default constructor
FuzzyRegressor - Class in keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy: It is the abstract class for the remaining basic classes related with Fuzzy Regression defined in keel.Algorithms.Symbolic_Regression.
FuzzyRegressor() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyRegressor
FuzzyRule - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting
FuzzyRule(int[], double[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyRule
FuzzyRule - Class in keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy: Represents a fuzzy rule.
FuzzyRule() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyRule: Constructor by default.
FuzzyRule(int, double) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyRule: A constructor for a fuzzy rule, given its consequent and weight in the base rules.
FuzzyRule(FuzzyRule) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyRule: A copy constructor for fuzzy rule, given other fuzzy rule.
fuzzyRule - Class in keel.Algorithms.LQD.methods.FGFS_costInstances: File: fuzzyRule.java Properties and functions of the fuzzy rule
fuzzyRule(Vector<fuzzyPartition>, int, int) - Constructor for class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
fuzzyRule - Class in keel.Algorithms.LQD.methods.FGFS_Minimum_Risk: File: fuzzyRule.java Properties and functions of the fuzzy rule as obtain the antecedent and the consequent of the rule from the confidence the this rule with the instances
fuzzyRule(Vector<fuzzyPartition>, int, int) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
fuzzyrule - Class in keel.Algorithms.LQD.preprocess.Prelabelling: File: fuzzyRule.java Properties and functions of the fuzzy rule as obtain the antecedent and the consequent of the rule from the confidence the this rule with the instances
fuzzyrule(Vector<fuzzypartition>, Vector<fuzzy>) - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzyrule
fuzzyrule - Class in keel.Algorithms.LQD.preprocess.Prelabelling_Expert: File: fuzzyRule.java Properties and functions of the fuzzy rule as obtain the antecedent and the consequent of the rule from the confidence the this rule with the instances
fuzzyrule(Vector<fuzzypartition>, Vector<fuzzy>) - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzyrule
FuzzyRuleSet - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS: Fuzzy Rule Set built from the PDFC
FuzzyRuleSet(SMO.BinarySMO, Instances, Kernel) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.FuzzyRuleSet: Builds a new Fuzzy Rule Set from a Binary SMO
FuzzySAPClassifier - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySAP: FuzzySAPClassifier is designed to allow a Fuzzy Classifier evolve by means of an Simulated Annealing Algorithm and Programming (SAP).
FuzzySAPClassifier(FuzzyPartition[], FuzzyPartition, int, int, Randomize) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySAP.FuzzySAPClassifier: A constructor of the class specifying the input and class variables partitions, the maximum height of the tree, the fitness type and the Randomize object to use.
FuzzySAPClassifier(FuzzySAPClassifier) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySAP.FuzzySAPClassifier: The copy constructor for this class.
FuzzySingleton - Class in keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy: Represents a singleton fuzzy set.
FuzzySingleton(double) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzySingleton: A constructor for a singleton fuzzy set, given the point.
FuzzySingleton(FuzzySingleton) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzySingleton: A copy constructor for a singleton fuzzy set, given other singleton fuzzy set.
fuzzySymRegGAP - Class in keel.Algorithms.Symbolic_Regression.fuzzySymRegGAP: Wrapper for symbolicRegression with fuzzy sets and based on GAP (Genetic Algorithm Programming) paradigm).
fuzzySymRegGAP() - Constructor for class keel.Algorithms.Symbolic_Regression.fuzzySymRegGAP.fuzzySymRegGAP
fuzzySymRegSAP - Class in keel.Algorithms.Symbolic_Regression.fuzzySymRegSAP: Wrapper for symbolicRegression with fuzzy sets and based on SA (Simulated Annealing) paradigm).
fuzzySymRegSAP() - Constructor for class keel.Algorithms.Symbolic_Regression.fuzzySymRegSAP.fuzzySymRegSAP
fuzzyTolerance - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Tolerance added to input examples in Fuzzy Symbolic Regression
fuzzyTolerance - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Tolerance added to input examples in Fuzzy Symbolic Regression.
fuzzyYet - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.NumericAntd
fw - Variable in class keel.GraphInterKeel.experiments.EducationalReport

G

g(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.LinearSearchBrent
g - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividual: The genotype of the individual.
g(double) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.LinearSearchBrent: Returns the error of the weights dSearch.
g(double) - Method in class keel.Algorithms.Shared.ClassicalOptim.LinearSearchBrent: Returns the error of the weights dSearch.
GA - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift: Title: Genetic algorithm Description: Genetic algorithm for Thrift Company: KELL
GA(myDataset, myDataset, BaseD, BaseR, int, int, int, double, double, String) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.GA
GA - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist: GA algorithm for the GAssist.
GA() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.GA: Creates a new instance of GA
GA - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS
GA() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.GA: Creates a new instance of GA
GA - Class in keel.Algorithms.Genetic_Rule_Learning.UCS: This is the main class of the genetic algorithm.
GA() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.GA: Constructs the GA
GA - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: This is the main class of the genetic algorithm.
GA() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.GA: Constructs the GA
GA - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
GA_Large() - Method in class keel.Algorithms.Decision_Trees.DT_GA.Poblacion: Performs the large GA to generate the different rules for the decision tree.
GA_LARGE_SN - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.DT_GA: Number to represent type of GA used (Large_SN scheme).
GA_MSE_CC_FSM - Class in keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM: File: GA_MSE_CC_FSM.java The GA MSE CC FSM Instance Selection algorithm.
GA_MSE_CC_FSM(String) - Constructor for class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.GA_MSE_CC_FSM: Default constructor.
GA_MSE_CC_FSM - Class in keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM: File: GA_MSE_CC_FSM.java The GA MSE CC FSM Instance Selection algorithm.
GA_MSE_CC_FSM(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.GA_MSE_CC_FSM: Default constructor.
GA_SMALL - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.DT_GA: Number to represent type of GA used (Small scheme).
GA_Small() - Method in class keel.Algorithms.Decision_Trees.DT_GA.Poblacion: Performs the small GA to generate the different rules for the decision tree.
GAFuzzyKNN - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.GAFuzzyKNN: File: GAFuzzyKNN.java The GAFuzzyKNN algorithm.
GAFuzzyKNN(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.GAFuzzyKNN.GAFuzzyKNN: Main builder.
gain(double[][], double) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Computes value of splitting criterion after split.
gain(double[][], double) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Computes value of splitting criterion after split.
gain(double[][], double) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Computes value of splitting criterion after split.
Gain - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate: This class is defined to manage the information gain of each attribute of the dataset
Gain() - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Gain
Gain - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate: This class is defined to manage the information gain of each attributev of the dataset
Gain() - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Gain
Gain - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate: This class is defined to manage the information gain of each attributev of the dataset
Gain() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Gain
GainInit(TableDat, String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Computes and stores the information gain of each attribute (variable) of the dataset
GainInit(TableDat, String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Computes and stores the information gain of each attribute (variable) of the dataset
GainInit(TableDat, String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Computes and stores the information gain of each attribute (variable) of the dataset
gainRatio(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ContingencyTables: Computes gain ratio for contingency table (split on rows).
gainRatio(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.ContingencyTables: Computes gain ratio for contingency table (split on rows).
gainRatio(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.ContingencyTables: Computes gain ratio for contingency table (split on rows).
gainRatioCutCrit(Classification, double, double) - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Function to compute the gain ratio.
gainRatioCutCrit(Classification, double, double) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to compute the gain ratio.
gainRatioCutCrit(Classification, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Function to compute the gain ratio.
gainRatioCutCrit(Classification, double, double) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Function to compute the gain ratio.
gainRatioCutCrit(Classification, double, double) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to compute the gain ratio.
gainRatioCutCrit(Classification, double, double) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Function to compute the gain ratio.
gainRatioCutCrit(Classification, double, double) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Function to compute the gain ratio.
gainRatioCutCrit(Classification, double, double) - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Function to compute the gain ratio.
gainRatioCutCrit(Classification, double, double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Function to compute the gain ratio.
gaIteration() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.populationWrapper
gamma - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Discount factor (used in multiple step problems)
GAMMA - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
gamma - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
gamma - Variable in class org.libsvm.svm_parameter
gammaTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.RBFKernel: Returns the tip text for this property
Gann - Class in keel.Algorithms.Neural_Networks.gann: Implementation of the Genetic Algorithm Neural Networks
Gann() - Constructor for class keel.Algorithms.Neural_Networks.gann.Gann: Empty (default) constructor
GAPCROSSGA - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OperatorIdent: Indentifier for GAPCROSSGA crossover.
GAPCROSSGA - Static variable in class keel.Algorithms.Shared.Parsing.OperatorIdent: Indentifier for GAPCROSSGA crossover.
GAPCROSSGP - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OperatorIdent: Indentifier for GAPCROSSGP crossover.
GAPCROSSGP - Static variable in class keel.Algorithms.Shared.Parsing.OperatorIdent: Indentifier for GAPCROSSGP crossover.
GAPMUTAGA - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OperatorIdent: Indentifier for GAPMUTAGA mutation.
GAPMUTAGA - Static variable in class keel.Algorithms.Shared.Parsing.OperatorIdent: Indentifier for GAPMUTAGA mutation.
GAPMUTAGP - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OperatorIdent: Indentifier for GAPMUTAGP mutation.
GAPMUTAGP - Static variable in class keel.Algorithms.Shared.Parsing.OperatorIdent: Indentifier for GAPMUTAGP mutation.
GAR - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR: Title: Algorithm Description: It contains the implementation of the GAR algorithm Company: KEEL
GAR() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.GAR: Default constructor
GAR(parseParameters) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.GAR: It reads the data from the input files and parse all the parameters from the parameters array.
GARProcess - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR: It provides the implementation of the GAR algorithm to be run in a process
GARProcess(myDataset, int, int, int, double, double, double, double, double, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.GARProcess
gaussian(double) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.StatFunc: Standard Normal Distribution Function.
gaussian(double, double, double) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.StatFunc: Normal Distribution Function with given mean and standard deviation.
Gaussian - Static variable in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: Type of Positive Definite Functions supported (Gaussian)
gaussian(double) - Method in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: Computes the result of the Gaussian PDRF
gaussianDensity(double) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.StatFunc: Density function of the Standard Normal Distribution.
gaussianDensity(double, double, double) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.StatFunc: Density function of the Normal Distribution with given mean and standard deviation.
gaussianPercentage(double) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.StatFunc: Percentage point of the standard normal distribution.
gaussianPercentage(double, double, double) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.StatFunc: Percentage point of the normal distribution with given mean and standard deviation.
gCenter - Class in keel.Algorithms.Preprocess.Missing_Values.kmeansImpute: This class represents a group of centers (centroids) of a set of clusters
gCenter() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.gCenter: Creates a new instance of gCenter
gCenter(int, int, int) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.gCenter: Creates a new instance of gCenter with provided number of centers, number of instances of the data set and number of attributes
gcerteza - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.T_Consequent: Consequent Certainty.
GCNet - Class in keel.Algorithms.Preprocess.NoiseFilters.ANR: Wrapper for a perceptron (ConjGradNN).
GCNet() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.ANR.GCNet
GCNet - Class in keel.Algorithms.Shared.ClassicalOptim: Wrapper for a perceptron (ConjGradNN).
GCNet() - Constructor for class keel.Algorithms.Shared.ClassicalOptim.GCNet
GCNN - Class in keel.Algorithms.Instance_Selection.GCNN: File: GCNN.java The GCNN Instance Selection algorithm.
GCNN(String) - Constructor for class keel.Algorithms.Instance_Selection.GCNN.GCNN: Default constructor.
GCNN - Class in keel.Algorithms.Preprocess.Instance_Selection.GCNN: File: GCNN.java The GCNN Instance Selection algorithm.
GCNN(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.GCNN.GCNN: Default constructor.
GCQuad - Class in keel.Algorithms.Shared.ClassicalOptim: Wrapper for a perceptron (ConjGradQUAD).
GCQuad() - Constructor for class keel.Algorithms.Shared.ClassicalOptim.GCQuad
Gcvfctn - Class in keel.Algorithms.Preprocess.Missing_Values.EM: Implements the GCV function.
Gcvfctn(double[], double[], double, int) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EM.Gcvfctn: Copy constructor
gcvridge(DenseMatrix, double[], double, int, int, double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: MATLAB - Finds minimum of GCV function for ridge regression.
Gen - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
Gen - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs: Class that defines the type Gen
Gen() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Gen
Gen - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs: Class that defines the type Gen
Gen() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Gen
Gen - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs: Class that defines the type Gen
Gen() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Gen
Gen - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs: Class that defines the type Gen
Gen() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Gen
Gen - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs: Class that defines the type Gen
Gen() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Gen
Gen - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs: Class that defines the type Gen
Gen() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Gen
Gen - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs: Class that defines the type Gen
Gen() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Gen
Gen - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs: Class that defines the type Gen
Gen() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Gen
GENAR - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR: Title: Algorithm Description: It contains the implementation of the GENAR algorithm Company: KEEL
GENAR() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.GENAR: Default constructor
GENAR(parseParameters) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.GENAR: It reads the data from the input files and parse all the parameters from the parameters array.
GENARProcess - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR: It provides the implementation of the GENAR algorithm to be run in a process
GENARProcess(myDataset, int, int, int, double, double, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.GENARProcess: It creates a new process for the algorithm by setting up its parameters
Gene - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Individuo
Gene - Class in keel.Algorithms.Genetic_Rule_Learning.COGIN: This class implements a gene as specified by the COGIN algorithm
Gene() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.COGIN.Gene: Default constructor
Gene(Attribute) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.COGIN.Gene: this constructor builds up a gene from the information of an attribute
Gene(Gene) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.COGIN.Gene: Copy constructor, performs a deep copy of the passed object
Gene - Class in keel.Algorithms.Genetic_Rule_Learning.CORE: This class represents a gene (i.e. a relation for a set of attribute values).
Gene(Attribute, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.CORE.Gene: Parameterized constructor
Gene(Gene) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.CORE.Gene: Deep-copy constructor
gene(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Cromosoma: Get the value of a gene (normal double representation)
Gene() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Cromosoma: Returns the values of all genes (normal double representation)
gene(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Cromosoma: Get the value of a gene
Gene() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Cromosoma: Returns the values of all genes
gene(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Cromosoma: Get the value of a gene (normal double representation)
Gene() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Cromosoma: Returns the values of all genes (normal double representation)
gene(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Cromosoma: Get the value of a gene (normal double representation)
Gene() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Cromosoma: Returns the values of all genes (normal double representation)
gene(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Cromosoma: Get the value of a gene (normal double representation)
Gene() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Cromosoma: Returns the values of all genes (normal double representation)
gene(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Cromosoma: Get the value of a gene
Gene() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Cromosoma: Returns the values of all genes
gene(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Cromosoma: Get the value of a gene (normal double representation)
Gene() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Cromosoma: Returns the values of all genes (normal double representation)
gene(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Cromosoma: Get the value of a gene (normal double representation)
Gene() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Cromosoma: Returns the values of all genes (normal double representation)
Gene - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate: This implementation uses integer values to store the genes values (only 0/1).
Gene(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Gene: Create new instances of gene
Gene - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF: This implementation uses boolean values to store the genes values It is used to store DNF rules, so that each variable can can get more than one value at a time Each gene is an array of boolean values, false indicates that the value is not present, true indicates that the value is present
Gene(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Gene: Creates new instance of gene
Gene - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate: This implementation uses integer values to store the genes values (only 0/1).
Gene(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Gene: Create new instances of gene
Gene - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD: This implementation uses boolean values to store the genes values.
Gene(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Gene: Creates new instance of gene
Gene - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate: This implementation uses integer values to store the genes values (only 0/1).
Gene(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Gene: Create new instances of gene
Gene - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA: This implementation uses boolean values to store the genes values.
Gene(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Gene: Creates new instance of gene
Gene - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal: It is used for representing and handling a gene throughout the evolutionary learning
Gene(double[]) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Gene: It creates a new gene by setting up its displacements
Gene - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori: It is used for representing and handling a gene throughout the evolutionary learning
Gene(MembershipFunction[]) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Gene: It creates a new gene by setting up its membership functions
Gene - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC: It is used for representing and handling a gene throughout the evolutionary learning.
Gene(MembershipFunction[]) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Gene: It creates a new gene by setting up its membership functions
Gene - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal: It is used for representing and handling a Gene throughout the evolutionary learning
Gene() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene: It creates a new gene
Gene - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA
Gene() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
Gene - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA
Gene() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
Gene - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR
Gene() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Gene
Gene - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR
Gene() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Gene
Gene - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR
Gene() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene
Gene - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh
Gene() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene: It creates a new gene
Gene - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR
Gene() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene: It creates a new gene
Gene - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII
Gene() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene: It creates a new gene
geneA(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Cromosoma: Get the value of a gene ("A" double representation)
GeneA() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Cromosoma: Returns the values of all genes ("A" double representation)
geneA(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Cromosoma: Get the value of a gene ("A" double representation)
GeneA() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Cromosoma: Returns the values of all genes ("A" double representation)
geneA(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Cromosoma: Get the value of a gene ("A" double representation)
GeneA() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Cromosoma: Returns the values of all genes ("A" double representation)
geneA(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Cromosoma: Get the value of a gene ("A" double representation)
GeneA() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Cromosoma: Returns the values of all genes ("A" double representation)
geneR(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Cromosoma: Get the value of a gene ("R" char representation)
GeneR() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Cromosoma: Returns the values of all genes ("R" char representation)
geneR(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Cromosoma: Get the value of a gene ("R" char representation)
GeneR() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Cromosoma: Returns the values of all genes ("R" char representation)
geneR(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Cromosoma: Get the value of a gene ("R" char representation)
GeneR() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Cromosoma: Returns the values of all genes ("R" char representation)
geneR(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Cromosoma: Get the value of a gene ("R" char representation)
GeneR() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Cromosoma: Returns the values of all genes ("R" char representation)
geneR(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Cromosoma: Get the value of a gene ("R" int representation)
GeneR() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Cromosoma: Returns the values of all genes ("R" int representation)
geneR(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Cromosoma: Get the value of a gene ("R" char representation)
GeneR() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Cromosoma: Returns the values of all genes ("R" char representation)
generaAleatorio(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Individuo
Generacion() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseR: It generates the initial set of rules from each data partition space from 2 to L (number of labels)
Generacion() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseR: Genero el conjunto de reglas inicial para cada particion del espacio (2...L)
Generacion(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Poblacion
generalC - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Summary of classification data for multiple algorithms identifier.
generalI - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Summary of data, multiple algorithms imbalanced
generaListas() - Method in class keel.Algorithms.Decision_Trees.SLIQ.SLIQ: Constructs the lists of attriibutes and classes used during SLIQ execution.
Generalization_operator(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
Generalization_operator(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
generalizeContinuous(int, double, double, int) - Method in class keel.Algorithms.Hyperrectangles.INNER.Rule: Generalizes a continuous attribute
generalizeExemplar(double[], int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Hyperrectangle: Adds a new example to the hyperrectangle
generalizeNominal(int, double, double, int) - Method in class keel.Algorithms.Hyperrectangles.INNER.Rule: Generalizes a nominal attribute
generalizeProbability - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
generalizeRule(int[], int[], int, int[], int, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
generalR - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Summary of regression data for multiple algorithms identifier.
generaPosicion(Vector, Vector, int, ConjuntoDatos, Randomize, float[][]) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Generates the particle position using the given posible conditions, its heuristic values and the data to be covered.
generarFicherosSalida(String, String, boolean[]) - Method in class keel.Algorithms.Preprocess.Feature_Selection.Datos: this method generates the output files .tra and .tst, removing the non-selected features
generate(myDataset, int[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Apriori: Generates the Rule Base with the whole Classification Association Rules set by using the Apriori Method.
generateActualMPA(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ruleOrderAgent
generateARs() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TotalSupportTree: Initiates process of generating Association Rules (ARs) from a T-tree.
generateARs(short[], int, TtreeNode[]) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TotalSupportTree: Continues process of generating association rules from a T-tree by recursively looping through T-tree level by level.
generateARs() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TotalSupportTree: Initiates process of generating Association Rules (ARs) from a T-tree.
generateARs(short[], int, TtreeNode[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TotalSupportTree: Continues process of generating association rules from a T-tree by recursively looping through T-tree level by level.
generateARs2() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TotalSupportTree: Loops through top level of T-tree as part of the AR generation process.
generateARs2() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TotalSupportTree: Loops through top level of T-tree as part of the AR generation process.
generateCAR() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Apriori: Generates the Rule Base with the whole Classification Association Rules set by using the Apriori Method
generateCAR() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Apriori: Generates the Rule Base with the whole Classification Association Rules set by using the Apriori Method
generateCARs(short[], int, int, short[], TtreeNode[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFP_CMAR: Continues process of generating classificationh association rules from a T-tree by recursively looping through T-tree level by level.
generateCSVOutput() - Method in class keel.GraphInterKeel.statistical.statTableModel: Exports the contents of the table in CSV format
generatedDataSet - Variable in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Condensed data
generatedDataSet - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Condensed data
generateDifferentRandomIntegers(int, int) - Static method in class keel.Algorithms.Instance_Generation.utilities.RandomGenerator: Generate a random sequence of integer between the bounds.
generateDifferentRandomIntegers(int, int, int) - Static method in class keel.Algorithms.Instance_Generation.utilities.RandomGenerator: Generate a random sequence of integer between the bounds.
generateDifferentRandomIntegers(int, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.RandomGenerator: Generate a random sequence of integer between the bounds.
generateDifferentRandomIntegers(int, int, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.RandomGenerator: Generate a random sequence of integer between the bounds.
generateDifferentRandomIntegersWithStep(int, int, int) - Static method in class keel.Algorithms.Instance_Generation.utilities.RandomGenerator: Generate a random sequence of integer between the bounds.
generateDifferentRandomIntegersWithStep(int, int, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.RandomGenerator: Generate a random sequence of integer between the bounds.
generateDifferentRandomNumbers(double, double) - Static method in class keel.Algorithms.Instance_Generation.utilities.RandomGenerator: Generate a random sequence of double values between the bounds.
generateDifferentRandomNumbers(double, double, int) - Static method in class keel.Algorithms.Instance_Generation.utilities.RandomGenerator: Generate a random sequence of double values between the bounds.
generateDifferentRandomNumbers(double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.RandomGenerator: Generate a random sequence of double values between the bounds.
generateDifferentRandomNumbers(double, double, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.RandomGenerator: Generate a random sequence of double values between the bounds.
generateDifferentRandomNumbersWithStep(double, double, double) - Static method in class keel.Algorithms.Instance_Generation.utilities.RandomGenerator: Generate a random sequence of double values between the bounds.
generateDifferentRandomNumbersWithStep(double, double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.RandomGenerator: Generate a random sequence of double values between the bounds.
generateDirectedMatrix() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams
generateDirectedMatrix() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Generates the complete directed social network matrix with the list of examples covered by each rule.
generateFile(String, double, String, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FingramsProcess: It runs the algorithm for mining association rules
generateFiles() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerCSVM: Generates the configuration files and the results files for the CSVM algorithm.
generateFiles() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerNB: Executes the algorithm and generates the files with the results.
generateFiles() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerSMO: Generates the configuration files and the results files for the algorithm.
generateFiles_Instance(InstanceSet, InstanceSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerNB: Executes the algorithms with the datasets given and generates the files with the results.
generateFingramImages(String, String, String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams
generateID() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation: Generates an ID based on the current settings and returns it.
generateID() - Method in class keel.Algorithms.SVM.SMO.core.TechnicalInformation: Generates an ID based on the current settings and returns it.
generateLevel2() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Generates level 2 of the T-tree.
generateLevelN(TtreeNode[], int, short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Commences process of generating remaining levels in the T-tree (other than top and 2nd levels).
generateMatrix() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Generates the complete social network matrix with the list of examples covered by each rule.
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.CFKNN.CFKNN: Generates the model of the algorithm
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.D_SKNN.D_SKNN: Generates the model of the algorithm
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FCMKNN.FCMKNN: Generates the model of the algorithm
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FENN.FENN: Generates the model of the algorithm
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRKNNA.FRKNNA: Generates the model of the algorithm
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN.FRNN: Generates the model of the algorithm
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_FRS.FRNN_FRS: Generates the model of the algorithm
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_VQRS.FRNN_VQRS: Generates the model of the algorithm
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyKNN.FuzzyKNN: Generates the model of the algorithm
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyNPC.FuzzyNPC: Generates the model of the algorithm
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.GAFuzzyKNN.GAFuzzyKNN: Generates the model of the algorithm
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IF_KNN.IF_KNN: Generates the model of the algorithm
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFSKNN.IFSKNN: Generates the model of the algorithm
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFV_NP.IFV_NP: Generates the model of the algorithm
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IT2FKNN.IT2FKNN: Generates the model of the algorithm
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN.JFKNN: Generates the model of the algorithm
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.PFKNN.PFKNN: Generates the model of the algorithm
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.PosIBL.PosIBL: Generates the model of the algorithm
generateModel() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.VWFuzzyKNN.VWFuzzyKNN: Generates the model of the algorithm
generateNeighborhood(HeterDisc.DiscretizationScheme, int, int, double) - Method in class keel.Algorithms.Discretizers.HeterDisc.HeterDisc: It generates the neighborhood of ds scheme discretization and adds each neighbor to variable GD
generateNextLevel(TtreeNode[], int, short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Generates a new level in the T-tree from a given "parent" node.
GenerateOutput(double[]) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network: Generate output at activation using input
GenerateOutput(double[], double[]) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network: Generate output using input
GenerateOutput(double[]) - Method in class keel.Algorithms.Neural_Networks.gann.Network: Generate output at activation using input
GenerateOutput(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.gann.Network: Generate output using input
GenerateOutput(double[]) - Method in class keel.Algorithms.Neural_Networks.gmdh.Network: Generate output at activation using an input
GenerateOutput(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.gmdh.Network: Generate output using an input
GenerateOutput(double[]) - Method in class keel.Algorithms.Neural_Networks.gmdh.sonn: Obtains the fitness of the sonn
GenerateOutput(double[]) - Method in class keel.Algorithms.Neural_Networks.net.Network: Generate output at activation using input
GenerateOutput(double[], double[]) - Method in class keel.Algorithms.Neural_Networks.net.Network: Generate output using input
generateParseException() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
generateParseException() - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
generateParseException() - Static method in class keel.Dataset.DataParser
generatePR() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.PRM: Main function of the method: it prunes the rulebase to obtain the final set of rules.
generateProfiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.matchProfileAgent
generateRB() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Apriori: Generate the rule set (Stage 1 and 2)
generateRB() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual: Generates the Rule Base with adjusted to the optimization done.
generateRB(double, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Apriori: Generates the Rule Base with the whole Classification Association Rules set with a minimum value of support and confidence by using the Apriori Method
generateRB(Apriori, double, double, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Individual: Generates the Rule Base with adjusted fuzzy confidences
generateRB() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Apriori
generateRB() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual
generateReducedDataSet() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Execute the reduction of the data set
generateReducedDataSet() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Execute the reduction of the data set
generateResultsClasification(String, int[], int[]) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Generates output file for a clasification problem
generateResultsClasification(String, int[], int[]) - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Generates output file for a clasification problem
generateResultsClasification(String, int[], int[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Generates output file for a clasification problem
generateResultsClasification(String, int[], int[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Generates output file for a clasification problem
generateResultsClasification(String, int[], int[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Generates output file for a clasification problem
generateResultsClasification(String, int[], int[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Generates output file for a clasification problem
generateResultsClasification(String, int[], int[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Generates output file for a clasification problem
generateResultsModeling(String, double[], double[]) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Generates output file for a modelling problem
generateResultsModeling(String, int[], int[]) - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Generates output file for a modelling problem
generateResultsModeling(String, double[], double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Generates output file for a modelling problem
generateResultsModeling(String, int[], int[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Generates output file for a modelling problem
generateResultsModeling(String, double[], double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Generates output file for a modelling problem
generateResultsModeling(String, int[], int[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Generates output file for a modelling problem
generateResultsModeling(String, double[], double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Generates output file for a modelling problem
generateResultsModeling(String, int[], int[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Generates output file for a modelling problem
generateResultsModeling(String, double[], double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Generates output file for a modelling problem
generateResultsModeling(String, int[], int[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Generates output file for a modelling problem
generateResultsModeling(String, double[], double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Generates output file for a modelling problem
generateResultsModeling(String, int[], int[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Generates output file for a modelling problem
generateResultsModeling(String, double[], double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Generates output file for a modelling problem
generateRules() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.DataSqueezer: Run the DataSqueezer algorithm Francisco Charte - 16-ene-2010
generateRulesPareto() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_GhoshProcess
generateRulesPareto() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_GoshProcess
generateRulesPareto() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.MOPNARProcess
generateRulesPareto() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.QAR_CIP_NSGAIIProcess
generateRulesSet() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.FPgrowthProcess: It constructs a rules set once the algorithm has been carried out
generateRulesSet(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AlatasetalProcess: It constructs a rules set once the whole evolutionary learning has been carried out.
generateRulesSet() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AprioriProcess: It constructs a rules set once the algorithm has been carried out
generateRulesSet() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.EclatProcess: It constructs a rules set once the algorithm has been carried out
generateRulesSet() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.FPgrowthProcess: It constructs a rules set once the algorithm has been carried out
generateScaledNetwork(String, String, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams
generateScienceMapFile(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams
generateScienceMapFile(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams
generateTree() - Method in class keel.Algorithms.Decision_Trees.C45.C45: Generates the tree.
generateTree(Dataset) - Method in class keel.Algorithms.Decision_Trees.C45.C45: Generates the tree.
generateTree() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.C45: It generates the decision tree from the dataset
generateTree(Dataset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.C45: Generates the tree.
generateTree() - Method in class keel.Algorithms.Decision_Trees.ID3.ID3: Run the algorithm.
generateTree() - Method in class keel.Algorithms.Decision_Trees.SLIQ.SLIQ: Generates the tree with the SLIQ algorithm.
generateTree(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.C45: Generates the tree.
generateTree(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.C45CS: Generates the tree.
generateTree() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.C45: Generates the tree with the model dataset.
generateTree(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.C45: Generates the tree.
generateTree(Dataset) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.C45: Generates the tree.
generateTree(Dataset) - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.C45: Generates the tree.
generateTree(Dataset) - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.C45: Generates the tree.
generateTree(Dataset) - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.C45: Generates the tree.
generateTree() - Method in class keel.Algorithms.Rule_Learning.ART.ART: Run the algorithm.
generateTree(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.C45: Generates the tree.
generateTree(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.C45: Generates the tree.
generateTree(MyDataset) - Method in class keel.Algorithms.Rule_Learning.PART.C45: Generates the tree.
generateTree(Dataset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45: Generates the tree.
generateUndirectedMatrix() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Generates the complete undirected social network matrix with the list of examples covered by each rule.
Generation() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Population: Run the CHC algorithm (Stage 3)
Generation(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Population: Main procedure of the GA
Generation(myDataset) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.RuleBase: Rule Learning Mechanism for the Chi et al.'
Generation() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Population: It performs the generation process
Generation(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Population: Main procedure of the GA
Generation() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Population: Run the CHC algorithm (Stage 3)
generation - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Current generation
GenerationalCrossover() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation: Generational crossover operator
GenerationalCrossover() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
GenerationalCrossover() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation: Generational crossover operator
GenerationalCrossover() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
GENERATIONS - Static variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
generationsTipText() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
GENERICMUTATION - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OperatorIdent: Indentifier for generic mutation.
GENERICMUTATION - Static variable in class keel.Algorithms.Shared.Parsing.OperatorIdent: Indentifier for generic mutation.
GENERICROSSOVER - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OperatorIdent: Indentifier for generic crossover.
GENERICROSSOVER - Static variable in class keel.Algorithms.Shared.Parsing.OperatorIdent: Indentifier for generic crossover.
genes - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome: It is used for representing and handling a Chromosome throughout the evolutionary learning
genes - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome: It is used for representing and handling a Chromosome throughout the evolutionary learning
genes - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome: It is used for representing and handling a Chromosome throughout the evolutionary learning
genes0Activos(int[]) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.Chromosome
genes1Activos(int[]) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.Chromosome
genesActivos() - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Cromosoma: Count the number of genes set to 1
genesActivos() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.Chromosome
genesActivos() - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma: Count the number of genes set to 1
genesActivos() - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma: Count the number of genes set to 1
genesActivos() - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma: Count the number of genes set to 1
genesActivos() - Method in class keel.Algorithms.Instance_Selection.CHC.Cromosoma: Count the number of genes set to 1
genesActivos() - Method in class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.Cromosoma
genesActivos() - Method in class keel.Algorithms.Instance_Selection.GGA.Cromosoma: Count the number of genes set to 1
genesActivos() - Method in class keel.Algorithms.Instance_Selection.IGA.Cromosoma: Count the number of genes set to 1
genesActivos() - Method in class keel.Algorithms.Instance_Selection.PBIL.Cromosoma: Count the number of genes set to 1
genesActivos() - Method in class keel.Algorithms.Instance_Selection.SGA.Cromosoma: Count the number of genes set to 1
genesActivos() - Method in class keel.Algorithms.Instance_Selection.SSMA.Cromosoma: Count the number of genes set to 1
genesActivos() - Method in class keel.Algorithms.Instance_Selection.ZhangTS.Cromosoma: Return the number of actived genes
genesActivos() - Method in class keel.Algorithms.Preprocess.Instance_Selection.CHC.Cromosoma: Count the number of genes set to 1
genesActivos() - Method in class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Count the number of genes set to 1
genesActivos() - Method in class keel.Algorithms.Preprocess.Instance_Selection.GGA.Cromosoma: Count the number of genes set to 1
genesActivos() - Method in class keel.Algorithms.Preprocess.Instance_Selection.IGA.Cromosoma: Count the number of genes set to 1
genesActivos() - Method in class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Cromosoma: Count the number of genes set to 1
genesActivos() - Method in class keel.Algorithms.Preprocess.Instance_Selection.SGA.Cromosoma: Count the number of genes set to 1
genesActivos() - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma: Count the number of genes set to 1
genesActivos() - Method in class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.Cromosoma: Return the number of actived genes
Genesis - Class in keel.Algorithms.Neural_Networks.ensemble: Class that creates individuals
Genesis() - Constructor for class keel.Algorithms.Neural_Networks.ensemble.Genesis: Empty constructor
Genesis - Class in keel.Algorithms.Neural_Networks.gann: Class that creates the individuals
Genesis() - Constructor for class keel.Algorithms.Neural_Networks.gann.Genesis: Empty constructor
Genesis - Class in keel.Algorithms.Neural_Networks.gmdh: Class Genesis
Genesis() - Constructor for class keel.Algorithms.Neural_Networks.gmdh.Genesis: Empty constructor
Genesis - Class in keel.Algorithms.Neural_Networks.net: Class for generating the individuals
Genesis() - Constructor for class keel.Algorithms.Neural_Networks.net.Genesis: Empty constructor
genetcode - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV
genetcode - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE
genetcode - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2
genetcode - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0
Genetic - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF: Methods to define the genetic algorithm and to apply operators and reproduction schema
Genetic() - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Creates a new instance of Genetic
Genetic - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD: Methods to define the genetic algorithm and to apply operators and reproduction schema
Genetic() - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic
Genetic - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA: Methods to define the genetic algorithm and to apply operators and reproduction schema
Genetic() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Creates a new instance of Genetic
GeneticAlgorithm - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms: GeneticAlgorithm is the base clase for all genetic algorithm and programming algorithms, including the simulate annealing based one.
GeneticAlgorithm() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms.GeneticAlgorithm
geneticAlgorithm - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
geneticAlgorithm(classifierFactory) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.geneticAlgorithm
GeneticAlgorithm(TableVar, TableDat, int, String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Composes the genetic algorithm applying the operators
GeneticAlgorithm(TableVar, TableDat, String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Composes the genetic algorithm applying the operators
GeneticAlgorithm(TableVar, TableDat, String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Composes the genetic algorithm applying the operators
GeneticAlgorithmForBoosting - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting
GeneticAlgorithmForBoosting(GenotypeBoosting) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GeneticAlgorithmForBoosting
GeneticAlgorithmGenerational - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms: GeneticAlgorithmGenerational is the genetic algorithm (GA) algorithm when the generational option is chosen, that is, the Steady parameter of the given method is not marked.
GeneticAlgorithmGenerational(GeneticIndividual, int, int, double, double, double, double, int, int, Randomize, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms.GeneticAlgorithmGenerational: Class constructor with the following parameters:
GeneticAlgorithmSteady - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms: GeneticAlgorithmSteady is the genetic algorithm (GA) algorithm when the steady option is chosen, that is, the Steady parameter of the given method is marked.
GeneticAlgorithmSteady(GeneticIndividual, int, int, int, double, double, double, double, int, int, Randomize, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms.GeneticAlgorithmSteady: Class constructor with the following parameters:
GeneticFuzzyApriori - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori: It gathers all the parameters, launches the algorithm, and prints out the results
GeneticFuzzyApriori() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.GeneticFuzzyApriori: Default constructor
GeneticFuzzyApriori(parseParameters) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.GeneticFuzzyApriori: It reads the data from the input files and parse all the parameters from the parameters array.
GeneticFuzzyAprioriDC - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC: It gathers all the parameters, launches the algorithm, and prints out the results
GeneticFuzzyAprioriDC() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.GeneticFuzzyAprioriDC: Default constructor
GeneticFuzzyAprioriDC(parseParameters) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.GeneticFuzzyAprioriDC: It reads the data from the input files and parse all the parameters from the parameters array.
GeneticFuzzyAprioriDCProcess - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC: It provides the implementation of the algorithm to be run in a process.
GeneticFuzzyAprioriDCProcess(myDataset, int, int, double, double, double, int, boolean, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.GeneticFuzzyAprioriDCProcess: It creates a new process for the algorithm by setting up its parameters
GeneticFuzzyAprioriProcess - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori: It provides the implementation of the algorithm to be run in a process
GeneticFuzzyAprioriProcess(myDataset, int, int, double, double, double, int, boolean, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.GeneticFuzzyAprioriProcess: It creates a new process for the algorithm by setting up its parameters
GeneticIndividual - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual: Methods for genetic individual management.
GeneticIndividual(int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividual: Constructor.
GeneticIndividualForClassification - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual: Class for management of genetic individuals in classification.
GeneticIndividualForClassification(int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForClassification: Constructor.
GeneticIndividualForModels - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual: Class for management of genetic individuals in Models
GeneticIndividualForModels(int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForModels: Constructor.
GeneticIndividualForSymbRegr - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual: Class for management of genetic individuals in symbolic regression
GeneticIndividualForSymbRegr(int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForSymbRegr: Constructor.
genetico(int, int, int, int, double, double) - Method in class keel.Algorithms.Decision_Trees.DT_GA.BaseR: Executes a genetic algorithm to generate the rules of the cassifier.
genetico() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.Poblacion: Performs the GA to generate the different rules for the decision tree.
genLatex - Class in keel.Algorithms.Statistical_Tests.Shared: Generates an output file in LaTeX format.
genLatex() - Constructor for class keel.Algorithms.Statistical_Tests.Shared.genLatex
GENNAlgorithm - Class in keel.Algorithms.Instance_Generation.GENN: GENN algorithm calling.
GENNAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.GENN.GENNAlgorithm
GENNGenerator - Class in keel.Algorithms.Instance_Generation.GENN: Generalized Edited Nearest Neighbor
GENNGenerator(PrototypeSet, int) - Constructor for class keel.Algorithms.Instance_Generation.GENN.GENNGenerator: Constructor of GENNGenerator objects.
GENNGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.GENN.GENNGenerator: Constructor of GENNGenerator objects.
Genotype - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes: Genotype is the base clase to represent the genotype of any GeneticIndividual.
Genotype(Randomize) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.Genotype: Class constructor with the following parameters:
GenotypeBoosting - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting
GenotypeBoosting(int, int, Randomize) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoosting
GenotypeBoostingMaxMin - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting
GenotypeBoostingMaxMin(int, int, int, int, Randomize) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoostingMaxMin
GenotypeFuzzyGAP - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes: GenotypeFuzzyGAP is the base clase to represent the genotype when a fuzzy model is to be learned with the genetic algorithm and programming (GAP).
GenotypeFuzzyGAP(FuzzyPartition[], FuzzyPartition, int, Randomize) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGAP: Class constructor with the following parameters:
GenotypeFuzzyGAP(GenotypeFuzzyGAP) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGAP: The copy constructor.
GenotypeFuzzyGP - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes: GenotypeFuzzyGP is the base clase to represent the genotype when a fuzzy model is to be learned with the genetic programming (GP).
GenotypeFuzzyGP(FuzzyPartition[], FuzzyPartition, int, Randomize) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGP: Class constructor with the following parameters:
GenotypeFuzzyGP(GenotypeFuzzyGP) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGP: The copy constructor.
GenotypeFuzzyGPRegSym - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes: GenotypeFuzzyGPRegSym is the base clase to represent the genotype when a fuzzy model symbolic regression is to be learned with the genetic programming (GP).
GenotypeFuzzyGPRegSym(double, double, int, int, int, int, Randomize) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGPRegSym: Class constructor with the following parameters:
GenotypeFuzzyGPRegSym(GenotypeFuzzyGPRegSym) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGPRegSym: The copy constructor.
GenotypePitts - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes: GenotypePitts is the base clase to represent the genotype when a fuzzy model is to be learned with the genetic algorithm and the Pittsburg approach.
GenotypePitts(int, int, Randomize) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypePitts: Class constructor with the following parameters:
GenotypePitts(GenotypePitts) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypePitts: The copy constructor.
genrand_gaussian() - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.MTwister
genrand_gaussian() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.MTwister
genrand_gaussian() - Method in class org.core.MTwister
genrand_real1() - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.MTwister
genrand_real1() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.MTwister
genrand_real1() - Method in class org.core.MTwister
genrand_real2() - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.MTwister
genrand_real2() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.MTwister
genrand_real2() - Method in class org.core.MTwister
genrand_real3() - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.MTwister
genrand_real3() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.MTwister
genrand_real3() - Method in class org.core.MTwister
genrand_res53() - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.MTwister
genrand_res53() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.MTwister
genrand_res53() - Method in class org.core.MTwister
geomDistance(double[], int, double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils: Computes the geometric mean of the distance between the given center and the 2 nearest vectors in a double[][]
geomDistance(double[], int, double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils: Computes the geometric mean of the distance between the given center and the 2 nearest vectors in a double[][]
geomDistance(double[], int, double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils: Computes the geometric mean of the distance between the given center and the 2 nearest vectors in a double[][]
geomDistance(double[], int, double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils: Computes the geometric mean of the distance between the given center and the 2 nearest vectors in a double[][]
geomDistance(double[], int, double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils: Computes the geometric mean of the distance between the given center and the 2 nearest vectors in a double[][]
geomDistance(double[], int, double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils: Computes the geometric mean of the distance between the given center and the 2 nearest vectors in a double[][]
get(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset: It returns the item located in the given position of the itemset.
get(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.RuleBase: Function to get a rule from the rule base
get(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset: It returns the item located in the given position of the itemset
get(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.RuleBase: Function to get a rule from the rule base
get(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.RuleBase: Function to get a rule from the rule base
get(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Itemset: It returns the item located in the given position of the itemset
get(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.RuleBase: Function to get a rule from the rule base
get(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Itemset: It returns the item located in the given position of the itemset
get(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: Function to get a rule from the rule base
get(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Itemset: It returns the item located in the given position of the itemset
get(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.RuleBase: Function to get a rule from the rule base
get(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase: It returns a given rule from the RB
get(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Itemset
get(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
get(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.PredictionArray: Returns the value of that position in the prediction array
get(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.PredictionArray: Returns the value of that position in the prediction array
get(int) - Method in class keel.Algorithms.Instance_Generation.GMCA.ClusterSet: Gets cluster in ith position.
get(Prototype) - Method in class keel.Algorithms.Instance_Generation.GMCA.ClusterSet: Gets the cluster of a prototype.
get(Prototype, Prototype) - Method in class keel.Algorithms.Instance_Generation.PNN.MatrixOfDistances: Get the distance between two prototypes.
get(int) - Method in class keel.Algorithms.Instance_Generation.VQ.Cluster: Get one element of the cluster.
get(int) - Method in class keel.Algorithms.Lazy_Learning.IDIBL.NQueue: Get a neighbor
get(int) - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster: Get an element from the cluster
get(int) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyPartition
get(int) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyPartition
get(int) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.partition
get(int) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.partition
get(int) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.partition
get(int) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzypartition
get(int) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzypartition
get(int) - Method in class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome: Returns the binary value (0 or 1) of the position given.
get(int) - Method in class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome: Returns the binary value (0 or 1) of the position given.
get(int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Gets a single element.
get(int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector: Gets the value of an element.
get(int, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Get a single element.
get(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Itemset: It returns the item stored at the index "pos" within an itemset
get(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Itemset: It returns the item stored at the index "pos" within an itemset
get(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Itemset: It returns the item stored at the index "pos" within an itemset
get(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Itemset: It returns the item stored at the index "pos" within an itemset
get(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Itemset: It returns the item stored at the index "pos" within an itemset
get(int) - Method in class keel.GraphInterKeel.experiments.DinamicParameter
get(int, int) - Method in class keel.GraphInterKeel.statistical.tests.Distribution2KeyTable: Get a value
get1Value() - Method in class keel.Algorithms.Rule_Learning.Riona.Selector: Returns the value or high extrem of the associated attribute
get_equivalence_set() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
get_equivalence_set() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal
get_fp() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.QualitySubgroup: Gets the value of the fp quality measure
get_FPm() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.QualitySubgroup: Gets the value of the FP missing quality measure
get_instancesByClass() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal
get_lower_aproximation() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
get_lower_aproximation() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal
get_precision(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
get_precision(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal
get_q() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.QualitySubgroup: Gets the value of the q quality measure
get_tp() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.QualitySubgroup: Gets the value of the tp quality measure
get_TPm() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.QualitySubgroup: Gets the value of the TP missing quality measure
get_upper_aproximation() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
get_upper_aproximation() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal
Get_Weight(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.ruleset
Get_Weight(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.ruleset
Get_Weight(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.ruleset
getA() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
geta() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
geta() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
geta() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.fuzzy
geta() - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
geta() - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
geta() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
geta() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
geta() - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
getAcc() - Method in class keel.Algorithms.ImbalancedClassification.Auxiliar.AUC.AccAUC: Provides the accuracy of a given classifier
getAccu() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Antd
getAccu() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Retuns the value of ACCU
getAccuracy() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.RuleBase: It returns the accuracy of the rule base
getAccuracy() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.RuleBase: It returns the accuracy of the rule base
getAccuracy() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Gets the value of the accuracyt field.
getAccuracy() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.RuleBase: Function to return the fitness of the rule base
getAccuracy() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual: Function to return the accuracy of the individual
getAccuracy() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: Function to return the fitness of the rule base
getAccuracy() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Individual: Function to return the accuracy of the individual
getAccuracy() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.RuleBase: Function to return the fitness of the rule base
getAccuracy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase: It returns the accuracy rate for the rule set
getAccuracy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.RuleBase: It returns the accuracy rate of the Rule Base
getAccuracy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseR
getAccuracy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual
getAccuracy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
getAccuracy() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformance
getAccuracy() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformanceTraining
getAccuracy() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
getAccuracy() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_aggregated
getAccuracy() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PerformanceAgent
getAccuracy() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PerformanceAgent
getAccuracy() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ruleOrderAgent
getAccuracy() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Returns the accuracy of the classifier.
getAccuracy() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters: Returns the accuracy of the classifier.
getAccuracy() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Returns the accuracy of the classifier.
getAccuracy() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Returns the accuracy of the classifier.
getAccuracy2() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformanceTraining
getAccuracy2() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
getAccuracyTest() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
getAccuracyTest() - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleQualityEvaluation: Get Accuracyy Test
getAccuracyTest() - Method in class keel.Algorithms.Rule_Learning.UnoR.EvaluaCalidadReglas: Returns the accuracy for the test dataset.
getAccuracyTrain() - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleQualityEvaluation: Get Accuracyy Train
getAccuracyTrain() - Method in class keel.Algorithms.Rule_Learning.UnoR.EvaluaCalidadReglas: Returns the accuracy for the training dataset.
getAccuRate() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Antd
getActAs() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene: It returns whether a gene is involved in the chromosome being considered.
getActAs() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene: It returns whether a gene is involved in the chromosome being considered.
getActAs() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene: It returns whether a gene is involved in the chromosome being considered.
getActAs() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene: It returns whether a gene is involved in the chromosome being considered.
getAction() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Returns the action of the classifier
getAction() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Returns the action of the classifier
getAction() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: It returns the action of the current classifier.
getActivation(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Rule: Obtains the activation state of the attribute
getActivation(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Rule: Obtains the activation state of the attribute
getActivationsOfClassifier(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformance
getActivationsOfRule(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PerformanceAgent
getActivationsOfRule(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PerformanceAgent
getActive() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule: It returns the activation of this rule
getActivePair() - Method in class keel.GraphInterKeel.experiments.Test: Get active parameters
getActualIndex(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: returns actual index in the Instances object.
getActualJobSentences() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: Return the Actual jobs Sentences
getActualNameExperiment() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: Get the experiment that is running
GetAdaSample(Ensemble, double[][], int, int) - Method in class keel.Algorithms.Neural_Networks.ensemble.Sample: Returns an Ada sample
GetAdaWeights() - Method in class keel.Algorithms.Neural_Networks.ensemble.Ensemble: Calculate weights using Ada method
getAdditionalOutputFiles() - Method in class keel.GraphInterKeel.experiments.Parameters: return additional output files
getAdjustedFuzzyAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.AlcalaetalProcess: It returns the mined fuzzy attributes once the genetic learning has been accomplished
getAlgorithmName() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.parseParameters: It returns the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.parseParameters: It returns the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.parseParameters: It returns the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.parseParameters: It returns the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.parseParameters: It returns the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.parseParameters: It returns the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.parseParameters: It returns the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.parseParameters: It returns the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.Decision_Trees.DT_GA.parseParameters: It returns the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.parseParameters: It returns the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.Decision_Trees.Target.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.parseParameters: It returns the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.parseParameters: It returns the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.parseParameters: It returns the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.ParametersParser: It returns the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.parseParameters: It returns the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.parseParameters: Gets the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.PSO_Learning.CPSO.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.PSO_Learning.REPSO.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Rule_Learning.C45Rules.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Rule_Learning.LEM1.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Rule_Learning.LEM2.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Rule_Learning.PART.parseParameters: It returns the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.Rule_Learning.Ripper.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Rule_Learning.Ritio.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Rule_Learning.Rules6.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Rule_Learning.Slipper.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Rule_Learning.SRI.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.parseParameters: Gets the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.SVM.C_SVM.parseParameters: Gets the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.SVM.EPSILON_SVR.parseParameters: Gets the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.SVM.NU_SVM.parseParameters: Gets the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.SVM.NU_SVR.parseParameters: Gets the algorithm name
getAlgorithmName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.parseParameters: It returns the name of the algorithm
getAlgorithmName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.parseParameters: It returns the name of the algorithm
getAlgorithmType() - Method in class keel.GraphInterKeel.experiments.Parameters: Gets the algorithm type
getAliveClassifiers() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformance
getAll() - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster: Get all elements from the cluster
getAll() - Method in class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome: Returns the chromosome representation (array of integers).
getAll() - Method in class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome: Returns the chromosome representation (array of integers).
getAll_support() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
getAll_support() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
getAll_support() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule
getAll_support() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
getAll_support() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
getAllDifferentFromClass(double) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Select all the patterns of different classs.
getAllDifferentFromClass(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Select all the patterns of different classs.
getAllele() - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute: It returns the allele as a double
getAllele() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep
getAllele() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep
getAllele() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep: Returns the value of the alelle
getAllErrors() - Method in class keel.Algorithms.Rule_Learning.Swap1.FormatErrorKeeper: It does return all the errors
getAllErrors() - Method in class keel.Dataset.FormatErrorKeeper: It does return all the errors
getAllInputs() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Returns a matrix with all the inputs of the dataSet in rows
getAllInputValues() - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: It does return all the input values.
getAllInputValues() - Method in class keel.Dataset.Instance: It does return all the input values.
getAllInstances() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.instanceSet
getAllNames() - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription: Gets all the names
getAllOutputs() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Returns a matrix with all the outputs of the dataSet in rows.
getAllOutputValues() - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: It does return all the output values.
getAllOutputValues() - Method in class keel.Dataset.Instance: It does return all the output values.
getAllowedIndices() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: returns the indices that are allowed to check for the attribute type
getAllowUnclassifiedInstances() - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Get the value of NumFolds.
getAllowUnclassifiedInstances() - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Get the value of NumFolds.
getAllowUnclassifiedInstances() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Get the value of NumFolds.
getAllValues(Vector, int) - Method in class keel.Algorithms.Decision_Trees.ID3.ID3: Function to return all the values of the specified attribute in the data set.
getAlphaInput() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Returns the alpha coeficient for the input weigths
getAlphaOutput() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Returns the alpha coeficient for the output weigths
getAmplitude() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ExpNeuronParametricMutator: Returns the amplitude coefficient for allowed weights
getAmplitude() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.LinearNeuronParametricMutator: Returns the amplitude coefficient for allowed weights
getAmplitude() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Returns the amplitude coefficient for the weights in mutations
getAmplitude() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.SigmNeuronParametricMutator: Returns the amplitude coefficient for allowed weights
getAmplitude(myDataset) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
getAmplitude(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: Returns the amplitude of the attribute with the given id.
getAmplitude(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: Returns the amplitude of the attribute with the given id.
getAmplitude(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: Returns the amplitude of the attribute with the given id.
getAmplitudeInterv() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
getantecedent() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
getantecedent(int) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
getantecedent() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
getantecedent(int) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
getantecedent() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.rule
getantecedent(int) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.rule
getantecedent() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
getantecedent(int) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
getAntecedent() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.AssociationRule: It retrieves the antecedent part of an association rule
getAntecedent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.AssociationRule: It retrieves the antecedent part of an association rule
getAntecedent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.AssociationRule: It retrieves the antecedent part of an association rule
getAntecedent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.AssociationRule: It retrieves the antecedent part of an association rule
getAntecedent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.AssociationRule: It retrieves the antecedent part of an association rule
getAntecedent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule: It retrieves the antecedent part of an association rule
getAntecedent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
getAntecedent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
getAntecedent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule: It retrieves the antecedent part of an association rule
getAntecedent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.AssociationRule: It retrieves the antecedent part of an association rule
getAntecedent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule
getAntecedent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
getAntecedent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
getAntecedente() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
getAntecedente(int) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
getAntecedente() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzyrule
getAntecedente(int) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzyrule
getAntecedente() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzyrule
getAntecedente(int) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzyrule
getAntecedente() - Method in class keel.Algorithms.Rule_Learning.Rules6.Regla: Returns the list with the antecedents of the rule.
getAntecedente() - Method in class keel.Algorithms.Rule_Learning.SRI.Regla: Returns the list with the antecedents of the rule.
getAntecedents() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AssociationRule: It retrieves the antecedent part of an association rule
getAntecedents() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule: It retrieves the antecedent part of an association rule
getAntecedents() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule: It retrieves the antecedent part of an association rule
getAntecedents() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule: It retrieves the antecedent part of an association rule
getAntecedentSupport() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.AssociationRule: It returns the antecedent support of an association rule
getAntecedentSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.AssociationRule: It returns the antecedent support of an association rule
getAntecedentSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.AssociationRule: It returns the antecedent support of an association rule
getAntecedentSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.AssociationRule: It returns the antecedent support of an association rule
getAntecedentSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.AssociationRule: It returns the antecedent support of an association rule
getAntecedentSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AssociationRule: Returns the support of the antecedent of this rule.
getAntecedentSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome
getAntecedentSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule: It returns the antecedent support of an association rule
getAntecedentSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule: It returns the antecedent support of an association rule
getAntecedentSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.AssociationRule: It returns the antecedent support of an association rule
getAntsSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome: It returns the support of the association rule represented by a chromosome
getAntsSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome: It returns the support of the association rule represented by a chromosome
getAntsSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome: It returns the support of the association rule represented by a chromosome
getAntSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
getAntSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
getAntSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
getApplication() - Static method in class keel.GraphInterKeel.datacf.DataCFApp: A convenient static getter for the application instance.
getArcAt(int) - Method in class keel.GraphInterKeel.experiments.Graph: Gets the arc at the indicated position
GetArcingSample(Ensemble, double[][], int, int) - Method in class keel.Algorithms.Neural_Networks.ensemble.Sample: Returns an Arcing sample
getArcs() - Method in class keel.GraphInterKeel.experiments.Graph: Gets all the arcs in the graph
getArea() - Method in class keel.Algorithms.Hyperrectangles.BNGE.Rule: Returns the area of the rule
getArg() - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription: Get args
getArray() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Access the internal one-dimensional array.
getArray() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector: Access the internal one-dimensional array.
getArray() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Access the internal two-dimensional array.
getArrayClass(Class) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Returns the basic class of an array class (handles multi-dimensional arrays).
getArrayClass(Class) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Returns the basic class of an array class (handles multi-dimensional arrays).
getArrayClass(Class) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Returns the basic class of an array class (handles multi-dimensional arrays).
getArrayClass(Class) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Returns the basic class of an array class (handles multi-dimensional arrays).
getArrayClass(Class) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Returns the basic class of an array class (handles multi-dimensional arrays).
getArrayClass(Class) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Returns the basic class of an array class (handles multi-dimensional arrays).
getArrayClass(Class) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Returns the basic class of an array class (handles multi-dimensional arrays).
getArrayCopy() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Returns a copy of the DoubleVector usng a double array.
getArrayCopy() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector: Returns a copy of the internal one-dimensional array.
getArrayCopy() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Copy the internal two-dimensional array.
getArrayDimensions(Class) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Returns the dimensions of the given array.
getArrayDimensions(Object) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Returns the dimensions of the given array.
getArrayDimensions(Class) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Returns the dimensions of the given array.
getArrayDimensions(Object) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Returns the dimensions of the given array.
getArrayDimensions(Class) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Returns the dimensions of the given array.
getArrayDimensions(Object) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Returns the dimensions of the given array.
getArrayDimensions(Class) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Returns the dimensions of the given array.
getArrayDimensions(Object) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Returns the dimensions of the given array.
getArrayDimensions(Class) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Returns the dimensions of the given array.
getArrayDimensions(Object) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Returns the dimensions of the given array.
getArrayDimensions(Class) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Returns the dimensions of the given array.
getArrayDimensions(Object) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Returns the dimensions of the given array.
getArrayDimensions(Class) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Returns the dimensions of the given array.
getArrayDimensions(Object) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Returns the dimensions of the given array.
getASize() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Returns the estimated action set size.
getASize() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Returns the estimated action set size.
getAssoc_rules_Pareto() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess
getAssociationMeasuresFile() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.parseParameters: Gets the name of the measure file
getAssociationRulesFile() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.parseParameters: Gets the name of the rule file
getAssociationRulesFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.parseParameters: It returns the name of the association rules file
getAssociationRulesFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.parseParameters: It returns the name of the association rules file
getAssociationRulesFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.parseParameters: It returns the name of the association rules file
getAssociationRulesFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.parseParameters: It returns the name of the association rules file
getAssociationRulesFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.parseParameters: It returns the name of the association rules file
getAssociationRulesFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.parseParameters: It returns the name of the association rules file
getAssociationRulesFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.parseParameters: It returns the name of the association rules file
getAssociationRulesFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.parseParameters: It returns the name of the association rules file
getAssociationRulesFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.parseParameters: It returns the name of the association rules file
getAssociationRulesFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.parseParameters: It returns the name of the association rules file
getAssociationRulesFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.parseParameters: It returns the name of the association rules file
getAssociationRulesFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.parseParameters: It returns the name of the association rules file
getAssociationRulesFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.parseParameters: It returns the name of the association rules file
getAssociationRulesFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.parseParameters: It returns the name of the association rules file
getAssociationRulesFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.parseParameters: It returns the name of the association rules file
getAssociationRulesFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.parseParameters: It returns the name of the association rules file
getAtributo() - Method in class keel.Algorithms.Decision_Trees.DT_GA.Selector: Returns the antecedent attribute of this selector.
getAtributo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Atributo: Returns the attribute id represented on this object.
getAtributo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Atributo: Returns the attribute id represented on this object.
getAtributo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Atributo: Returns the attribute id represented on this object.
getAtributo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Atributo: Returns the attribute id represented on this object.
getAtributo() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Atributo: Returns the attribute id represented on this object.
getAtributo() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Condition: It returns the attribute id
getAtributo() - Method in class keel.Algorithms.Rule_Learning.LEM2.Atributo_valor
getAtributo(int) - Method in class keel.Algorithms.Rule_Learning.Prism.Muestra: Returns the value of the attribute 'i' of the example
getAtributo() - Method in class keel.Algorithms.Rule_Learning.Prism.Selector: Returns the attribute's id
getAtributo() - Method in class keel.Algorithms.Rule_Learning.Rules6.Atributo_valor: Returns the attribute index.
getAtributo() - Method in class keel.Algorithms.Rule_Learning.SRI.Atributo_valor: Returns the attribute index.
getAtributo(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.Muestra: Returns the value of the attribute 'i' of the example
getAtributo() - Method in class keel.Algorithms.Rule_Learning.UnoR.Selector: Returns the attribute's id
getAtributo(int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Muestra: Returns the value of the attribute 'i' of the example
getAtributo(int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Returns the ith antecedent
getAtributo(int) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Muestra: Returns the value of the attribute 'i' of the example
getAtributo() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Selector: Returns the attribute's id
getAtributo() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Selector: Return the id of the attribute
getAtributo(int) - Method in class keel.GraphInterKeel.datacf.util.AttributeTable: Returns the attribute of a row
getAtributos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Returns the all attributes names.
getAtributtes() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances
getAttHeader() - Method in class keel.Dataset.InstanceSet
getAttr() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Antd
getAttr() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene
getAttr() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
getAttr() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
getAttr() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Gene
getAttr() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Gene
getAttr() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene
getAttr() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene
getAttr() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene
getAttr() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene
getAttrib(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyDataset: It returns the ID of the attribute with the given position.
getAttrib(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyDataset
getAttribute(int) - Method in class keel.Algorithms.Decision_Trees.C45.Dataset: Returns the attribute that has the index.
getAttribute(String) - Method in class keel.Algorithms.Decision_Trees.C45.Dataset: Returns the attribute that has the name.
getAttribute(int) - Method in class keel.Algorithms.Decision_Trees.C45.Itemset: Returns the attribute with the given index.
getattribute() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Selector: Returns the attribute considered in this selector.
getAttribute(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Returns the attribute that has the index.
getAttribute(String) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Returns the attribute that has the name.
getAttribute(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset: Returns the attribute with the given index.
getAttribute() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.Split: Gets the position of the attribute for the split
getAttribute(int) - Method in class keel.Algorithms.Decision_Trees.ID3.Dataset: Returns the attribute that has the index.
getAttribute(String) - Method in class keel.Algorithms.Decision_Trees.ID3.Dataset: Returns the attribute that has the name.
getAttribute(int) - Method in class keel.Algorithms.Decision_Trees.ID3.Itemset: Returns the attribute with the given index.
getAttribute() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Split: Gets the position of the attribute for the split
getAttribute(int) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Returns the attribute that has the index.
getAttribute(String) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Returns the attribute that has the name.
getAttribute(int) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Itemset: Returns the attribute with the given index.
getAttribute() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.FuzzyAntecedent: Obtains the variable related to the fuzzy antecedent
getAttribute(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Instance: It returns the value of the attribute i-th of the example
getAttribute() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Gene: Gets the attribute associated to this gene
getAttribute() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.Condition
getAttribute(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Returns the attribute with the given index.
getAttribute(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns the attribute that has the index.
getAttribute(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns the attribute that has the name.
getAttribute() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleRule: Returns the attribute's id
getAttribute(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Returns the attribute that has the index.
getAttribute(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Returns the attribute that has the name.
getAttribute(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset: Returns the attribute with the given index.
getAttribute(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset: Returns the attribute with the given index.
getAttribute(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Returns the attribute that has the index.
getAttribute(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Returns the attribute that has the name.
getAttribute() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.SimpleRule: Returns the attribute's id
getAttribute(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Instance: It returns the value of the attribute i-th of the example
getAttribute() - Method in class keel.Algorithms.Hyperrectangles.EACH.Selector: Returns the attribute's id
getAttribute(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Returns the attribute that has the index.
getAttribute(String) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Returns the attribute that has the name.
getAttribute(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset: Returns the attribute with the given index.
getAttribute(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Returns the attribute that has the index.
getAttribute(String) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Returns the attribute that has the name.
getAttribute(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset: Returns the attribute with the given index.
getattribute() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Selector: Returns the attribute considered in this selector.
getAttribute() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Obtains the variable related to the fuzzy antecedent
getAttribute(String) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IMetadata: Get mining attribute by name
getAttribute(int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IMetadata: Get mining attribute by index of the array of attributes of mining data specification
getAttribute(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.Metadata: Get mining attribute by name
getAttribute(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.Metadata: Get mining attribute by index of the array of attributes of mining data specification
getAttribute(int) - Method in class keel.Algorithms.Rule_Learning.AQ.Instance: It returns the value of the attribute i-th of the example
getAttribute() - Method in class keel.Algorithms.Rule_Learning.AQ.Selector: It returns the attribute id
getAttribute(int) - Method in class keel.Algorithms.Rule_Learning.ART.Dataset: Returns the attribute that has the index.
getAttribute(String) - Method in class keel.Algorithms.Rule_Learning.ART.Dataset: Returns the attribute that has the name.
getAttribute(int) - Method in class keel.Algorithms.Rule_Learning.ART.Itemset: Returns the attribute with the given index.
getAttribute(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Itemset: Returns the attribute with the given index.
getAttribute(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Returns the attribute that has the index.
getAttribute(String) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Returns the attribute that has the name.
getAttribute() - Method in class keel.Algorithms.Rule_Learning.C45Rules.SimpleRule: Returns the attribute's id
getAttribute(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset: Returns the attribute with the given index.
getAttribute(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Returns the attribute that has the index.
getAttribute(String) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Returns the attribute that has the name.
getAttribute() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.SimpleRule: Returns the attribute's id
getAttribute(int) - Method in class keel.Algorithms.Rule_Learning.CN2.Instance: It returns the value of the attribute i-th of the example
getAttribute() - Method in class keel.Algorithms.Rule_Learning.CN2.Selector: It returns the attribute id
getAttribute(int) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Returns the attribute that has the index.
getAttribute(String) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Returns the attribute that has the name.
getAttribute(int) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset: Returns the attribute with the given index.
getAttribute(int) - Method in class keel.Algorithms.Rule_Learning.PART.Itemset: Returns the attribute with the given index.
getAttribute(int) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Returns the attribute that has the index.
getAttribute(String) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Returns the attribute that has the name.
getAttribute() - Method in class keel.Algorithms.Rule_Learning.PART.SimpleRule: Returns the attribute's id
getAttribute() - Method in class keel.Algorithms.Rule_Learning.Riona.Selector: Return the id of the attribute
getAttribute() - Method in class keel.Algorithms.Rule_Learning.Ripper.SimpleRule: Returns the attribute's id
getAttribute() - Method in class keel.Algorithms.Rule_Learning.Slipper.SimpleRule: Returns the attribute's id
getAttribute(String) - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It returns the attribute requested.
getAttribute(int) - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It returns the attribute being int the position passed as an argument.
getAttribute(String) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It returns the attribute requested.
getAttribute(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It returns the attribute being int the position passed as an argument.
getAttribute(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Returns the attribute that has the index.
getAttribute(String) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Returns the attribute that has the name.
getAttribute(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset: Returns the attribute with the given index.
getAttribute(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Instance: It returns the value of the attribute i-th of the example
getAttribute(String) - Static method in class keel.Dataset.Attributes: It returns the attribute requested.
getAttribute(int) - Static method in class keel.Dataset.Attributes: It returns the attribute being int the position passed as an argument.
getAttribute(String) - Method in class keel.Dataset.InstanceAttributes: It returns the attribute requested.
getAttribute(int) - Method in class keel.Dataset.InstanceAttributes: It returns the attribute being int the position passed as an argument.
getAttributeAllele() - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute
getAttributeAllele() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: Returns the value of the alelle
getAttributeAllele() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: Returns the value of the alelle
getAttributeAllele() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep: Returns the attribute of this allele
getAttributeDefinitions() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: getAttributeDefinitions It does return the definition of the attibutes contained in the dataset.
getAttributeDefinitions() - Method in class keel.Dataset.InstanceSet: getAttributeDefinitions It does return the definition of the attibutes contained in the dataset.
getAttributeI(int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset: Gets all the information about ith the attribute that the dataset uses
getAttributeI(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset: Gets all the information about ith the attribute that the dataset uses
getAttributeIndex(int) - Method in class keel.GraphInterKeel.datacf.util.Dataset: Return attribute name at index position
getAttributeIndices() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: Returns the indices of the attributes.
getAttributeName(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It returns the name of the attribute in "id_attr"
getAttributeName(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It returns the name of the attribute in "id_attr"
getAttributeName(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It returns the name of the attribute in "id_attr"
getAttributeName(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: It returns the name of the attribute in "id_attr"
getAttributeName(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: It returns the name of the attribute in "id_attr"
getAttributeName(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: It returns the name of the attribute in "id_attr"
getAttributeName(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: It returns the name of the attribute in "id_attr"
getAttributeName(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: It returns the name of the attribute in "id_attr"
getAttributeName(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: It returns the name of the attribute in "id_attr"
getAttributeNum() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceParser: It returns the number of attributes
getAttributeNum() - Method in class keel.Dataset.InstanceParser: It returns the number of attributes
GetAttributePresence(int) - Method in class keel.Algorithms.PSO_Learning.CPSO.Particle
GetAttributePresence(int) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
GetAttributePresence(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.Particle
getAttributes() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset: Gets all the information about the attributes that the dataset uses
getAttributes() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset: Gets all the information about the attributes that the dataset uses
getAttributes() - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
getAttributes() - Static method in class keel.Algorithms.RST_Learning.RSTData
getAttributes() - Method in class keel.Algorithms.Rule_Learning.ART.Rule
getAttributes() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It does return an array with all attributes
getAttributes() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It does return an array with all attributes
getAttributes() - Static method in class keel.Dataset.Attributes: It does return an array with all attributes
getAttributes() - Method in class keel.Dataset.InstanceAttributes: It does return an array with all attributes
getAttributes() - Method in class keel.GraphInterKeel.datacf.util.Dataset: Returns a vector with variable names
getAttributesPerRule() - Method in class keel.Algorithms.Decision_Trees.C45.Tree: Function to compute the number of attributes of the tree
getAttributesPerRule() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: Function to compute the number of attributes of the tree
getAttributeType() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute: Gets the data type of the data of the attribute
getAttributeType() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute: Gets the data type of the data of the attribute
getAttributeType(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It returns the type of the attribute in "id_attr"
getAttributeType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It returns the type of the attribute in "id_attr"
getAttributeType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It returns the type of the attribute in "id_attr"
getAttributeType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: It returns the type of the attribute in "id_attr"
getAttributeType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: It returns the type of the attribute in "id_attr"
getAttributeType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: It returns the type of the attribute in "id_attr"
getAttributeType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: It returns the type of the attribute in "n_attr"
getAttributeType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: It returns the type of the attribute in "id_attr"
getAttributeType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: It returns the type of the attribute in "n_attr"
getAttributeType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: It returns the type of the attribute in "n_attr"
getAttributeType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: It returns the type of the attribute in "id_attr"
getAttributeType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: It returns the type of the attribute in "id_attr"
getAttributeType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: It returns the type of the attribute in "n_attr"
getAttributeType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: It returns the type of the attribute in "n_attr"
getAttributeType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: It returns the type of the attribute in "n_attr"
getAttributeType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: It returns the type of the attribute in "n_attr"
getAttributeType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: It returns the type of the attribute in "n_attr"
getAttributeType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: It returns the type of the attribute in "n_attr"
getAttributeTypeIndex(int) - Method in class keel.GraphInterKeel.datacf.util.Dataset: Return attribute type at index position
getAttributeTypeString(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It returns the type of the attribute as String in "id_attr"
getAttributeTypeString(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: It returns the type of the attribute as String in "id_attr"
getAttributeTypeString(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: It returns the type as String of the attribute in "id_attr"
getAttributeTypeString(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: Returns the type of the attribute given as a String.
getAttributeValue() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Register: Gets the value stored in the register
getAttrValue() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Antd
getAUC() - Method in class keel.Algorithms.ImbalancedClassification.Auxiliar.AUC.AccAUC: Provides the AUC of a given classifier
getAuthors() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation: splits the authors on the " and " and returns a vector with the names
getAuthors() - Method in class keel.Algorithms.SVM.SMO.core.TechnicalInformation: splits the authors on the " and " and returns a vector with the names
getAV(int) - Method in class keel.Algorithms.Rule_Learning.Rules6.Regla: Returns the pair attribute - values asked from the antecedents list.
getAV(int) - Method in class keel.Algorithms.Rule_Learning.SRI.Regla: Returns the pair attribute - values asked from the antecedents list.
getAverage(ArrayList) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Utils
getAverage(ArrayList) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Utils
getAverageAccuracies() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.populationWrapper
getAverageAccuracy() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Gets value for average accuracy field.
getAverageActivation() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformance
getAverageClTime() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Returns the average time of classifiers in the population.
getAverageClTime() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Returns the average time of classifiers in the population.
getAverageCompTime() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Returns the average reduction time of classifiers in the population.
getAverageLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.populationWrapper
getAverageNumCRs() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Gets value for average number of generated classification rules field.
getAverageNumFreqSets() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Gets value for average umber of frequent sets field.
getAveragePerClass() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: Returns the average values per class.
getAveragePerClass() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Returns the average values per class.
getAvergaeNumUpdates() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Gets value for average number of updates field.
getb() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
getb() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
getb() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.fuzzy
getb() - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
getb() - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
getb() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
getb() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
getb() - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
getB() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getB(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getB() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getB(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getB() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getB(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getB() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getB(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getB() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getB(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getB() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getB(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getB() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getB(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getB() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getB(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getB() - Method in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: This method returns the 'b' threshold value of this SMO
GetBaggingSample() - Method in class keel.Algorithms.Neural_Networks.ensemble.Sample: Return a bagging sample
getBase() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
getBase() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Histogram: Returns the Base of the histogram.
getBaseCodificada() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Codificacion
getBc() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion
getBd() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Discretizacion
getBDatos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getBDatos(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getBDatos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getBDatos(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getBDatos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getBDatos(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getBDatos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getBDatos(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getBDatos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getBDatos(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getBDatos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getBDatos(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getBDatos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getBDatos(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getBDatos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getBDatos(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getBDatos_x0(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getBDatos_x0(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getBDatos_x0(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getBDatos_x0(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getBDatos_x0(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getBDatos_x0(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getBDatos_x0(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getBDatos_x0(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getBDatos_x1(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getBDatos_x1(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getBDatos_x1(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getBDatos_x1(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getBDatos_x1(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getBDatos_x1(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getBDatos_x1(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getBDatos_x1(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getBDatos_x3(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getBDatos_x3(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getBDatos_x3(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getBDatos_x3(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getBDatos_x3(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getBDatos_x3(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getBDatos_x3(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getBDatos_x3(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getBeginColumn() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
getBeginColumn() - Static method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
getBeginColumn() - Static method in class keel.Dataset.SimpleCharStream
getBeginLine() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
getBeginLine() - Static method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
getBeginLine() - Static method in class keel.Dataset.SimpleCharStream
getBest(Classifier[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper: Obtains the best classifier of population.
getBest(Classifier[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper: Obtains the best classifier of population.
getBest() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.EUSCHCQstat
getBest() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
getBest() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
getBest() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getBest() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
getBest() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
getBest() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
getBest() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getBest() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
getBEST_CROM() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
getBEST_CROM() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
getBEST_CROM() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getBEST_CROM() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
getBEST_CROM() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
getBEST_CROM() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getBestAction() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.PredictionArray: Returns the best action in the prediction array.
getBestAction() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.PredictionArray: Returns the best action in the prediction array.
getBestCCRIndividual() - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.algorithm.classification.CCRElitistNeuralNetAlgorithm: Returns the best individual of the population
getBestFeatures() - Method in class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome: Returns the best features for the chromosome representation.
getBestGuy() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Return the position of the best individual of the main population
getBestGuy() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Population: Gets the position of the better individual of the population
getBestIndividual() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Returns the best individual of the population
getBestMinFC() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Population: It returns the best minimum confidence
getBestMinFS() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Population: It returns the best minimum support
getBestModelResultFile() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlusReporterClas: Returns file name where the best model obtained will be written
getBestModelResultFile() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.IRPropPlusReporterRegr: Returns file name where the best model obtained will be written
getBestModelResultFile() - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.listener.NeuralNetReporterClas: Returns file name where the best model obtained will be written
getBestModelResultFile() - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.listener.NeuralNetReporterRegr: Returns file name where the best model obtained will be written
getBestOutputs() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.EUSCHCQstat
getBestOverall() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.populationWrapper
getBestPopulation() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.populationWrapper
getBestRB() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Population: Return the best individual in the population
getBestRB() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Population: It returns the best RB found so far
getBestRB() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Population: Return the best individual in the population
getBestrules() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess: Returns the best rules mined.
getBestValue() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.PredictionArray: Returns the best action in the prediction array.
getBestValue() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.PredictionArray: Returns the best action in the prediction array.
getBeta0() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the regression intercept (Beta0)
getBeta1() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the regression Slope (Beta1)
getBin(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Histogram: Returns the bin summation in the given position.
GetBinary1() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
GetBinary1() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
GetBinary1() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
GetBinary2() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
GetBinary2() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
GetBinary2() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
GetBinary3() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
GetBinary3() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
GetBinary3() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
getBinaryAttributesNominal() - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Gets if binary attributes are to be treated as nominal ones.
getBins() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Histogram: Returns all the Bins values.
getBinsize() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Histogram: Returns the Histogram Bin size.
getBit(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Gene: Get the bit indicated
getBITS_GEN() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
getBITS_GEN() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
getBITS_GEN() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getBITS_GEN() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
getBITS_GEN() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
getBITS_GEN() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
getBITS_GEN() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getBITS_GEN() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
getBody() - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Cromosoma: Get the body of a chromosome (all values)
getBody(int) - Method in class keel.Algorithms.Instance_Selection.CoCoIS.Subpopulation: Returns the body of an individual, given its ID
getBody(int) - Method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.Subpopulation: Returns the body of an individual, given its ID
getBondad() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Corte
getBondad() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Union
getBorderColor() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.InfoNode: Returns the Color of the border of the node.
getBoundary(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
getBregla() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getBregla(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getBregla(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getBregla() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getBregla(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getBregla(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getBregla() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getBregla(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getBregla(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getBregla() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getBregla(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getBregla(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getBregla() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getBregla(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getBregla(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getBregla() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getBregla(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getBregla(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getBregla() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getBregla(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getBregla(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getBregla() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getBregla(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getBregla(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getBuildLogisticModels() - Method in class keel.Algorithms.SVM.SMO.SMO: Get the value of buildLogisticModels.
getC() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: Returns the output of the data-set as integer values
getC() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: Returns the output of the data-set as integer values
getC() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: Returns the output of the data-set as integer values
getC() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: Returns the output of the data-set as integer values
getC() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: It returns the values for the output (class)
getC() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: Returns the output of the data-set as integer values
getC() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: It returns the values for the output (class)
getC() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Returns the values for the out-put(class)
getC(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Returns the value of the output attribute (class) for the example with the given index.
getc() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
getc() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
getc() - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
getc() - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
getc() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
getc() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
getc() - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
getC() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Returns the outputs of each example (classification).
getC() - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Returns the outputs of each example (classification).
getC() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Returns the outputs of each example (classification).
getC() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Returns the outputs of each example (classification).
getC() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Returns the outputs of each example (classification).
getC() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Returns the outputs of each example (classification).
getC() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Returns the outputs of each example (classification).
getC() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: Returns classes for classification problems.
getC() - Method in class keel.Algorithms.Rule_Learning.AQ.Dataset: It returns the values for the output (class)
getC() - Method in class keel.Algorithms.Rule_Learning.CN2.Dataset: It returns the values for the output (class)
getC() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Returns the values for the out-put(class)
getC() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Returns the values for the out-put(class)
getC(int) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Returns the value of the attributes of the out-put for an instance
getC() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Returns the values for the out-put(class)
getC() - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Returns classes for classification problems.
getC() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: It returns the values for the output (class)
getC() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset: It returns the values for the output (class)
getC() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset: It returns the values for the output (class)
getC() - Method in class keel.Algorithms.SVM.SMO.SMO: Get the value of C.
getC() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Get the value of C.
getC() - Method in class keel.Algorithms.SVM.SMO.SVMreg: Get the value of C.
getC() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.MembershipFunction: It returns the center of an isosceles-triangle
getC() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.MembershipFunction: It returns the center of an isosceles-triangle
getC2() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Returns the values for the out-put(class)
getC2() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Returns the string values for the out-put(class)
getC2() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset
getC2() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Returns the values for the out-put(class)
getCa() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene
getCabecera() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
getCabecera() - Method in class keel.Algorithms.RE_SL_Methods.LEL_TSK.MiDataset: It returns the header
getCabecera() - Method in class keel.Algorithms.RE_SL_Methods.MamWM.MiDataset
getCabecera() - Method in class keel.Algorithms.RE_SL_Methods.mogulHC.MiDataset: It returns the header
getCabecera() - Method in class keel.Algorithms.RE_SL_Methods.mogulIRL.MiDataset: It returns the header
getCabecera() - Method in class keel.Algorithms.RE_SL_Methods.mogulSC.MiDataset: It returns the header
getCabecera() - Method in class keel.Algorithms.RE_SL_Methods.TSK_IRL.MiDataset: It returns the header
getCabecera() - Method in class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.MiDataset: It returns the header
getCabecera() - Method in class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.MiDataset: It returns the header
getCabecera() - Method in class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.MiDataset: It returns the header
getCabecera() - Method in class keel.Algorithms.RE_SL_Postprocess.MamSelect.MiDataset: It returns the header
getCabecera() - Method in class keel.Algorithms.RE_SL_Postprocess.MamWSelect.MiDataset: It returns the header
getCabecera() - Method in class keel.Algorithms.RE_SL_Postprocess.MamWTuning.MiDataset: It returns the header
getCabecera() - Method in class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.MiDataset: It returns the header
getCabecera() - Method in class keel.Algorithms.RE_SL_Postprocess.TSKSelect.MiDataset: It returns the header
getCache(Class, String) - Static method in class keel.Algorithms.SVM.SMO.core.ClassDiscovery: returns the list of classnames associated with this class and package, if available, otherwise null
getCacheHits() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: return the number of kernel cache hits
getCacheSize() - Method in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: Gets the size of the cache
getCacheSize() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Gets the size of the cache
getCalidad() - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Cromosoma: Get the quality of a chromosome
getCalidad() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.Chromosome: Ite returns the fitness of the chrom.
getCalidad() - Method in class keel.Algorithms.Instance_Selection.CHC.Cromosoma: Get the quality of a chromosome
getCalidad() - Method in class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Get the quality of a chromosome
getCalidad() - Method in class keel.Algorithms.Instance_Selection.GGA.Cromosoma: Get the quality of a chromosome
getCalidad() - Method in class keel.Algorithms.Instance_Selection.IGA.Cromosoma: Get the quality of a chromosome
getCalidad() - Method in class keel.Algorithms.Instance_Selection.PBIL.Cromosoma: Get the quality of a chromosome
getCalidad() - Method in class keel.Algorithms.Instance_Selection.SGA.Cromosoma: Get the quality of a chromosome
getCalidad() - Method in class keel.Algorithms.Instance_Selection.ZhangTS.Cromosoma: Get the quality of a chromosome
getCalidad() - Method in class keel.Algorithms.Preprocess.Instance_Selection.CHC.Cromosoma: Get the quality of a chromosome
getCalidad() - Method in class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Get the quality of a chromosome
getCalidad() - Method in class keel.Algorithms.Preprocess.Instance_Selection.GGA.Cromosoma: Get the quality of a chromosome
getCalidad() - Method in class keel.Algorithms.Preprocess.Instance_Selection.IGA.Cromosoma: Get the quality of a chromosome
getCalidad() - Method in class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Cromosoma: Get the quality of a chromosome
getCalidad() - Method in class keel.Algorithms.Preprocess.Instance_Selection.SGA.Cromosoma: Get the quality of a chromosome
getCalidad() - Method in class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.Cromosoma: Get the quality of a chromosome
getCambio() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: Returns the initial values of the original dataset before making any change.
getCategories() - Static method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.WrapperManager
getCCnf() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Retuns the value of CCNF
getCellEditorValue() - Method in class keel.GraphInterKeel.datacf.util.EachRowEditor: Returns the editor value
getCellEditorValue() - Method in class keel.GraphInterKeel.statistical.StatCellEditor: Get the value of a cell
getcent() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.fuzzy
getcent() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.fuzzy
getCenter() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbf: Gets the vector of centres of a neuron
getCentre() - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbf: Gets the vector of centres of a neuron
getCentre() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbf: Gets the vector of centres of a neuron
getCentre() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbf: Gets the vector of centres of a neuron
getCentre() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf: Gets the vector of centres of a neuron
getCentre() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbf: Gets the vector of centres of a neuron
getCentre() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf: Gets the vector of centres of a neuron
getCentroid() - Method in class keel.Algorithms.Instance_Generation.VQ.Cluster: Returns the center of the cluster.
getCentroid() - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster: Get the centroid of the cluster
getCep() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion
getcero() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
getcero() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
getcero() - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
getcero() - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
getcero() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
getcero() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
getcero() - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
getCF() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AssociationRule: It returns the certain factor of an association rule
getCF() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule: It returns the CF of an association rule
getCF() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
getCF() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
getCF() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule: It returns the CF of an association rule
getCF() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule: It returns the CF of an association rule
getCF() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
getCF() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
getCF() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
getCF() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
getCF() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
getCF() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
getCF() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
getCF() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
getChainValue() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGPRegSym: This method returns the centre of weights of the vector fsChain.
getChart() - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelAttribute: Gets Chart for paint attributes values
getChart() - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelCharts2D: Gets a JFreeChart from the image
getCheckErrorRate() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Gets whether to check for error rate is in stopping criterion
getChecksTurnedOff() - Method in class keel.Algorithms.SVM.SMO.SMO: Returns whether the checks are turned off or not.
getChecksTurnedOff() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Returns whether the checks are turned off or not.
getChecksTurnedOff() - Method in class keel.Algorithms.SVM.SMO.supportVector.Kernel: Returns whether the checks are turned off or not.
getChild(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree: Returns the son with the given index.
getChild(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Tree: Returns the son with the given index.
getChild(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree: Returns the son with the given index.
getChild(int) - Method in class keel.Algorithms.Rule_Learning.PART.Tree: Returns the son with the given index.
getChildren() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Returns the children of the node.
getChildren(int) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Returns the child with the given index.
getChildren() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.Item: It returns the children of an item
getChildren() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.Item: It returns the children of an item
getChromosome(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.SEM: Gets the i-th chromosome
getChromosome(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.SEM: Gets the i-th chromosome
getChromosome() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.LimitRoulette
getChronCrossover() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
getChronCrossover() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
getChronEvaluation() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
getChronEvaluation() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
getChronMutation() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
getChronMutation() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
getChronReplacement() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
getChronReplacement() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
getChrons() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
getChrons() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
getChronSelection() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
getChronSelection() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
getClas() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset: It returns the output class of the itemset.
getClas() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: It returns the output class of the rule.
getClas() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset: It returns the output class of the itemset
getClas() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: It returns the output class of the rule
getClas() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.Rule: It returns the output class of the rule
getClas() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Itemset: It returns the output class of the itemset
getClas() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule: It returns the output class of the rule
getClas() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Itemset: It returns the output class of the itemset
getClas() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Rule: It returns the output class of the rule
getClas() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Itemset: It returns the output class of the itemset
getClas() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Rule: It returns the output class of the rule
getClas() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule: It returns the class of the rule
getClas() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Obtains the class associated to this rule, consequent of the fuzzy rule
getClas() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Itemset
getClas() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule
getClas() - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Chromosome: Gets the class output associated to this individual
getClas() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Instance: It returns the class of the example
getClas() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome: Gets the class of this rule
getClas() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Rule: Returns the class associated to this rule
getClas() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Rule: Returns the class associated to this rule
getClas() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Instance: It returns the class of the example
getClas() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Rule: It returns the consequent class of the rule
getClas() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Obtains the class associated to this rule, consequent of the fuzzy rule
getClas() - Method in class keel.Algorithms.Rule_Learning.AQ.Complex: It return the class id
getClas() - Method in class keel.Algorithms.Rule_Learning.AQ.Instance: It returns the class of the example
getClas() - Method in class keel.Algorithms.Rule_Learning.ART.Rule
getClas() - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: It return the class id
getClas() - Method in class keel.Algorithms.Rule_Learning.CN2.Instance: It returns the class of the example
getClas() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeDat: Gets the class
getClas() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeDat: Gets the class
getClas() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Return the class of the complex
getClas() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Instance: It returns the class of the example
getClas() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeDat: Gets the class
getClase() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Returns the class of the node.
getClase() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Muestra: Returns the class of this example.
getClase() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Muestra: Returns the class of this example.
getClase() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Muestra: Returns the class of this example.
getClase() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Muestra: Returns the class of this example.
getClase() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
getClase(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_aggregated
getClase() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list
getClase() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
getClase() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
getClase() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Corte
getClase() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Union
getClase() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual: Returns the class in the consequent of the individual
getClase() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Muestra: Returns the class of this example.
getClase() - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Returns the class that defines the comples
getClase() - Method in class keel.Algorithms.Rule_Learning.Prism.Muestra: Returns the example's class
getClase() - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Return the class that define the complex
getClase() - Method in class keel.Algorithms.Rule_Learning.UnoR.Muestra: Returns the example's class
getClase() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Muestra: Returns the example's class
getClase() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Returns the rule class.
getClase() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Returns the class value
getClase() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Muestra: Returns the example's class
getClaseEjemplo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Resultado
getClaseOptima(int[][][], long) - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Returns a vector with the optimum class for each pair attribute-value
getClaseRegla() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Resultado
getClases() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Returns the classes, as a vector of integers, of all the prototypes included in this set.
getClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableDat: Returns the class of the example in position pos
getClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableDat: Returns the class of the example in position pos
getClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableDat: Returns the class of the example in position pos
getClassAsString(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyDataset: Return the name of the class in position "pos"
getClassAt(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.OpenDataset: Returns the class of a given element.
getClassAttribute() - Method in class keel.Algorithms.Decision_Trees.C45.Dataset: Returns class attribute.
getClassAttribute() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Returns class attribute.
getClassAttribute() - Method in class keel.Algorithms.Decision_Trees.ID3.Dataset: Returns class attribute.
getClassAttribute() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Returns class attribute.
getClassAttribute() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Returns class attribute.
getClassAttribute() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns class attribute.
getClassAttribute() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Returns class attribute.
getClassAttribute() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Returns class attribute.
getClassAttribute() - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Devuelve la clase que define el complejo
getClassAttribute() - Method in class keel.Algorithms.Hyperrectangles.EACH.Hyperrectangle
getClassAttribute() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Returns class attribute.
getClassAttribute() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Returns class attribute.
getClassAttribute() - Method in class keel.Algorithms.Rule_Learning.ART.Dataset: Returns class attribute.
getClassAttribute() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Returns class attribute.
getClassAttribute() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Returns class attribute.
getClassAttribute() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Returns class attribute.
getClassAttribute() - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Returns class attribute.
getClassAttribute() - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Return the class that define the complex
getClassAttribute() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Returns class attribute.
getclassCasesCovered(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: It returns the number of examples covered by the rule for the class "class".
getclassCasesCovered(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: It returns the number of examples covered by the rule for the class "class"
getClassDistribution(int) - Method in class keel.Algorithms.Rule_Learning.AQ.Complex: It returns the class distribution of a specified class
getClassDistribution(int) - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: It returns the class distribution of a specified class
getClasses() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Gets an array list with the different classes in the node
getClasses() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It returns the class labels
getClasses() - Method in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: This method returns the transformed class values of each instance
getClassFequency() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns the frequency (number of instances) of each class.
getClassFequency(Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns the frequency (number of instances) of each class.
getClassFequency() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Returns the frequency (number of instances) of each class.
getClassFequency(Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Returns the frequency (number of instances) of each class.
getClassFequency() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Returns the frequency (number of instances) of each class.
getClassFequency(Mask) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Returns the frequency (number of instances) of each class.
getClassFequency() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Returns the frequency (number of instances) of each class.
getClassFequency(Mask) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Returns the frequency (number of instances) of each class.
getClassFequency() - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Returns the frequency (number of instances) of each class.
getClassFequency(Mask) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Returns the frequency (number of instances) of each class.
getClassificationResults(DoubleTransposedDataSet) - Method in class keel.Algorithms.Decision_Trees.CART.CART: It gets the classification results
getClassifier() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.RuleBase: Function to get stored classifier
getClassifier() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.RuleBase: Function to get stored classifier
getClassifier(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Returns the classifier in the given position.
getClassifier(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Returns the classifier in the given position.
getClassIndex() - Method in class keel.Algorithms.Decision_Trees.C45.Dataset: Returns the index of the class attribute.
getClassIndex() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Returns the index of the class attribute.
getClassIndex() - Method in class keel.Algorithms.Decision_Trees.ID3.Dataset: Returns the index of the class attribute.
getClassIndex() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Returns the index of the class attribute.
getClassIndex() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns the index of the class attribute.
getClassIndex() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Returns the index of the class attribute.
getClassIndex() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Returns the index of the class attribute.
getClassIndex() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Returns the index of the class attribute.
getClassIndex() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Returns the index of the class attribute.
getClassIndex() - Method in class keel.Algorithms.MIL.AbstractMIAlgorithm
getClassIndex() - Method in class keel.Algorithms.Rule_Learning.ART.Dataset: Returns the index of the class attribute.
getClassIndex() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Returns the index of the class attribute.
getClassIndex() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Returns the index of the class attribute.
getClassIndex() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Returns the index of the class attribute.
getClassIndex() - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Returns the index of the class attribute.
getClassIndex() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Returns the index of the class attribute.
getClassLearned() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexResult
getClassSelector() - Method in class keel.Algorithms.Hyperrectangles.EACH.Sample: Returns the example's class
getClassValue() - Method in class keel.Algorithms.Decision_Trees.C45.Itemset: Returns the index of the value of the class.
getClassValue() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset: Returns the index of the value of the class.
getClassValue() - Method in class keel.Algorithms.Decision_Trees.ID3.Itemset: Returns the index of the value of the class.
getClassValue() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Itemset: Returns the index of the value of the class.
getClassValue() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Returns the index of the value of the class.
getClassValue() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset: Returns the index of the value of the class.
getClassValue() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset: Returns the index of the value of the class.
getClassValue() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset: Returns the index of the value of the class.
getClassValue() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset: Returns the index of the value of the class.
getClassValue() - Method in class keel.Algorithms.Rule_Learning.ART.Itemset: Returns the index of the value of the class.
getClassValue() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Itemset: Returns the index of the value of the class.
getClassValue() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset: Returns the index of the value of the class.
getClassValue() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset: Returns the index of the value of the class.
getClassValue() - Method in class keel.Algorithms.Rule_Learning.PART.Itemset: Returns the index of the value of the class.
getClassValue() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset: Returns the index of the value of the class.
getClusterOf(Prototype) - Method in class keel.Algorithms.Instance_Generation.GMCA.ClusterSet: Returns the cluster of a prototype.
getClusterOf(Instance) - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.fuzzygCenter: Returns the cluster to which the given instance belongs to
getClusterOf(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.gCenter: Returns the cluster to which the given instance belongs to
getClusters() - Method in class keel.Algorithms.Instance_Generation.GMCA.ClusterSet: Returns the set of clusters.
getCnf() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.QualityMeasures: Gets the value of the confidence
getCnf() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.QualityMeasures: Method to return the value of the confidence
getCnfValue() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Gets the value of confidence of the individual
getCo() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForClassification: This method return the result of classification
getCob() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
getCoefficientOfVariation() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Sample: Returns the Variation coefficient.
getCoefficients() - Method in interface keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IOptimizableFunc: Returns the initial value of a[], that is, the coefficients of the model
getCoefficients() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.MSEOptimizablePUNeuralNetClassifier: Returns the initial value of a[], that is, the coefficients of the model B01 B02 ...
getCoefficients() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.MSEOptimizableSigmNeuralNetClassifier: Returns the initial value of a[], that is, the coefficients of the model B01 B02 ...
getCoefficients() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.MSEOptimizablePUNeuralNetRegressor: Returns the initial value of a[], that is, the coefficients of the model B01 B02 ...
getCoefficients() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.MSEOptimizableSigmNeuralNetRegressor: Returns the initial value of a[], that is, the coefficients of the model B01 B02 ...
getCoefficients() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.LinearRegression: returns the calculated coefficients
getColumn() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream: Deprecated.
getColumn() - Static method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream: Deprecated.
getColumn(int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Gets a column of the matrix and returns it as a double array.
getColumn() - Static method in class keel.Dataset.SimpleCharStream: Deprecated.
getColumnas() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Item: Returns a copy of the columns references for each value.
getColumnClass(int) - Method in class keel.GraphInterKeel.datacf.util.AttributeTable: Returns the class of the elements of a column
getColumnClass(int) - Method in class keel.GraphInterKeel.datacf.util.DatasetTable
getColumnClass(int) - Method in class keel.GraphInterKeel.datacf.util.VariableTable: Gets the class of a given column
getColumnClass(int) - Method in class keel.GraphInterKeel.statistical.statTableModel: Get the class of a column
getColumnCount() - Method in class keel.GraphInterKeel.datacf.util.AttributeTable: Returns the number of columns
getColumnCount() - Method in class keel.GraphInterKeel.datacf.util.DatasetTable
getColumnCount() - Method in class keel.GraphInterKeel.datacf.util.VariableTable: Gets the number of columns
getColumnCount() - Method in class keel.GraphInterKeel.experiments.ParametersTable: Gets the number of columns
getColumnCount() - Method in class keel.GraphInterKeel.statistical.statTableModel: Gets the number of columns of the table
getColumnDimension() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Get column dimension.
getColumnName(int) - Method in class keel.GraphInterKeel.datacf.util.AttributeTable: Returns the name of a given column
getColumnName(int) - Method in class keel.GraphInterKeel.datacf.util.DatasetTable
getColumnName(int) - Method in class keel.GraphInterKeel.datacf.util.VariableTable: Gets the name of a given column
getColumnName(int) - Method in class keel.GraphInterKeel.experiments.ParametersTable: Get the specified column's name
getColumnName(int) - Method in class keel.GraphInterKeel.statistical.statTableModel: Get the name of a column
getColumnNames() - Method in class keel.GraphInterKeel.datacf.util.DatasetTable: Return columnNames
getColumnPackedCopy() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Make a one-dimensional column packed copy of the internal array.
getComment() - Method in enum keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation.Field: returns the comment string
getComment() - Method in enum keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation.Type: returns the comment string
getComment() - Method in enum keel.Algorithms.SVM.SMO.core.TechnicalInformation.Field: returns the comment string
getComment() - Method in enum keel.Algorithms.SVM.SMO.core.TechnicalInformation.Type: returns the comment string
getCommonClass(Vector, int) - Method in class keel.Algorithms.Decision_Trees.ID3.ID3: Function to return the most common class of the itemsets in data.
getComp() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Retuns the value of COMP
getComparadorCondiciones() - Static method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Condicion: Returns the Condition comparative method.
getComparadorCondiciones() - Static method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Condicion: Returns the Condition comparative method.
getComparadorCondiciones() - Static method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Condicion: Returns the Condition comparative method.
getComparadorCondiciones() - Static method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Condicion: Returns the Condition comparative method.
getComparadorParticulas() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Returns the Particles comparative method.
getComparator() - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.SoftmaxClassificationProblemEvaluator: Returns a ValueFitnessComparator
getComparator() - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.problem.regression.RegressionProblemEvaluator: Returns a ValueFitnessComparator
getComplement(Vector, Vector) - Method in class keel.Algorithms.Decision_Trees.ID3.ID3: Function to returns a subset of data, which is the complement of the second argument.
getComponent(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.FuzzyClassifier: This method returns a FuzzyRule component.
getComponent(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyPartition: Returns the characteristic points of the fuzzy set n.
getComponent(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase: Returns rule n.
getComprehensibility() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
getCompTime() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Returns the time of the classifier.
getCompTime() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Returns the time of the classifier.
getCondicionContinua(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Returns the continuous condition with the given index.
getCondicionNominal(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Returns the nominal condition with the given index.
getCondition() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Gets the relationship between this node and its descendats, that's the way to split this node in two
getCondition() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode: Gets the relationship between this node and its descendats, that's the way to split this node in two
getCondition() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Condition
getCondition(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual: It returns the condition for the variable in position "atributo"
getCondition(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Rule: It returns a condition defined for the i-th attribute
getConfidence() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: It returns the confidence of the rule.
getConfidence() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: It returns the confidence of the rule
getConfidence() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Gets the current confidence setting.
getConfidence(short[], double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Calculates and returns the conidence for an AR given the antecedent item set and the support for the total item set.
getConfidence(double, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Calculates and returns the conidence for an AR given the support for both the antecedent and the entire item set.
getConfidence() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule: It returns the Confidence of the rule
getConfidence() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Rule: It returns the Confidence of the rule
getConfidence() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Rule: It returns the Confidence of the rule
getConfidence() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule
getConfidence() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule: Returns the confidence of the last element of the antecetents.
getConfidence() - Method in class keel.Algorithms.Rule_Learning.ART.Rule
getConfidence() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.AssociationRule: It returns the confidence of an association rule
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.AssociationRule: It returns the confidence of an association rule
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.AssociationRule: It returns the confidence of an association rule
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.AssociationRule: It returns the confidence of an association rule
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.AssociationRule: It returns the confidence of an association rule
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AssociationRule: It returns the confidence of an association rule
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule: It returns the confidence of an association rule
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule: It returns the confidence of an association rule
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule: It returns the confidence of an association rule
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.AssociationRule: It returns the confidence of an association rule
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule: It returns the confidence of an association rule
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome: It returns the confidence of the association rule represented by a chromosome
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule: It returns the confidence of an association rule
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome: It returns the confidence of the association rule represented by a chromosome
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule: It returns the confidence of an association rule
getConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome: It returns the confidence of the association rule represented by a chromosome
getConfigs() - Method in class keel.GraphInterKeel.experiments.Parameters: return config files generated by method
getConjReglas() - Method in class keel.Algorithms.Rule_Learning.Prism.ConjReglas: Returns the complet set if rules
getConjReglas() - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjReglas: Returns the complet set if rules
getConjReglas() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjReglas: Returns the complet set if rules
getConjReglas() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjReglas: Returns the complet set if rules
getConjReglas() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetRules: Return the set of rules
getConsecuente() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
getConsecuente(int) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
getConsecuente() - Method in class keel.Algorithms.Rule_Learning.Rules6.Regla: Returns the consequent of the rule.
getConsecuente() - Method in class keel.Algorithms.Rule_Learning.SRI.Regla: Returns the consequent of the rule.
getConsecuentes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getConsecuentes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getConsecuentes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getConsecuentes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getConsecuentes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getConsecuentes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getConsecuentes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getConsecuentes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getConsequent() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule: Gets the internal representation of the class label to be predicted
getConsequent() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Rule: Get the consequent of this rule, i.e. the predicted class
getconsequent() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
getconsequent(int) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
getconsequent() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.rule
getconsequent(int) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.rule
getconsequent() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
getconsequent(int) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
getconsequent() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
getconsequent(int) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
getConsequent() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.AssociationRule: It retrieves the consequent part of an association rule
getConsequent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.AssociationRule: It retrieves the consequent part of an association rule
getConsequent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.AssociationRule: It retrieves the consequent part of an association rule
getConsequent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.AssociationRule: It retrieves the consequent part of an association rule
getConsequent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.AssociationRule: It retrieves the consequent part of an association rule
getConsequent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule: It retrieves the consequent part of an association rule
getConsequent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule: It retrieves the consequent part of an association rule
getConsequent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
getConsequent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule: It retrieves the consequent part of an association rule
getConsequent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.AssociationRule: It retrieves the consequent part of an association rule
getConsequent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule
getConsequent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
getConsequent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
getConsequents() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AssociationRule: It retrieves the consequent part of an association rule
getConsequents() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
getConsequents() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
getConsequents() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
getConsequentSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.AssociationRule: It returns the consequent support of an association rule
getConsequentSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.AssociationRule: It returns the consequent support of an association rule
getConsequentSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.AssociationRule: It returns the consequent support of an association rule
getConsequentSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.AssociationRule: It returns the consequent support of an association rule
getConsequentSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AssociationRule: Returns the support of the consequent of this rule.
getConsequentSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome
getConsequentSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule: It returns the consequent support of an association rule
getConsequentSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule: It returns the consequent support of an association rule
getConsistencia() - Method in class keel.Algorithms.Rule_Learning.Rules6.Regla: Return the consistency of the rule.
getConsistencia() - Method in class keel.Algorithms.Rule_Learning.SRI.Regla: Return the consistency of the rule.
getConsSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
getConsSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
getConsSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
getConsSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
getConsSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
getConsSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
getConsts() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.RegSymFuzzyGP: This methods return a copy of the constant part
getContinuous(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Returns if the variable "pos" is or not continua
getContinuous() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeVar: Gets if the variable is continuous or discrete
getContinuous(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Returns if the variable "pos" is or not continua
getContinuous() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeVar: Gets if the variable is continuous or discrete
getContinuous(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Returns if the variable "pos" is or not continua
getContinuous() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeVar: Gets if the variable is continuous or discrete
getContribution(int, int) - Static method in class keel.Algorithms.Instance_Selection.CoCoIS.CoCoIS: Computes the contribution of a given selector
getContribution(int, int) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.CoCoIS: Computes the contribution of a given selector
getConv() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AssociationRule: It returns the conviction of an association rule
getConv() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule: It returns the conviction of an association rule
getConv() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
getConv() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
getConv() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule: It returns the conviction of an association rule
getConv() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule: It returns the conviction of an association rule
getConv() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
getConv() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
getConv() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
getConv() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
getConv() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
getConv() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
getConv() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
getConv() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
getCopy() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule: It returns a copy of this rule
getCopy() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleRule: It returns a copy of this simple rule
getCopy() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule: It returns a copy of this rule
getCopy() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.SimpleRule: It returns a copy of this simple rule
getCopy() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Rule: It returns a copy of this rule
getCopy() - Method in class keel.Algorithms.Rule_Learning.C45Rules.SimpleRule: It returns a copy of this simple rule
getCopy() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule: It returns a copy of this rule
getCopy() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.SimpleRule: It returns a copy of this simple rule
getCopy() - Method in class keel.Algorithms.Rule_Learning.PART.Rule: It returns a copy of this rule
getCopy() - Method in class keel.Algorithms.Rule_Learning.PART.SimpleRule: It returns a copy of this simple rule
getCopy() - Method in class keel.Algorithms.Rule_Learning.Ripper.Rule: It returns a copy of this rule
getCopy() - Method in class keel.Algorithms.Rule_Learning.Ripper.SimpleRule: It returns a copy of this simple rule
getCopy() - Method in class keel.Algorithms.Rule_Learning.Slipper.Rule: It returns a copy of this rule
getCopy() - Method in class keel.Algorithms.Rule_Learning.Slipper.SimpleRule: It returns a copy of this simple rule
getCorrectClassifications() - Method in class keel.Algorithms.Statistical_Tests.Shared.StatTest.InformationAboutClass: Returns the number of correct classifications done.
getCorrelation() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the correlation value R.
getCorte() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Corte
getCorte() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Union
getCortes() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Discretizacion
getCortesCod() - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Codificacion
getCost_instances() - Method in class keel.GraphInterKeel.experiments.Parameters: Gets cost instances
getCoste() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Returns the associated cost.
getCount() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.ExampleWeight: Returns the number of times counted.
getCount() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.ExampleWeight
getCountSupport(int, int, myDataset, DataB) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
getCovarianceMatrix() - Method in class keel.Algorithms.Discretizers.UCPD.PCA: It returns the matrix of covariance
getCove() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Retuns the value of
getCover() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Antd
getCoverage() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformanceTraining
getCoverage() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
getCovered() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Instance: It returns the "covered" value, that is, the number of rules that covers the example
getCovered() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Instance: It returns the "covered" value, that is, the number of rules that covers the example
getCovered() - Method in class keel.Algorithms.Hyperrectangles.EACH.Sample: Returns the number of times that the example has benn matched
getCovered() - Method in class keel.Algorithms.Rule_Learning.AQ.Instance: It returns the "covered" value, that is, the number of rules that covers the example
getCovered() - Method in class keel.Algorithms.Rule_Learning.CN2.Instance: It returns the "covered" value, that is, the number of rules that covers the example
getCovered(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableDat: Returns if the example in position pos is yet covered or not
getCovered() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeDat: Gets if the example is covered
getCovered(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableDat: Returns if the example in position pos is yet covered or not
getCovered() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeDat: Gets if the example is covered
getCovered() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Instance: It returns the "covered" value, that is, the number of rules that covers the example
getCovered(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableDat: Returns if the example in position pos is yet covered or not
getCovered() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeDat: Gets if the example is covered
getCoveredInstances() - Method in class keel.Algorithms.Discretizers.MVD.Interval: Provides the indexes of the instances in the data set covered by this interval
getCoveredInstances() - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Chromosome: Gets the number of instances covered by this chromosome
getCoveredRecords(short[]) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TotalSupportTree: Retrieves the records of given itemset which are covered by the association rules
getCoveredRecords() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TotalSupportTree: Retrieves all the records which are covered by the association rules
getCoveredRecords(short[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TotalSupportTree
getCoveredRecords() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TotalSupportTree: Retrieves all the records which are covered by the association rules
getCoveredTIDs() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AssociationRule: It indicates the dataset records which have been covered by an association rule
getCoveredTIDs() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome: It indicates the dataset records which have been covered by a chromosome
getCoveredTIDs(int, int, myDataset, DataB) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
getCoveredTIDs(myDataset) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome: It compares a chromosome with another one in order to accomplish ordering later.
getCoveredTIDs(myDataset) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome: It compares a chromosome with another one in order to accomplish ordering later.
getCoveredTIDs(myDataset) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome: It compares a chromosome with another one in order to accomplish ordering later.
getCR() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.SEM: Gets the Classification Rate of this SEM
getCR() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.SEM: Gets the Classification Rate of this SEM
getCr() - Method in class keel.Algorithms.Rule_Learning.Slipper.Rule: Returns the confidence of the rule.
getCR() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess
getCratio() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Returns creation ratio of the algorithm
getCrisp() - Method in class keel.GraphInterKeel.experiments.Parameters: Get crisp status
getCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Chromosome: Retuns the value of the position of the gene indicated
getCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual: Returns the indicated value of the gene of the Chromosome
getCromElem(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population: Returns the indicated gene of the Chromosome
getCromElem(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.CromCAN: Retuns the value of the gene indicated
getCromElem(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndCAN: Returns the indicated gene of the Chromosome
getCromElem(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndDNF: Returns the indicated gene of the Chromosome
getCromElem(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Returns the indicated gene of the Chromosome
getCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Returns the indicated gene of the CromCAN
getCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Chromosome: Retuns the value of the position of the gene indicated
getCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual: Returns the indicated value of the gene of the Chromosome
getCromElem(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population: Returns the indicated gene of the Chromosome
getCromElem(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.CromCAN: Retuns the value of the gene indicated
getCromElem(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndCAN: Returns the indicated gene of the Chromosome
getCromElem(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndDNF: Returns the indicated gene of the Chromosome
getCromElem(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Returns the indicated gene of the Chromosome
getCromElem(int, int, int, String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Population: Returns the indicated gene of the Chromosome
getCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Chromosome: Retuns the value of the position of the gene indicated
getCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual: Returns the indicated value of the gene of the Chromosome
getCromElem(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population: Returns the indicated gene of the Chromosome
getCromElem(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.CromCAN: Retuns the value of the gene indicated
getCromElem(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndCAN: Returns the indicated gene of the Chromosome
getCromElem(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndDNF: Returns the indicated gene of the Chromosome
getCromElem(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual: Returns the indicated gene of the Chromosome
getCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Population: Returns the indicated gene of the CromCAN
getCromElemGene(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.CromDNF: Retuns the value of the gene indicated
getCromElemGene(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndCAN: Returns the value of the indicated gene for the variable
getCromElemGene(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndDNF: Returns the value of the indicated gene for the variable
getCromElemGene(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Retuns the value of the gene indicated
getCromElemGene(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Returns the indicated gene of the CromCAN
getCromElemGene(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.CromDNF: Retuns the value of the gene indicated
getCromElemGene(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndCAN: Returns the value of the indicated gene for the variable
getCromElemGene(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndDNF: Returns the value of the indicated gene for the variable
getCromElemGene(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual: Retuns the value of the gene indicated
getCromElemGene(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Population: Returns the indicated gene of the CromCAN
getCromGeneElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.CromDNF: Retuns the value of the gene indicated
getCromGeneElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndCAN: Returns the value of the indicated gene for the variable
getCromGeneElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndDNF: Returns the indicated gene of the Chromosome
getCromGeneElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Retuns the value of the gene indicated
getCromGeneLenght(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.CromDNF: Retuns the gene lenght of the chromosome
getCromGeneLength(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Chromosome: Retuns the gene lenght of the chromosome
getCromGeneLength(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.CromDNF: Retuns the gene lenght of the chromosome
getCromGeneLength(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Chromosome: Retuns the gene lenght of the chromosome
getCromGeneLength(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Chromosome: Retuns the gene lenght of the chromosome
getCromLenght() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.CromDNF: Retuns the lenght of the chromosome
getCromLenght() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.CromCAN: Retuns the gene lenght of the chromosome
getCromLenght() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.CromDNF: Retuns the gene lenght of the chromosome
getCromLength() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Chromosome: Retuns the gene lenght of the chromosome
getCromLength() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.CromCAN: Retuns the length of the chromosome
getCromLength() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.CromDNF: Retuns the length of the chromosome
getCromLength() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Chromosome: Retuns the gene lenght of the chromosome
getCromLength() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.CromCAN: Retuns the gene lenght of the chromosome
getCromLength() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Chromosome: Retuns the gene lenght of the chromosome
getCrossPercent() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
getCrowdingDistance() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Returns the crowdingDistance of the individual
getcRule() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Replace: It returns the position of the best rule that correctly classifies the example stored in the structure.
getcRule() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Structure: It returns the position of the best rule that correctly classifies the example stored in the structure
getcRule() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Replace: It returns the position of the best rule that correctly classifies the example stored in the structure
getcRule() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Structure: It returns the position of the best rule that correctly classifies the example stored in the structure
getCSize() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Returns the estimated action set size.
getCSize() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters: Returns the estimated correct set size.
getCSup() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Retuns the value of CSUP
getCubierta() - Method in class keel.Algorithms.Rule_Learning.Prism.Muestra: Returns the number of times that the example has benn matched
getCubierta() - Method in class keel.Algorithms.Rule_Learning.UnoR.Muestra: Returns the number of times that the example has benn matched
getCubierta() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Muestra: Returns the number of times that the example has benn matched
getCubierta() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Muestra: Returns the number of times that the example has benn matched
getCummulative() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Histogram: Returns the cummulative bins.
getCumulatedProbability(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.NormalDistribution: Returns the accumulated probability for the given value.
getCumulatedProbability(double) - Method in class keel.GraphInterKeel.statistical.tests.NormalDistribution: Computes cumulated distribution frequency for a given value
getCurrent() - Method in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.FreqList: Obtains the element pointed currently by the iterator
getCurrent() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.FreqList: Obtains the element pointed currently by the iterator
getCurrent() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.FreqList: Obtains the element pointed currently by the iterator
getCurrent() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqList: Obtains the element pointed currently by the iterator
getCurrent() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqListPair: Obtains the element pointed currently by the iterator
getCurrent() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.FreqList: Obtains the element pointed currently by the iterator
getCurrent() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.FreqList: Obtains the element pointed currently by the iterator
getCurrent() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.FreqList: Obtains the element pointed currently by the iterator
getCurrent() - Method in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.FreqList: Obtains the element pointed currently by the iterator
getCurrent() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.FreqList: Obtains the element pointed currently by the iterator
getCurrent() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.FreqList: Obtains the element pointed currently by the iterator
getCurrentBest() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Returns best individual fitness of the population
getCurrentClass() - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Environment: The function returns the current class
getCurrentClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.MPEnvironment: Return the class of the environmental state.
getCurrentClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.SSFileEnvironment: Returns the class of the current example
getCurrentFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.valueAssociations: Returns the frequency of the class pointed by the current state of the list's iterator
getCurrentFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.valueAssociations: Returns the frequency of the class pointed by the current state of the list's iterator
getCurrentFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.valueAssociations: Returns the frequency of the class pointed by the current state of the list's iterator
getCurrentFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.valueAssociations: Returns the frequency of the class pointed by the current state of the list's iterator
getCurrentFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.valueAssociations: Returns the frequency of the class pointed by the current state of the list's iterator
getCurrentFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.valueAssociations: Returns the frequency of the class pointed by the current state of the list's iterator
getCurrentFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.valueAssociations: Returns the frequency of the class pointed by the current state of the list's iterator
getCurrentFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.valueAssociations: Returns the frequency of the class pointed by the current state of the list's iterator
getCurrentMean() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Returns fitness mean of the percentageFirstMutator of population
getCurrentRuleListObject() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Gets the current instance of the RuleList class.
getCurrentState() - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Environment: The function returns the current state.
getCurrentState() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.MPEnvironment: The function returns the current state.
getCurrentState() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.SSFileEnvironment: The function returns the current state.
getCurrentState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.DECEnvironment: The function returns the current state.
getCurrentState() - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Environment: The function returns the current state.
getCurrentState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MPEnvironment: The function returns the current state.
getCurrentState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MSEnvironment: The function returns the current state.
getCurrentState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.PAREnvironment: The function returns the current state.
getCurrentState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.POSEnvironment: The function returns the current state.
getCurrentState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RMPEnvironment: The function returns the current state.
getCurrentState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.SSFileEnvironment: The function returns the current state.
getCurrentValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.valueAssociations: Obtains the value of the class pointed by the current state of the list's iterator
getCurrentValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.valueAssociations: Obtains the value of the class pointed by the current state of the list's iterator
getCurrentValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.valueAssociations: Obtains the value of the class pointed by the current state of the list's iterator
getCurrentValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.valueAssociations: Obtains the value of the class pointed by the current state of the list's iterator
getCurrentValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.valueAssociations: Obtains the value of the class pointed by the current state of the list's iterator
getCurrentValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.valueAssociations: Obtains the value of the class pointed by the current state of the list's iterator
getCurrentValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.valueAssociations: Obtains the value of the class pointed by the current state of the list's iterator
getCurrentValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.valueAssociations: Obtains the value of the class pointed by the current state of the list's iterator
getCurrentVersion() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.instanceSet
getCurrentVersion() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowing
getCurrentVersion() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowingGWS
getCurrentVersion() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowingILAS
getCurrentVersion() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper
getCurrentVersion() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Windowing
getCurrentVersion() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper
getCurrentVersion() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Windowing
getCursorPosition() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset: Returns the cursor position
getCutPoint(int, int) - Method in class keel.Algorithms.Discretizers.Basic.Discretizer: Returns the cp-th cut point of the given attribute.
getCutPoint(int, int) - Method in class keel.Algorithms.Discretizers.Chi2_Discretizer.Discretizer: Returns the cp-th cut point of the given attribute.
getCutPoint(int, int) - Method in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Discretizer: Returns the cp-th cut point of the given attribute.
getCutPoint(int, int) - Method in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Discretizer: Returns the cp-th cut point of the given attribute.
getCutPoint(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Basic.Discretizer
getCutPoint() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: It returns the cutpoint
getCutPoint(int, int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Discretizer
getCutPoint() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Returns the cut point.
getCutPoint() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Returns the cut point where the node will be divided in its children.
getCutPoint() - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Returns the cut point.
getd() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
getd() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
getd() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.fuzzy
getd() - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
getd() - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
getd() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
getd() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
getd() - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
getD() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.EigenvalueDecomposition: Return the block diagonal eigenvalue matrix
getD() - Method in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: Gets the gamma value.
getDat(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableDat: Get the value of the variable "pos" of the example "numEj"
getDat(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeDat: Gets the value of a variable
getDat(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableDat: Get the value of the variable "pos" of the example "numEj"
getDat(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeDat: Gets the value of a variable
getDat(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableDat: Get the value of the variable "pos" of the example "numEj"
getDat(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeDat: Gets the value of a variable
getData() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Returns the dataset that satisfies the node condition.
getData() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: returns the underlying data
getData() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Get the data of the stats
getData(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.myDataset: It return an example
getData(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.myDataset: It return an example
getData(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.EachDataSet: Returns an example
getData(int) - Method in class keel.Algorithms.Rule_Learning.AQ.myDataset: It return an example
getData() - Method in class keel.Algorithms.Rule_Learning.ART.TBAR
getData(int) - Method in class keel.Algorithms.Rule_Learning.CN2.myDataset: It return an example
getData() - Method in class keel.Algorithms.Rule_Learning.Ripper.Ripper: Returns the training dataset.
getData() - Method in class keel.GraphInterKeel.datacf.editData.EditDataPanel: Get Dataset
getData() - Method in class keel.GraphInterKeel.datacf.util.DatasetTable: Get Data
getData() - Method in class keel.GraphInterKeel.datacf.util.VariableTable: Returns the data array
getData() - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanel: Gets a Datasets
getDataCFView() - Method in class keel.GraphInterKeel.datacf.importData.ImportPanel: Gets a view of Data
getDataI(int, int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset: Gets the value of the jth attribute for the ith instance in the dataset
getDataI(int, int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset: Gets the value of the jth attribute for the ith instance in the dataset
getDataIndex(int, int) - Method in class keel.GraphInterKeel.datacf.util.Dataset: Return data at position (i,j)
getDataItem(int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset: Gets the value of the ith instance in the dataset
getDataItem(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset: Gets the value of the ith instance in the dataset
getdataSelected() - Method in class keel.GraphInterKeel.experiments.Joint
getDataset() - Method in class keel.Algorithms.Decision_Trees.C45.Itemset: Returns the dataset of this itemset.
getDataset() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset: Returns the dataset of this itemset.
getDataset() - Method in class keel.Algorithms.Decision_Trees.ID3.Itemset: Returns the dataset of this itemset.
getDataset() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Itemset: Returns the dataset of this itemset.
getDataset() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Returns the dataset of this itemset.
getDataset() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset: Returns the dataset of this itemset.
getDataset() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset: Returns the dataset of this itemset.
getDataset() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset: Returns the dataset of this itemset.
getDataset() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset: Returns the dataset of this itemset.
getDataset() - Method in class keel.Algorithms.MIL.ExceptionDatasets
getDataset() - Method in class keel.Algorithms.Rule_Learning.ART.Itemset: Returns the dataset of this itemset.
getDataset() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Itemset: Returns the dataset of this itemset.
getDataset() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset: Returns the dataset of this itemset.
getDataset() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset: Returns the dataset of this itemset.
getDataset() - Method in class keel.Algorithms.Rule_Learning.PART.Itemset: Returns the dataset of this itemset.
getDataset() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset: Returns the dataset of this itemset.
getdataset_used() - Method in class keel.GraphInterKeel.experiments.Parameters: Gets datasets useds
getDataVector() - Method in class keel.GraphInterKeel.datacf.util.Dataset: Returns a vector of vectors in which each vector is an example/pattern Note: each value is stored as a String (must be converted)
getDateFormat() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Returns the Date format pattern in case this attribute is of type DATE, otherwise an empty string.
getDato(int) - Method in class keel.Algorithms.Rule_Learning.Prism.ConjDatos: Returns an example
getDato(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjDatos: Returns an example
getDato(int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjDatos: Returns an example
getDato(int) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjDatos: Returns an example
getDato(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetData: Return an instance
getDatos() - Method in class keel.GraphInterKeel.datacf.util.DatasetTable: Get datos
getDatosAt(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.OpenDataset: Return example data at index in a string separated by comma without spaces
getDatosAt(int) - Method in class keel.Algorithms.Neural_Networks.gann.OpenDataset: Return example data at index in a string separated by comma without spaces
getDatosAt(int) - Method in class keel.Algorithms.Neural_Networks.gmdh.OpenDataset: Return example data at index in a string separated by comma without spaces
getDatosAt(int) - Method in class keel.Algorithms.Neural_Networks.net.OpenDataset: Return example data at index in a string separated by comma without spaces
getDatosIndex(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Returns the value of a given attribute for a given instance.
getDebug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Classifier: Get whether debugging is turned on.
getDebug() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Gets whether debug information is output to the console
getDebug() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Gets whether debugging output will be printed.
getDebug() - Method in class keel.Algorithms.SVM.SMO.core.Check: Get whether debugging is turned on
getDebug() - Method in class keel.Algorithms.SVM.SMO.supportVector.Kernel: Gets whether debugging output is turned on or not.
getDecompositionAttribute() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Retuns the dataset that satisfies the node's condition.
getDecompositionValue() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Returns the value used to divide the node.
getDecreasedNI() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Node: Obtains a list of lists with n1,...
getDecreasedNIV() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Node: Obtains a list of lists with the k classes in decreasing order of ni - V(Si).
getDefaultClass() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Selected: It returns the default class in the structure.
getDefaultClass() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Selected: It returns the default class in the structure
getDefaultCr(MyDataset, Mask, Mask, double[]) - Method in class keel.Algorithms.Rule_Learning.Slipper.Ruleset: Computes the confidence of the default rule, according to the equation 4 of [AAAI99]: Cr=1/2ln((W+ + 1/(2n))/(W_ + 1/(2n))) W+: sum of the weights of the positive instances that are covered by the current rule W_: sum of the weights of the negative instances that are covered by the current rule n: |p|+|n|
getDefaultCr() - Method in class keel.Algorithms.Rule_Learning.Slipper.Ruleset: Returns the confidence of the default rule.
getDefaultValue(int) - Method in class keel.GraphInterKeel.experiments.Parameters: return default value for parameter at index position
getDefaultValues() - Method in class keel.GraphInterKeel.experiments.Parameters: return parameter default values
getDefaultW(MyDataset, Mask, double[]) - Static method in class keel.Algorithms.Rule_Learning.Slipper.Rule: Computes W+ or W- for the default rule, according to the function W=sum(Di) i e R
getdere() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.fuzzy
getdere() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.fuzzy
getDescription() - Method in class keel.GraphInterKeel.datacf.util.KeelFileFilter: Returns the description of the file filter
getDescription() - Method in class keel.GraphInterKeel.experiments.ArchiveFilter2: Get the description of the archive
getDescription() - Method in class keel.GraphInterKeel.experiments.KeelFileFilter: Get the filter name
getDescription() - Method in class keel.GraphInterKeel.statistical.CSVFileFilter: Get the filter name
getDescriptions(int) - Method in class keel.GraphInterKeel.experiments.Parameters: return parameter name for parameter at index position
getDestination() - Method in class keel.GraphInterKeel.experiments.Arc: Gets the destination node
getDestination2() - Method in class keel.GraphInterKeel.experiments.Arc: Gets the destination node
getDeviation(ArrayList) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Utils
getDeviation(ArrayList) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Utils
getDF() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule: It returns the DF of the rule
getdID() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Replace: It returns the position in the training dataset for the example stored in the structure.
getdID() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Structure: It returns the position in the training dataset for the example stored in the structure
getdID() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Replace: It returns the position in the training dataset for the example stored in the structure
getdID() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Structure: It returns the position in the training dataset for the example stored in the structure
getDifferentValuesAttributeI(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Node: Gets the number of different values for the ith attribute
getDimensions() - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Returns the number of dimensions
getDimensions() - Method in class keel.Algorithms.Hyperrectangles.EACH.Hyperrectangle
getDimensions() - Method in class keel.Algorithms.Rule_Learning.Riona.Complex
getDirectionAttribute() - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It returns if the attribute is an input attribute
getDirectionAttribute() - Method in class keel.Dataset.Attribute: It returns if the attribute is an input attribute
getDiscretizedValue(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.InstanceWrapper
getDiscretizedValue(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.InstanceWrapper
getDiscretizedValues() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.InstanceWrapper
getDiscretizedValues() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.InstanceWrapper
getDiscretizer(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.DiscretizationManager
getDiscretizer(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.DiscretizationManager
getDisplacements() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Gene: It returns the displacements of a gene
getDisplay() - Method in enum keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation.Field: returns the display string
getDisplay() - Method in enum keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation.Type: returns the display string
getDisplay() - Method in enum keel.Algorithms.SVM.SMO.core.TechnicalInformation.Field: returns the display string
getDisplay() - Method in enum keel.Algorithms.SVM.SMO.core.TechnicalInformation.Type: returns the display string
getDistrib() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Return the value of the complete distribution
getDistrib() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.AssociationRule: Return the value of the complete distribution
getDistribClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Return the value of a distribution
getDistribClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.AssociationRule: Return the value of a distribution
getDistribClassEx(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Return the value of the distribution for the example of a class
getDistribClassEx(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.AssociationRule: Return the value of the distribution for the example of a class
getDistribEx() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Return the value of the complete distribution for the example of a class
getDistribEx() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.AssociationRule: Return the value of the complete distribution for the example of a class
getDistribucion() - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Returns the value of the distribution
getDistribucion() - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Return the value of the distribution
getDistribucion() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Return the value of the distribution
getDistribucion() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Return the value of the distribution
getDistribucionClase(int) - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Returns the value of the distribution for a given class
getDistribucionClase(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Return the value of the distribution
getDistribucionClase(int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Return the value of the distribution
getDistribucionClase(int) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Return the value of the distribution
getDistribucionClaseEj(int) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Return the value of the distribution for the given class
getDistribucionEj() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Return the value of the distribution
getDistribution() - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Returns the distribution vector.
getDistribution() - Method in class keel.Algorithms.Rule_Learning.AQ.Complex: It returns the class distribution
getDistribution() - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: It returns the class distribution
getDistribution() - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Return the value of the distribution
getDistributionClass(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Returns the distribution value for the class given.
getDistributionClass(int) - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Return the value of the distribution
getDistributions(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Get the class distribution predicted by the rule in given position
getDiversity() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Gets the type of diversity of the algorithm
getDomain(int) - Method in class keel.GraphInterKeel.experiments.Parameters: return parameter domain for parameter at index position
getDomains() - Method in class keel.GraphInterKeel.experiments.Parameters: return parameter domains
getDomainValue(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.Particle
getDomainValue(int, int) - Method in class keel.GraphInterKeel.experiments.Parameters: returns domain value at position pos for parameter at index position
getDouble(int) - Method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Parses the parameter with the index given and returns it as real
getDouble(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Parses as Double the parameter with the given index
getDoublePivot() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.LUDecomposition: Return pivot permutation vector as a one-dimensional double array
getDoubleTail() - Static method in class keel.GraphInterKeel.statistical.tests.WilcoxonDistribution: Returns double-tailed p-value of the last comparison
getDs() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess
getDuration(double, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Returns the difference between two given times as a string.
getE() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
getE() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
getE() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getE() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
getE() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
getE() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
getE() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getE() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
getEC() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
getEc_tra() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Chc
getEc_tra() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Chc
getEc_tra() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Chc
getEc_tra() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Chc
getEc_tra() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Chc
getEc_tra() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Chc
getEc_tra() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Chc
getEc_tra() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Chc
getEc_tst() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Chc
getEc_tst() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Chc
getEc_tst() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Chc
getEc_tst() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Chc
getEc_tst() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Chc
getEc_tst() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Chc
getEc_tst() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Chc
getEc_tst() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Chc
getEditDataPanel() - Method in class keel.GraphInterKeel.datacf.editData.EditVariablePanel: Get Edit Data Panel
getEducationlRun() - Method in class keel.GraphInterKeel.experiments.EducationalRunEvent
getEigenvalues() - Method in class keel.Algorithms.Discretizers.UCPD.PCA: It returns the selected eigenvalues vector
getEigenvectors(double) - Method in class keel.Algorithms.Discretizers.UCPD.PCA: It computes the most representative eigenvectors
getElement(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.IntegerSet: It returns the element at position i
getElement(int, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Returns the value of a cell in the matrix.
getEliteIndivCromCAN(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Returns de hole chromosome of the selected individual of the elite population
getEliteIndivCromDNF(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Returns de hole chromosome of the selected individual iof the elite pupulation
getElitismRate() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
getemax() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: It returns an array with the maximum values of the attributes.
getemax() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: It returns an array with the maximum values of the attributes
getemax() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: It returns an array with the maximum values of the attributes
getemaximo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: It returns an array with the maximum values of the attributes
getemaximo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: It returns an array with the maximum values of the attributes
getemaximo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: It returns an array with the maximum values of the attributes
getemaximo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: It returns an array with the maximum values of the attributes
getemaximo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: It returns an array with the maximum values of the input attributes
getemaximo() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: It returns an array with the maximum values of the attributes
getemaximo() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: It returns an array with the maximum values of the input attributes
getemaximo() - Method in class keel.Algorithms.Rule_Learning.AQ.Dataset: It returns an array with the maximum values of the input attributes
getemaximo() - Method in class keel.Algorithms.Rule_Learning.CN2.Dataset: It returns an array with the maximum values of the input attributes
getemaximo() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Returns an array with the maximum values of the in-put attributes
getemaximo() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Returns an array with the maximum values of the in-put attributes
getemaximo() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: It returns an array with the maximum values of the input attributes
getemaximo() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset: It returns an array with the maximum values of the input attributes
getemaximo() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset: It returns an array with the maximum values of the input attributes
getEMaximum() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Returns an array with the minium values of the attributes of the in-put
getemin() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: It returns an array with the minimum values of the attributes.
getemin() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: It returns an array with the minimum values of the attributes
getemin() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: It returns an array with the minimum values of the attributes
geteminimo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: It returns an array with the minimum values of the attributes
geteminimo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: It returns an array with the minimum values of the attributes
geteminimo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: It returns an array with the minimum values of the attributes
geteminimo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: It returns an array with the minimum values of the attributes
geteminimo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: It returns an array with the minimum values of the input attributes
geteminimo() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: It returns an array with the minimum values of the attributes
geteminimo() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: It returns an array with the minimum values of the input attributes
geteminimo() - Method in class keel.Algorithms.Rule_Learning.AQ.Dataset: It returns an array with the minimum values of the input attributes
geteminimo() - Method in class keel.Algorithms.Rule_Learning.CN2.Dataset: It returns an array with the minimum values of the input attributes
geteminimo() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Returns an array with the minimum values of the in-put values
geteminimo() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Returns an array with the minimum values of the in-put values
geteminimo() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: It returns an array with the minimum values of the input attributes
geteminimo() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset: It returns an array with the minimum values of the input attributes
geteminimo() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset: It returns an array with the minimum values of the input attributes
getEMinimum() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Returns an array with the minium values of the in-put attributes
getEndColumn() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
getEndColumn() - Static method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
getEndColumn() - Static method in class keel.Dataset.SimpleCharStream
getEndLine() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
getEndLine() - Static method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
getEndLine() - Static method in class keel.Dataset.SimpleCharStream
getEntradas() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.SMOTE_RSB
getEntropy() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Returns the class entropy of this set.
getEntropy(Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Returns the class entropy of this set.
getEntropy() - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Returns the class entropy of this set.
getEntropy(Mask) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Returns the class entropy of this set.
getEnvironmentClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.DECEnvironment: Returns the class of the environmental state.
getEnvironmentClass() - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Environment: Returns the class of the environmental state.
getEnvironmentClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MPEnvironment: Return the class of the environmental state.
getEnvironmentClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MSEnvironment: Return the class of the environmental state.
getEnvironmentClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.PAREnvironment: Returns the class of the environmental state.
getEnvironmentClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.POSEnvironment: Returns the class of the environmental state.
getEnvironmentClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RMPEnvironment: Returns the class of the environmental state.
getEnvironmentClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.SSFileEnvironment: Does return if the class of the environmental state.
getEps() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Get the value of eps.
getEpsilon() - Method in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
getEpsilon() - Method in class keel.Algorithms.SVM.SMO.SMO: Get the value of epsilon.
getEpsilon() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Get the value of epsilon.
getEpsilon() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMO: Get the value of epsilon.
getEpsilonParameter() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: Get the value of epsilon parameter of the epsilon insensitive loss function.
GetEqualSample() - Method in class keel.Algorithms.Neural_Networks.ensemble.Sample: Uniform sample
getError() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformance
getError(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.FormatErrorKeeper: Return the information about one error.
getError(int) - Method in class keel.Dataset.FormatErrorKeeper: Return the information about one error.
getErroresClase() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion
getErroresClase(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion
getErrorFunction() - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.SoftmaxClassificationProblemEvaluator: Returns error function
getErrorFunction() - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.problem.regression.RegressionProblemEvaluator: Returns error function
getErrorRate() - Method in class keel.Algorithms.Instance_Selection.IGA.Cromosoma: Returns the error rate of a chromosome
getErrorRate() - Method in class keel.Algorithms.Instance_Selection.ZhangTS.Cromosoma: Get the error rate of a chromosome
getErrorRate() - Method in class keel.Algorithms.Preprocess.Instance_Selection.IGA.Cromosoma: Get the error rate of a chromosome
getErrorRate() - Method in class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.Cromosoma: Returns the error rate of a chromosome
getErrorSumOfSquares() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the sum of squared errors (SSe)
getErrorVariance() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the variance error (sigma^2)
getEvaluator() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.KEELIRPropPlusWrapperClas: Access to system evaluator.
getEvaluator() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.KEELIRPropPlusWrapperRegr: Access to system evaluator.
getEvaluator() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Access to system evaluator
getEvolution() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Population: Type of evolution
getExample(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Output a specific example.
getExample(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Output a specific example
getExample(Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Output a specific example
getExample(Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Output a specific example
getExample(Mask) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Output a specific example
getExample(Mask) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Output a specific example
getExample(Mask) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: Output a specific example
getExample(Mask) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: Output a specific example
getExample(Mask) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Rule_Learning.SRI.Instances: Returns the example in the position given.
getExample(int) - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: Output a specific example
getExample(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: Returns the example with the given id.
getExample(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: Returns the example with the given id.
getExample(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: Returns the example with the given id.
getExample() - Method in class keel.GraphInterKeel.experiments.UseCase
getExampleFGTTFS(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: Output a specific example
getExamplesClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableDat: Returns the number of examples belonging to the class specified
getExamplesClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableDat: Returns the number of examples belonging to the class specified
getExamplesClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetData: Return the examples for a class
getExamplesClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableDat: Returns the number of examples belonging to the class specified
getExamplesClassObj() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableDat: Returns the number of examples of the target class
getExamplesClassObj() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableDat: Returns the number of examples of the target class
getExamplesClassObj() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableDat: Returns the number of examples of the target class
getExamplesCovered() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableDat: Returns the number of examples covered by the rules generated
getExamplesCovered() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableDat: Returns the number of examples covered by the rules generated
getExampleString(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: Output a specific example
getExampleXf(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: Output a specific example
getException() - Method in class keel.GraphInterKeel.experiments.EducationalRunEvent
getException() - Method in class keel.GraphInterKeel.experiments.EducationalRunkeelEvent: Return a Exception
getException() - Method in class keel.GraphInterKeel.experiments.RunkeelEvent
getExceptionCost(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Rule: Returns the Minimum Data Length of a dataset given a theory (this rule).
getExceptionCost(MyDataset, int, int, int, int) - Static method in class keel.Algorithms.Rule_Learning.C45Rules.Rule: Static version.
getExceptionCost(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Ruleset: Returns the exception cost for the Minimum Data Length of a dataset given a theory (this ruleset).
getExceptionCost(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Ruleset: Returns the exception cost for the Minimum Data Length of a dataset given a theory (this ruleset).
getExceptionCost(MyDataset, Mask, Mask, IncrementalMask) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Ruleset: Returns the exception cost for the Minimum Data Length of a dataset given a theory (this ruleset).
getExceptionCost(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule: Returns the Minimum Data Length of a dataset given a theory (this rule).
getExceptionCost(MyDataset, int, int, int, int) - Static method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule: Static version.
getExceptionCost(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset: Returns the exception cost for the Minimum Data Length of a dataset given a theory (this ruleset).
getExceptionCost(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset: Returns the exception cost for the Minimum Data Length of a dataset given a theory (this ruleset).
getExceptionCost(MyDataset, Mask, Mask, IncrementalMask) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset: Returns the exception cost for the Minimum Data Length of a dataset given a theory (this ruleset).
getExceptionCost(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.Ripper.Rule: Returns the Minimum Data Length of a dataset given a theory (this rule).
getExceptionCost(MyDataset, int, int, int, int) - Static method in class keel.Algorithms.Rule_Learning.Ripper.Rule: Static version.
getExceptionCost(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset: Returns the exception cost for the Minimum Data Length of a dataset given a theory (this ruleset).
getExceptionCost(MyDataset) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset: Returns the exception cost for the Minimum Data Length of a dataset given a theory (this ruleset).
getExceptionCost(MyDataset, Mask, Mask, IncrementalMask) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset: Returns the exception cost for the Minimum Data Length of a dataset given a theory (this ruleset).
getExceptionsLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
getExceptionsLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
getExceptionsLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
getExe() - Method in class keel.GraphInterKeel.experiments.Parameters: return number of executions
getExecDocentWindowState() - Method in class keel.GraphInterKeel.experiments.Experiments: EDUCATIONAL KEEL **********************
getexecExternoFinalizado() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: This method return a control variable used for GUI EjecucionDocente.
getExitos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Individual: It gets the number of classified instances for each rule
getExpected() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexResult
getExperience() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Returns the experience of the classifier.
getExperience() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters: Returns the experience of the classifier.
getExperience() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Returns the experience of the classifier.
getExperience() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Returns the experience of the classifier.
getExperienceAverage() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Returns the experience average of the population.
getExperienceAverage() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Returns the experience average of the population.
getExperimentType() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: This methos get type problem, classification or regression
getExponent() - Method in class keel.Algorithms.SVM.SMO.supportVector.PolyKernel: Gets the exponent value.
getExternalObjectDescription() - Method in class keel.GraphInterKeel.experiments.Graph: Gets the External Object Description which describes this graph
getF() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
getF() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
getF() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getF() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
getF() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
getF() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
getF() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getF() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
getF0() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the statistical F0.
getFakeTransactions() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: Outputs an array of transactions with their recasted attribute values.
getFakeTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: Outputs an array of transactions with their recasted attribute values.
getFakeTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: Outputs an array of transactions with their recasted attribute values.
getFakeTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: Outputs an array of transactions with their recasted attribute values.
getFCnf() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Retuns the value of FCNF
getFeatures(ArrayList<Integer>) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: It return the prototypeset with the features specified in lista.
getFeatures1() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet
getFeatures2() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet
getFichEntTra() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
getFicheroSalida() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.SMOTE_RSB
getFicheroTest() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.SMOTE_RSB
getFicheroTraining() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.SMOTE_RSB
getFichRul() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
getFichSalTra() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
getFichSalTst() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
getFileBrowserPanel() - Method in class keel.GraphInterKeel.datacf.partitionData.PartitionPanel: Gets the file browser from this partition panel
getFileChooser() - Method in class keel.GraphInterKeel.datacf.util.FileBrowserPanel: Get a FileBrowser
getFileLocation() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Returns the files directory where every file will be stored.
getFileName() - Static method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Returns the file
getFileName() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Returns the filename used to read the observations and parameters
getFileName() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.FileDataset: Gets the file name
getFileName() - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Return the name of parameters file.
GetFileName(StringTokenizer) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Utils: Gets the name for the file, eliminating "" and skiping "="
getFileName(StringTokenizer) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Utils: Gets the name for the file, eliminating "" and skiping "="
GetFileName(StringTokenizer) - Static method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Utils: Gets the name for the file, eliminating "" and skiping "="
GetFileName(StringTokenizer) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Utils: Gets the name for the file, eliminating "" and skiping "="
getFilePath() - Static method in class keel.GraphInterKeel.util.Path: Generate a file with the path stored
getFiltered(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Get the data after filtering the given rule
getFilterType() - Method in class keel.Algorithms.SVM.SMO.SMO: Gets how the training data will be transformed.
getFilterType() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Gets how the training data will be transformed.
getFilterType() - Method in class keel.Algorithms.SVM.SMO.SVMreg: Gets how the training data will be transformed.
getFinales() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
getFiringDegrees() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams
getFirst() - Method in class keel.Algorithms.Instance_Generation.utilities.Pair: Get first element of the pair.
getFirst() - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Pair: Get first element of the pair.
getFirstTimeLimit() - Method in class keel.Algorithms.Lazy_Learning.IDIBL.IDIBL: Get first tunning algorithm time limit
getFirstToken(StreamTokenizer) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Gets next token, skipping empty lines.
getFit() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
getFit() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
getFit() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Chromosome
getFit() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Chromosome
getFitDif() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Returns significative fitness difference
getFitDif() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Returns the difference between two fitnesses that we consider enough to say that the fitness has improved
getFitness() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual: Function to return the fitness of the individual
getFitness() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Individual: Function to return the fitness of the individual
getFitness() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Obtains the fitness associated to this rule, its raw_fitness measure
getFitness() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual
getFitness(classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformanceTraining
getFitness() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
getFitness() - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Chromosome: Gives the fitness of the chromosome
getFitness() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
getFitness(Classifier) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PerformanceAgent: This function returns the fitness formula used
getFitness() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.RuleSet: Gets the fitness of this rule set
getFitness() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual: It returns the fitness of the individual
getFitness() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
getFitness(Classifier) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PerformanceAgent: This function returns the fitness formula used
getFitness() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.RuleSet: Gets the fitness of this rule set
getFitness() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Returns the fitness of the classifier.
getFitness() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Returns the fitness of the current classifier.
getFitness() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC_Chromosome: Obtains the fitness associated to this CHC_Chromosome, its fitness measure
getFitness() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Obtains the fitness associated to this rule, its raw_fitness measure
getFitness() - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma: Get the fitness of a chromosome
getFitness() - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma: Get the fitness of a chromosome
getFitness() - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma: Get the fitness of a chromosome
getFitness() - Method in class keel.Algorithms.Instance_Selection.SSMA.Cromosoma: Get the fitness of a chromosome
getfitness() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.IndMichigan
getfitness() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.IndMichigan
getfitness() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.IndMichigan
getfitness() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.IndMichigan
getfitness() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.IndMichigan
getFitness() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Gets the fitness of a RBF.
getFitness() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.Cromosoma: return the fitness of the chromosome
getFitness() - Method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.Chromosome: Get the fitness value
getFitness() - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma: Get the fitness of a chromosome
getFitness() - Method in class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome: Returns the fitness of the chromosome.
getFitness() - Method in class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome: Returns the fitness of the chromosome.
getFitness() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Retuns the value of the fitness
getFitness() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.QualityMeasures: Retuns the value of the fitness
getFitness() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Chromosome: It returns the fitness of a chromosome
getFitness() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Chromosome: It returns the fitness of a chromosome
getFitness() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Chromosome: It returns the fitness of a chromosome
getFitness() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome: It returns the fitness of a chromosome
getFitness() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Chromosome
getFitness_rank() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
getFitnessAc() - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma: Get the accuracy fitness of a chromosome
getFitnessAc() - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma: Get the accuracy fitness of a chromosome
getFitnessAc() - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma: Get the accuracy fitness of a chromosome
getFitnessAc() - Method in class keel.Algorithms.Instance_Selection.SSMA.Cromosoma: Get the accuracy fitness of a chromosome
getFitnessAc() - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma: Get the accuracy fitness of a chromosome
getFixedBounds() - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It returns the variable fixedBounds.
getFixedBounds() - Method in class keel.Dataset.Attribute: It returns the variable fixedBounds.
getFlag(char, String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Checks if the given array contains the flag "-Char".
getFlag(char, String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Checks if the given array contains the flag "-Char".
getFlag(String, String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Checks if the given array contains the flag "-String".
getFlag(char, String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Checks if the given array contains the flag "-Char".
getFlag(char, String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Checks if the given array contains the flag "-Char".
getFlag(String, String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Checks if the given array contains the flag "-String".
getFlag(char, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Checks if the given array contains the flag "-Char".
getFlag(String, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Checks if the given array contains the flag "-String".
getFlag(char, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Checks if the given array contains the flag "-Char".
getFlag(String, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Checks if the given array contains the flag "-String".
getFlag(char, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Checks if the given array contains the flag "-Char".
getFlag(String, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Checks if the given array contains the flag "-String".
getFlag(char, String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Checks if the given array contains the flag "-Char".
getFlag(String, String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Checks if the given array contains the flag "-String".
getFlag(char, String[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Checks if the given array contains the flag "-Char".
getFlag(String, String[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Checks if the given array contains the flag "-String".
getFMeasure() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformanceTraining
getFolds() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Gets the number of folds
getFontColor() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.InfoNode: Returns the Font color of the node.
getFontSize() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.InfoNode: Returns the Font size.
getFp() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
getFP() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Return the value of FP
getFracasos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Individual: It gets the number of misclassified instances for each rule
getFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.ValueFreq: Returns the frequency of this element
getFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.ValueFreq: Returns the frequency of this element
getFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValueFreq: Returns the frequency of this element
getFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValuesFreq: Returns the frequency of this pair
getFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.ValueFreq: Returns the frequency of this element
getFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.ValueFreq: Returns the frequency of this element
getFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.ValueFreq: Returns the frequency of this element
getFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.ValueFreq: Returns the frequency of this element
getFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.ValueFreq: Returns the frequency of this element
getFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.ValueFreq: Returns the frequency of this element
getFrequencyOfClasses() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Inform the frequency of each class of the set
getFrequencyOfClasses() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Inform the frequency of each class of the set
getFrequentItemsets(int[][], int[]) - Static method in class keel.Algorithms.Discretizers.UCPD.FrequentItemsets: It computes the frequent itemsets and returns them
getFromClass(double) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Select all the prototypes of a specific class.
getFromClass(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Select all the prototypes of a specific class.
getFS() - Static method in class keel.Algorithms.RST_Learning.KNNClassifier: Get a vector with the features currently selected
getFSup() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Retuns the value of FSUP
getFuncionEvaluacionXmlFileName() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion
getFunction() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule: It returns the right side (class) of the rule.
getFurtherNeighbor() - Method in class keel.Algorithms.Lazy_Learning.KSNN.KSNN: Calculates, for each train instance, the distance to its further K neighbour.
getFuzzy() - Method in class keel.GraphInterKeel.experiments.Parameters: Get fuzzy status
getFuzzyAttribute(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: Outputs the fuzzy regions of the specified attribute.
getFuzzyAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.GeneticFuzzyAprioriProcess: It returns the mined fuzzy attributes once the genetic learning has been accomplished
getFuzzyAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.GeneticFuzzyAprioriDCProcess: It returns the mined fuzzy attributes once the genetic learning has been accomplished
getFuzzyRegions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyAttribute: It returns the fuzzy regions composing the fuzzy attribute
getFuzzyRegions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyAttribute: It returns the fuzzy regions composing the fuzzy attribute
getFuzzyRegions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyAttribute: It returns the fuzzy regions composing the fuzzy attribute
getFuzzyTransaction(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: Outputs the fuzzy attribute values for the specified transaction.
getFuzzyTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyDataset: It returns the membership degrees associated with each fuzzy attribute and for all the transactions
getFuzzyTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyDataset: It returns the membership degrees associated with each fuzzy attribute and for all the transactions
getFuzzyTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyDataset: It returns the membership degrees associated with each fuzzy attribute and for all the transactions
getFuzzyValue(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyRegion: It returns the membership degree of an X value within a fuzzy region
getFuzzyValue(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyRegion: It returns the membership degree of an X value within a fuzzy region
getFuzzyValue(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyRegion: It returns the membership degree of an X value within a fuzzy region
getFuzzyValue(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyRegion: It returns the membership degree of an X value within a fuzzy region
getGain() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.Literal: It returns the gain of the literal
getGain(double, PNArray) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.Rule: It returns an array of literals whose gain is higher than a minimum threshold
getGain(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Returns the gain of the variable "pos"
getGain(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Returns the gain of the variable "pos"
getGain(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Returns the gain of the variable "pos"
getGainRatio() - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Returns the gain ratio for the cut.
getGainRatio() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Returns the gain ratio for the cut.
getGainRatio() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Returns the gain ratio for the cut.
getGainRatio() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Returns the gain ratio for the cut.
getGainRatio() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Returns the gain ratio for the cut.
getGainRatio() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Returns the gain ratio for the cut.
getGainRatio() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Returns the gain ratio for the cut.
getGainRatio() - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Returns the gain ratio for the cut.
getGainRatio() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Returns the gain ratio for the cut.
getGamma() - Method in class keel.Algorithms.SVM.SMO.supportVector.RBFKernel: Gets the gamma value.
GetGEMWeights(EnsembleParameters, double[][], int) - Method in class keel.Algorithms.Neural_Networks.ensemble.Ensemble: Calculate weights using GEM method
getGen(int) - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.Chromosome: It returns a given gen of the chromsome
getGen(int) - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.Instance_Selection.CHC.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.Instance_Selection.GGA.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.Instance_Selection.IGA.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.Instance_Selection.PBIL.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.Instance_Selection.SGA.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.Instance_Selection.SSMA.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.Instance_Selection.ZhangTS.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.Preprocess.Instance_Selection.CHC.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GGA.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.Preprocess.Instance_Selection.IGA.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SGA.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma: Get the value of a gene
getGen(int) - Method in class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.Cromosoma: Get the value of a gene
getGen() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Gets the value of a gene
getGen(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
getGen(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
getGen(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Chromosome
getGen(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Chromosome
getGene() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Individuo
getGene(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Individuo
getGene(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Chromosome: Gets the gene at specified position
getGene(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Individual: It returns the value of the gene at position i
getGene() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
getGene() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
getGene() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getGene() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
getGene() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
getGene() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
getGene() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getGene() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
getGene(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome: It returns the "i-th" gene of a chromosome
getGene(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome: It returns the "i-th" gene of a chromosome
getGene(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome: It returns the "i-th" gene of a chromosome
getGene(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome: It returns the "i-th" gene of a chromosome
getGeneElem(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Gene: Retuns the value of the gene indicated
getGeneElem(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Gene: Retuns the value of the gene indicated
getGeneElem(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Gene: Retuns the value of the gene indicated
getGeneElem(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Gene: Retuns the value of the gene indicated
getGeneElem(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Gene: Retuns the value of the gene indicated
getGeneElem(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Gene: Retuns the value of the gene indicated
getGeneLenght() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Gene: Retuns the gene lenght of the chromosome
getGeneLenght() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Gene: Retuns the gene lenght of the chromosome
getGeneLength() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Gene: Retuns the gene lenght of the chromosome
getGeneLength() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Gene: Retuns the gene length of the chromosome
getGeneLength() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Gene: Retuns the gene lenght of the chromosome
getGeneLength() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Gene: Retuns the gene lenght of the chromosome
getGenerality() - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Attribute: Returns the generality of the attribute.
getGenerality() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Gets the generality of the classifier.
getGenerality() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters: Gets the generality of the classifier.
getGenerality() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep: It returns the generality of the allele.
getGenerality() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep: It returns the generality of the allele.
getGenerality() - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute: Returns the generality of the attribute.
getGenerality() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Gets the generality of the classifier.
getGenerality() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: It returns the generality of the allele.
getGenerality() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Gets the generality of the classifier.
getGenerality() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: It returns the generality of the allele.
getGenerality() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: It returns the generality of the classifier.
getGenerality() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep: It returns the generality of the allele.
getGeneralityAverage() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Returns the generalization average of the classifiers in the population.
getGeneralityAverage() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Returns the generalization average of the classifiers in the population.
getGeneration() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.KEELIRPropPlusWrapperClas: Access to current generation.
getGeneration() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.KEELIRPropPlusWrapperRegr: Access to current generation.
getGeneration() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Access to current generation
getGenes() - Method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.Chromosome: Get the body of a chromosome
getGenes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
getGenes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
getGenes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getGenes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
getGenes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
getGenes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
getGenes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getGenes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
getGenes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Chromosome: It returns the genes of a chromosome
getGenes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Chromosome: It returns the genes of a chromosome
getGenes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Chromosome: It returns the genes of a chromosome
getGenes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome: It returns the genes of a chromosome
getGenes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
getGenes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
getGenes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Chromosome
getGenes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Chromosome
getGenes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Chromosome
getGenes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome: It returns the genes of a chromosome
getGenes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
getGenes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome: It returns the genes of a chromosome
getGenesRule() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
getGenesRule() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule
getGenesRule() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
getGeneticLearningLog() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.AlcalaetalProcess: It returns the XML string representing the genetic learning log
getGeneticLearningLog() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.GeneticFuzzyAprioriProcess: It returns the XML string representing the genetic learning log
getGeneticLearningLog() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.GeneticFuzzyAprioriDCProcess: It returns the XML string representing the genetic learning log
getGradoEmp() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getGradoEmp() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getGradoEmp() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getGradoEmp() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getGradoEmp() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getGradoEmp() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getGradoEmp() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getGradoEmp() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getGranularity() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Obtains the granularity of this rule as the maximum number of labels that a fuzzy antecedent can have
getH() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.QRDecomposition: Return the Householder vectors
getHeader() - Method in class keel.Algorithms.Decision_Trees.C45.Algorithm: Function to get the name of the relation and the names, types and possible values of every attribute in a dataset.
getHeader() - Static method in class keel.Algorithms.Decision_Trees.CART.dataset.DataSetManager: It returns the header
getHeader() - Method in class keel.Algorithms.Decision_Trees.CART.ResultPrinter: It returns the header
getHeader() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm: Function to get the name of the relation and the names, types and possible values of every attribute in a dataset.
getHeader() - Method in class keel.Algorithms.Decision_Trees.ID3.Algorithm: Function to get the name of the relation and the names, types and possible values of every attribute in a dataset.
getHeader(String) - Static method in class keel.Algorithms.Decision_Trees.M5.M5: Extracts the header of the file given.
getHeader() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm: Function to get the name of the relation and the names, types and possible values of every attribute in a dataset.
getHeader() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyDataset: It returns the header
getHeader() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm: Function to get the name of the relation and the names, types and possible values of every attribute in a dataset.
getHeader() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm: Function to get the name of the relation and the names, types and possible values of every attribute in a dataset.
getHeader() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm: Function to get the name of the relation and the names, types and possible values of every attribute in a dataset.
getHeader() - Method in class keel.Algorithms.Neural_Networks.gmdh.OpenDataset: Returns the header of the data set with all the attributes information
getHeader() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlusReporterClas: Returns KEEL file header
getHeader() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.IRPropPlusReporterRegr: Returns KEEL file header
getHeader() - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.listener.NeuralNetReporterClas: Returns KEEL file header
getHeader() - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.listener.NeuralNetReporterRegr: Returns KEEL file header
getHeader() - Method in class keel.Algorithms.Rule_Learning.ART.Algorithm: Function to get the name of the relation and the names, types and possible values of every attribute in a dataset.
getHeader() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Algorithm: Function to get the name of the relation and the names, types and possible values of every attribute in a dataset.
getHeader() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Algorithm: Function to get the name of the relation and the names, types and possible values of every attribute in a dataset.
getHeader() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm: Function to get the name of the relation and the names, types and possible values of every attribute in a dataset.
getHeader() - Method in class keel.Algorithms.Rule_Learning.PART.Algorithm: Function to get the name of the relation and the names, types and possible values of every attribute in a dataset.
getHeader() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceParser: It returns all the header read in parseHeader.
getHeader() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: It returns the header.
getHeader() - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Returns the header of the data set with the attributes' information
getHeader() - Method in class keel.Dataset.InstanceParser: It returns all the header read in parseHeader.
getHeader() - Method in class keel.Dataset.InstanceSet: It returns the header.
getHeaderNoData(String) - Static method in class keel.Algorithms.Decision_Trees.M5.M5: Extracts the header of the file given, when the data is not specified.
getHeuristic() - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Returns the heuristic value if the complex
getHeuristic() - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: Returns the heuristic value of the Complex.
getheuristic() - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: It returns the heuristic of the complex (LEF function)
getHeuristic() - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: return the heuristic value of the complex
getHeuristica() - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Returns the heuristic value if the complex
getHeuristica() - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: return the heuristic value of the complex
getHeuristica() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: return the heuristic value of the rule
getHeuristica() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: return the heuristic value of the complex
getHidden() - Method in class keel.GraphInterKeel.experiments.Parameters: Gets the hidden parameters
getHiddenLayerInitialMaxNofneurons(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Returns initial maximum number of neurons of a hidden layer
getHiddenLayerInitialMaxNofneurons(int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Returns initial maximum number of neurons of a hidden layer
getHiddenLayerInitiator(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Returns initiator of neurons of a hidden layer
getHiddenLayerInitiator(int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Returns initiator of neurons of a hidden layer
getHiddenLayerMaxNofneurons(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Returns maximum number of neurons of a hidden layer
getHiddenLayerMaxNofneurons(int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Returns maximum number of neurons of a hidden layer
getHiddenLayerMinNofneurons(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Returns minimum number of neurons of a hidden layer
getHiddenLayerMinNofneurons(int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Returns minimum number of neurons of a hidden layer
getHiddenLayerType(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Returns type of neurons of a hidden layer
getHiddenLayerType(int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Returns type of neurons of a hidden layer
getHiddenLayerWeightRange(int, int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Returns weight range of a hidden layer
getHiddenLayerWeightRange(int, int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Returns weight range of a hidden layer
getHits() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset: It returns the number of hits of the itemset against the training set.
getHits() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset: It returns the number of hits of the itemset against the training set
getHlayer(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Returns a specific hidden layer of the neural net
getHlayer(int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuralNet: Returns a specific hidden layer of the neural net
getHowWork() - Method in class keel.GraphInterKeel.experiments.UseCase
getHyperrectangle(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.HyperrectangleSet: Returns a hyperrectangles of the set
getiBit(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Condition
getiCondition(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.Rule
getiCondition(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule
getID() - Method in class keel.Algorithms.Decision_Trees.M5.Association: Gets the numeric ID of the Association.
getID() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation: returns the unique ID (either the one used in creating this instance or the automatically generated one)
getID() - Method in class keel.Algorithms.SVM.SMO.core.Tag: Gets the numeric ID of the Tag.
getID() - Method in class keel.Algorithms.SVM.SMO.core.TechnicalInformation: returns the unique ID (either the one used in creating this instance or the automatically generated one)
getId() - Method in class keel.GraphInterKeel.experiments.Graph: Gets the id of this graph
getId() - Method in class keel.GraphInterKeel.experiments.Node: Gets the id of the node
getIdAttr() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyAttribute: It returns the ID of the attribute being considered
getIdAttr() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyAttribute: It returns the ID of the attribute being considered
getIdAttr() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyAttribute: It returns the ID of the attribute being considered
getIDAttribute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Item: It returns the ID of the attribute involved in the item
getIDAttribute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Item: It returns the ID of the attribute involved in the item
getIDAttribute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Item: It returns the ID of the attribute involved in the item
getIDAttribute() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Item: It returns the ID of the attribute involved in the item
getIdentifier() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Gets the identifier of the node
getIdentifier() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Node: Gets the identifier of the node
getIdentifier() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Register: Gets the identifier of the register itself
getIdentifier() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode: Gets the identifier of the node
getIDLabel() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Item: It returns the ID of the label involved in the item
getIDLabel() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Item: It returns the ID of the label involved in the item
getIDLabel() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Item: It returns the ID of the label involved in the item
getIDLabel() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Item: It returns the ID of the label involved in the item
getIdOfAntecedents() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AssociationRule: It indicates the attributes which act as antecedents within an association rule
getIdOfConsequents() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AssociationRule: It indicates the attributes which act as consequents within an association rule
getIdRbf() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbf: Gets the id of the neuron
getIDsOfAllAttributeValues() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It returns suitable recasted IDs to recognize later each value belonging to an attribute
getIDsOfAllAttributeValues() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: It returns suitable recasted IDs to recognize later each value belonging to an attribute
getIDsOfNominalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It returns the IDs of the nominal attributes
getIDsOfNominalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It returns the IDs of the nominal attributes
getIDsOfNominalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: It returns the IDs of the nominal attributes
getIDsOfNominalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: It returns the IDs of the nominal attributes
getIDsOfNumericAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It returns the IDs of the numeric attributes
getIDsOfNumericAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It returns the IDs of the numeric attributes
getIDsOfNumericAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: It returns the IDs of the numeric attributes
getIDsOfNumericAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: It returns the IDs of the numeric attributes
getIDStr() - Method in class keel.Algorithms.SVM.SMO.core.Tag: Gets the string ID of the Tag.
getIfSeed() - Method in class keel.GraphInterKeel.experiments.Parameters: Checks if the algorithm need seed
GetImage() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
GetImage() - Static method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
GetImage() - Static method in class keel.Dataset.SimpleCharStream
getImagEigenvalues() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.EigenvalueDecomposition: Return the imaginary parts of the eigenvalues
getimax() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Returns the maximum values of each input attribute.
getimax() - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Returns the maximum values of each input attribute.
getimax() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Returns the maximum values of each input attribute.
getimax() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Returns the maximum values of each input attribute.
getimax() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Returns the maximum values of each input attribute.
getimax() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Returns the maximum values of each input attribute.
getimax() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Returns the maximum values of each input attribute.
getImaximum() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: Returns maximum value for each variable.
getImaximum() - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Returns maximum value for each variable.
getimin() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Returns the minimum values of each input attribute.
getimin() - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Returns the minimum values of each input attribute.
getimin() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Returns the minimum values of each input attribute.
getimin() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Returns the minimum values of each input attribute.
getimin() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Returns the minimum values of each input attribute.
getimin() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Returns the minimum values of each input attribute.
getimin() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Returns the minimum values of each input attribute.
getIminimum() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: Returns maximum value for each variable.
getIminimum() - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Returns maximum value for each variable.
getImpurities() - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode
getImpurityFunction() - Method in class keel.Algorithms.Decision_Trees.CART.CART: It returns the impurity function
getImpurityLevel() - Method in class keel.Algorithms.Hyperrectangles.INNER.Rule: Returns the impurity level of a rule
getIndex() - Method in class keel.Algorithms.Decision_Trees.ID3.Attribute: Returns the index of the attribute.
getIndex() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Attribute: Returns the index of the attribute.
getIndex() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeValue: This method return the index of the node
getIndex() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.SingleIndex: Gets the selected index
getIndex() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask: Returns the position pointed by the cursors.
getIndex() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Returns the position pointed by the cursors.
getIndex(StreamTokenizer) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Gets index, checking for a premature and of line.
getIndex() - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Informs of the index of the prototype.
getIndex(IAttribute) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IMetadata: Get index of given attribute in this specification
getIndex(String) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IMetadata: Get index of given attribute in this specification
getIndex(IAttribute) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.Metadata: Get index of given attribute in this specification
getIndex(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.Metadata: Get index of given attribute in this specification
getIndex() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputNeuron: Returns the index associated to this neuron
getIndex() - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbfn: Returns the list on index of the net neurons.
getIndex() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn: Returns the list on index of the net neurons.
getIndex() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn: Returns the list on index of the net neurons.
getIndex() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn: Returns the list on index of the net neurons.
getIndex() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn: Returns the list on index of the net neurons.
getIndex() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn: Returns the list on index of the net neurons.
getIndex() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getIndex(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getIndex() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getIndex(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getIndex() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getIndex(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getIndex() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getIndex(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getIndex() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getIndex(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getIndex() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getIndex(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getIndex() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getIndex(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getIndex() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getIndex(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getIndex() - Method in class keel.Algorithms.Rule_Learning.ART.Attribute: Returns the index of the attribute.
getIndex() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask: Returns the position pointed by the cursors.
getIndex() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Returns the position pointed by the cursors.
getIndex() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Attribute: Returns the index of the attribute.
getIndex() - Method in class keel.Algorithms.Rule_Learning.PART.Mask: Returns the position pointed by the cursors.
getIndex() - Method in class keel.Algorithms.Rule_Learning.Ripper.Mask: Returns the position pointed by the cursors.
getIndex() - Method in class keel.Algorithms.Rule_Learning.Slipper.Mask: Returns the position pointed by the cursors.
getIndex() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Informs of the index of the prototype.
getIndexes() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Returns the list on index of the net neurons.
getIndexOfAntecedentGenes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome: It indicates the genes which act as antecedents within a chromosome
getIndexOfConsequentGenes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome: It indicates the genes which act as consequents within a chromosome
getIndexOfInvolvedGenes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome: It indicates the genes which are involved to form an association rule later
getIndexOfNotInvolvedGenes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome: It indicates the genes which are excluded to form an association rule later
getIndice() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Condicion: Returns the attribute id.
getIndice() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Condicion: Returns the attribute id.
getIndice() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Condicion: Returns the attribute id.
getIndice() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Condicion: Returns the attribute id.
getIndiceGini() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Returns the the Gini index.
getIndiv(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Returns the indicated individual of the population
getIndiv(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Population: Returns the indicated individual of the population
getIndivCovered(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Returns the position i of the array cubre
getIndivCrom() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual: Returns the hole Chromosome
getIndivCrom(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population: Returns de hole cromosoma of the selected individual
getIndivCrom() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual: Returns the hole Chromosome
getIndivCrom(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population: Returns de hole cromosoma of the selected individual
getIndivCrom() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndCAN: Returns the Chromosome
getIndivCrom() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndDNF: Returns the Chromosome
getIndivCrom() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual: Returns the hole Chromosome
getIndivCrom(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population: Returns de hole cromosoma of the selected individual
getIndivCromCAN(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Returns de hole chromosome of the selected individual of the main population
getIndivCromCAN() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndCAN: Returns the Chromosome
getIndivCromCAN() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndDNF: Returns the Chromosome
getIndivCromCAN() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Returns the Canonical Chromosome
getIndivCromCAN(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Returns de hole cromosoma of the selected individual
getIndivCromCAN() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndCAN: Returns the indicated Chromosome
getIndivCromCAN() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndDNF: Returns the indicated Chromosome
getIndivCromCAN() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Returns the Canonical Chromosome
getIndivCromCAN(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Population: Returns de hole cromosoma of the selected individual
getIndivCromCAN(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Returns de hole chromosome of the selected individual
getIndivCromCAN() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndCAN: Returns the Chromosome
getIndivCromCAN() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndDNF: Returns the Chromosome
getIndivCromCAN() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual: Returns the Canonical Chromosome
getIndivCromCAN(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Population: Returns de hole cromosoma of the selected individual
getIndivCromDNF(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Returns de hole chromosome of the selected individual of the main population
getIndivCromDNF() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndCAN: Returns the Chromosome
getIndivCromDNF() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndDNF: Returns the Chromosome
getIndivCromDNF() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Returns the DNF Chromosome
getIndivCromDNF(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Returns de hole cromosoma of the selected individual
getIndivCromDNF() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndCAN: Returns the indicated Chromosome
getIndivCromDNF() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndDNF: Returns the indicated Chromosome
getIndivCromDNF() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Returns the DNF Chromosome
getIndivCromDNF(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Population: Returns de hole cromosoma of the selected individual
getIndivCromDNF(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Returns de hole chromosome of the selected individual
getIndivCromDNF() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndCAN: Returns the Chromosome
getIndivCromDNF() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndDNF: Returns the Chromosome
getIndivCromDNF() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual: Returns the DNF Chromosome
getIndivCromDNF(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Population: Returns de hole cromosoma of the selected individual
getIndivCubre(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Returns if the example number "pos" is covered by this individual
getIndivDensity(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Gets the density of the individual indicated
getIndivDom() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Returns if the individual is or not dominated by other individuals
getIndivDom(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Gets if the individual in the position is or not dominated
getIndivEvaluated() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual: Returns if the individual has been evaluated
getIndivEvaluated(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population: Returns if the individual of the population has been evaluated
getIndivEvaluated() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Returns if the individual has been evaluated
getIndivEvaluated(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Returns if the individual of the population has been evaluated
getIndivEvaluated() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual: Returns if the individual has been evaluated
getIndivEvaluated(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population: Returns if the individual of the population has been evaluated
getIndivEvaluated() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Returns if the individual has been evaluated
getIndivEvaluated(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Population: Returns if the individual of the population has been evaluated
getIndivEvaluated() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual: Returns if the individual has been evaluated
getIndivEvaluated(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population: Returns if the individual of the population has been evaluated
getIndivEvaluated() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual: Returns if the individual has been evaluated
getIndivEvaluated(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Population: Returns if the individual of the population has been evaluated
getIndivFitness() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Returns the fitness of the individual
getIndivFitness(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Returns fitness of the indicated inidividual of the population
getIndivFitness() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual: Returns the fitness of the individual
getIndivFitness(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Population: Returns fitness of the indicated inidividual of the population
getIndivNameClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population: Returns the class name of the individual of the population
getIndivNameClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population: Returns the class name of the individual of the population
getIndivNameClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population: Returns the class name of the individual of the population
getIndivNumClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population: Returns the class number of the individual of the population
getIndivNumClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population: Returns the class number of the individual of the population
getIndivNumClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population: Returns the class number of the individual of the population
getIndivNvar(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population: Returns the number of variables of the indicated individual (including the consequent)
getIndivNvar(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population: Returns the number of variables of the indicated individual (including the consequent)
getIndivNvar(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population: Returns the number of variables of the indicated individual (including the consequent)
getIndivOSup() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Returns the original support of the individual
getIndivPerf() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual: Returns the fitness of the individual
getIndivPerf(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population: Returns fitness of the indicated inidividual of the population
getIndivPerf() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual: Returns the fitness of the individual
getIndivPerf(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population: Returns fitness of the indicated inidividual of the population
getIndivPerf() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual: Returns the fitness of the individual
getIndivPerf(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population: Returns fitness of the indicated inidividual of the population
getIndivRawFit(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Gets the raw fitness of the individual indicated
getIndivSize() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual: Returns the size of the Chromosome
getIndivSize() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual: Returns the size of the Chromosome
getIndivSize() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual: Returns the size of the Chromosome
getIndivStrength(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Gets the strenght of the individual indicated
getIndivTotalClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population: Returns the number of examples of the DB belonging to the class of the individual
getIndivTotalClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population: Returns the number of examples of the DB belonging to the class of the individual
getIndivTotalClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population: Returns the number of examples of the DB belonging to the class of the individual
getInf() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets
getInfoGain() - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Returns information gain for the generated cut.
getInfoGain() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Returns information gain for the generated cut.
getInfoGain() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Returns information gain for the generated cut.
getInfoGain() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Returns information gain for the generated cut.
getInfoGain() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Returns information gain for the generated cut.
getInfoGain() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Returns information gain for the generated cut.
getInfoGain() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Returns information gain for the generated cut.
getInfoGain() - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Returns information gain for the generated cut.
getInfoGain() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Returns information gain for the generated cut.
getInhabitants() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.KEELIRPropPlusWrapperClas: Access to population inhabitants.
getInhabitants() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.KEELIRPropPlusWrapperRegr: Access to population inhabitants.
getInhabitants() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Access to population inhabitants
getInicial() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
getInicial(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
getIniOp() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
getInitialAlphaInput() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Returns the initial alpha coeficient for the input weigths
getInitialAlphaOutput() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Returns the initial alpha coeficient for the input weigths
getInitialmaxnofneurons() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Returns the initial maximum number of neurons of this layer
getInitiatorNeuronTypes(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Returns an array of initiators of neurons of hibrid layers
getInitiatorNeuronTypes(int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Returns an array of initiators of neurons of hibrid layers
getInnerBorder() - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster: Get the inner border of the cluster
getInput(int) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Returns a specific input of the protoype.
getInput(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Returns a specific input of the protoype.
getInputAsNominal(int) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Return an input as nominal.
getInputAsNominal(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Return an input as nominal.
getInputAsString(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: It returns the input value of the example "pos" as string.
getInputAsString(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It returns the input value of the example "pos" as string
getInputAsString(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It returns the input value of the example "pos" as string
getInputAsString(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It returns the input value of the example "pos" as string
getInputAttribute(int) - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It returns the input attribute being int the position passed as an argument.
getInputAttribute(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It returns the input attribute being int the position passed as an argument.
getInputAttribute(int) - Static method in class keel.Dataset.Attributes: It returns the input attribute being int the position passed as an argument.
getInputAttribute(int) - Method in class keel.Dataset.InstanceAttributes: It returns the input attribute being int the position passed as an argument.
getInputAttributes() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It returns all the input attributes
getInputAttributes() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It does return all the input attributes
getInputAttributes() - Static method in class keel.Dataset.Attributes: It does return all the input attributes
getInputAttributes() - Method in class keel.Dataset.InstanceAttributes: It does return all the input attributes
getInputAttributesHeader() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It returns a String with all the input attributes definition in keel format.
getInputAttributesHeader() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It does return a String with all the input attributes definition in keel format.
getInputAttributesHeader() - Static method in class keel.Dataset.Attributes: It does return a String with all the input attributes definition in keel format.
getInputAttributesHeader() - Method in class keel.Dataset.InstanceAttributes: It does return a String with all the input attributes definition in keel format.
getInputFile(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.parseParameters: It returns the input file of the specified index
getInputFile(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.parseParameters: It returns the input file of the specified index
getInputFile(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.parseParameters: It returns the input file of the specified index
getInputFile(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.parseParameters: It returns the input file of the specified index
getInputFile(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.parseParameters: It returns the input file of the specified index
getInputFile(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.parseParameters: It returns the input file of the specified index
getInputFile(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.parseParameters: It returns the input file of the specified index
getInputFile(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.parseParameters: It returns the input file of the specified index
getInputFile(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.parseParameters: It returns the input file of the specified index
getInputFile(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.parseParameters: It returns the input file of the specified index
getInputFile(int) - Method in class keel.Algorithms.Decision_Trees.Target.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.parseParameters: It returns the input file of the specified index
getInputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.parseParameters: It returns the input file of the specified index
getInputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.parseParameters: It returns the input file of the specified index
getInputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.ParametersParser: It returns the input file of the specified index
getInputFile(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.parseParameters: It returns the input file of the specified index
getInputFile(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.parseParameters: obtains the input file of index pos
getInputFile(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.PSO_Learning.CPSO.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Rule_Learning.LEM1.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Rule_Learning.LEM2.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Rule_Learning.PART.parseParameters: It returns the input file of the specified index
getInputFile(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Rule_Learning.Ritio.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Rule_Learning.Rules6.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Rule_Learning.SRI.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.SVM.C_SVM.parseParameters: obtains the input file of index pos
getInputFile(int) - Method in class keel.Algorithms.SVM.EPSILON_SVR.parseParameters: obtains the input file of index pos
getInputFile(int) - Method in class keel.Algorithms.SVM.NU_SVM.parseParameters: obtains the input file of index pos
getInputFile(int) - Method in class keel.Algorithms.SVM.NU_SVR.parseParameters: obtains the input file of index pos
getInputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.parseParameters: It returns the input file in the position "pos"
getInputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.parseParameters: It returns the input file in the position "pos"
getInputFiles() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.parseParameters: It returns the input files
getInputFiles() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.parseParameters: It returns the input files
getInputFiles() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.parseParameters: It returns the input files
getInputFiles() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.parseParameters: It returns the input files
getInputFiles() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.parseParameters: It returns the input files
getInputFiles() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.parseParameters: It returns the input files
getInputFiles() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.parseParameters: It returns the input files
getInputFiles() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.parseParameters: It returns the input files
getInputFiles() - Method in class keel.Algorithms.Decision_Trees.DT_GA.parseParameters: It returns the input files
getInputFiles() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.parseParameters: It returns the input files
getInputFiles() - Method in class keel.Algorithms.Decision_Trees.Target.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.parseParameters: It returns the input files
getInputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.parseParameters: It returns the input files
getInputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.parseParameters: It returns the input files
getInputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.ParametersParser: It returns the input files
getInputFiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.parseParameters: It returns the input files
getInputFiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.parseParameters: Obtains all the input files
getInputFiles() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.PSO_Learning.CPSO.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.PSO_Learning.REPSO.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Rule_Learning.C45Rules.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Rule_Learning.LEM1.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Rule_Learning.LEM2.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Rule_Learning.PART.parseParameters: It returns the input files
getInputFiles() - Method in class keel.Algorithms.Rule_Learning.Ripper.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Rule_Learning.Ritio.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Rule_Learning.Rules6.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Rule_Learning.Slipper.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Rule_Learning.SRI.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.SVM.C_SVM.parseParameters: Obtains all the input files
getInputFiles() - Method in class keel.Algorithms.SVM.EPSILON_SVR.parseParameters: Obtains all the input files
getInputFiles() - Method in class keel.Algorithms.SVM.NU_SVM.parseParameters: Obtains all the input files
getInputFiles() - Method in class keel.Algorithms.SVM.NU_SVR.parseParameters: Obtains all the input files
getInputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.parseParameters: It returns all the input files
getInputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.parseParameters: It returns all the input files
getInputFormat() - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Gets the currently set inputformat instances.
getInputFormat() - Method in class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter: Returns the format of the input instances.
getInputHeader() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It returns an String with the @inputs in keel format.
getInputHeader() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It does return an String with the @inputs in keel format.
getInputHeader() - Static method in class keel.Dataset.Attributes: It does return an String with the @inputs in keel format.
getInputHeader() - Method in class keel.Dataset.InstanceAttributes: It does return an String with the @inputs in keel format.
getInputInterval() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.problem.IProblem: Returns the input interval of normalized data
getInputInterval() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Returns the input interval of normalized data
getInputLayer() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Returns the input layer of this neural net
getInputLayer() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuralNet: Returns the input layer of this neural net
getInputMissingValues() - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: Get Input Missing Values
getInputMissingValues(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: Get Input Missing Values
getInputMissingValues() - Method in class keel.Dataset.Instance: Get Input Missing Values
getInputMissingValues(int) - Method in class keel.Dataset.Instance: Get Input Missing Values
getInputNominalValue(int, int) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: Returns the value of a nominal input attribute of an instance in the instanceSet.
getInputNominalValue(int, int) - Method in class keel.Dataset.InstanceSet: Returns the value of a nominal input attribute of an instance in the instanceSet.
getInputNominalValues() - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: Get Input Nominal Values
getInputNominalValues(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: Get Input Nominal Values
getInputNominalValues() - Method in class keel.Dataset.Instance: Get Input Nominal Values
getInputNominalValues(int) - Method in class keel.Dataset.Instance: Get Input Nominal Values
getInputNominalValuesInt(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: It does return the input nominal value at the specified position.
getInputNominalValuesInt() - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: It does return all the input nominal values.
getInputNominalValuesInt(int) - Method in class keel.Dataset.Instance: It does return the input nominal value at the specified position.
getInputNominalValuesInt() - Method in class keel.Dataset.Instance: It does return all the input nominal values.
getInputNumAttributes() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It return the number of input attributes in the API
getInputNumAttributes() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It return the number of input attributes in the API
getInputNumAttributes() - Static method in class keel.Dataset.Attributes: It return the number of input attributes in the API
getInputNumAttributes() - Method in class keel.Dataset.InstanceAttributes: It return the number of input attributes in the API
getInputNumericValue(int, int) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: Returns the value of an integer or a real input attribute of an instance in the instanceSet.
getInputNumericValue(int, int) - Method in class keel.Dataset.InstanceSet: Returns the value of an integer or a real input attribute of an instance in the instanceSet.
getInputRealValues() - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: Get Input Real Values
getInputRealValues(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: Get Input Real Values
getInputRealValues() - Method in class keel.Dataset.Instance: Get Input Real Values
getInputRealValues(int) - Method in class keel.Dataset.Instance: Get Input Real Values
getInputs() - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Returns the inputs of the protoype.
getInputs(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Returns the inputs of an specified observation
getInPuts() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Returns the number of in-put variables
getInputs() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Returns the inputs of the protoype.
getInputs() - Method in class keel.GraphInterKeel.datacf.util.Dataset: Return a vector that contains input variables
getInputs() - Method in class keel.GraphInterKeel.experiments.Multiplexor: Gets input
getInputStringIndex() - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Returns an array containing the indices of all string attributes in the input format.
getInstaceSet() - Method in class keel.Algorithms.Neural_Networks.net.OpenDataset: Returns the InstanceSet associated
getInstance(StreamTokenizer, boolean) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Reads a single instance using the tokenizer and appends it to the dataset.
getInstance(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.instanceSet
getInstance(StreamTokenizer, boolean) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Reads a single instance using the tokenizer and appends it to the dataset.
getInstance() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceParser: It returns an instance
getInstance(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: Gets the instance located at the cursor position.
getInstance() - Method in class keel.Dataset.InstanceParser: It returns an instance
getInstance(int) - Method in class keel.Dataset.InstanceSet: Gets the instance located at the cursor position.
getInstanceFull(StreamTokenizer, boolean) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Reads a single instance using the tokenizer and appends it to the dataset.
getInstanceFull(StreamTokenizer, boolean) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Reads a single instance using the tokenizer and appends it to the dataset.
getInstanceInit(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.instanceSet
getInstanceInit(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper
getInstanceInit(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper
getInstances() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Windowing
getInstances() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Windowing
getInstances() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.PartitionScheme: It returns all the original instances
getInstances() - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.PartitionScheme: It returns all the original instances
getInstances() - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.PartitionScheme: It returns all the original instances
getInstances() - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.PartitionScheme: It returns all the original instances
getInstances() - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.PartitionScheme: It returns all the original instances
getInstances() - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
getInstances() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: It returns all the instances of the class.
getInstances() - Method in class keel.Dataset.InstanceSet: It returns all the instances of the class.
getInstances() - Method in class keel.GraphInterKeel.datacf.partitionData.PartitioningSchemes.DistanceBased_best: It returns all the original instances
getInstances_by_index(int[]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff: Convert from a lisf of index to a list of Instances from m_Data
getInstancesByClass() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
getInstanceSet() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Returns the instance set
getInstanceSet() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Returns the instances set.
getInstanceSet() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Returns the instance set
getInstanceSet() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Returns the instance set
getInstancesInto(double[][], int, int[], double[], int, int, int) - Method in class keel.Algorithms.Discretizers.UCPD.UCPD: It computes the indexes of instances that fall into the interval selected
getInstancesOfIteration() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.instanceSet
getInstanceSparse(StreamTokenizer, boolean) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Reads a single instance using the tokenizer and appends it to the dataset.
getInstanceSparse(StreamTokenizer, boolean) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Reads a single instance using the tokenizer and appends it to the dataset.
getInstancesSizeByClass(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
getInt(int) - Method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Parses the parameter with the index given and returns it as integer
getInt(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Parses as Integer the parameter with the given index
getInteger(int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Globals.Rand: Returns a random int between [uLow,uHigh]
getInteger(int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Rand: Returns a random long between [uLow,uHigh]
GetInteger1() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
GetInteger1() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
GetInteger1() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
GetInteger2() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
GetInteger2() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
GetInteger2() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
GetInteger3() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
GetInteger3() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
GetInteger3() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
getIntercept() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the regression intercept (Beta0)
getInterestingness() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.AssociationRule: It returns the interest measure of an association rule
getInterestingness() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.AssociationRule: It returns the interest measure of an association rule
getInterestingness() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.AssociationRule: It returns the interest measure of an association rule
getInterestingness() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.AssociationRule: It returns the interest measure of an association rule
getInternalCacheSize() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Gets the size of the internal cache
getInterQuartileRange() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Histogram: Returns the interquartile range.
getInterval(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.DataB
getInterval(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.DataB
getInterval(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.DiscreteDataset: It returns the interval of an attribute
getInvert() - Method in class keel.Algorithms.Decision_Trees.M5.Interval: Gets whether the range sense is inverted, i.e. all except the values included by the range string are selected.
getInvert() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Range: Gets whether the range sense is inverted, i.e. all except the values included by the range string are selected.
getInvolvedRule(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypePitts: This method returns a rule in the rule base.
getIR(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Returns the Imbalanced Rate for the class given.
getIS() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Returns the Instance set stored.
getIS() - Static method in class keel.Algorithms.RST_Learning.KNNClassifier: Get a vector with the instances currently selected
getIsEvaluated() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
getIsEvaluated() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
getIsPositiveInterval() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene: It returns if a gene treats a positive or negative interval
getIsPositiveInterval() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene: It returns if a gene treats a positive or negative interval
getIsPositiveInterval() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene: It returns if a gene treats a positive or negative interval
getItem() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Item: Returns a copy of the values of the item.
getItems() - Method in class keel.Algorithms.Discretizers.UCPD.Itemset: It returns the items array of this itemset
getIterationsSinceBest() - Static method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.timerEvolutionStats
getIterationsSinceBest() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Statistics
getIterationsSinceBest() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Statistics
getizd() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.fuzzy
getizd() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.fuzzy
getJarName() - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription: Gets the JAR name (i.e. the name with the extesion ".jar") of the active layer
getJarName(int) - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription: Gets the JAR name at the indicated layer
getJTable() - Method in class keel.GraphInterKeel.datacf.editData.EditVariablePanel: Gets the current JTable
getJTable() - Method in class keel.GraphInterKeel.statistical.ExcelAdapter: Public Accessor methods for the Table on which this adapter acts.
getJTable1() - Method in class keel.GraphInterKeel.datacf.editData.EditDataPanel: Get JTable of the dataSet
getK() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: Returns the value of the parameter K.
getK() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.ENN: Returns the number k of the NN used.
getK() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
getK() - Static method in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN: Returns the current value of K.
getK() - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Returns the current value of K.
getK() - Method in class keel.GraphInterKeel.datacf.partitionData.KFoldOptionsJDialog: Gets the number of folds
getKavlr() - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Gets the matrix with the positions of non-missing values
getKernel() - Method in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: Returns the kernel to use
getKernel() - Method in class keel.Algorithms.SVM.SMO.SMO: Returns the kernel to use
getKernel() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Gets the kernel to use.
getKernel() - Method in class keel.Algorithms.SVM.SMO.SVMreg: Returns the kernel to use
getKernelEvaluations() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: returns the number of kernel evaluations
getKey(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Score: Returns the value at a given position of the vector (inverse method of findKey).
getKey() - Method in class keel.Algorithms.Rule_Learning.Ripper.Trio: Returns the attribute's value.
getKey(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Score: Returns the value at a given position of the vector (inverse method of findKey).
getKey() - Method in class keel.Algorithms.Rule_Learning.Slipper.Trio: Returns the attribute's value.
getKind() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset: Gets the kind of the dataset we are considering (training, reference, test)
getKind() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset: Gets the kind of the dataset we are considering (training, reference, test)
getKmisr() - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Gets the matrix with the positions of missing values
getKValue() - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Get the value of K.
getKValue() - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Get the value of K.
getKValue() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Get the value of K.
getL() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.CholeskyDecomposition: Return triangular factor.
getL() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Returns the L part of the matrix.
getL() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.LUDecomposition: Return lower triangular factor
getL() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Gene
getL() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Gene
getL() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene
getLabel() - Method in class keel.Algorithms.Instance_Generation.GMCA.Cluster: Gets the label of all prototypes of the cluster.
getLabel() - Method in class keel.Algorithms.Instance_Generation.VQ.Cluster: Get the class of the cluster.
getLabel() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyRegion: It returns the label associated with a fuzzy region
getLabel() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyRegion: It returns the label associated with a fuzzy region
getLabel() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyRegion: It returns the label associated with a fuzzy region
getLabel() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyRegion: It returns the label associated with a fuzzy region
getLabel() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.Item: It returns the label associated with an item
getLabel() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.Item: It returns the label associated with an item
getLagrangeMultipliers() - Method in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: This method returns the Lagrange Multipliers obtained for each instance.
getLambda() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Gets the lambda constant used in the string kernel
getLaplace() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.Rule: It returns the Laplace accuracy of the rule
getLaplaceAcc() - Method in class keel.Algorithms.Hyperrectangles.RISE.Rule: Returns the Laplace accuracy of the rule
getLastAV() - Method in class keel.Algorithms.Rule_Learning.Rules6.Regla: Returns the last pair attribute - values from the antecedents list.
getLastAV() - Method in class keel.Algorithms.Rule_Learning.SRI.Regla: Returns the last pair attribute - values from the antecedents list.
getLastChangeEval() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Population: Return the number of the evaluation with the last change
getLastElement(short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Gets the last element in the given item set, or '0' if the itemset is empty.
getLastError() - Method in interface keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IOptimizableFunc: Last error of the model
getLastError() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.MSEOptimizablePUNeuralNetClassifier: Last error of the model
getLastError() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.MSEOptimizableSigmNeuralNetClassifier: Last error of the model
getLastError() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.MSEOptimizablePUNeuralNetRegressor: Last error of the model
getLastError() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.MSEOptimizableSigmNeuralNetRegressor: Last error of the model
getLastIterations() - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Gets the last number of iterations performed
getLastRule() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.ruleSet: It returns the last rule (it is supossed to be the one with less strength)
getLastRule() - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleSet: Returns the last rule(normally the one with best weight)
getLastRule() - Method in class keel.Algorithms.Rule_Learning.AQ.ruleSet: It returns the last rule (normally, the one with best weight)
getLastRule() - Method in class keel.Algorithms.Rule_Learning.CN2.ruleSet: It returns the last rule (normally, the one with best weight)
getLastToken(StreamTokenizer, boolean) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Gets token and checks if its end of line.
getLeafs() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Gets the number of leaf nodes from this node and its descendants
getLeafs() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode: Gets the number of leaf nodes from this node and its descendants
getLeastFrequentClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_aggregated
getLeft() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Gets the left descendant of the node, if it is not a leaf node
getLeft() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode: Gets the left descendant of the node, if it is not a leaf node
getLeft() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Intervals
getLeft() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Interval: It returns the left bound of an interval
getLeft() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Intervals
getLeftChild() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode
getLeftSon() - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode: It gets the left son of the current node
getLeftTail() - Static method in class keel.GraphInterKeel.statistical.tests.WilcoxonDistribution: Returns left-tailed p-value of the last comparison
getLenghtElite() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Methods to get the lenght of the elite population
getLenghtPop() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Methods to get the lenght of the population
getLenghtPop() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Methods to get the lenght of the population
getLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
getLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
getLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierADI
getLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierGABIL
getLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierUBR
getLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
getLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
getLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
getLength() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule
getLength() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
getLengthAnt() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
getLengthAnt() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
getLengthAnt() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule
getLengthAntecedent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
getLengthAntecedent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
getLengthAntecedent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
getLengthConsequent() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
getLengthPopulation() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Gets the lenght of the population
getLengthRule() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
getLengthRule() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
getLevel() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Obtains the level of this rule; 1 for general rules, 2 for specific rules
getLevel() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Obtains the level of this rule; 1 for general rules, 2 for specific rules
getLift() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams
getLift() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AssociationRule: It returns the lift of an association rule
getLift() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule: It returns the lift of an association rule
getLift() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
getLift() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
getLift() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule: It returns the lift of an association rule
getLift() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule: It returns the lift of an association rule
getLift() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
getLift() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
getLift() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
getLift() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
getLift() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
getLift() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
getLimitHigh() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.LimitRoulette
getLimitLow() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.LimitRoulette
getLimits(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Rule: Returns the bounds of the attribute in the rule
getLimits(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Rule: Returns the bounds of the attribute in the rule
getLine() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream: Deprecated.
getLine() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceParser: This method reads one valid line of the file.
getLine() - Static method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream: Deprecated.
getLine() - Method in class keel.Dataset.InstanceParser: This method reads one valid line of the file.
getLine() - Static method in class keel.Dataset.SimpleCharStream: Deprecated.
getLink(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron: Returns the link with the neuron specified (0 is bias neuron)
getLinks() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron: Returns the links array
getListFilas() - Method in class keel.Algorithms.Rule_Learning.LEM2.Atributo_valor
getListValues() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Gets for each attribute the sorted list of its possible values
getListValues() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Gets for each attribute the ordered list of the possible values
getListValues() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Gets for each attribute the sorted list of its possible values
getLiteralsMatrix() - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Literals: It returns the matrix of literals
getLocalHierarchicalMeasure() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Obtains the local hierarchical measure associated to this rule, computed with the compute hierarchical measures procedure
getLocator(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: Returns the AttributeLocator at the given index.
getLocatorIndices() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: Returns the indices of the AttributeLocator objects.
getLogger() - Method in exception keel.Algorithms.Rule_Learning.Swap1.DatasetException: Gets the vector with the errors.
getLogger() - Method in exception keel.Dataset.DatasetException: Gets the vector with the errors.
getLost(int, int) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Calculate: Returns if the value of the gen of an example is a lost value or not lost = max value of the variable + 1
getLost(TableVar, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableDat: Returns if the value of the gen of an example is a lost value or not lost = max value of the variable + 1
getLost(int, int) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Calculate: Returns if the value of the gen of an example is a lost value or not lost = max value of the variable + 1
getLost(TableVar, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableDat: Returns if the value of the gen of an example is a lost value or not lost = max value of the variable + 1
getLost(int, int) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Calculate: Returns if the value of the gen of an example is a lost value or not lost = max value of the variable + 1
getLost(TableVar, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableDat: Returns if the value of the gen of an example is a lost value or not lost = max value of the variable + 1
getLower_aproximation(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
getLowerAllele() - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Attribute: It returns the lower allele.
getLowerAllele() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep: Returns the lower real value of the representation
getLowerAllele() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep
getLowerAllele() - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute: It returns the lower allele.
getLowerAllele() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: Returns the lower real value of the representation
getLowerAllele() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: Returns the lower real value of the representation
getLowerAllele(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: Returns the value of the alelle
getLowerAllele() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep
getLowerAndUpperValues(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Hyperrectangle: Returns the positions of the minimum and maximum value of the attribute id in the hyperrectangle
getLowerBound() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Condition: It returns the lower bound
getLowerBound() - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.Gcvfctn: Gets the lower bound of the evaluation
getLowerBound(int) - Method in interface keel.Algorithms.Preprocess.Missing_Values.EM.util.MultivariateFunction: get lower bound of argument n
getLowerBound() - Method in interface keel.Algorithms.Preprocess.Missing_Values.EM.util.UnivariateFunction
getLowerBound() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene: It returns the lower bound of the interval stored in a gene
getLowerBound() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene: It returns the lower bound of the interval stored in a gene
getLowerBound() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene: It returns the lower bound of the interval stored in a gene
getLowerNumericBound() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Returns the lower bound of a numeric attribute.
getLowerQuartile() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Histogram: Returns the lower quartile.
getLowerrealBound() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Returns the lower bound of a real attribute.
getLowerValues() - Method in class keel.Algorithms.Hyperrectangles.EACH.Hyperrectangle
getLSearch() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Methods to get if local search must be performed
getLSup() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.QualityMeasures: Method to return the value of the local support
getMacroClFitness() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Returns the fitness of the "macro-classifier"
getMacroClFitness() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters: Returns the fitness of the current macro-classifier.
getMacroClSum() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Returns the number of macro classifiers in the set.
getMacroClSum() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Returns the number of macro classifiers in the set.
getMajorOutputClass() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Node: Gets the output class of the majority of the instances.
getMark() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: It returns the mark of the rule.
getMark() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: It returns the mark of the rule
getMatrix() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

returns the internal matrix
getMatrix(int, int, int, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Get a submatrix.
getMatrix(int[], int[]) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Get a submatrix.
getMatrix(int, int, int[]) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Get a submatrix.
getMatrix(int[], int, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Get a submatrix.
getMatrixRulesXExamples() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Returns the matrix that stores the examples that fire the rules.
getMax(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Gets the maximum value of the variable as argument.
getMax(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It returns the maximum value of the given attribute
getMax(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It returns the maximum value of the given attribute
getMax(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It returns the maximum value of the given attribute
getMax(int, int) - Method in class keel.Algorithms.Complexity_Metrics.Statistics: It returns the maximum of the given attribute within the given class
getMax(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It returns the maximum value of the given attribute
getMax(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Gets the maximum value of the variable as argument.
getMax(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It returns the maximum value of the attribute specified
getMax() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute: Gets the maximum value for the attribute if it is not a nominal attribute
getMax() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute: Gets the maximum value for the attribute if it is not a nominal attribute
getMax(int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It returns the maximum value of the given attribute
getMax(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It returns the maximum value of the given attribute
getMax(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It returns the maximum value of the given attribute
getMax(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It returns the maximum value of the attribute "variable"
getMax(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It returns the maximum value of the attribute "variable"
getMax(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It returns the maximum value of the attribute "variable"
getMax(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It returns the maximum value of the attribute "variable"
getMax(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It returns the maximum value of the attribute "variable"
getMax(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It returns the maximum value of the attribute "variable"
getMax(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It returns the maximum value of the given attribute
getMax(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It returns the maximum value of the given attribute
getMax(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It returns the maximum value of the given attribute
getMax(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It returns the maximum value of the given attribute
getMax(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It returns the maximum value of the attribute "variable"
getMax(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It returns the maximum value of the attribute specified
getMax(double[], long) - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Returns the index where is the maximum in an array of doubles
getMax(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It returns the maximum value of the given attribute
getMax(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It returns the maximum value of the attribute specified
getmax() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.Interval
getmax() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.Interval
getmax() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.Interval
getmax() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.Interval
getmax() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.Interval
getmax() - Method in class keel.Algorithms.LQD.preprocess.Expert.interval
getmax() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.interval
getmax() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.interval
getmax() - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.interval
getMax() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Sample: Returns the maximum value of all samples.
getMax(int) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It returns the maximum value of the attribute "variable"
getMax(int) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It returns the maximum value of the attribute "variable"
getMax(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It returns the maximum value of the attribute specified
getMax(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Returns the maximum valid value for the variable "pos"
getMax() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeVar: Gets the maximum value for the variable
getMax(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Returns the maximum valid value for the variable "pos"
getMax() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeVar: Gets the maximum value for the variable
getMax(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Returns the maximum valid value for the variable "pos"
getMax() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeVar: Gets the maximum value for the variable
getMax(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It returns the upper bound of the variable <(p>
getMax(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It returns the upper bound of the variable
getMax(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It returns the upper bound of the variable
getMax(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: It returns the upper bound of the variable
getMax(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: It returns the upper bound of the variable
getMax(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: It returns the upper bound of the variable
getMax(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: It returns the upper bound of the variable
getMax(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: It returns the upper bound of the variable
getMax(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: It returns the upper bound of the variable
getMax(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: It returns the upper bound of the variable
getMax(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: It returns the upper bound of the variable
getMax(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: It returns the upper bound of the variable
getMax(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: It returns the upper bound of the variable
getMax(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: It returns the upper bound of the variable
getMax(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: It returns the upper bound of the variable
getMax(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: It returns the upper bound of the variable
getMax(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: It returns the upper bound of the variable
getMax(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: It returns the upper bound of the variable
getMax_attr() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene
getMax_attr() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene
getMaxAccuracy() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.populationWrapper
getMaxAttribute() - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It returns the maximum possible value in a integer or real attribute
getMaxAttribute() - Method in class keel.Dataset.Attribute: It returns the maximum possible value in a integer or real attribute
getMaxDepth() - Method in class keel.Algorithms.Decision_Trees.CART.CART: It returns the maximal depth
getMaxDepth() - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Get the maximum depth of trh tree, 0 for unlimited.
getMaxDepth() - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Get the maximum depth of trh tree, 0 for unlimited.
getMaxDepth() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Get the maximum depth of trh tree, 0 for unlimited.
getMaxim(double[], long) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Returns the index where is the maximum of a double array If there are more than one. returns one of them * @param num array of doubles given.
getMaximo(double[], long) - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Returns the index where is the maximum in an array of doubles
getMaximum(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.Classifier: public method to obtain the class which performs with maximum membership value for the given example .
getMaximum() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Returns an array with the maximum values of the in-put attributes
getMaximum(int[], long) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Returns the index where is the maximum of an array of integers If there are more than one, returns one of them
getMaximumDelta() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus: Returns the maximum delta value, that is, the maximum increment or step size of the corresponding coefficients
getMaximumDistance() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Returns the maximum distance between train data
getMaxInfoGain() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Antd
getMaxIterations() - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Gets the maximum number of allowed iterations
getMaxIts() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Get the value of MaxIts.
getMaxLabel() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Returns the maximum number of labels of all the variables
getMaxLabel() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Returns the maximum number of labels of all the variables
getMaxLabel() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Returns the maximum number of labels of all the variables
getMaxLabels() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Obtains the maximum number of labels this fuzzy antecedent can handle
getMaxLift() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams
getMaxLinksAdd() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Returns the maximum number of links to add in mutations
getMaxLinksDel() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Returns the maximum number of links to remove in mutations
getMaxNeuronsAdd() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Returns the maximum number of neurons to add in mutations
getMaxNeuronsDel() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Returns the maximum number of neurons to remove in mutations
getMaxnofneurons() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.ILayer: Returns the maximum number of neurons of this layer
getMaxnofneurons() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputLayer: Returns the maximum number of neurons of this layer
getMaxnofneurons() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Returns the maximum number of neurons of this layer
getMaxOfGenerations() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Get the maximum number of iterations for this algorithm
getMaxPayoff() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.DECEnvironment: Returns the environment maximum payoff
getMaxPayoff() - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Environment: Returns the environment maximum payoff
getMaxPayoff() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MPEnvironment: Returns the environment maximum payoff
getMaxPayoff() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MSEnvironment: Returns the environment maximum payoff
getMaxPayoff() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.PAREnvironment: Returns the environment maximum payoff
getMaxPayoff() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.POSEnvironment: Returns the environment maximum payoff
getMaxPayoff() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.SSFileEnvironment: Does return the environment maximum payoff
getMaxRange() - Method in class keel.Algorithms.Decision_Trees.C45.Attribute: Gets the bigger value of a continuous attribute.
getMaxRange() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Attribute: Gets the bigger value of a continuous attribute.
getMaxRange() - Method in class keel.Algorithms.Decision_Trees.ID3.Attribute: Gets the bigger value of a continuous attribute.
getMaxRange() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Attribute: Gets the bigger value of a continuous attribute.
getMaxRange() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Gets the bigger value of a continuous attribute.
getMaxRange() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA_Attribute: Gets the bigger value of a continuous attribute.
getMaxRange() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyAttribute: Gets the bigger value of a continuous attribute.
getMaxRange() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Attribute: Gets the bigger value of a continuous attribute.
getMaxRange() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Attribute: Gets the bigger value of a continuous attribute.
getMaxRange() - Method in class keel.Algorithms.Rule_Learning.ART.Attribute: Gets the bigger value of a continuous attribute.
getMaxRange() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyAttribute: Gets the bigger value of a continuous attribute.
getMaxRange() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyAttribute: Gets the bigger value of a continuous attribute.
getMaxRange() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Attribute: Gets the bigger value of a continuous attribute.
getMaxRange() - Method in class keel.Algorithms.Rule_Learning.PART.MyAttribute: Gets the bigger value of a continuous attribute.
getMaxSize() - Method in class keel.Algorithms.Rule_Learning.ART.TBAR
getMaxSubsequenceLength() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Returns the maximum length of the subsequence
getMaxSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams
getMaxVal() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Returns the maximum number of values of all the variables
getMaxVal() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Returns the maximum number of values of all the variables
getMaxVal() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Returns the maximum number of values of all the variables
getMaxValue(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It returns the maximum value of the attribute specified
getMaxValueOf(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Returns the maximum value of a specific variable
getMaxX() - Method in class keel.GraphInterKeel.experiments.Graph: Gets the maximum X value of the graph
getMaxY() - Method in class keel.GraphInterKeel.experiments.Graph: Gets the maximum Y value of the graph
getMDL(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule: Returns the Minimum Data Length of a dataset given a theory (this rule).
getMDL(MyDataset, int, int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule: Static version.
getMDL(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Ruleset: Returns the Minimum Data Length of a dataset given a theory (this ruleset).
getMDL(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Ruleset: Returns the Minimum Data Length of a dataset given a theory (this ruleset).
getMDL(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule: Returns the Minimum Data Length of a dataset given a theory (this rule).
getMDL(MyDataset, int, int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule: Static version.
getMDL(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Ruleset: Returns the Minimum Data Length of a dataset given a theory (this ruleset).
getMDL(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Ruleset: Returns the Minimum Data Length of a dataset given a theory (this ruleset).
getMDL(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Ruleset: Returns the Minimum Data Length of a dataset given a theory (this ruleset).
getMDL(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset: Returns the Minimum Data Length of a dataset given a theory (this ruleset).
getMDL(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.PART.Rule: Returns the Minimum Data Length of a dataset given a theory (this rule).
getMDL(MyDataset, int, int, int, int) - Static method in class keel.Algorithms.Rule_Learning.PART.Rule: Static version.
getMDL(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.PART.Ruleset: Returns the Minimum Data Length of a dataset given a theory (this ruleset).
getMDL(MyDataset) - Method in class keel.Algorithms.Rule_Learning.PART.Ruleset: Returns the Minimum Data Length of a dataset given a theory (this ruleset).
getMDL(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset: Returns the Minimum Data Length of a dataset given a theory (this ruleset).
getMDL(MyDataset) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset: Returns the Minimum Data Length of a dataset given a theory (this ruleset).
getMDL(MyDataset, Mask, Mask, IncrementalMask) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset: Returns the Minimum Data Length of a dataset given a theory (this ruleset).
getMean(int, int) - Method in class keel.Algorithms.Complexity_Metrics.Statistics: It returns the mean of the given attribute within the given class
getMean() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Sample: Returns the mean
getMean(double, double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Sample: Returns a Confidence Interval for the mean value with the given confidence and sigma.
getMean(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Sample: Returns a Confidence Interval for the mean value with the given confidence.
getMean() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.NormalDistribution: Returns the mean value of the distribution.
getMean() - Method in class keel.GraphInterKeel.statistical.tests.NormalDistribution: Get the mean of the distribution
getMeanSquares() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Sample: Returns the mean of the samples squares.
getMeanValue(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: Does return the mean value for that attribute.
getMeanValue(int) - Method in class keel.Dataset.Attribute: Does return the mean value for that attribute.
getMeasure(String) - Method in class keel.Algorithms.Decision_Trees.M5.M5: Returns the value of the named measure
getMeasure(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Returns the value of the named measure
getMeasure(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: Returns the value of the named measure
getMeasure(String) - Method in interface keel.Algorithms.SVM.SMO.core.AdditionalMeasureProducer: Returns the value of the named measure
getMeasure(String) - Method in class keel.Algorithms.SVM.SMO.SVMreg: Returns the value of the named measure
getMeasures() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Return the quality measure of the individual
getMeasureValue(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Gets the value of the quality measure in the position pos
getMedian() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Histogram: Returns the median.
getMedidas(Genetic) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Return the quality measures of the individual
getMedidas(Genetic, int, String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Get the measurements of a single rule
getMedidas() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual: Return the quality measure of the individual
getMedidas(int, String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Population: Get the measurements of a single rule
getMejor() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Poblacion
getMejor() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Poblacion: Returns the best solution obtained by the GA.
getMejorPosicion() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Returns the best position of the particle.
getMembershipFunctions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Gene: It returns the membership functions of a gene
getMembershipFunctions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Gene: It returns the membership functions of a gene
getMembershipOf(int, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.fuzzygCenter: Returns the membership degree of the instance for a given cluster
getMessage() - Method in exception keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParseException: This method has the standard behavior when this object has been created using the standard constructors.
getMessage() - Method in error keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.TokenMgrError: You can also modify the body of this method to customize your error messages.
getMessage() - Method in exception keel.Algorithms.Rule_Learning.Swap1.ParseException: This method has the standard behavior when this object has been created using the standard constructors.
getMessage() - Method in error keel.Algorithms.Rule_Learning.Swap1.TokenMgrError: You can also modify the body of this method to customize your error messages.
getMessage() - Method in exception keel.Dataset.ParseException: This method has the standard behavior when this object has been created using the standard constructors.
getMessage() - Method in error keel.Dataset.TokenMgrError: You can also modify the body of this method to customize your error messages.
getMetadata() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Returns the properties supplied for this attribute.
getMetadata() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Returns the properties supplied for this attribute.
getMetadata() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset: Access to this dataset specification
getMetadata() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IDataset: Access to this dataset specification
getMicroClFitness() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Returns the fitness of the "micro-classifier"
getMicroClFitness() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters: Returns the fitness of the current micro-classifier.
getMicroClSum() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Returns the number of micro classifiers in the set.
getMicroClSum() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Returns the number of micro classifiers in the set.
getMin(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Gets the minimum value of the variable as argument.
getMin(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It returns the minimum value of the given attribute
getMin(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It returns the minimum value of the given attribute
getMin(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It returns the minimum value of the given attribute
getMin(int, int) - Method in class keel.Algorithms.Complexity_Metrics.Statistics: It returns the minimum of the given attribute within the given class
getMin(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It returns the minimum value of the given attribute
getMin(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Gets the minimum value of the variable as argument.
getMin(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It returns the minimum value of the attribute specified
getMin() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute: Gets the minimum value for the attribute if it is not a nominal attribute
getMin() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute: Gets the minimum value for the attribute if it is not a nominal attribute
getMin(int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It returns the minimum value of the given attribute
getMin(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It returns the minimum value of the given attribute
getMin(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It returns the minimum value of the given attribute
getMin(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It returns the minimum value of the attribute "variable"
getMin(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It returns the minimum value of the attribute "variable"
getMin(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It returns the minimum value of the attribute "variable"
getMin(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It returns the minimum value of the attribute "variable"
getMin(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It returns the minimum value of the attribute "variable"
getMin(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It returns the minimum value of the attribute "variable"
getMin(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It returns the minimum value of the given attribute
getMin(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It returns the minimum value of the given attribute
getMin(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It returns the minimum value of the given attribute
getMin(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It returns the minimum value of the given attribute
getMin(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It returns the minimum value of the attribute "variable"
getMin(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It returns the minimum value of the given attribute
getMin(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It returns the minimum value of the attribute specified
getmin() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.Interval
getmin() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.Interval
getmin() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.Interval
getmin() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.Interval
getmin() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.Interval
getmin() - Method in class keel.Algorithms.LQD.preprocess.Expert.interval
getmin() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.interval
getmin() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.interval
getmin() - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.interval
getMin() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Sample: Returns the minimum value of all samples.
getMin(int) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It returns the minimum value of the attribute "variable"
getMin(int) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It returns the minimum value of the attribute "variable"
getMin(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It returns the minimum value of the attribute specified
getMin(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Returns the minimum valid value for the variable "pos"
getMin() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeVar: Gets the minimum value for the variable
getMin(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Returns the minimum valid value for the variable "pos"
getMin() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeVar: Gets the minimum value for the variable
getMin(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Returns the minimum valid value for the variable "pos"
getMin() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeVar: Gets the minimum value for the variable
getMin(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It returns the lower bound of the variable
getMin(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It returns the lower bound of the variable
getMin(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It returns the lower bound of the variable
getMin(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: It returns the lower bound of the variable
getMin(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: It returns the lower bound of the variable
getMin(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: It returns the lower bound of the variable
getMin(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: It returns the lower bound of the variable
getMin(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: It returns the lower bound of the variable
getMin(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: It returns the lower bound of the variable
getMin(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: It returns the lower bound of the variable
getMin(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: It returns the lower bound of the variable
getMin(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: It returns the lower bound of the variable
getMin(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: It returns the lower bound of the variable
getMin(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: It returns the lower bound of the variable
getMin(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: It returns the lower bound of the variable
getMin(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: It returns the lower bound of the variable
getMin(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: It returns the lower bound of the variable
getMin(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: It returns the lower bound of the variable
getMin_attr() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene
getMin_attr() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene
getMinAttribute() - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It returns the minimum possible value in a integer or real attribute
getMinAttribute() - Method in class keel.Dataset.Attribute: It returns the minimum possible value in a integer or real attribute
getMinCnf() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Gets the minimum confidence
getMinConf() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Methods to get the value for the minimum confidence of the rules to be generated
getMinConf() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Methods to get the value for the minimum confidence of the rules to be generated
getMinFC() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Individual: Function to return the minimum confidence of the individual
getMinFS() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Individual: Function to return the minimum support of the individual
getMinFunction() - Method in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
getMinFunction() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization: Get the minimal function value
getMinimum() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Returns an array with the minimum values of the in-put values
getMinimumDelta() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus: Returns the minimum delta value, that is, the minimum increment or step size of the corresponding coefficients
getMinLift() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams
getMinLinksAdd() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Returns the minimum number of links to add in mutations
getMinLinksDel() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Returns the minimum number of links to remove in mutations
getMinNeuronsAdd() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Returns the minimum number of neurons to add in mutations
getMinNeuronsDel() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Returns the minimum number of neurons to remove in mutations
getMinNo() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Gets the minimum total weight of the instances in a rule
getMinnofneurons() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Returns the minimum number of neurons of this layer
getMinNum() - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Get the value of MinNum.
getMinNum() - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Get the value of MinNum.
getMinNum() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Get the value of MinNum.
getMinPayoff() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.DECEnvironment: Returns the environment minimum payoff
getMinPayoff() - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Environment: Returns the environment minimum payoff
getMinPayoff() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MPEnvironment: Returns the environment minimum payoff
getMinPayoff() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MSEnvironment: Returns the environment minimum payoff
getMinPayoff() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.PAREnvironment: Returns the environment minimum payoff
getMinPayoff() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.POSEnvironment: Returns the environment minimum payoff
getMinPayoff() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.SSFileEnvironment: Does return the environment minimum payoff
getMinRange() - Method in class keel.Algorithms.Decision_Trees.C45.Attribute: Returns the minor value of a continuous attribute.
getMinRange() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Attribute: Returns the minor value of a continuous attribute.
getMinRange() - Method in class keel.Algorithms.Decision_Trees.ID3.Attribute: Returns the minor value of a continuous attribute.
getMinRange() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Attribute: Returns the minor value of a continuous attribute.
getMinRange() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Returns the minor value of a continuous attribute.
getMinRange() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA_Attribute: Returns the minor value of a continuous attribute.
getMinRange() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyAttribute: Returns the minor value of a continuous attribute.
getMinRange() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Attribute: Returns the minor value of a continuous attribute.
getMinRange() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Attribute: Returns the minor value of a continuous attribute.
getMinRange() - Method in class keel.Algorithms.Rule_Learning.ART.Attribute: Returns the minor value of a continuous attribute.
getMinRange() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyAttribute: Returns the minor value of a continuous attribute.
getMinRange() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyAttribute: Returns the minor value of a continuous attribute.
getMinRange() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Attribute: Returns the minor value of a continuous attribute.
getMinRange() - Method in class keel.Algorithms.Rule_Learning.PART.MyAttribute: Returns the minor value of a continuous attribute.
getMinSupp() - Method in class keel.Algorithms.Rule_Learning.ART.TBAR
getMinSupport() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Returns the minimum support threshold value in terms of a number records.
getMinSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams
getMinValue(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It returns the minimum value of the attribute specified
getMinValueOf(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Returns the minimum value of a specific variable
getMisses() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset: It returns the number of misses of the itemset against the training set.
getMisses() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset: It returns the number of misses of the itemset against the training set
getMissing(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It returns an array showing if the value of each attribute for the instance "pos" is missing (TRUE) or not (FALSE)
getMissing(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It returns an array showing if the value of each attribute for the instance "pos" is missing (TRUE) or not (FALSE)
getMissing(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It returns an array with boolean values stating the missing values for an individual
getMissing(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It returns an array showing if the value of each attribute for the instance "pos" is missing (TRUE) or not (FALSE)
getMissing(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It returns an array with boolean values stating the missing values for an individual
getMissing(int) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It returns an array showing if the value of each attribute for the instance "pos" is missing (TRUE) or not (FALSE)
getMissing(int) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It returns an array showing if the value of each attribute for the instance "pos" is missing (TRUE) or not (FALSE)
getMissing(int) - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It gets an array that indicates the possible missing values for an example
getMissing(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It returns an array indicating the position of the missing values on a specific example
getMissing(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It returns an array indicating the position of the missing values on a specific example
getMissing(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It returns an array indicating the position of the missing values on a specific example
getMissing(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: It returns an array indicating the position of the missing values on a specific example
getMissing(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: It returns an array indicating the position of the missing values on a specific example
getMissing(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: It returns an array indicating the position of the missing values on a specific example
getMissing(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: It returns an array indicating the position of the missing values on a specific example
getMissing(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: It returns an array indicating the position of the missing values on a specific example
getMissing(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: It returns an array indicating the position of the missing values on a specific example
getMissing(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: It returns an array indicating the position of the missing values on a specific example
getMissing(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: It returns an array indicating the position of the missing values on a specific example
getMissing(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: It returns an array indicating the position of the missing values on a specific example
getMissing(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: It returns an array indicating the position of the missing values on a specific example
getMissing(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: It returns an array indicating the position of the missing values on a specific example
getMissing(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: It returns an array indicating the position of the missing values on a specific example
getMissing(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: It returns an array indicating the position of the missing values on a specific example
getMissing(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: It returns an array indicating the position of the missing values on a specific example
getMissing(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: It returns an array indicating the position of the missing values on a specific example
getMissingValue() - Static method in class keel.Algorithms.Decision_Trees.C45.Itemset: Returns the missing value.
getMissingValue() - Static method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset: Returns the missing value.
getMissingValue() - Static method in class keel.Algorithms.Decision_Trees.ID3.Itemset: Returns the missing value.
getMissingValue() - Static method in class keel.Algorithms.Decision_Trees.SLIQ.Itemset: Returns the missing value.
getMissingValue() - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Returns the missing value.
getMissingValue() - Static method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset: Returns the missing value.
getMissingValue() - Static method in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset: Returns the missing value.
getMissingValue() - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset: Returns the missing value.
getMissingValue() - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset: Returns the missing value.
getMissingValue() - Static method in class keel.Algorithms.Rule_Learning.ART.Itemset: Returns the missing value.
getMissingValue() - Static method in class keel.Algorithms.Rule_Learning.C45Rules.Itemset: Returns the missing value.
getMissingValue() - Static method in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset: Returns the missing value.
getMissingValue() - Static method in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset: Returns the missing value.
getMissingValue() - Static method in class keel.Algorithms.Rule_Learning.PART.Itemset: Returns the missing value.
getMissingValue() - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset: Returns the missing value.
getMissingVector() - Method in class keel.GraphInterKeel.experiments.DataSet: Gets the missing partitions vector
getMistakeClass() - Method in class keel.Algorithms.Statistical_Tests.Shared.StatTest.InformationAboutClass: Returns the list of mistaken classes
getMistakeClassifications() - Method in class keel.Algorithms.Statistical_Tests.Shared.StatTest.InformationAboutClass: Returns the number of incorrect classifications done.
getMode() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Histogram: Returns the mode.
getModelResultFile() - Method in class keel.Algorithms.Decision_Trees.CART.ResultPrinter: It gets the model result file
getModelTime() - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.Timer: Get model time
getModelTime() - Static method in class keel.Algorithms.RST_Learning.Timer: Get model time
getModelType() - Method in class keel.Algorithms.Decision_Trees.M5.M5: Get the value of Model.
getModified() - Method in class keel.GraphInterKeel.experiments.Graph: Gets the current modified status
getMogbest() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Returns maximum number of generations allowed without improving best fitness
getMogmean() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Returns maximum number of generations allowed without improving mean fitness
getMostFrequentClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_aggregated
getMostFrequentClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Returns the most frequent class.
getMostFrequentClass() - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Returns the most frequent class.
getMostFrequentClass() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Returns the class most frecuent of the set of instances
getMostFrequentValue(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It does return the value most frequent for the class
getMostFrequentValue(int) - Method in class keel.Dataset.Attribute: It does return the value most frequent for the class
getMu() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Cochromosome: Gets the mu parameter
getMu(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Population: Gets the mu value of one subpopulation
getMuest() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Instance: It returns the example itself (array of values)
getMuest() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Instance: It returns the example itself (array of values)
getMuest() - Method in class keel.Algorithms.Rule_Learning.AQ.Instance: It returns the example itself (array of values)
getMuest() - Method in class keel.Algorithms.Rule_Learning.CN2.Instance: It returns the example itself (array of values)
getMuest() - Method in class keel.Algorithms.Rule_Learning.Prism.Muestra: Returns the attributes(array of values)
getMuest() - Method in class keel.Algorithms.Rule_Learning.UnoR.Muestra: Returns the attributes(array of values)
getMuest() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Muestra: Returns the attributes(array of values)
getMuest() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Muestra: Returns the attributes(array of values)
getMuest() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Instance: It returns the example itself (array of values)
getMultiInstanceData() - Method in class keel.Algorithms.MIL.Diverse_Density.EMDD.EMDD
getMutationProbability() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
getMutationRate() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
getMutator1() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Returns first individual mutator
getMutator2() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Returns first second mutator
getN() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.PNArray: It returns the value of the negative weight of the PNArray
getN(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.PNArray: It returns the negative weight of a given literal
getN() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeVariable: This method returns the variable number
getN() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
getN() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the number of samples.
getN_e() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Individual: It checks if the chromosome has been evaluated
getN_etiquetas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getN_etiquetas(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getN_etiquetas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getN_etiquetas(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getN_etiquetas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getN_etiquetas(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getN_etiquetas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getN_etiquetas(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getN_etiquetas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getN_etiquetas(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getN_etiquetas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getN_etiquetas(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getN_etiquetas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getN_etiquetas(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getN_etiquetas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getN_etiquetas(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getN_reglas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getN_reglas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getN_reglas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getN_reglas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getN_reglas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getN_reglas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getN_reglas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getN_reglas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getN_var_estado() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getN_var_estado() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getN_var_estado() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getN_var_estado() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getN_var_estado() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getN_var_estado() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getN_var_estado() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getN_var_estado() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getN_variables() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getN_variables() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getN_variables() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getN_variables() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getN_variables() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getN_variables() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getN_variables() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getN_variables() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getnActive() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask: Return the number of active entries
getnActive() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Return the number of active entries
getnActive() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask: Return the number of active entries
getnActive() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Return the number of active entries
getnActive() - Method in class keel.Algorithms.Rule_Learning.PART.Mask: Return the number of active entries
getnActive() - Method in class keel.Algorithms.Rule_Learning.Ripper.Mask: Return the number of active entries
getnActive() - Method in class keel.Algorithms.Rule_Learning.Slipper.Mask: Return the number of active entries
getNAlgorithms() - Static method in class keel.GraphInterKeel.statistical.Configuration: Gets the number of algorithms of the test
getName() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Fuzzy: It returns the name of the fuzzy set
getName() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Fuzzy: It returns the name of the fuzzy set
getName() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Fuzzy: It returns the name of the fuzzy set
getName() - Method in class keel.Algorithms.Decision_Trees.C45.Dataset: Returns the name of the dataset.
getName() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Returns the name of the dataset.
getName() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute: Gets the name of the attribute
getName() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset: Gets the name of the dataset
getName() - Method in class keel.Algorithms.Decision_Trees.ID3.Dataset: Returns the name of the dataset.
getName() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute: Gets the name of the attribute
getName() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset: Gets the name of the dataset
getName() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Returns the name of the dataset.
getName() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Fuzzy: It returns the name of the fuzzy set
getName() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns the name of the dataset.
getName() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Returns the name of the dataset.
getName() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Returns the name of the dataset.
getName() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Returns the name of the dataset.
getName() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Returns the name of the dataset.
getName(int) - Method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Returns and specified name of a parameter.
getName() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractAttribute: Access to the attribute name *
getName() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset: Get name of this dataset
getName() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IAttribute: Access to the attribute name
getName() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IDataset: Get name of this dataset
getName() - Method in class keel.Algorithms.Rule_Learning.ART.Dataset: Returns the name of the dataset.
getName() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Returns the name of the dataset.
getName() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Returns the name of the dataset.
getName() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Returns the name of the dataset.
getName() - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Returns the name of the dataset.
getName() - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It gets the attribute name
getName() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Returns the name of the dataset.
getName(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Returns and specified name of a parameter.
getName() - Method in class keel.Algorithms.Statistical_Tests.Shared.StatTest.InformationAboutClass: Returns the algorithm's name.
getName() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeVar: Gets the name of the variable
getName() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeVar: Gets the name of the variable
getName() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeVar: Gets the name of the variable
getName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fuzzy: It returns the name of the fuzzy set
getName() - Method in class keel.Dataset.Attribute: It gets the attribute name
getName() - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription: Gets the name of the object at the active layer Currently, only layer 0 is used in KEEL
getName(int) - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription: The name of the object at the layer indicated
getName() - Method in class keel.GraphInterKeel.experiments.Graph: Gets the name of this graph
getName() - Method in class keel.GraphInterKeel.experiments.UseCase
getNameClass() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual: Returns the string with the name of the class of the individual
getNameClass() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual: Returns the string with the name of the class of the individual
getNameClass() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual: Returns the string with the name of the class of the individual
getNameClassObj() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Returns the name of the class of the target variable
getNameClassObj() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Returns the name of the class of the target variable
getNameClassObj() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Returns the name of the class of the target variable
getNameExperiment() - Method in class keel.GraphInterKeel.experiments.EducationalReport: Get name of the experiments
getNameObj(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Method to get the name of the objective indicated
getNames() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It returns the attribute labels for the input features
getNames() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Returns the name of the problem's variables
getNamesLength() - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription: Gets the size of the names array
getNameVar(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: It returns the name of the variable in "pos"
getNameVar(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: It returns the name of the variable in "pos"
getNameVar(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: It returns the name of the variable in "pos"
getNameVar(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: It returns the name of the variable in "pos"
getNameVar(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: It returns the name of the variable in "pos"
getNameVar(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: It returns the name of the variable in "pos"
getNameVar(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: It returns the name of the variable in "pos"
getNameVar(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: It returns the name of the variable in "pos"
getNameVar(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: It returns the name of the variable in "pos"
getnAnts() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.Rule: It returns the number of antecedents of the rule
getnAnts() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome
getnAnts() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
getnAnts() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
getnAnts() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
getnAnts() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
getnAnts() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
getnAnts() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
getNatributos() - Method in class keel.Algorithms.Rule_Learning.Prism.Muestra: Returns the number of attributes of the example
getNatributos() - Method in class keel.Algorithms.Rule_Learning.UnoR.Muestra: Returns the number of attributes of the example
getNatributos() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Muestra: Returns the number of attributes of the example
getNatributos() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Muestra: Returns the number of attributes of the example
getNattributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Instance: It returns the number of attributes of the example
getNattributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Instance: It returns the number of attributes of the example
getNAttributes() - Method in class keel.Algorithms.Hyperrectangles.EACH.Sample: Returns the number of attributes of the example
getNattributes() - Method in class keel.Algorithms.Rule_Learning.AQ.Instance: It returns the number of attributes of the example
getNattributes() - Method in class keel.Algorithms.Rule_Learning.CN2.Instance: It returns the number of attributes of the example
getNattributes() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Instance: It returns the number of attributes of the example
getNC() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformance
getNC() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformanceTraining
getNChildren() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree: Returns the number of children of the root.
getNChildren() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Tree: Returns the number of children of the root.
getNChildren() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree: Returns the number of children of the root.
getNChildren() - Method in class keel.Algorithms.Rule_Learning.PART.Tree: Returns the number of children of the root.
getnclases() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnclases() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnclases() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnclases() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnclases() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getNClases() - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Returns the number of classes of the problem
getNClases() - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Returns the number of classes of the problem
getnclases() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Returns the total number of classes
getNClases() - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Return the number of classes
getNClases() - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Return the number of classes
getnclases() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Returns the total number of classes
getnclases() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: It returns the total number of classes
getNClases() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Return the number of classes
getnclases() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset: It returns the total number of classes
getnClass() - Method in class keel.Algorithms.Rule_Learning.SRI.Instances: Returns the number of classes or output values.
getNClass() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Returns the number of classes of the target variable
getNClass() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Returns the number of classes of the target variable
getNClass() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Return the number of classes
getNClass() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Returns the number of classes of the target variable
getnClasses() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification).
getnClasses() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: It returns the total number of classes
getnClasses() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: It returns the total number of classes
getNClasses() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Returns the total number of classes
getnClasses() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnclasses() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Returns the number of classes.
getnclasses() - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Returns the number of classes.
getnclasses() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Returns the number of classes.
getnclasses() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Returns the number of classes.
getnclasses() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Returns the number of classes.
getnclasses() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Returns the number of classes.
getnclasses() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Returns the number of classes.
getNclasses() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: Returns the number of classes for classification problems.
getnClasses() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getNclasses() - Method in class keel.Algorithms.Rule_Learning.AQ.Complex: It returns the number of classes
getnClasses() - Method in class keel.Algorithms.Rule_Learning.AQ.Dataset: It returns the total number of classes
getNclasses() - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: It returns the number of classes
getnClasses() - Method in class keel.Algorithms.Rule_Learning.CN2.Dataset: It returns the total number of classes
getnClasses() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getNClasses() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Returns the total number of classes
getnClasses() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getnClasses() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getNclasses() - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Returns the number of classes for classification problems.
getnClasses() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset: It returns the total number of classes
getnCond() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Obtains the number of different conditions used in this rule antecedent
getnCond() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Obtains the number of different conditions used in this rule antecedent
getnCons() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
getnData() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: It gets the size of the data-set.
getnData() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It gets the size of the data-set
getNData() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Return the number of examples
getnData() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It gets the size of the data-set
getndata() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Returns the number of examples.
getndata() - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Returns the number of examples.
getndata() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Returns the number of examples.
getndata() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Returns the number of examples.
getndata() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Returns the number of examples.
getndata() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Returns the number of examples.
getndata() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Returns the number of examples.
getNdata() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: Returns the size of input data.
getnData() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It gets the size of the data-set
getNData() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Returns the number of examples
getnData() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It gets the size of the data-set
getnData() - Method in class keel.Algorithms.Rule_Learning.SRI.Instances: Returns the number of examples.
getnData() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It gets the size of the data-set
getNdata() - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Returns the size of input data.
getnData() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It gets the size of the data-set
getNData() - Method in class keel.GraphInterKeel.datacf.util.Dataset: Return example/patterns number
getNDatasets() - Static method in class keel.GraphInterKeel.statistical.Configuration: Gets the number of data sets of the test
getndatos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: It gets the size of the data-set
getndatos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: It gets the size of the data-set
getndatos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: It gets the size of the data-set
getndatos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: It gets the size of the data-set
getndatos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: It returns the number of examples
getNdatos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It gets the size of the data-set
getndatos() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: It gets the size of the data-set
getndatos() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: It returns the number of examples
getndatos() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.OpenDataset: Gets the number of examples
getndatos() - Method in class keel.Algorithms.Neural_Networks.gann.OpenDataset: Access to ndatos
getndatos() - Method in class keel.Algorithms.Neural_Networks.gmdh.OpenDataset: Gets the number of examples
getndatos() - Method in class keel.Algorithms.Neural_Networks.net.OpenDataset: Gets the number of examples
getndatos() - Method in class keel.Algorithms.Rule_Learning.AQ.Dataset: It returns the number of examples
getndatos() - Method in class keel.Algorithms.Rule_Learning.CN2.Dataset: It returns the number of examples
getndatos() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Return the number of examples
getndatos() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Return the number of examples
getndatos() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: It returns the number of examples
getndatos() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset: It returns the number of examples
getndatos() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset: It returns the number of examples
getNearest(Prototype, PrototypeSet, boolean) - Static method in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN: Return the nearest prototype to another with the same of different class
getNearest(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN: Return the nearest prototype to another in a set.
getNearest(Prototype, PrototypeSet, boolean) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Return the nearest prototype to another with the same of different class
getNearest(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Return the nearest prototype to another in a set.
getNearestNeighbors(Prototype, PrototypeSet, int) - Static method in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN: Return some nearest prototypes to another with different class
getNearestNeighbors(Prototype, PrototypeSet, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Return some nearest prototypes to another with different class
getNearestNeighborsWithDifferentClassAs(Prototype, PrototypeSet, int) - Static method in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN: Return some nearest prototypes to another with different class
getNearestNeighborsWithDifferentClassAs(Prototype, PrototypeSet, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Return some nearest prototypes to another with different class
getNearestNeighborsWithSameClassAs(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN: Return the nearest prototypes to another with the same class
getNearestNeighborsWithSameClassAs(Prototype, PrototypeSet, int) - Static method in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN: Return some nearest prototypes to another with the same class
getNearestNeighborsWithSameClassAs(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Return the nearest prototypes to another with the same class
getNearestNeighborsWithSameClassAs(Prototype, PrototypeSet, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Return some nearest prototypes to another with the same class
getNearestWithDifferentClassAs(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN: Return the nearest prototype to another with the same class
getNearestWithDifferentClassAs(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Return the nearest prototype to another with the same class
getNearestWithSameClassAs(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN: Return the nearest prototype to another with the same class.
getNearestWithSameClassAs(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Return the nearest prototype to another with the same class.
getNegationBit() - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Gene: Obtains the status of the negation bit
getNegative(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Score: Returns the number of negative instances of the dataset that contains the value at a given position of the vector.
getNegative(double) - Method in class keel.Algorithms.Rule_Learning.Ripper.Score: Returns the number of negative instances that contains the given value.
getNegative() - Method in class keel.Algorithms.Rule_Learning.Ripper.Trio: Returns the number of negative instances of a given dataset that contains the value.
getNegative(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Score: Returns the number of negative instances of the dataset that contains the value at a given position of the vector.
getNegative(double) - Method in class keel.Algorithms.Rule_Learning.Slipper.Score: Returns the number of negative instances that contains the given value.
getNegative() - Method in class keel.Algorithms.Rule_Learning.Slipper.Trio: Returns the number of negative instances of a given dataset that contains the value.
getNegativeEta() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus: Returns the negative eta value, that is, the increment of the step size at each ephoc
getNegEx() - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.pnPair: It returns the negEx value
getNeighbourSet(double[], int) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Calculates the neighbour of one test example
getnentradas() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: It gets the number of input attributes of the data-set
getnentradas() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: It gets the number of input attributes of the data-set
getnentradas() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: It gets the number of input attributes of the data-set
getnentradas() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: It gets the number of input attributes of the data-set
getnentradas() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: It returns the number of input variables
getnentradas() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: It gets the number of input attributes of the data-set
getnentradas() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: It returns the number of input variables
getnentradas() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.OpenDataset: Gets the number of inputs
getNEntradas() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.SMOTE_RSB
getnentradas() - Method in class keel.Algorithms.Neural_Networks.gann.OpenDataset: Access to nentradas
getnentradas() - Method in class keel.Algorithms.Neural_Networks.gmdh.OpenDataset: Gets the number of inputs
getnentradas() - Method in class keel.Algorithms.Neural_Networks.net.OpenDataset: Gets the number of inputs
getnentradas() - Method in class keel.Algorithms.Rule_Learning.AQ.Dataset: It returns the number of input variables
getnentradas() - Method in class keel.Algorithms.Rule_Learning.CN2.Dataset: It returns the number of input variables
getnentradas() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Return the number of in-put variables
getnentradas() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Return the number of input variables
getnentradas() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: It returns the number of input variables
getnentradas() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset: It returns the number of input variables
getnentradas() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset: It returns the number of input variables
getnetConf() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AssociationRule: It returns the Netconf of an association rule
getNetConf() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule: It returns the netconf of an association rule
getNetConf() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
getNetConf() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
getNetConf() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule: It returns the netconf of an association rule
getNetConf() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule: It returns the netconf of an association rule
getNetConf() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
getNetConf() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
getNetConf() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
getNetConf() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
getNetConf() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
getNetConf() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
getNetConf() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
getNetConf() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
getNeuralNetType() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Returns a neural net type
getNeuron(int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.ILayer: Returns a neuron of the layer, using its index
getNeuron(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputLayer: Returns a neuron of the layer using its index
getNeuron(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Returns a neuron of the layer using its index
getNeuronTypes(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Returns an array of neuron types of a concrete layer (this is an hibrid layer)
getNeuronTypes(int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Returns an array of neuron types of a concrete layer
getNEval() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Methods to get the number of evaluation to perform in an iteration of the GA
getNEval() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Gets the number of evalutions of the algorithms
getNEval() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Returns the number of evaluation when the individual was created
getNEval() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Methods to get the number of evaluation to perform in an iteration of the GA
getNewHeader() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: It does return a new header (not necessary the same header as the input file one).
getNewHeader() - Method in class keel.Dataset.InstanceSet: It does return a new header (not necessary the same header as the input file one).
getNewInstancesIndex() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
getNewRule(int) - Method in class keel.Algorithms.Rule_Learning.AQ.ruleSet: It returns the rule as a new copy
getNewRule(int) - Method in class keel.Algorithms.Rule_Learning.CN2.ruleSet: It returns the rule as a new copy
getNewTree(Dataset) - Method in class keel.Algorithms.Decision_Trees.C45.Tree: Function to create a new tree.
getNewTree(Dataset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Tree: Function to create a new tree.
getNewTree(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree: Function to create a new tree.
getNewTree(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Tree: Function to create a new tree.
getNewTree(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree: Function to create a new tree.
getNewTree(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Tree: Function to create a new tree.
getNewTree(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree: Function to create a new tree.
getNewTree(MyDataset) - Method in class keel.Algorithms.Rule_Learning.PART.Tree: Function to create a new tree.
getNewTree(Dataset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: Function to create a new tree.
getNewValuesInTest() - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It returns a vector with all new nominal values read in test.
getNewValuesInTest() - Method in class keel.Dataset.Attribute: It returns a vector with all new nominal values read in test.
getNEx() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableDat: Returns the number of examples of the DataSet
getNEx() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableDat: Returns the number of examples of the DataSet
getNEx() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableDat: Returns the number of examples of the DataSet
getNext(int) - Method in class keel.Algorithms.SVM.SMO.supportVector.SMOset: Gets the next element in the set. -1 gets the first one.
getNextAsDouble() - Method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Return next parameter as double.
getNextAsDouble() - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Return next parameter as double.
getNextAsInt() - Method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Return next parameter as int.
getNextAsInt() - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Return next parameter as int.
getNextAsString() - Method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Return next parameter as string.
getNextAsString() - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Return next parameter as string.
getNextAsStringArray() - Method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Return next parameter as string.
getNextAsStringArray() - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Return next parameter as string.
getNextToken(StreamTokenizer) - Method in class keel.Algorithms.Decision_Trees.C45.Algorithm: Puts the tokenizer in the first token of the next line.
getNextToken(StreamTokenizer) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm: Puts the tokenizer in the first token of the next line.
getNextToken(StreamTokenizer) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm: Puts the tokenizer in the first token of the next line.
getNextToken() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
getNextToken() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserTokenManager
getNextToken(StreamTokenizer) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm: Puts the tokenizer in the first token of the next line.
getNextToken(StreamTokenizer) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm: Puts the tokenizer in the first token of the next line.
getNextToken(StreamTokenizer) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Gets next token, checking for a premature and of line.
getNextToken(StreamTokenizer) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Algorithm: Puts the tokenizer in the first token of the next line.
getNextToken(StreamTokenizer) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Algorithm: Puts the tokenizer in the first token of the next line.
getNextToken(StreamTokenizer) - Method in class keel.Algorithms.Rule_Learning.PART.Algorithm: Puts the tokenizer in the first token of the next line.
getNextToken() - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
getNextToken() - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParserTokenManager
getNextToken() - Static method in class keel.Dataset.DataParser
getNextToken() - Static method in class keel.Dataset.DataParserTokenManager
getnFeatures() - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
getnFeatures() - Static method in class keel.Algorithms.RST_Learning.RSTData
getNGenerations() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
getNGenes() - Method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.Chromosome: Get the number of genes selected
getNiche() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
getNiche() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierADI
getNiche() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierGABIL
getNiche() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierUBR
getNiche() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
getNiche() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
getNiche() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
getnInputs() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: It gets the number of input attributes of the data-set.
getnInputs() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It gets the number of input attributes of the data-set
getNInPuts() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Return the number of in-put variables
getnInputs() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It gets the number of input attributes of the data-set
getninputs() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Returns the number of input attributes.
getninputs() - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Returns the number of input attributes.
getninputs() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Returns the number of input attributes.
getninputs() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Returns the number of input attributes.
getninputs() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Returns the number of input attributes.
getninputs() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Returns the number of input attributes.
getninputs() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Returns the number of input attributes.
getNinputs() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: Returns the number of input variables.
getnInputs() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It gets the number of input attributes of the data-set
getnInputs() - Method in class keel.Algorithms.Rule_Learning.SRI.Instances: Returns the number of attributes of the dataset.
getnInputs() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It gets the number of input attributes of the data-set
getNinputs() - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Returns the number of input variables.
getnInputs() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It gets the number of input attributes of the data-set
getNInputs() - Method in class keel.GraphInterKeel.datacf.util.Dataset: Return input variable number
getnInstances() - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
getNInstancesI(int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset: Gets the number of instances of each class that there are in the dataset
getNInstancesI(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset: Gets the number of instances of each class that there are in the dataset
getnIntervals() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.DataB
getnIntervals() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.DataB
getnJobs() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: This method get number of jobs
getNLabel() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Returns the number of labels for all the continuous variables
getNLabel() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Returns the number of labels for all the continuous variables
getNLabel() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Returns the number of labels for all the continuous variables
getnLabels() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.DataBase: It return the whole array of number of labels for every attribute
getnLabels() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.DataBase: It return the whole array of number of labels for every attribute
getnLabels() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.DataBase: It return the whole array of number of labels for every attribute
getnLabels() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.DataBase: It return the whole array of number of labels for every attribute
getnLabels() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.DataBase: It return the whole array of number of labels for every attribute
getnLabels() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.DataBase: It return the whole array of number of labels for every attribute
getnLabels() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.DataBase: It return the whole array of number of labels for every attribute
getnLabels() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.FuzzyAntecedent: Obtains the number of labels used in this fuzzy antecedent as condition
getnLabels() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.DataBase
getnLabels() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Obtains the number of labels used in this fuzzy antecedent as condition
getNLabels() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeVar: Gets the number of labels used by the continuous variable
getNLabels() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeVar: Gets the number of labels used by the continuous variable
getNLabels() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeVar: Gets the number of labels used by the continuous variable
getnLabels() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.DataBase: Returns the number of labels of each variable.
getNLabelsOfAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: Outputs the number of labels for all attributes in the dataset.
getnLabelsReal() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.DataBase: Returns the number of total real labels held by the input attributes.
getnLabelsReal() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.DataBase
getnLabelsReal() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.DataBase: Returns the number of real labels
getNLabelVar(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Returns the number of labels of the var indicated by "pos"
getNLabelVar(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Returns the number of labels of the var indicated by "pos"
getNLabelVar(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Returns the number of labels of the var indicated by "pos"
getNN(double[], int) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Calculates the k examples most near of the set
getNObjectives() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Retuns the number of objectives used
getNObjectives(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Gets the name of the objective
getnoCruce() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
getnoCruce() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getnoCruce() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
getnoCruce() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getnoCruceR() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getnoCruceR() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getNode(int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Searches a descendant node from this node with a specific id
getNode(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode: Searches a descendant node from this node with a specific id
getNode() - Method in class keel.GraphInterKeel.experiments.Joint
getNodeAt(int) - Method in class keel.GraphInterKeel.experiments.Graph: Returns the node at position indicated
getNodeColor() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.InfoNode: Returns the Color of the node.
getNodeError() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Obtains the error of a leave node in a the tree from all the instances in the leaf in a two number array
getNodeInfo() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.InfoNode: Returns Info printed in the node
getNodeInfoToolTipText() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.InfoNode: Returns Text showed when moving the mouse over a node.
getNodes() - Method in class keel.GraphInterKeel.experiments.Graph: Gets all the nodes from this graph
getNodeSize() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.InfoNode: Returns Size of the node.
getNOfHiddenLayers() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Returns number of hidden layers of the neural nets
getNOfHiddenLayers() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Returns number of hidden layers of the neural nets
getNofhlayers() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Returns the number of hidden layers of the neural net
getNofhlayers() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuralNet: Returns the current number of hidden layers of the neural net
getNofhneurons() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Returns the number of hidden neurons of this neural net
getNofhneurons() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuralNet: Returns the number of hidden neurons of this neural net
getNOfInputs() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Returns number of inputs of the neural nets
getNofinputs() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Returns the number of inputs of the observations stored in the data set
getNOfInputs() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Returns number of inputs of the neural nets
getNoflinks() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Returns the number of effective links of this neural net
getNoflinks() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuralNet: Returns the number of effective links of this neural net
getNoflinks() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Returns the number of effective links of the layer
getNoflinks() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron: Returns the number of effective links of the neuron
getNofneurons() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.ILayer: Returns the number of neurons of this layer
getNofneurons() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputLayer: Returns the number of neurons of this layer
getNofneurons() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Returns the number of neurons of this layer
getNofobservations() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Returns the number of observations stored in the data set
getNOfOutputs() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Returns number of outputs of the neural nets
getNofoutputs() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Returns the number of outputs of the observations stored in the data set
getNOfOutputs() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Returns number of outputs of the neural nets
getNOfPartitions() - Method in class keel.GraphInterKeel.datacf.partitionData.HoldOutOptionsJDialog: Gets the number of partitions to be performed, i.e. the number of times that a random division using the configuration selected is going to be performed.
getNofvariables() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Returns the number of variables stored in the data set
getNogbest() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Returns current number of generations without improving best fitness
getNogmean() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Returns current number of generations without improving mean fitness
getNoisyInstances() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ConjGradNN: Returns a vector with the noisy instances.
getNoisyInstances() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.GCNet
getNombres() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
getNominales() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: Returns an array of booleans that indicates for each variable wether it is nominal or not
getNominals() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It computes an array that stores a boolean value that indicates if a given attribute has nominal type or not
getNominals() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It computes an array that stores a boolean value that indicates if a given attribute has nominal type or not
getNominals() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It computes an array that stores a boolean value that indicates if a given attribute has nominal type or not
getNominals() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It computes an array that stores a boolean value that indicates if a given attribute has nominal type or not
getNominalValue(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.InstanceWrapper
getNominalValue(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.InstanceWrapper
getNominalValue(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It returns de ith value of that nominal attribute
getNominalValue(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It returns the nominal value "id_val" within the attribute "id_attr"
getNominalValue(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It returns the nominal value "id_val" within the attribute "id_attr"
getNominalValue(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It returns the nominal value "id_val" within the attribute "id_attr"
getNominalValue(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: It returns the nominal value "id_val" within the attribute "id_attr"
getNominalValue(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: It returns the nominal value "id_val" within the attribute "id_attr"
getNominalValue(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: It returns the nominal value "id_val" within the attribute "id_attr"
getNominalValue(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: It returns the nominal value "id_val" within the attribute "id_attr"
getNominalValue(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: It returns the nominal value "id_val" within the attribute "id_attr"
getNominalValue(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: It returns the nominal value "id_val" within the attribute "id_attr"
getNominalValue(int) - Method in class keel.Dataset.Attribute: It returns de ith value of that nominal attribute
getNominalValues() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.InstanceWrapper
getNominalValues() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.InstanceWrapper
getNominalValuesList() - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: Returns all the possible nominal values
getNominalValuesList() - Method in class keel.Dataset.Attribute: Returns all the possible nominal values
getNormalizedInputValues() - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: It does return the normalized values in a double[].
getNormalizedInputValues(InstanceAttributes) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: Obtains the normalized input attributes from a InstanceAttribute definition
getNormalizedInputValues() - Method in class keel.Dataset.Instance: It does return the normalized values in a double[].
getNormalizedInputValues(InstanceAttributes) - Method in class keel.Dataset.Instance: Obtains the normalized input attributes from a InstanceAttribute definition
getNormalizedOutputValues() - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: It does return the normalized values in a double[].
getNormalizedOutputValues(InstanceAttributes) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: Obtains the normalized output attributes from a InstanceAttribute definition
getNormalizedOutputValues() - Method in class keel.Dataset.Instance: It does return the normalized values in a double[].
getNormalizedOutputValues(InstanceAttributes) - Method in class keel.Dataset.Instance: Obtains the normalized output attributes from a InstanceAttribute definition
getNormalizer() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Returns the normalizer associated to the trainData DataSet
getNotClassified(int) - Method in class keel.Algorithms.Statistical_Tests.Shared.StatTest.InformationAboutClass: Sets the number of not classified examples with the one given.
getNotClassified() - Method in class keel.Algorithms.Statistical_Tests.Shared.StatTest.InformationAboutClass: Returns the number of not classified examples
getnOutput() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It gets the number of output variables
getNOutputFiles() - Method in class keel.Algorithms.Rule_Learning.Ripper.parseParameters: It returns the number of output files
getnOutputs() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It gets the number of output attributes of the data-set (for example number of classes in classification)
getNOutputs() - Method in class keel.GraphInterKeel.datacf.util.Dataset: Return output variable number
getNParameters() - Method in class keel.Algorithms.Rule_Learning.Ripper.parseParameters: It returns the number of parameters
getNParticion() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: This method return number of th actual partition
getNRealPartition() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: Get number partition of a experiment (not global partition)
getNreglasTotal() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
getNreglasTotal() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getNreglasTotal() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
getNreglasTotal() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
getNreglasTotal() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getNreglasTotal() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
getnReplace() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: It returns the size of the Replace list in the rule.
getnReplace() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: It returns the size of the Replace list in the rule
getnsalidas() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.OpenDataset: Gets the number of outputs
getnsalidas() - Method in class keel.Algorithms.Neural_Networks.gann.OpenDataset: Access to nsalidas
getnsalidas() - Method in class keel.Algorithms.Neural_Networks.gmdh.OpenDataset: Gets the number of outputs
getnsalidas() - Method in class keel.Algorithms.Neural_Networks.net.OpenDataset: Gets the number of outputs
getnSelected() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual: Returns the number of genes selected.
getnSelected() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual
getnSV(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_model
getnSV(int) - Method in class org.libsvm.svm_model
getnTrans() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It gets the size of the data-set
getnTrans() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It gets the size of the data-set
getnTrans() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It gets the size of the data-set
getnTrans() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: It gets the size of the data-set
getnTrans() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: It gets the size of the data-set
getnTrans() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: It gets the size of the data-set
getnTrans() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: It gets the size of the data-set
getnTrans() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: It gets the size of the data-set
getnTrans() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: It gets the size of the data-set
getnTrans() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: It gets the size of the data-set
getnTrans() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: It gets the size of the data-set
getnTrans() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: It gets the size of the data-set
getnTrans() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: It gets the size of the data-set
getnTrans() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: It gets the size of the data-set
getnTrans() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: It gets the size of the data-set
getnTrans() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: It gets the size of the data-set
getnTrans() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: It gets the size of the data-set
getnTrans() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: It gets the size of the data-set
getNuevaRegla(int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjReglas: Returns a rule as a copy of the list
getNumAliveRules() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
getNumAliveRules() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PerformanceAgent
getNumAliveRules() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
getNumAliveRules() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PerformanceAgent
getNumArguments() - Method in interface keel.Algorithms.Preprocess.Missing_Values.EM.util.MultivariateFunction: get number of arguments
getNumAtr() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset: Gets the number of attributes that the dataset has
getNumAtr() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset: Gets the number of attributes that the dataset has
getNumAtributos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
getNumAttribute(String) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Calculate: It returns the number (position) of the attribute name indicated.
getNumAttribute(String) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Calculate: It returns the number (position) of the attribute name indicated.
getNumAttribute(String) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Calculate: It returns the number (position) of the attribute name indicated.
getNumAttributes() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It return the total number of attributes in the API
getNumAttributes() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It return the total number of attributes in the API
getNumAttributes() - Static method in class keel.Dataset.Attributes: It return the total number of attributes in the API
getNumAttributes() - Method in class keel.Dataset.InstanceAttributes: It return the total number of attributes in the API
getNumber() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Cluster: Gets the number of this cluster
getNumberAntecedents() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Returns the number of antecedents of the rules.
getNumberCategories() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.CategoricalAttribute: Access to Number of categories
getNumberExamples() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Returns the number of examples managed.
getNumberFeatures() - Method in class keel.Algorithms.MIL.AbstractMIAlgorithm
getNumberMatches() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Gets the number of matches of the classifier.
getNumberMatches() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Gets the number of matches of the classifier.
getNumberOfExamples() - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Environment: It returns the number of examples of the database.
getNumberOfExamples() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.MPEnvironment: It returns the number of examples of the database.
getNumberOfExamples() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.SSFileEnvironment: It return the number of the examples of the database.
getNumberOfExamples() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.DECEnvironment: It returns the number of examples of the database.
getNumberOfExamples() - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Environment: It returns the number of examples of the database.
getNumberOfExamples() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MPEnvironment: It returns the number of examples of the database.
getNumberOfExamples() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MSEnvironment: It returns the number of examples of the database.
getNumberOfExamples() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.PAREnvironment: It returns the number of examples of the database.
getNumberOfExamples() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.POSEnvironment: It returns the number of examples of the database.
getNumberOfExamples() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.SSFileEnvironment: It return the number of the examples of the database.
getNumberOfFuzzyAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyDataset: It returns the number of fuzzy attributes composing a fuzzy dataset
getNumberOfFuzzyAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyDataset: It returns the number of fuzzy attributes composing a fuzzy dataset
getNumberOfFuzzyAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyDataset: It returns the number of fuzzy attributes composing a fuzzy dataset
getNumberOfFuzzyRegions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyAttribute: It returns the number of fuzzy regions composing a fuzzy attribute
getNumberOfFuzzyRegions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyDataset: It returns the number of fuzzy regions of each involved fuzzy attributes
getNumberOfFuzzyRegions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyAttribute: It returns the number of fuzzy regions composing a fuzzy attribute
getNumberOfFuzzyRegions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyDataset: It returns the number of fuzzy regions of each involved fuzzy attributes
getNumberOfFuzzyRegions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyAttribute: It returns the number of fuzzy regions composing a fuzzy attribute
getNumberOfFuzzyRegions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyDataset: It returns the number of fuzzy regions of each involved fuzzy attributes
getNumberofJobFinished() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: Get the number of Jobs that is finished actually
getNumberOfNodes() - Static method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TtreeNode: It returns the number of nodes in a T-tree
getNumberOfNodes() - Static method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TtreeNode: It returns the number of nodes.
getNumberOfNodes() - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TtreeNode: It returns the number of nodes.
getNumberOfOneFrequentItemsets() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.AlcalaetalProcess: It returns the number of 1-Frequent Itemsets
getNumberOfOneFrequentItemsets() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyAprioriProcess: It returns the number of 1-Frequent Itemsets
getNumberOfOneFrequentItemsets() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.GeneticFuzzyAprioriProcess: It returns the number of 1-Frequent Itemsets
getNumberOfOneFrequentItemsets() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.GeneticFuzzyAprioriDCProcess: It returns the number of 1-Frequent Itemsets
getNumberOfSubfronts() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Ranking: Returns the total number of subFronts founds.
getNumberRules() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Returns the number of rules of the system.
getNumberViolatedConstraints() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Returns the numberOfViolatedConstraints of the individual
getNumBins() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Histogram: Returns the number of the bins.
getNumBits() - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Gene: Obtains the number of bits (computed from the attribute associated)
getNumClass() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual: Returns the number of the class of the individual
getNumClass() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual: Returns the number of the class of the individual
getNumClass() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual: Returns the number of the class of the individual
getNumClasses() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset: Gets the number of different classes that there are in the dataset
getNumClasses() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Gets the number of different classes in the node
getNumClasses() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset: Gets the number of different classes that there are in the dataset
getNumClasses() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Node: Gets the number of different classes in the node
getNumClassifiers() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_aggregated
getNumClassObj() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Returns the number of the class of the target variable
getNumClassObj() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Returns the number of the class of the target variable
getNumClassObj() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Returns the number of the class of the target variable
getNumCMAR_CRs() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList: Returns the number of generated CMAR classification rules.
getNumCond() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Returns the number of possible conditions.
getNumCond() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Returns the number of possible conditions.
getNumCond() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: Returns the number of possible conditions.
getNumConsequents() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.FuzzyClassifier: This method returns the RuleBase number of consequents.
getNumCRs() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList: Returns the number of generated rules (usually used in conjunction with classification rule mining algorithms rather than ARM algorithms).
getNumDimensions() - Method in class keel.Algorithms.Discretizers.UCPD.PCA: It returns the number of dimensions
getNumDiscretizers() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.DiscretizationManager
getNumDiscretizers() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.DiscretizationManager
getNumEjemplos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
getNumElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.VAList: This method returns the total of elements ever added to this list.
getNumElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.VAList: This method returns the total of elements ever added to this list.
getNumElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.VAList: This method returns the total of elements ever added to this list.
getNumElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.VAList: This method returns the total of elements ever added to this list.
getNumElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.VAList: This method returns the total of elements ever added to this list.
getNumElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.VAList: This method returns the total of elements ever added to this list.
getNumElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.VAList: This method returns the total of elements ever added to this list.
getNumElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.VAList: This method returns the total of elements ever added to this list.
getNumerosity() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Returns the numerosity of the classifier.
getNumerosity() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters: Returns the numerosity of the classifier.
getNumerosity() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Returns the numerosity of the classifier.
getNumerosity() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Returns the numerosity of the classifier.
getNumError() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformance
getNumErrors() - Method in class keel.Algorithms.Rule_Learning.Swap1.FormatErrorKeeper: Returns the number of errors.
getNumErrors() - Method in class keel.Dataset.FormatErrorKeeper: Returns the number of errors.
getNumExamples() - Method in class keel.Algorithms.Hyperrectangles.EACH.Hyperrectangle
getNumFolds() - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Get the value of NumFolds.
getNumFolds() - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Get the value of NumFolds.
getNumFolds() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Get the value of NumFolds.
getNumFolds() - Method in class keel.Algorithms.SVM.SMO.SMO: Get the value of numFolds.
getNumFreqSets() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Returns number of frequent/large (supported) sets in T-tree.
getNumFreqSets() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TotalSupportTree: Commences the process of counting and returning number of supported nodes in the T-tree.
getNumFreqSets() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TotalSupportTree: Commences the process of counting and returning number of supported nodes in the T-tree.
getNumGenes() - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Chromosome: the number of genes of this individual
getNumGenes() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome: Gets the number of genes of this chromosome
getNumGenes() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Cochromosome: Gets the TOTAL number of genes across all chromosomes in this object
getNumGenes(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Population: Gets the number of genes of one subpopulation
getNumGenes() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Subpopulation: Gets the TOTAL number of genes of this subpopulation
getNumIndiv() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Population: Return the number of individuals of the population
getNumInputs() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGPRegSym: Returns the number of inputs
getNumInputs() - Method in class keel.GraphInterKeel.experiments.Parameters: return number of input connections
getNumIns() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset: Gets the number of instances that the dataset has
getNumIns() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset: Gets the number of instances that the dataset has
getNumInstances() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.instanceSet
getNumInstances() - Method in class keel.Algorithms.Rule_Learning.Riona.Selector: Returns the numebr of instances
getNumInstances() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: It returns the number of instances.
getNumInstances() - Method in class keel.Dataset.InstanceSet: It returns the number of instances.
getNumInstancesOfIteration() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.instanceSet
getNumInstancesOfIteration() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Windowing
getNumInstancesOrig() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.instanceSet
getNumIntervals(int) - Method in class keel.Algorithms.Discretizers.Basic.Discretizer: Returns the number of intervals in the given attribute.
getNumIntervals(int) - Method in class keel.Algorithms.Discretizers.Chi2_Discretizer.Discretizer: Returns the number of intervals in the given attribute.
getNumIntervals(int) - Method in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Discretizer: Returns the number of intervals in the given attribute.
getNumIntervals(int) - Method in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Discretizer: Returns the number of intervals in the given attribute.
getNumIntervals(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Basic.Discretizer
getNumIntervals(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Discretizer
getNumItemsClassI(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Node: Gets the number of items that belongs to class i
getNumKO() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformanceTraining
getNumLiterals() - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Literals: It returns the number the literals
getNumNC() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformance
getNumNegExamples(Complex) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Calculates the number of negative examples that match with the rule
getNumNiches() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
getNumNiches() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierADI
getNumNiches() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierGABIL
getNumNiches() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierUBR
getNumNiches() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
getNumNiches() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
getNumNiches() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
getNumNodes() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Gets the number of internal nodes from this node and its descendants.
getNumNodes() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode: Gets the number of internal nodes from this node and its descendants.
getNumNominalValues() - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It returns the number of different values that can take a nominal attribute.
getNumNominalValues(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It returns the number of different values that can take a nominal attribute.
getNumNominalValues() - Method in class keel.Dataset.Attribute: It returns the number of different values that can take a nominal attribute.
getNumObjectives() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Gets the number of objectives
getNumObjectives() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.QualityMeasures: Returns the num_objetivos of the individual
getNumOfFeatures() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
getNumOfGenerations() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
getNumOfRuns() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
getNumOK() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformanceTraining
getNumOneFrequentItemsets() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Chromosome: It returns the number of 1-frequent itemsets of a chromosome
getNumOneFrequentItemsets() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Chromosome: It returns the number of 1-frequent itemsets of a chromosome
getNumOneFrequentItemsets() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Chromosome: It returns the number of 1-frequent itemsets of a chromosome
getNumOutputs() - Method in class keel.GraphInterKeel.experiments.Parameters: return number of extra outputs
getNumOutputValue() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It returns the number of different values that can take the class.
getNumParameters() - Method in class keel.GraphInterKeel.experiments.Parameters: Gets the number of parameters
getNumPos() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformanceTraining
getNumPosExamples(Complex) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Calculates the number of positive examples the math with the rule
getNumPtreeNodes() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree: Gets number of nodes in P-tree.
getNumRegisters() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Node: Gets the number of different registers of data that there are in the node
getNumRules() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.FuzzyRuleSet: This method returns the number of rules of this Rule Set
getNumRules() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Cochromosome: Gets the number of rules (chromosomes)
getNumRules(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Population: Gets the number of rules of the specified subpopulation
getNumRules() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Subpopulation: Gets the number of rules of this subpopulation
getNumRules() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
getNumRules() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierADI
getNumRules() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierGABIL
getNumRules() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierUBR
getNumRules() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
getNumRules() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
getNumRules() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
getNumRules() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ruleOrderAgent
getNumSupOneItemSets() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Gets number of supported attributess (note this is not necessarily the same as the number of columns/attributes in the input set) plus the number of classifiers.
getNumSupOneItemSets() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Gets number of supported single item sets (note this is not necessarily the same as the number of columns/attributes in the input set).
getNumSupOneItemSets() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Gets number of supported single item sets (note this is not necessarily the same as the number of columns/attributes in the input set).
getNumSupOneItemSets() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Gets number of supported single item sets (note this is not necessarily the same as the number of columns/attributes in the input set).
getNumTotal() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformanceTraining
getNumVA() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.VAList: This method gives the number of VA elements stored (i.e. the number of different values)
getNumVA() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.VAList: This method gives the number of VA elements stored (i.e. the number of different values)
getNumVA() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.VAList: This method gives the number of VA elements stored (i.e. the number of different values)
getNumVA() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.VAList: This method gives the number of VA elements stored (i.e. the number of different values)
getNumVA() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.VAList: This method gives the number of VA elements stored (i.e. the number of different values)
getNumVA() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.VAList: This method gives the number of VA elements stored (i.e. the number of different values)
getNumVA() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.VAList: This method gives the number of VA elements stored (i.e. the number of different values)
getNumVA() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.VAList: This method gives the number of VA elements stored (i.e. the number of different values)
getNumValues() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Returns for each attribute the number of different values
getNumValues() - Method in class keel.Algorithms.Hyperrectangles.EACH.Selector: Returns the number of values for this selector
getNumValues() - Method in class keel.Algorithms.Rule_Learning.Riona.Selector: Returns the number of values
getNumValues() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Returns for each attribute the number of different values
getNumValues() - Method in class keel.Algorithms.Rule_Learning.UnoR.Interval: Returns the number of values in it.
getNumValues2() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Returns for each attribute the number of different values
getNumValues2() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Returns for each attributes the number of values for the set
getNumValues2() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Returns for each attribute the number of different values
getNumVar() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual: Returns the number of variables of the rule (including the consequent)
getNumVar() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual: Returns the number of variables of the rule (including the consequent)
getNumVar() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual: Returns the number of variables of the rule (including the consequent)
getnv(String) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.MultipleClassifierSystem
getnVA(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.VAList: This method returns the valueAssociations object which is in position 'i'.
getnVA(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.VAList: This method returns the valueAssociations object which is in position 'i'.
getnVA(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.VAList: This method returns the valueAssociations object which is in position 'i'.
getnVA(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.VAList: This method returns the valueAssociations object which is in position 'i'.
getnVA(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.VAList: This method returns the valueAssociations object which is in position 'i'.
getnVA(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.VAList: This method returns the valueAssociations object which is in position 'i'.
getnVA(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.VAList: This method returns the valueAssociations object which is in position 'i'.
getnVA(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.VAList: This method returns the valueAssociations object which is in position 'i'.
getNValOutput() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It gets the number of values for the target variable
getNValue() - Method in class keel.Algorithms.Hyperrectangles.EACH.Selector: Returns the nominal value of the associated value
getNValue() - Method in class keel.Algorithms.Rule_Learning.Riona.Selector: Returns the nominal value of the attribute
getnValues() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Condition
getNValues() - Method in class keel.Algorithms.Hyperrectangles.EACH.Selector: Return the set of nominal values of the selector
getNValues() - Method in class keel.Algorithms.Rule_Learning.Riona.Selector: Returns the set of nominal values ofthe selector
getnVar() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Obtains the number of variables used in this rule antecedent
getnVar() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Obtains the number of variables used in this rule antecedent
getnvariables() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: It gets the number of variables of the data-set (including the output)
getnvariables() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: It gets the number of variables of the data-set (including the output)
getnvariables() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: It gets the number of variables of the data-set (including the output)
getnvariables() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: It gets the number of variables of the data-set (including the output)
getnvariables() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: It returns the number of variables
getNvariables() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It gets the number of variables of the data-set (including the output)
getnvariables() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: It gets the number of variables of the data-set (including the output)
getnvariables() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: It returns the number of variables
getNVariables() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Returns the number of variables
getnvariables() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.OpenDataset: Gets the number of variables
getnvariables() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Returns the number of variables.
getnvariables() - Method in class keel.Algorithms.Neural_Networks.gann.OpenDataset: Access to nvariables
getnvariables() - Method in class keel.Algorithms.Neural_Networks.gmdh.OpenDataset: Gets the number of variables
getnvariables() - Method in class keel.Algorithms.Neural_Networks.net.OpenDataset: Gets the number of variables
getnvariables() - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Returns the number of variables.
getnvariables() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Returns the number of variables.
getnvariables() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Returns the number of variables.
getnvariables() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Returns the number of variables.
getnvariables() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Returns the number of variables.
getnvariables() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Returns the number of variables.
getNvariables() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: Returns the number of input variables plus output variables.
getnvariables() - Method in class keel.Algorithms.Rule_Learning.AQ.Dataset: It returns the number of variables
getnvariables() - Method in class keel.Algorithms.Rule_Learning.CN2.Dataset: It returns the number of variables
getnvariables() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Returns the number of variables
getNVariables() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Returns the number of variables
getnvariables() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Returns the number of variables
getNvariables() - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Returns the number of input variables plus output variables.
getnvariables() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: It returns the number of variables
getnvariables() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset: It returns the number of variables
getnvariables() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset: It returns the number of variables
getNVariables() - Method in class keel.GraphInterKeel.datacf.util.Dataset: Return variable number
getnVars() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: It gets the number of variables of the data-set (including the output).
getnVars() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It gets the number of variables of the data-set (including the output)
getnVars() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It gets the number of variables of the data-set (including the output)
getNVars() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Returns the number of variables
getNVars() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Returns the number of variables
getNVars() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Returns the number of variables
getnVars() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It gets the number of variables of the data-set
getnVars() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It gets the number of variables of the data-set
getnVars() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It gets the number of variables of the data-set
getnVars() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: It gets the number of variables of the data-set
getnVars() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: It gets the number of variables of the data-set
getnVars() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: It gets the number of variables of the data-set
getnVars() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: It gets the number of variables of the data-set
getnVars() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: It gets the number of variables of the data-set
getnVars() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: It gets the number of variables of the data-set
getnVars() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: It gets the number of variables of the data-set
getnVars() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: It gets the number of variables of the data-set
getnVars() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: It gets the number of variables of the data-set
getnVars() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: It gets the number of variables of the data-set
getnVars() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: It gets the number of variables of the data-set
getnVars() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: It gets the number of variables of the data-set
getnVars() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: It gets the number of variables of the data-set
getnVars() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: It gets the number of variables of the data-set
getnVars() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: It gets the number of variables of the data-set
getNVisibleParams() - Method in class keel.GraphInterKeel.experiments.Parameters: Gets the number of visible paramters
getObj(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Method to get the name of the quality measure in position pos
getObj(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Methods to get the name of the quality measure as objective in a position
getObj1() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Methods to get the name of the quality measure as objective 1
getObj2() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Methods to get the name of the quality measure as objective 2
getObj3() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Methods to get the name of the quality measure as objective 3
getObject() - Method in class keel.Algorithms.Decision_Trees.M5.SerializedObject: Gets the object stored in this SerializedObject.
getObject() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.SerializedObject: Returns a serialized object.
getObject() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SerializedObject: Returns a serialized object.
getObject() - Method in class keel.Algorithms.SVM.SMO.core.SerializedObject: Returns a serialized object.
getObjective(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
getObjective(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
getObjective(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
getObjective() - Method in class keel.GraphInterKeel.experiments.UseCase
getObjective() - Static method in class keel.GraphInterKeel.statistical.Configuration: Gets the objective of the test
getObjectives() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome: It returns the objectives of a chromosome
getObjectives() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome: It returns the objectives of a chromosome
getObjectives() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome: It returns the objectives of a chromosome
getObjectiveValue(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.QualityMeasures: Gets the value of the objective pos
getObservation(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Returns an specified observation
getObservationsOf(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Returns all the values of a variable in the data set
getObserved(String, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Cluster: Returns the number of times a determined attribute value appears in the instances of this cluster
getomax() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Returns the maximum value of the output.
getomax() - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Returns the maximum value of the output.
getomax() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Returns the maximum value of the output.
getomax() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Returns the maximum value of the output.
getomax() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Returns the maximum value of the output.
getomax() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Returns the maximum value of the output.
getomax() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Returns the maximum value of the output.
getOmaximum() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: Returns maximum value for output.
getOmaximum() - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Returns maximum value for output.
getOmega() - Method in class keel.Algorithms.SVM.SMO.supportVector.Puk: Gets the omega value.
getomin() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Returns the minimun value of the output.
getomin() - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Returns the minimun value of the output.
getomin() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Returns the minimun value of the output.
getomin() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Returns the minimun value of the output.
getomin() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Returns the minimun value of the output.
getomin() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Returns the minimun value of the output.
getomin() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Returns the minimun value of the output.
getOminimum() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: Returns maximum value for output.
getOminimum() - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Returns maximum value for output.
getOperacion(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.FuncionEvaluacionBean
getOperacion() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
getOperador() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Condicion: Returns the operator of this condition.
getOperador() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Condicion: Returns the operator of this condition.
getOperador() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Condicion: Returns the operator of this condition.
getOperador() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Condicion: Returns the operator of this condition.
getOperador() - Method in class keel.Algorithms.Rule_Learning.Prism.Selector: Return the operator's id
getOperador() - Method in class keel.Algorithms.Rule_Learning.UnoR.Selector: Return the operator's id
getOperador() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Selector: Return the operator's id
getOperador() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Selector: Return the id of the operator
getOperator() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleRule: Returns the operator of the rule
getOperator() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.SimpleRule: Returns the operator of the rule
getOperator() - Method in class keel.Algorithms.Hyperrectangles.EACH.Selector: Return the operator's id
getOperator(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.Particle
getOperator() - Method in class keel.Algorithms.Rule_Learning.AQ.Selector: It return the operator id (!
getOperator() - Method in class keel.Algorithms.Rule_Learning.C45Rules.SimpleRule: Returns the operator of the rule
getOperator() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.SimpleRule: Returns the operator of the rule
getOperator() - Method in class keel.Algorithms.Rule_Learning.CN2.Selector: It return the operator id (!
getOperator() - Method in class keel.Algorithms.Rule_Learning.PART.SimpleRule: Returns the operator of the rule
getOperator() - Method in class keel.Algorithms.Rule_Learning.Riona.Selector: Returns the id of the operator
getOperator() - Method in class keel.Algorithms.Rule_Learning.Ripper.SimpleRule: Returns the operator of the rule
getOperator() - Method in class keel.Algorithms.Rule_Learning.Slipper.SimpleRule: Returns the operator of the rule
getOperator() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene: It returns the operator used.
getOptClass() - Method in class keel.Algorithms.Discretizers.OneR.Opt: Computes the optimum class for the explanatory value
getOptimalClass() - Method in class keel.Algorithms.Rule_Learning.UnoR.Interval: Returns the optimal class.
getOptimalPopulation - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It's set to true if the optimal population has to be read from a file.
GETOPTIMALPOPULATION - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
getOptimizations() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Gets the the number of optimization runs
getOptimumClass(int[][][], long) - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Returns a vector with the optimum class for each pair attribute-value
getOptimumClass(int[][][], long) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Returns a vector with the class for each pair attribute-value
getOption(char, String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Gets an option indicated by a flag "-Char" from the given array of strings.
getOption(char, String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Gets an option indicated by a flag "-Char" from the given array of strings.
getOption(String, String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Gets an option indicated by a flag "-String" from the given array of strings.
getOption(char, String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Gets an option indicated by a flag "-Char" from the given array of strings.
getOption(char, String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Gets an option indicated by a flag "-Char" from the given array of strings.
getOption(String, String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Gets an option indicated by a flag "-String" from the given array of strings.
getOption(char, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Gets an option indicated by a flag "-Char" from the given array of strings.
getOption(String, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Gets an option indicated by a flag "-String" from the given array of strings.
getOption(char, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Gets an option indicated by a flag "-Char" from the given array of strings.
getOption(String, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Gets an option indicated by a flag "-String" from the given array of strings.
getOption(char, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Gets an option indicated by a flag "-Char" from the given array of strings.
getOption(String, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Gets an option indicated by a flag "-String" from the given array of strings.
getOption(char, String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Gets an option indicated by a flag "-Char" from the given array of strings.
getOption(String, String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Gets an option indicated by a flag "-String" from the given array of strings.
getOption(char, String[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Gets an option indicated by a flag "-Char" from the given array of strings.
getOption(String, String[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Gets an option indicated by a flag "-String" from the given array of strings.
getOptionPos(char, String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Gets the index of an option or flag indicated by a flag "-Char" from the given array of strings.
getOptionPos(String, String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Gets the index of an option or flag indicated by a flag "-String" from the given array of strings.
getOptionPos(char, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Gets the index of an option or flag indicated by a flag "-Char" from the given array of strings.
getOptionPos(String, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Gets the index of an option or flag indicated by a flag "-String" from the given array of strings.
getOptionPos(char, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Gets the index of an option or flag indicated by a flag "-Char" from the given array of strings.
getOptionPos(String, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Gets the index of an option or flag indicated by a flag "-String" from the given array of strings.
getOptionPos(char, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Gets the index of an option or flag indicated by a flag "-Char" from the given array of strings.
getOptionPos(String, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Gets the index of an option or flag indicated by a flag "-String" from the given array of strings.
getOptionPos(char, String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Gets the index of an option or flag indicated by a flag "-Char" from the given array of strings.
getOptionPos(String, String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Gets the index of an option or flag indicated by a flag "-String" from the given array of strings.
getOptionPos(char, String[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Gets the index of an option or flag indicated by a flag "-Char" from the given array of strings.
getOptionPos(String, String[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Gets the index of an option or flag indicated by a flag "-String" from the given array of strings.
getOptions() - Method in class keel.Algorithms.Decision_Trees.M5.M5: Gets the current settings of the Classifier.
getOptions() - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Gets the current settings of the filter.
getOptions() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Classifier: Gets the current settings of the Classifier.
getOptions() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: Gets the current settings of the Classifier.
getOptions() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
getOptions() - Method in interface keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.OptionHandler: Gets the current option settings for the OptionHandler.
getOptions() - Method in interface keel.Algorithms.Statistical_Classifiers.Logistic.core.OptionHandler: Gets the current option settings for the OptionHandler.
getOptions() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Gets the current settings of the classifier.
getOptions() - Method in class keel.Algorithms.SVM.SMO.core.Check: Gets the current settings of the CheckClassifier.
getOptions() - Method in interface keel.Algorithms.SVM.SMO.core.OptionHandler: Gets the current option settings for the OptionHandler.
getOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: Gets the current settings of the Kernel.
getOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.Kernel: Gets the current settings of the Kernel.
getOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: Gets the current settings of the Kernel.
getOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.PolyKernel: Gets the current settings of the Kernel.
getOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.Puk: Gets the current settings of the Kernel.
getOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.RBFKernel: Gets the current settings of the Kernel.
getOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: Gets the current settings of the classifier.
getOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMO: Gets the current settings of the classifier.
getOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: Gets the current settings of the object.
getOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Gets the current settings of the Kernel.
getOptionValue(String) - Method in class keel.GraphInterKeel.datacf.util.OptionsDialog: Returns the value of an option
getOrderAttribute(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.DataBase: It returns the sum of the number of labels until a given attribute
getOrderClas() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.DataBase: It returns the sum of the number of labels until a given attribute
getOrigin() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.Link: Returns the origin neuron of the link, used to obtain its output value
getOriginalElementsIndex() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.SMOTE_RSB
getOriginalHeaderWithoutInOut() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: It does return the original header definiton but without @input and @output in there
getOriginalHeaderWithoutInOut() - Method in class keel.Dataset.InstanceSet: It does return the original header definiton but without @input and @output in there
getOrigInstances() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.instanceSet
getOSup() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Retuns the value of the original support measure
getOutAttribute() - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanel: Returns the index of the output attribute
getOuterBorder() - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster: Get the outer border of the cluster
getOutOfRange() - Method in class keel.GraphInterKeel.experiments.ParametersTable: EDUCATIONAL KEEL ****************************
getOutput(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Random_Sets.FSS98.RSFSS: This methods returns a evaluation of the given example using all the clusters found.
getOutput() - Method in class keel.Algorithms.Hyperrectangles.BNGE.Rule: Returns the output class of the rule
getOutput() - Method in class keel.Algorithms.Hyperrectangles.INNER.Rule: Returns the output class of the rule
getOutput() - Method in class keel.Algorithms.Hyperrectangles.RISE.Rule: Returns the output class of the rule
getOutput(int) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Returns a specific output of the protoype.
getOutput(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Returns all the values of an output in the data set
getOutput(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Returns a specific output of the protoype.
getOutputAsInteger() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Returns the output of the data-set as integer values.
getOutputAsInteger(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: It returns the output value of the example "pos".
getOutputAsInteger() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It returns the output value of the example "pos"
getOutputAsInteger() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: Returns the output of the data-set as integer values
getOutputAsInteger(int) - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It returns the output value of the example "pos"
getOutputAsNominal(int) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Return an output as nominal.
getOutputAsNominal(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Return an output as nominal.
getOutputAsReal() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Returns the output of the data-set as real values.
getOutputAsReal(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: It returns the output value of the example "pos".
getOutputAsReal() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It returns the output value of the example "pos"
getOutputAsReal() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: Returns the output of the data-set as real values
getOutputAsReal(int) - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Returns the output of the data-set as nominal values.
getOutputAsString(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: It returns the output value of the example "pos".
getOutputAsString() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It returns the output value of the example "pos"
getOutputAsString(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyDataset: Return the output value of the example in position "pos"
getOutputAsString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It returns the output value of the example "pos"
getOutputAsString(int) - Method in class keel.Algorithms.Rule_Learning.SRI.Instances: Returns the output value of the example in the position given.
getOutputAsString() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It returns the output value of the example "pos"
getOutputAsString() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: Returns the output of the data-set as nominal values
getOutputAsString(int) - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It returns the output value of the example "pos"
getOutputAttribute() - Static method in class keel.Algorithms.Decision_Trees.CART.dataset.DataSetManager: It returns the output attribute
getOutputAttribute() - Method in class keel.Algorithms.Decision_Trees.CART.ResultPrinter: If returns the output attribute
getOutputAttribute() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset: Gets all the information about the output attribute of the dataset
getOutputAttribute() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset: Gets all the information about the output attribute of the dataset
getOutputAttribute() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlusReporterClas: Returns output attribute metadata
getOutputAttribute() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.IRPropPlusReporterRegr: Returns output attribute metadata
getOutputAttribute() - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.listener.NeuralNetReporterClas: Returns output attribute metadata
getOutputAttribute() - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.listener.NeuralNetReporterRegr: Returns output attribute metadata
getOutputAttribute(int) - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It returns the output attribute being int the position passed as an argument.
getOutputAttribute(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It returns the output attribute being int the position passed as an argument.
getOutputAttribute(int) - Static method in class keel.Dataset.Attributes: It returns the output attribute being int the position passed as an argument.
getOutputAttribute(int) - Method in class keel.Dataset.InstanceAttributes: It returns the output attribute being int the position passed as an argument.
getOutputAttributes() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It does return all the output attributes.
getOutputAttributes() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It does return all the output attributes.
getOutputAttributes() - Static method in class keel.Dataset.Attributes: It does return all the output attributes.
getOutputAttributes() - Method in class keel.Dataset.InstanceAttributes: It does return all the output attributes.
getOutputAttributesHeader() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It does return a String with all the output attributes definition in keel format.
getOutputAttributesHeader() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It does return a String with all the output attributes definition in keel format.
getOutputAttributesHeader() - Static method in class keel.Dataset.Attributes: It does return a String with all the output attributes definition in keel format.
getOutputAttributesHeader() - Method in class keel.Dataset.InstanceAttributes: It does return a String with all the output attributes definition in keel format.
getOutputBuffer() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.ThreadedStreamHandler
getOutputClass() - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode
getOutputClass() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Gets the output class stored for the node.
getOutputClass() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Node: Gets the output class of the first register of the node.
getOutputClass() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Register: Gets the class of the register
getOutputClass() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode: Gets the output class stored for the node.
getOutputFile(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.parseParameters: It returns the output file of the specified index
getOutputFile(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.parseParameters: It returns the output file of the specified index
getOutputFile(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.parseParameters: It returns the output file of the specified index
getOutputFile(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.parseParameters: It returns the output file of the specified index
getOutputFile(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.parseParameters: It returns the output file of the specified index
getOutputFile(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.parseParameters: It returns the output file of the specified index
getOutputFile(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.parseParameters: It returns the output file of the specified index
getOutputFile(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.parseParameters: It returns the output file of the specified index
getOutputFile(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.parseParameters: It returns the output file of the specified index
getOutputFile(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.parseParameters: It returns the output file of the specified index
getOutputFile(int) - Method in class keel.Algorithms.Decision_Trees.Target.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.parseParameters: It returns the output file of the specified index
getOutputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.parseParameters: It returns the output file of the specified index
getOutputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.parseParameters: It returns the output file of the specified index
getOutputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.ParametersParser: It returns the output file of the specified index
getOutputFile(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.parseParameters: It returns the output file of the specified index
getOutputFile(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.parseParameters: obtains the output file of index pos
getOutputFile(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.PSO_Learning.CPSO.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Rule_Learning.LEM1.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Rule_Learning.LEM2.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Rule_Learning.PART.parseParameters: It returns the output file of the specified index
getOutputFile(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Rule_Learning.Ritio.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Rule_Learning.Rules6.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Rule_Learning.SRI.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.SVM.C_SVM.parseParameters: obtains the output file of index pos
getOutputFile(int) - Method in class keel.Algorithms.SVM.EPSILON_SVR.parseParameters: obtains the output file of index pos
getOutputFile(int) - Method in class keel.Algorithms.SVM.NU_SVM.parseParameters: obtains the output file of index pos
getOutputFile(int) - Method in class keel.Algorithms.SVM.NU_SVR.parseParameters: obtains the output file of index pos
getOutputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.parseParameters: It returns the output file in the position "pos"
getOutputFile(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.parseParameters: It returns the output file in the position "pos"
getOutputFilePaths() - Method in class keel.GraphInterKeel.experiments.EducationalReport: Return array of strings with file output paths
getOutputFiles() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.parseParameters: It returns the output files
getOutputFiles() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.parseParameters: It returns the output files
getOutputFiles() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.parseParameters: It returns the output files
getOutputFiles() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.parseParameters: It returns the output files
getOutputFiles() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.parseParameters: It returns the output files
getOutputFiles() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.parseParameters: It returns the output files
getOutputFiles() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.parseParameters: It returns the output files
getOutputFiles() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.parseParameters: It returns the output files
getOutputFiles() - Method in class keel.Algorithms.Decision_Trees.DT_GA.parseParameters: It returns the output files
getOutputFiles() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.parseParameters: It returns the output files
getOutputFiles() - Method in class keel.Algorithms.Decision_Trees.Target.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.parseParameters: It returns the output files
getOutputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.parseParameters: It returns the output files
getOutputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.parseParameters: It returns the output files
getOutputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.ParametersParser: It returns the output files
getOutputFiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.parseParameters: It returns the output files
getOutputFiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.parseParameters: Obtains all the output files
getOutputFiles() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.PSO_Learning.CPSO.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.PSO_Learning.REPSO.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Rule_Learning.C45Rules.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Rule_Learning.LEM1.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Rule_Learning.LEM2.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Rule_Learning.PART.parseParameters: It returns the output files
getOutputFiles() - Method in class keel.Algorithms.Rule_Learning.Ripper.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Rule_Learning.Ritio.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Rule_Learning.Rules6.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Rule_Learning.Slipper.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Rule_Learning.SRI.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.SVM.C_SVM.parseParameters: Obtains all the output files
getOutputFiles() - Method in class keel.Algorithms.SVM.EPSILON_SVR.parseParameters: Obtains all the output files
getOutputFiles() - Method in class keel.Algorithms.SVM.NU_SVM.parseParameters: Obtains all the output files
getOutputFiles() - Method in class keel.Algorithms.SVM.NU_SVR.parseParameters: Obtains all the output files
getOutputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.parseParameters: It returns all the output files
getOutputFiles() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.parseParameters: It returns all the output files
getOutputFileTra() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.parseParameters: Gets the name of the output for train file
getOutputFileTst() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.parseParameters: Gets the name of the output for test file
getOutputFormat() - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Gets the format of the output instances.
getOutputFormat() - Method in class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter: Returns the output format.
getOutputHeader() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It does return an String with the @outputs in keel format.
getOutputHeader() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It does return an String with the @outputs in keel format.
getOutputHeader() - Static method in class keel.Dataset.Attributes: It does return an String with the @outputs in keel format.
getOutputHeader() - Method in class keel.Dataset.InstanceAttributes: It does return an String with the @outputs in keel format.
getOutputI(int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset: Gets the class of each instance in the dataset
getOutputI(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset: Gets the class of each instance in the dataset
getOutputInterval() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.problem.IProblem: Returns the input interval of normalized data
getOutputInterval() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Returns the input interval of normalized data
getOutputLayer() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Returns the output layer of this neural net
getOutputLayer() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuralNet: Returns the output layer of this neural net
getOutputLayerInitiator() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Returns initiator of neurons of the output layer
getOutputLayerInitiator() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Returns initiator of neurons of the output layer
getOutputLayerType() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Returns type of neurons of the output layer
getOutputLayerType() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Returns type of neurons of the output layer
getOutputLayerWeightRange(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Returns weight range of the output layer
getOutputLayerWeightRange(int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Returns weight range of the output layer
getOutputMean(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Returns the mean of a specific number of output
getOutputMissingValues() - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: Get Output Missing Values
getOutputMissingValues(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: Get Output Missing Values
getOutputMissingValues() - Method in class keel.Dataset.Instance: Get Output Missing Values
getOutputMissingValues(int) - Method in class keel.Dataset.Instance: Get Output Missing Values
getOutputName() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: Returns the class name.
getOutputNominalValue(int, int) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: Returns the value of a nominal output attribute of an instance in the instanceSet.
getOutputNominalValue(int, int) - Method in class keel.Dataset.InstanceSet: Returns the value of a nominal output attribute of an instance in the instanceSet.
getOutputNominalValues() - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: Get Output Nominal Values
getOutputNominalValues(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: Get Output Nominal Values
getOutputNominalValues() - Method in class keel.Dataset.Instance: Get Output Nominal Values
getOutputNominalValues(int) - Method in class keel.Dataset.Instance: Get Output Nominal Values
getOutputNominalValuesInt(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: It does return the output value at the specified position
getOutputNominalValuesInt() - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: It does return the output value at the specified position
getOutputNominalValuesInt(int) - Method in class keel.Dataset.Instance: It does return the output value at the specified position
getOutputNominalValuesInt() - Method in class keel.Dataset.Instance: It does return the output value at the specified position
getOutputNumAttributes() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It return the number of output attributes in the API
getOutputNumAttributes() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It return the number of output attributes in the API
getOutputNumAttributes() - Static method in class keel.Dataset.Attributes: It return the number of output attributes in the API
getOutputNumAttributes() - Method in class keel.Dataset.InstanceAttributes: It return the number of output attributes in the API
getOutputNumericValue(int, int) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: Returns the value of an integer or a real output attribute of an instance in the instanceSet.
getOutputNumericValue(int, int) - Method in class keel.Dataset.InstanceSet: Returns the value of an integer or a real output attribute of an instance in the instanceSet.
getOutputRealValues() - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: Get Output Real Values
getOutputRealValues(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: Get Output Real Values
getOutputRealValues() - Method in class keel.Dataset.Instance: Get Output Real Values
getOutputRealValues(int) - Method in class keel.Dataset.Instance: Get Output Real Values
getOutputs() - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Returns the outputs of the protoype.
getOutputs(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Returns the outputs of an specified observation
getOutputs() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Returns the outputs of the protoype.
getOutputs() - Method in class keel.GraphInterKeel.datacf.util.Dataset: Return a vector that contains output variables
getOutputStringIndex() - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Returns an array containing the indices of all string attributes in the output format.
getOutputValue(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Returns the nominal output value which corresponds to the integer passed as argument.
getOutputValue(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It returns the output value (string) which matchs with a given integer value
getOutputValue(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It returns the output value (string) which matchs with a given integer value
getOutputValue(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It returns the output value (string) which matchs with a given integer value
getOutputValue(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It returns the output value (string) which matchs with a given integer value
getOutputValue() - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode
getOutputValue(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Returns the nominal output value which corresponds to the integer passed as argument.
getOutputValue(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It returns the class label (string) given a class id (int)
getOutputValue(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It returns the output value (string) which matchs with a given integer value
getOutputValue(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It returns the output value (string) which matchs with a given integer value
getOutputValue(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It returns the nominal value for the class in the position "intValue"
getOutputValue(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It returns the nominal value for the class in the position "intValue"
getOutputValue(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It returns the nominal value for the class in the position "intValue"
getOutputValue(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It returns the nominal value for the class in the position "intValue"
getOutputValue(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It returns the nominal value for the class in the position "intValue"
getOutputValue(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It returns the nominal value for the class in the position "intValue"
getOutputValue(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It returns the output value (string) which matchs with a given integer value
getOutputValue(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It returns the output value (string) which matchs with a given integer value
getOutputValue(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It returns the output value (string) which matchs with a given integer value
getOutputValue(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It returns the output value (string) which matchs with a given integer value
getOutputValue(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It returns the output value (string) which matchs with a given integer value
getOutputValue(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It returns the nominal value for the class in the position "intValue"
getOutputValue(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It returns the output value (string) which matchs with a given integer value
getOutputValue(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It returns the nominal value for the class in the position "intValue"
getOutputValue(int) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It returns the nominal value for the class in the position "intValue"
getOutputValue(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It returns the name of the class of index intValue
getOutputValue(int) - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It gets the string value for the class number specified in the parameter
getOverallConstraintViolation() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Returns the overallConstraintViolation of the individual
getOverlapping(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: Return the class-overlapping rate
getP() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.PNArray: It returns the value of the positive weight of the PNArray
getP(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.PNArray: It returns the positive weight of a given literal
getP() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Chc: FIN DEL FICHERO
getP() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Chc: FIN DEL FICHERO
getP() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Chc: FIN DEL FICHERO
getP() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Chc: FIN DEL FICHERO
getP() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Chc: FIN DEL FICHERO
getP() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Chc
getP() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Chc: FIN DEL FICHERO
getP() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Chc: FIN DEL FICHERO
getPairFreq(String, String) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqListPair: Searches for the first pair which has its elements equals to the provided ones, and return it frequency
getParameter(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.parseParameters: It returns the name of the parameter specified
getParameter(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.parseParameters: It returns the name of the parameter specified
getParameter(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.parseParameters: It returns the name of the parameter specified
getParameter(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.parseParameters: It returns the name of the parameter specified
getParameter(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.parseParameters: It returns the name of the parameter specified
getParameter(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.parseParameters: It returns the name of the parameter specified
getParameter(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.parseParameters: It returns the name of the parameter specified
getParameter(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.parseParameters: It returns the name of the parameter specified
getParameter(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.parseParameters: It returns the name of the parameter specified
getParameter(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.parseParameters: It returns the name of the parameter specified
getParameter(int) - Method in class keel.Algorithms.Decision_Trees.Target.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.parseParameters: It returns the name of the parameter specified
getParameter(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.parseParameters: It returns the name of the parameter specified
getParameter(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.parseParameters: It returns the name of the parameter specified
getParameter(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.ParametersParser: It returns the name of the parameter specified
getParameter(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.parseParameters: It returns the name of the parameter specified
getParameter(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.parseParameters: Returns the parameter of index pos
getParameter(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.PSO_Learning.CPSO.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Rule_Learning.LEM1.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Rule_Learning.LEM2.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Rule_Learning.PART.parseParameters: It returns the name of the parameter specified
getParameter(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Rule_Learning.Ritio.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Rule_Learning.Rules6.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Rule_Learning.SRI.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.parseParameters: Gets a string with the parameter of a determined position used by the algorithm
getParameter(int) - Method in class keel.Algorithms.SVM.C_SVM.parseParameters: Returns the parameter of index pos
getParameter(int) - Method in class keel.Algorithms.SVM.EPSILON_SVR.parseParameters: Returns the parameter of index pos
getParameter(int) - Method in class keel.Algorithms.SVM.NU_SVM.parseParameters: Returns the parameter of index pos
getParameter(int) - Method in class keel.Algorithms.SVM.NU_SVR.parseParameters: Returns the parameter of index pos
getParameter(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.parseParameters: It returns the parameter in the position "pos"
getParameter(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.parseParameters: It returns the parameter in the position "pos"
getParameters() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.parseParameters: It returns the name of the parameters
getParameters() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.parseParameters: It returns the name of the parameters
getParameters() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.parseParameters: It returns the name of the parameters
getParameters() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.parseParameters: It returns the name of the parameters
getParameters() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.parseParameters: It returns the name of the parameters
getParameters() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.parseParameters: It returns the name of the parameters
getParameters() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.parseParameters: It returns the name of the parameters
getParameters() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.parseParameters: It returns the name of the parameters
getParameters() - Method in class keel.Algorithms.Decision_Trees.DT_GA.parseParameters: It returns the name of the parameters
getParameters() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.parseParameters: It returns the name of the parameters
getParameters() - Method in class keel.Algorithms.Decision_Trees.Target.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.parseParameters: It returns the name of the parameters
getParameters() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.parseParameters: It returns the name of the parameters
getParameters() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.parseParameters: It returns the name of the parameters
getParameters() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.ParametersParser: Returns the parameters obtained.
getParameters() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.parseParameters: It returns the name of the parameters
getParameters() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.parseParameters: Gets all the parameters lines from the file
getParameters() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.PSO_Learning.CPSO.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.PSO_Learning.REPSO.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Rule_Learning.C45Rules.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Rule_Learning.LEM1.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Rule_Learning.LEM2.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Rule_Learning.PART.parseParameters: It returns the name of the parameters
getParameters() - Method in class keel.Algorithms.Rule_Learning.Ripper.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Rule_Learning.Ritio.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Rule_Learning.Rules6.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Rule_Learning.Slipper.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Rule_Learning.SRI.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.parseParameters: Gets an array with the parameters used by the algorithm
getParameters() - Method in class keel.Algorithms.SVM.C_SVM.parseParameters: Gets all the parameters lines from the file
getParameters() - Method in class keel.Algorithms.SVM.EPSILON_SVR.parseParameters: Gets all the parameters lines from the file
getParameters() - Method in class keel.Algorithms.SVM.NU_SVM.parseParameters: Gets all the parameters lines from the file
getParameters() - Method in class keel.Algorithms.SVM.NU_SVR.parseParameters: Gets all the parameters lines from the file
getParameters() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.parseParameters: It returns all the parameters as an array of Strings
getParameters() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.parseParameters: It returns all the parameters as an array of Strings
getparameters() - Method in class keel.GraphInterKeel.experiments.Joint
getParameters() - Method in class keel.GraphInterKeel.experiments.UseCase
getParametersP(int) - Method in class keel.GraphInterKeel.experiments.Joint
getParametersToString() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.ParametersParser: It returns the name of the parameters
getParameterType(int) - Method in class keel.GraphInterKeel.experiments.Parameters: return parameter name for parameter at index position
getParameterTypes() - Method in class keel.GraphInterKeel.experiments.Parameters: return parameter types
getParameterValue(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.ParametersParser: Returns the parameter value of a given parameter name.
GetParamFloat(StringTokenizer) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Utils: Gets an float from param file, skiping "="
getParamFloat(StringTokenizer) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Utils: Gets an float from param file, skiping "="
GetParamFloat(StringTokenizer) - Static method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Utils: Gets an float from param file, skiping "="
GetParamFloat(StringTokenizer) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Utils: Gets an float from param file, skiping "="
GetParamInt(StringTokenizer) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Utils: Gets an integer from param file, skiping "="
getParamInt(StringTokenizer) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Utils: Gets an integer from param file, skiping "="
GetParamInt(StringTokenizer) - Static method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Utils: Gets an integer from param file, skiping "="
GetParamInt(StringTokenizer) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Utils: Gets an integer from param file, skiping "="
GetParamString(StringTokenizer) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Utils: Gets an String from param file, skiping "="
getParamString(StringTokenizer) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Utils: Gets an String from param file, skiping "="
GetParamString(StringTokenizer) - Static method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Utils: Gets an String from param file, skiping "="
GetParamString(StringTokenizer) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Utils: Gets an String from param file, skiping "="
getParent() - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode: It gets the parent of the current node
getParent() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Returns the father node.
getParentRef() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Returns a reference of the parent set.
getParentRef() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Returns a reference of the parent set.
getParetos() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess
getParetos() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_GhoshProcess
getParetos() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_GoshProcess
getParetos() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.MOPNARProcess
getParetos() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.QAR_CIP_NSGAIIProcess
getPartitionOfInstance(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.PartitionScheme: Returns the partition where the instance with the given index belongs to.
getPartitionOfInstance(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.PartitionScheme: Returns the partition where the instance with the given index belongs to.
getPartitionOfInstance(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.PartitionScheme: Returns the partition where the instance with the given index belongs to.
getPartitions() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.PartitionScheme: It returns the indexes of the original instances in all partitions
getPartitions() - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.PartitionScheme: It returns the indeces of the original instances in all partitions
getPartitions() - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.PartitionScheme: It returns the indexes of the original instances in all partitions
getPartitions() - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.PartitionScheme: It returns the indeces of the original instances in all partitions
getPartitions() - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.PartitionScheme: It returns the indeces of the original instances in all partitions
getPartitions() - Method in class keel.GraphInterKeel.datacf.partitionData.PartitioningSchemes.DistanceBased_best: It returns the indexes of the original instances in all partitions
getPartitionTime() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: This method return the compute time for a partition
getPartitionType() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: This methos get type partitions, k-fold or 5x2
getPath() - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription: Gets the path at the active layer
getPath(int) - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription: Get the path from the indicated layer
getPath() - Static method in class keel.GraphInterKeel.statistical.Configuration: Gets the path of the file to store the results of the test
getPath() - Static method in class keel.GraphInterKeel.util.Path: Get the path
getPathResultFilesTxt() - Method in class keel.GraphInterKeel.experiments.EducationalFSReport: Return paths of result.txt files
GetPattern() - Method in class keel.Algorithms.Neural_Networks.ensemble.Sample: Return a random pattern
getPatternIndex(int) - Method in class keel.GraphInterKeel.datacf.util.Dataset: Return example/pattern at index position
getPatterns() - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode
getPenalizedFitness() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Obtains the penalized fitness associated to this rule, computed with the token competition procedure
getPenalizedFitness() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Obtains the penalized fitness associated to this rule, computed with the token competition procedure
getPenaltyFactor() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
getPer() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset: It returns the Pessimistic Error Rate of the itemset.
getPer() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset: It returns the Pessimistic Error Rate of the itemset
getPercentages(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Returns an array of percentages of a concrete layer (this is an hibrid layer)
getPercentages(int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Returns an array of percentages of a concrete layer for an hibrid layer
getPercentageSecondMutator() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Returns percentage of census selected to be mutated with second mutator
getPercentile(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Histogram: Returns percentile value with the percentage given.
getPerf() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Individual: It gets the fitness value for the chromosome
getpeso() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
getpeso() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
getpeso() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
getpeso() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
getPivot() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.LUDecomposition: Return pivot permutation vector
getPNpairs(IntegerSet, Instance[], int) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.pnPair: Returns the PN pairs with the integer set and instances given.
getPobBestPerf() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population: Gets the fitness of the best individual of the population
getPobBestPerf() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population: Gets the fitness of the best individual of the population
getPobBestPerf() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population: Gets the fitness of the best individual of the population
getPoblacion() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
getPoblacion(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
getPoblacion() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
getPoblacion(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
getPoblacion() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getPoblacion(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getPoblacion() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
getPoblacion(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
getPoblacion() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
getPoblacion(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
getPoblacion() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
getPoblacion(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
getPoblacion() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getPoblacion(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getPoblacion() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
getPoblacion(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
getPopBestGuy() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population: Gets the position of the best individual of the population
getPopBestGuy() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population: Gets the position of the best individual of the population
getPopBestGuy() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population: Gets the position of the best individual of the population
getPopsize() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
getPOPSIZE() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
getPopsize() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
getPOPSIZE() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
getPopsize() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getPOPSIZE() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getPopsize() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
getPOPSIZE() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
getPopsize() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
getPOPSIZE() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
getPopsize() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
getPOPSIZE() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
getPopsize() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getPOPSIZE() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getPopsize() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
getPOPSIZE() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
getPopSize() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess
getPopulationSize() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
getPopulationSize() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
getPopulationSize() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Returns population size of the algorithm
getPorcCob() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Gets the percentage of biased initialisation in the re-initialisation based on coverage
getPos() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Cover: Returns the position of the covered example.
getPos() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Cover: Returns the position stored.
getPosActive(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule: It returns the number of valid labels for the rule until a given position
getPosActual() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Returns the actual position of the particle.
getPosEx() - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.pnPair: It returns the posEx value
getPosFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Instance: It returns the position of the example in the input data file
getPosFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Instance: It returns the position of the example in the input data file
getPosFile() - Method in class keel.Algorithms.Hyperrectangles.EACH.Sample: Returns the position of the example inf the in-put file of data
getPosFile() - Method in class keel.Algorithms.Rule_Learning.AQ.Instance: It returns the position of the example in the input data file
getPosFile() - Method in class keel.Algorithms.Rule_Learning.CN2.Instance: It returns the position of the example in the input data file
getPosFile() - Method in class keel.Algorithms.Rule_Learning.Prism.Muestra: Returns the position of the example inf the in-put file of data
getPosFile() - Method in class keel.Algorithms.Rule_Learning.UnoR.Muestra: Returns the position of the example inf the in-put file of data
getPosFile() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Muestra: Returns the position of the example inf the in-put file of data
getPosFile() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Muestra: Returns the position of the example inf the in-put file of data
getPosFile() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Instance: It returns the position of the example in the input data file
getPosibleValuesOfOutput() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Return all the existing classes in our universe.
getPosibleValuesOfOutput() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Return all the existing classes in our universe.
getPosicion() - Method in class keel.GraphInterKeel.experiments.Node: Gets the current position of the node
getPosition() - Method in class keel.GraphInterKeel.experiments.Node: Gets the current position of the node
getPositionRuleMatch() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier: positionRuleMatch contains the position within the classifier (e.g. the rule) that matched the last classified input instance
getPositionRuleMatch() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier: positionRuleMatch contains the position within the classifier (e.g. the rule) that matched the last classified input instance
getPositive(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Score: Returns the number of positive instances of the dataset that contains the value at a given position of the vector.
getPositive(double) - Method in class keel.Algorithms.Rule_Learning.Ripper.Score: Returns the number of positive instances that contains the given value.
getPositive() - Method in class keel.Algorithms.Rule_Learning.Ripper.Trio: Returns the number of positive instances of a given dataset that contains the value.
getPositive(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Score: Returns the number of positive instances of the dataset that contains the value at a given position of the vector.
getPositive(double) - Method in class keel.Algorithms.Rule_Learning.Slipper.Score: Returns the number of positive instances that contains the given value.
getPositive() - Method in class keel.Algorithms.Rule_Learning.Slipper.Trio: Returns the number of positive instances of a given dataset that contains the value.
getPositiveEta() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus: Returns the positive eta value, that is, the increment of the step size at each epoch
getPosListFilas(int) - Method in class keel.Algorithms.Rule_Learning.LEM2.Atributo_valor
getPossibleInstancesOfRule(int, int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ruleOrderAgent
getPossibleLabelConclusions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Returns the Set of label conclusions that can appear.
getPossibleVbleConclusions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams
getPosString(Vector, String) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Utils: Returns the position of the element at the vector, -1 if does not appear
getposString(Vector, String) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Utils: Returns the position of the element at the vector, -1 if does not appear
getPosString(Vector, String) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Utils: Returns the position of the element at the vector, -1 if does not appear
getposString(Vector, String) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Utils: Returns the position of the element at the vector, -1 if does not appear
getposString(Vector, String) - Static method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Utils: Returns the position of the element at the vector, -1 if does not appear
getPosString(Vector, String) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Utils: Returns the position of the element at the vector, -1 if does not appear
getposString(Vector, String) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Utils: Returns the position of the element at the vector, -1 if does not appear
getPredError() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Returns the prediction error of the classifier.
getPredError() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Returns the prediction error of the classifier.
getPredErrorAverage() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: It is used by specify.
getPredicted() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexResult
getPrediction() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Returns the prediction of the classifier.
getPrediction() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Returns the prediction of the classifier.
getPrediction() - Method in class keel.Algorithms.ImbalancedClassification.Auxiliar.AUC.PredPair: Provides the predicted class associated to a given instance
getPredictions() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.C45: Returns the predicted classes for each test instance.
getPredictions() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: Returns the predicted classes for each test instance.
getPredictions() - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.C45: Returns the predicted classes for each test instance.
getPredictions() - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.C45: Returns the predicted classes for each test instance.
getPredictions() - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: Returns the predicted classes for each test instance.
getPredictions() - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.LDA: Returns the predicted classes for each test instance.
GetPredictions(String, int) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.MultipleClassifierSystem
getPredictions() - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.C45: Returns the predicted classes for each test instance.
getPredictions() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45: Predicted classes for the test instances.
getPredictions(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerCSVM: Returns the predictions for a given partition.
getPredictions() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerNB: Returns the predictions for each instance.
getPredictions(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerSMO: Returns the predictions for a given partition.
getPredictions() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.NumericalNaiveBayes: Returns the predicted classes for each test instance.
getPreferredScrollableViewportSize() - Method in class keel.GraphInterKeel.experiments.DinamicDataset
getPreferredScrollableViewportSize() - Method in class keel.GraphInterKeel.experiments.GraphPanel
getPreferredScrollableViewportSize() - Method in class keel.GraphInterKeel.experiments.SelectData: scroll control
getPreviousBest() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Returns previous best fitness
getPreviousMean() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Returns previous mean fitness
getPriorProbabilities() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.C45: Returns the prior probabilities
getProb() - Method in class keel.Algorithms.ImbalancedClassification.Auxiliar.AUC.PosProb: Provides the probability associated to a given instance
getProbabilities() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45: Classes probabilities for the test instances.
getProbabilities() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerCSVM: Returns the classes probabilities.
getProbabilities() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerNB: Returns the classes probabilities.
getProbabilities() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerSMO: Returns the classes probabilities.
getProbabilities() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.NumericalNaiveBayes: Returns the classes probabilities for each test instance.
getProbability(double) - Method in class keel.Algorithms.Decision_Trees.M5.M5Kernel: Get a probability estimate for a value.
getProbability(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.NormalDistribution: Returns the probability for the given value.
getProbability(double) - Method in class keel.GraphInterKeel.statistical.tests.NormalDistribution: Computes punctual probability for a given point
getProbCross() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Methods to get the value for the crossover probability
getProbCross() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Gets the cross probability
getProbCross() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Methods to get the value for the crossover probability
getproblem() - Method in class keel.GraphInterKeel.experiments.Joint
getProblemType() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: Return type of problem
getProbMut() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Methods to get the value for the mutation probability
getProbMut() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Methods to get the value for the mutation probability
getProbMutacion() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion
getProbMutation() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Gets the mutation probability
getProbMutExtremo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion
getProperties() - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster: See if values calculated for the cluster are valid
getPrototypeSet() - Method in class keel.Algorithms.Instance_Generation.GMCA.Cluster: Gets the prototype set of the cluster.
getPrototypeSet() - Method in class keel.Algorithms.Instance_Generation.VQ.Cluster: Get the elements of the cluster.
getPrototypeWithSelectedInputs(ArrayList<Integer>) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Returns the prototype selected inputs of the prototype specified in lista.
getProvider() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Returns individual provider
getPruneExamplesFactor() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
getPruningFactor() - Method in class keel.Algorithms.Decision_Trees.M5.M5: Get the value of PruningFactor.
getPruningFactor() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: Get the value of PruningFactor.
getPruningMethod() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Gets the method used for pruning.
getQ() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.QRDecomposition: Generate and return the (economy-sized) orthogonal factor
getQg() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Return the value of Qg
getQualityMeasures(int, String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Get the measurements of a single rule of the main population
getQualityMeasures(int, String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Get the measures of a single rule of the main population
getQualityMeasuresElite(int, String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Get the measurements of a single rule of the elite population
getR() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the correlation value R.
getR() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.QRDecomposition: Return the upper triangular factor
getRadius() - Method in class keel.Algorithms.Instance_Generation.GMCA.Cluster: Gets the radius of the cluster.
getRadius() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbf: Gets the radius of a neuron
getRadius() - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbf: Gets the radius of a neuron
getRadius() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbf: Gets the radius of a neuron
getRadius() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbf: Gets the radius of a neuron
getRadius() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf: Gets the radius of a neuron
getRadius() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbf: Gets the radius of a neuron
getRadius() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf: Gets the radius of a neuron
getRadiusLength() - Method in class keel.Algorithms.Instance_Generation.GMCA.Cluster: Gets the radius length of the cluster.
getRandgen() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ExpNeuronParametricMutator: Returns the random generator used in mutation
getRandgen() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.LinearNeuronParametricMutator: Returns the random generator used in mutation
getRandgen() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.SigmNeuronParametricMutator: Returns the random generator used in mutation
getRandgen() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.ExpNeuronStructuralMutator: Returns the random generator used in mutation
getRandgen() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.LinearNeuronStructuralMutator: Returns the random generator used in mutation
getRandgen() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.SigmNeuronStructuralMutator: Returns the random generator used in mutation
getRandGenFactory() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Returns randgen factory
getRandom() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Get a random prototype
getRandom() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Get a random prototype
getRandomNumberGenerator(long) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns a random number generator.
getRandomNumberGenerator(long) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns a random number generator.
getRandomSeed() - Method in class keel.Algorithms.SVM.SMO.SMO: Get the value of randomSeed.
getRange() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Sample: Returns the range (max-min).
getRange(int) - Method in class keel.GraphInterKeel.datacf.util.Dataset: Return range of index variable
getRangeOutput() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.OpenDataset: It returns the list of values for the output class
getRangeOutput() - Method in class keel.Algorithms.Neural_Networks.net.OpenDataset: It returns the list of values for the output class
getRanges() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
getRanges() - Method in class keel.Algorithms.Decision_Trees.M5.Interval: Gets the string representing the selected range of values
getRanges() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It returns the discourse universe for the input and output variables
getRanges() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It returns the discourse universe for the input and output variables
getRanges() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It returns the discourse universe for the input and output variables
getRanges() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It returns the discourse universe for the input and output variables
getRanges() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Range: Gets the string representing the selected range of values
getRanges() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: It returns the upper and lower ranges for each attribute of the data-set
getRanges() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It returns the discourse universe for the input and output variables
getRanges() - Method in class keel.GraphInterKeel.datacf.util.Dataset: Return a vector of vectors in which each vector contains the ranges for the variable
getRangesEnum(int, int) - Method in class keel.GraphInterKeel.datacf.util.Dataset: Returns range value at index of var variable
getRangesInt(int, int) - Method in class keel.GraphInterKeel.datacf.util.Dataset: Return range value at index of var variable
getRangesReal(int, int) - Method in class keel.GraphInterKeel.datacf.util.Dataset: Return range value at index of var variable
getRangesVar(String) - Method in class keel.GraphInterKeel.datacf.util.Dataset: Return the range of a variable
getRangos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
getRangos(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
getRangos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Returns the minimum and maximum values of every attributes as a matrix.
getRangos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getRangos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getRangos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getRangos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getRangos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getRangos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getRangos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getRangos() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getRangosEnum(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
getRangosVar(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.OpenDataset: It returns the nominal value list of an attribute
getRangosVar(int) - Method in class keel.Algorithms.Neural_Networks.gann.OpenDataset: It returns the list of nominal values of an attribute
getRangosVar(int) - Method in class keel.Algorithms.Neural_Networks.gmdh.OpenDataset: Returns the nominal value list of an attribute
getRangosVar(int) - Method in class keel.Algorithms.Neural_Networks.net.OpenDataset: It returns the nominal value list of an attribute
getRank() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Returns the rank of the individual
getRank() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Chromosome
getRank() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
getRank() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
getRank() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
getRank() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
getRank() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
getRank() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
getRanking() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Individuo
getRanks() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
getRanks() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
getRanks() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
getRbf(String) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Gets an RBF from the net given its identifier
getRbf(String) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbfn: Gets an RBF from the net given its identifier
getRbf(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn: Gets an RBF from the net given its identifier
getRbf(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn: Gets an RBF from the net given its identifier
getRbf(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn: Gets an RBF from the net given its identifier
getRbf(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn: Gets an RBF from the net given its identifier
getRbf(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn: Gets an RBF from the net given its identifier
getReadable() - Method in class keel.Algorithms.Decision_Trees.M5.Association: Gets the string description of the Association.
getReadable() - Method in class keel.Algorithms.SVM.SMO.core.Tag: Gets the string description of the Tag.
getReal() - Static method in class keel.Algorithms.Genetic_Rule_Learning.Globals.Rand: Returns a random real between [0,1)
getReal() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Rand: Returns a random real between [0,1)
GetReal1() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
GetReal1() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
GetReal1() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
GetReal2() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
GetReal2() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
GetReal2() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
GetReal3() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
GetReal3() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
GetReal3() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
getRealEigenvalues() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.EigenvalueDecomposition: Return the real parts of the eigenvalues
getRealTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: Outputs an array of transactions with their corresponding attribute values.
getRealTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: Outputs an array of transactions with their corresponding attribute values.
getRealTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: Outputs an array of transactions with their corresponding attribute values.
getRealTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: Outputs an array of transactions with their corresponding attribute values.
getRealTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: Outputs an array of transactions with their corresponding attribute values.
getRealValue() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Gene: Retrieves the real value of this gene
getRealValue(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.InstanceWrapper
getRealValue(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.InstanceWrapper
getRealValue(int, String) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Gets the real value
getRealValues() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.InstanceWrapper
getRealValues() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.InstanceWrapper
getRecall() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.agentPerformanceTraining
getReduccionIni() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
getReduccionIni() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getReduccionIni() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
getReduccionIni() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getReduccionR() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getReduccionR() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getReducedStepSize() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus: Returns an array of booleans, indicating which coefficients have to be treated as more sensible, using reduced step size for these coefficients
getReferences() - Method in class keel.GraphInterKeel.experiments.UseCase
getregla() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.IndMichigan
getregla() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.IndMichigan
getregla() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.IndMichigan
getregla() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.IndMichigan
getregla() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.IndMichigan
getRegla(int) - Method in class keel.Algorithms.Rule_Learning.Prism.ConjReglas: Returns a rule of the list
getRegla(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjReglas: Returns a rule of the list
getRegla(int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjReglas: Returns a rule of the list
getRegla(int) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjReglas: Returns a rule of the list
getReglasOutputFile() - Method in class keel.Algorithms.Rule_Learning.LEM1.parseParameters: It returns the name of the file containing the rules
getReglasOutputFile() - Method in class keel.Algorithms.Rule_Learning.LEM2.parseParameters: It returns the name of the file containing the rules
getReglasOutputFile() - Method in class keel.Algorithms.Rule_Learning.Ritio.parseParameters: It returns the name of the file containing the rules
getReglasOutputFile() - Method in class keel.Algorithms.Rule_Learning.Rules6.parseParameters: It returns the name of the file containing the rules
getReglasOutputFile() - Method in class keel.Algorithms.Rule_Learning.SRI.parseParameters: It returns the name of the file containing the rules
getRegOptimizer() - Method in class keel.Algorithms.SVM.SMO.SVMreg: returns the learning algorithm
getRegressionResults(DoubleTransposedDataSet) - Method in class keel.Algorithms.Decision_Trees.CART.CART: It gets the regression results
getReInitCob() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Gets if the algorithm uses re-initialisation based on coverage
getRelacion() - Method in class keel.GraphInterKeel.datacf.util.Dataset: Return the relation name
getRelation() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.SMOTE_RSB
getRelation() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Returns the last token after read the last relation in results file.
getRelation() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceParser: It returns the relation name
getRelation() - Method in class keel.Algorithms.Shared.Parsing.ProcessConfig: Returns the last token after read the last relation in results file.
getRelation() - Method in class keel.Dataset.InstanceParser: It returns the relation name
getRelationName() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It gets the relation name.
getRelationName() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It gets the relation name.
getRelationName() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It gets the relation name.
getRelationName() - Static method in class keel.Dataset.Attributes: It gets the relation name.
getRelationName() - Method in class keel.Dataset.InstanceAttributes: It gets the relation name.
getRemainingParameters() - Method in class keel.Algorithms.Instance_Generation.utilities.Parameters: Returns the remained not parsed parameters
getRemainingParameters() - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Returns the parameters that have not been parsed yet.
getRemoved(int) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: Returns True if the i-th instance was removed, false otherwise.
getRemoved(int) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: Returns True if the i-th instance was removed, false otherwise.
getRemoved(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: Returns True if the i-th instance was removed, false otherwise.
getRep() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Returns the representation of the classifier.
getReplace(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: It returns the Replace element of the rule in the position "pos".
getReplace(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: It returns the Replace element of the rule in the position "pos"
getRepresentative() - Method in class keel.Algorithms.Instance_Generation.GMCA.Cluster: Gets the representative of the cluster
getResultingAccuracy(String, String, int, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Internal function that gets only the accuracy of the condensation.
getResultingAccuracy(String, int, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Internal function that gets only the accuracy of the condensation.
getResultingAccuracy(String, String, int, PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Internal function that gets only the accuracy of the condensation.
getResults(String, String, int, int, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Internal function that gets the parameters of accuracy of the condensation.
getResultsOfAccuracy(String, int, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Internal function that gets the parameters of accuracy of the condensation.
getResultsOfAccuracy(String, String, PrototypeSet, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Internal function that gets the parameters of accuracy of the condensation.
getResultsOfAccuracy(String, String, PrototypeSet, PrototypeSet) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Internal function that gets the parameters of accuracy of the condensation.
getReturnValue(int) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Recursion-ending function that is called at the end of each recursion branch.
getRevision() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule: String "1.0"
getRevision() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Returns revision ( string "1.0")
getRidge() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Gets the ridge in the log-likelihood.
getRight() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Gets the right descendant of the node, if it is not a leaf node
getRight() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode: Gets the right descendant of the node, if it is not a leaf node
getRight() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Intervals
getRight() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Interval: It returns the right bound of an interval
getRight() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Intervals
getRightChild() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode
getRightN() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule: It returns the number of right of the rule
getRightSon() - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode
getRightTail() - Static method in class keel.GraphInterKeel.statistical.tests.WilcoxonDistribution: Returns right-tailed p-value of the last comparison
getRoot() - Method in class keel.Algorithms.Decision_Trees.CART.tree.DecisionTree: It returns the root of the tree
getRoot() - Method in class keel.Algorithms.Rule_Learning.ART.TBAR
getRootNode() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGAP: This method returns the root node.
getRootNode() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGP: This method returns the root node.
getRootNode() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGPRegSym: This method returns the root node.
getRow(int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Gets a row of the matrix and returns it as double array.
getRow(int) - Method in class keel.GraphInterKeel.datacf.util.AttributeTable: Returns a row of the table as a vector
getRowCount() - Method in class keel.GraphInterKeel.datacf.util.AttributeTable: Returns the number of rows
getRowCount() - Method in class keel.GraphInterKeel.datacf.util.DatasetTable
getRowCount() - Method in class keel.GraphInterKeel.datacf.util.VariableTable: Gets the number of rows
getRowCount() - Method in class keel.GraphInterKeel.experiments.ParametersTable: Gets the number of rows (i.e. the number of visible parameters)
getRowCount() - Method in class keel.GraphInterKeel.statistical.statTableModel: Gets the number of rows of the table
getRowDimension() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Get row dimension.
getRowPackedCopy() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Make a one-dimensional row packed copy of the internal array.
getRule() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Selected: It returns the rule in the structure.
getRule() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Selected: It returns the rule in the structure
getRule(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Population: Gets the indicated rule
getRule(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Population: Gets one rule using its global index, that is, the index across all subpopulations
getRule(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Subpopulation: Gets a rule from this subpopulation
getRule() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.Rule
getRule(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.RuleSet
getRule() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule
getRule(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.RuleSet
getRule(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Ruleset: Returns the rule in the i-th position of the ruleset.
getRule(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
getRule(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ruleOrderAgent
getRule(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Ruleset: Returns the rule in the i-th position of the ruleset.
getRule(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.ruleSet: It returns the i-th rule
getRule(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleSet: Returns a rule of the list
getRule(int) - Method in class keel.Algorithms.Rule_Learning.AQ.ruleSet: Returns the rule of the list with the index given.
getRule(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Ruleset: Returns the rule in the i-th position of the ruleset.
getRule(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset: Returns the rule in the i-th position of the ruleset.
getRule(int) - Method in class keel.Algorithms.Rule_Learning.CN2.ruleSet: Returns the rule of the list with the given index.
getRule(int) - Method in class keel.Algorithms.Rule_Learning.PART.Ruleset: Returns the rule in the i-th position of the ruleset.
getRule(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset: Returns the rule in the i-th position of the ruleset.
getRule(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Ruleset: Returns the rule in the i-th position of the ruleset.
getRule(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetRules: Return a complex of the rule
getRuleCF() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome
getRuleConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome: It returns the confidence of the association rule represented by a chromosome
getRuleConfidence() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Chromosome
getRuleConv() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome
getRuleLift() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome
getRuleNetconf() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome
getRules() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
getRules() - Method in class keel.Algorithms.Hyperrectangles.BNGE.BNGE: Extract the rules from the training set.
getRules() - Method in class keel.Algorithms.Hyperrectangles.INNER.INNER: The core of INNER algorithm.
getRules() - Method in class keel.Algorithms.Hyperrectangles.RISE.RISE: Extract the rules from the training set.
getRules() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.ARMMGAProcess: Returns the mined association rules
getRuleset() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Get the ruleset of the stats
getRuleset() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Get the ruleset generated by Ripper
getRuleSet() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.RuleSet
getRuleSet() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.RuleSet
getRuleSet() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.ruleSet: It returns the whole rule set
getruleSet() - Method in class keel.Algorithms.Rule_Learning.AQ.ruleSet: It returns the complete rule-set
getruleSet() - Method in class keel.Algorithms.Rule_Learning.CN2.ruleSet: It returns the complete rule-set
getRulesetMask(MyDataset) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset: Returns the combine mask of all the rules in the set.
getRulesetSize() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Get the size of the ruleset in the stats
getRulesIdentifiers() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Returns the Rules identifiers
getRulesRep() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Gets the representation of the rules
getRulesRep() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Gets the rules representation of the algorithm
getRulesRep() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Gets the representation of the rules
getRulesSet() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: It constructs a rules set once the algorithm has been carried out.
getRulesSet() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.AlcalaetalProcess: It returns a rules set once the algorithm has been carried out
getRulesSet() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyAprioriProcess: It returns a rules set once the algorithm has been carried out
getRulesSet() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.GeneticFuzzyAprioriProcess: It returns a rules set once the algorithm has been carried out
getRulesSet() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.GeneticFuzzyAprioriDCProcess: It returns a rules set once the algorithm has been carried out
getRulesSet() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: It constructs a rules set once the algorithm has been carried out.
getRulesStage1() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Apriori: Returns the rules generated on the Stage 1.
getRulesStage1() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Apriori
getRuleStats(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Get the statistics of the ruleset in the given position
getRuleSupport() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.AssociationRule: It returns the support of an association rule
getRuleSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.AssociationRule: It returns the support of an association rule
getRuleSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.AssociationRule: It returns the support of an association rule
getRuleSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.AssociationRule: It returns the support of an association rule
getRuleSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.AssociationRule: It returns the support of an association rule
getRuleSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome: It returns the support of the association rule represented by a chromosome
getRuleSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule: It returns the support of an association rule
getRuleSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule: It returns the support of an association rule
getRuleSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.AssociationRule: It returns the support of an association rule
getRuleSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Chromosome
getRuleWeight(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypePitts: This method returns the weight of a rule
getRuleYulesQ() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome
getRunkeel() - Method in class keel.GraphInterKeel.experiments.EducationalRunkeelEvent: Return a runkeel Class
getRunkeel() - Method in class keel.GraphInterKeel.experiments.RunkeelEvent
getS() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.SingularValueDecomposition: Return the diagonal matrix of singular values
getSalida() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.SMOTE_RSB
getSalidaPDEF() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
getSalidaPDEF() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
getSalidaPDEF() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
getSalidaPDEF() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
getSalidaPDEF() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
getSalidaPDEF() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
getSalidaPDEF() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
getSalidaPDEF() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
getSample() - Method in class keel.Algorithms.Discretizers.UCPD.Sampling: It returns one value of the sampling
getSample() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Sampling
getSample() - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.Sampling
getSample() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Sampling: Returns a sample contained sampling vector. if the Sampling vector is not instantiated, the function will do it.
getSample() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Sampling
getSample() - Method in class keel.Algorithms.Hyperrectangles.EACH.Sample: Returns the attributes(array of values)
getSample(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Sample: Returns the value of the attribute 'i' of the example
getSample() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
getSample() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
getSample() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getSample() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
getSample() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
getSample() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
getSample() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getSample() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
getSampleSize() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Sample: Returns the number of samples.
getScaledFitness() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
getScaledSocialNetwork() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Returns the matrix that stores the scaled social network
getScore(Instances) - Method in class keel.Algorithms.Rule_Learning.SRI.Regla: Computes the quality of the rule with the m-probability-estimation.
getScore() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: Compute the value of the objective function.
getScoringFunction() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
getScrollableBlockIncrement(Rectangle, int, int) - Method in class keel.GraphInterKeel.experiments.DinamicDataset
getScrollableBlockIncrement(Rectangle, int, int) - Method in class keel.GraphInterKeel.experiments.GraphPanel
getScrollableBlockIncrement(Rectangle, int, int) - Method in class keel.GraphInterKeel.experiments.SelectData
getScrollableTracksViewportHeight() - Method in class keel.GraphInterKeel.experiments.DinamicDataset
getScrollableTracksViewportHeight() - Method in class keel.GraphInterKeel.experiments.GraphPanel
getScrollableTracksViewportHeight() - Method in class keel.GraphInterKeel.experiments.SelectData
getScrollableTracksViewportWidth() - Method in class keel.GraphInterKeel.experiments.DinamicDataset
getScrollableTracksViewportWidth() - Method in class keel.GraphInterKeel.experiments.GraphPanel
getScrollableTracksViewportWidth() - Method in class keel.GraphInterKeel.experiments.SelectData
getScrollableUnitIncrement(Rectangle, int, int) - Method in class keel.GraphInterKeel.experiments.DinamicDataset
getScrollableUnitIncrement(Rectangle, int, int) - Method in class keel.GraphInterKeel.experiments.GraphPanel
getScrollableUnitIncrement(Rectangle, int, int) - Method in class keel.GraphInterKeel.experiments.SelectData
getScrollPane() - Method in class keel.GraphInterKeel.datacf.editData.EditDataPanel: Return datajScrollPane
getSeBeta0() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the Standard errors intercept - Se(beta0)
getSeBeta1() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the Standard errors slope - Se(beta1)
getSecond() - Method in class keel.Algorithms.Instance_Generation.utilities.Pair: Get second element of the pair.
getSecond() - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Pair: Get second element of the pair.
getSecondTimeLimit() - Method in class keel.Algorithms.Lazy_Learning.IDIBL.IDIBL: Get second tunning algorithm time limit
getSeed() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Gets the current seed value to use in randomizing the data
getSeed() - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Get the seed of the random generator.
getSeed() - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Gets the seed for the random number generations
getSeed() - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Get the seed of the random generator.
getSeed() - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Gets the seed for the random number generations
getSeed() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Gets the seed for the random number generations
getSeed() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: Gets the current seed value for the random number generator
getSeed() - Method in class keel.GraphInterKeel.experiments.Graph: Gets the seed of this graph
getSeed(int) - Method in class keel.GraphInterKeel.experiments.Parameters: return seed at index position
getSeeds() - Method in class keel.GraphInterKeel.experiments.Parameters: return actual seeds
getSeizedSamples() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Computes the number of training samples that match this rule.
getSelectedTag() - Method in class keel.Algorithms.Decision_Trees.M5.SelectedAssociation: Gets the selected Tag.
getSelectedTag() - Method in class keel.Algorithms.SVM.SMO.core.SelectedTag: Gets the selected Tag.
getSelection() - Method in class keel.Algorithms.Decision_Trees.M5.Interval: Gets an array containing all the selected values, in the order that they were selected (or ascending order if range inversion is on)
getSelection() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Range: Gets an array containing all the selected values, in the order that they were selected (or ascending order if range inversion is on)
getSelectionAlgorithm() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
getSelector(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Returns the selector in a position given the complex
getSelector(int) - Method in class keel.Algorithms.Rule_Learning.AQ.Complex: It returns one of the selectors associated to the complex
getSelector(int) - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: It returns one of the selectors associated to the complex
getSelector(int) - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Returns the selector in a position given the complex
getSelector(int) - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Return a selector in one position by giving a complex
getSelector(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Return a selector in one position by giving a complex
getSelector(int) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Return a selector in one position by giving a complex
getSelector(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Get a selector
getSelectorTabbedPane() - Method in class keel.GraphInterKeel.datacf.DataCFFrame: Returns Selector Tabbed Pane
getSelectorToolbar() - Method in class keel.GraphInterKeel.datacf.DataCFFrame: Returns Selector ToolBar
getSequentialState() - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Environment: It returns the new Example of a single step file environment.
getSequentialState() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.MPEnvironment: It returns the new Example of a single step file environment.
getSequentialState() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.SSFileEnvironment: It returns the new Example of a single step file environment.
getSequentialState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.DECEnvironment: It returns the new Example of a single step file environment.
getSequentialState() - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Environment: It returns the new Example of a single step file environment.
getSequentialState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MPEnvironment: It returns the new Example of a single step file environment.
getSequentialState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MSEnvironment: It returns the new Example of a single step file environment.
getSequentialState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.PAREnvironment: It returns the new Example of a single step file environment.
getSequentialState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.POSEnvironment: It returns the new Example of a single step file environment.
getSequentialState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.SSFileEnvironment: It returns the new Example of a single step file environment.
getSet() - Method in class keel.Algorithms.Instance_Generation.GMCA.Cluster: Gets the prototype set of the cluster.
getSetRules() - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleSet: Returns the complet set if rules
getSetRules(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.EARMGAProcess
getSetRules(double, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.GARProcess: Returns the rules that have their confidence and support values higher than the minimum ones given.
getSetRules(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.GENARProcess: Returns the rules that have their support values higher than the minimum given.
getSetSizeFromPercentage(double) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Number of prototypes corresponding of the desired percentage of the reduced set.
getSetSizeFromPercentage(PrototypeSet, double) - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Number of prototypes corresponding of the desired percentage of the reduced set.
getSetSizeFromPercentage(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Number of prototypes corresponding of the desired percentage of the reduced set.
getSetSizeFromPercentage(PrototypeSet, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Number of prototypes corresponding of the desired percentage of the reduced set.
getSigma() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.NormalDistribution: Returns the sigma value of the distribution.
getSigma() - Method in class keel.Algorithms.SVM.SMO.supportVector.Puk: Gets the sigma value.
getSigma() - Method in class keel.GraphInterKeel.statistical.tests.NormalDistribution: Get the sigma value of the distribution
getSigmaSq() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the variance error (sigma^2)
getSign() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Retuns the value of SIGN
getSignificativeWeight() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.initiators.RandomInitiator: Access to significative weight value
getSignificativeWeigth() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.ExpNeuronStructuralMutator: Returns the minimum value of new weigths
getSignificativeWeigth() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.LinearNeuronStructuralMutator: Returns the minimum value of new weigths
getSignificativeWeigth() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.SigmNeuronStructuralMutator: Returns the minimum value of new weigths
getSignificativeWeigth() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Returns the significative weigth for new links
getSilent() - Method in class keel.Algorithms.SVM.SMO.core.Check: Get whether silent mode is turned on
getSimpleRule(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule: Returns the i-ieth simple rule of this rule.
getSimpleRule(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule: Returns the i-ieth simple rule of this rule.
getSimpleRule(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Rule: Returns the i-ieth simple rule of this rule.
getSimpleRule(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule: Returns the i-ieth simple rule of this rule.
getSimpleRule(int) - Method in class keel.Algorithms.Rule_Learning.PART.Rule: Returns the i-ieth simple rule of this rule.
getSimpleRule(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Rule: Returns the i-ieth simple rule of this rule.
getSimpleRule(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Rule: Returns the i-ieth simple rule of this rule.
getSimpleStats(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Get the simple stats of one rule, including 6 parameters: 0: coverage; 1:uncoverage; 2: true positive; 3: true negatives; 4: false positives; 5: false negatives
getSingleIndex() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.SingleIndex: Gets the string representing the selected range of values
getSingularValues() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.SingularValueDecomposition: Return the one-dimensional array of singular values
getSize() - Method in class keel.Algorithms.Instance_Selection.IGA.Cromosoma: Returns the size of the chromosome
getSize() - Method in class keel.Algorithms.Lazy_Learning.IDIBL.NQueue: Get actual size of the queue
getSize() - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster: See size.
getSize() - Method in class keel.Algorithms.Preprocess.Instance_Selection.IGA.Cromosoma: Chromosome size
getSize() - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome: Returns the size of the chromosome.
getSize() - Static method in class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome: Returns the size of the chromosome.
getSize() - Method in class keel.Algorithms.Rule_Learning.AQ.Complex: It returns the number of selectors of the complex
getSize() - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: It returns the number of selectors of the complex
getSizeAntecedentes() - Method in class keel.Algorithms.Rule_Learning.Rules6.Regla: Returns the size of the antecedents list.
getSizeAntecedentes() - Method in class keel.Algorithms.Rule_Learning.SRI.Regla: Returns the size of the antecedents list.
getSizeCondition() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Condition
getSlope() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the regression Slope (Beta1)
getSmoothedFunction() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode
getSnextNminus(double[][], double[][], int[][], boolean[][], int[], boolean, Vector) - Method in class keel.Algorithms.Instance_Selection.ZhangTS.Cromosoma: Function that returns the best neighbor in N-
getSnextNminus(double[][], double[][], int[][], boolean[][], int[], boolean, Vector) - Method in class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.Cromosoma: Function that returns the best neighbor in N-
getSnextNplus(double[][], double[][], int[][], boolean[][], int[], boolean, Vector) - Method in class keel.Algorithms.Instance_Selection.ZhangTS.Cromosoma: Function that returns the best neighbor in N+
getSnextNplus(double[][], double[][], int[][], boolean[][], int[], boolean, Vector) - Method in class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.Cromosoma: Function that returns the best neighbor in N+
getSocialNetwork() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Returns the matrix that stores the complete social network.
getSolicitaGarbageColector() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoBC.CoBCGenerator: Asks for the garbage collector
getSolicitaGarbageColector() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoTraining.CoTrainingGenerator: Asks for the garbage collector.
getSolicitaGarbageColector() - Method in class keel.Algorithms.Semi_Supervised_Learning.DE_TriTraining.DE_TriTrainingGenerator: Asks for the garbage collector.
getSolicitaGarbageColector() - Method in class keel.Algorithms.Semi_Supervised_Learning.Democratic.DemocraticGenerator: Asks for the garbage collector.
getSolicitaGarbageColector() - Method in class keel.Algorithms.Semi_Supervised_Learning.RASCO.RASCOGenerator: Asks for the garbage collector.
getSolicitaGarbageColector() - Method in class keel.Algorithms.Semi_Supervised_Learning.Rel_RASCO.Rel_RASCOGenerator: Asks for the garbage collector.
getSolicitaGarbageColector() - Method in class keel.Algorithms.Semi_Supervised_Learning.SelfTraining.SelfTrainingGenerator: Asks for the garbage collector.
getSolicitaGarbageColector() - Method in class keel.Algorithms.Semi_Supervised_Learning.TriTraining.TriTrainingGenerator: Asks for the garbage collector.
getSource() - Method in class keel.GraphInterKeel.experiments.Arc: Gets the source node of this arc
getSource2() - Method in class keel.GraphInterKeel.experiments.Arc: Gets the source node of this arc
getSpecialImpurityLevel(Rule[]) - Method in class keel.Algorithms.Hyperrectangles.INNER.Rule: Computes the impurity level of a rule, by considering only those instances which are not already covered by other rules
getSpecies() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.KEELIRPropPlusWrapperClas: Access to system species.
getSpecies() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.KEELIRPropPlusWrapperRegr: Access to system species.
getSpecies() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Access to system species
getSplitingAttribute() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode
getSplitingValue() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode
getSplitPoint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.NumericAntd: Get split point of this numeric antecedent
getSSe() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the sum of squared errors (SSe)
getSSEadj() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ConjGradNN: Returns the SSE statistical variable
getSSr() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the sum of squares due to regression (SSr)
getStagnationTolerance() - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Gets the stagnation tolerance
getStandardDeviation() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Sample: Returns the Standard deviation.
getStandardDeviation(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Sample: Returns a Confidence Interval for the standard deviation value with the given confidence.
getStandardErrorFromCommand() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.SystemCommandExecutor
getStandardOutputFromCommand() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.SystemCommandExecutor
getStartOfPtree() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree: Gets reference to start of P-tree.
getState() - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.MTwister
getState() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.MTwister
getState() - Method in class org.core.MTwister
getStateAddButton() - Method in class keel.GraphInterKeel.datacf.editData.EditDataPanel: Gets the state of Add Button
getStateDeleteButton() - Method in class keel.GraphInterKeel.datacf.editData.EditDataPanel: Gets the state of Delete Button
getStdDev() - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster: Get stdDev of the cluster
getStdErrorIntercept() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the Standard errors intercept - Se(beta0)
getStdErrorSlope() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the Standard errors slope - Se(beta1)
getStdPerClass() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: Returns the standard deviation per class.
getStdPerClass() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Returns the standard deviation per class.
getSteps() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It returns an array with the step values of each attribute depending on the chosen number of partitions
getSteps() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: It returns an array with the step values of each attribute depending on the chosen number of partitions
getSteps() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: It returns an array with the step values of each attribute depending on the chosen number of partitions
getSteps() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: It returns an array with the step values of each attribute depending on the chosen number of partitions
getStrength() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Rule: It gets the strength of the rule
getStrictDominance() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Gets if the algorithm considers strict dominance
getStringIndices(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Gets an array containing the indices of all string attributes.
getStringIndices(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter: Returns the indeces of the given instances.
getStringValue(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Returns the value of the given attribute.
getStringValue(MyAttribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Returns the value of the given attribute.
getSubDirectories(String, File, HashSet) - Static method in class keel.Algorithms.SVM.SMO.core.ClassDiscovery: adds all the sub-directories recursively to the list
getSubfront(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Ranking: Returns a Population containing the solutions of a given rank.
getSubsequenceLength() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Returns the length of the subsequence
getSubset(Vector, int, int) - Method in class keel.Algorithms.Decision_Trees.ID3.ID3: Function to returns a subset of data.
getSubtype() - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription: Gets the subtype of this object
getSubtypelqd() - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription: Gets the subtype of this object
getSuccessRatio() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricSRMutator: Returns the success ratio of last generation
GetSuffix(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
GetSuffix(int) - Static method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
GetSuffix(int) - Static method in class keel.Dataset.SimpleCharStream
getSuitability() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Chromosome: It returns the suitability of a chromosome
getSuitability() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Chromosome: It returns the suitability of a chromosome
getSuitability() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Chromosome: It returns the suitability of a chromosome
getSum() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Sample: Returns the summation of samples values.
getSumFuzzySupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Chromosome: It returns the sum of fuzzy supports of the 1-Frequent Itemsets covered by a chromosome
getSumInterval() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
getSumInterval() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
getSumInterval() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
getSumInterval() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
getSumSquares() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Sample: Returns the summation of the samples squares.
getSup() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets
getSup() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Return the value of Sup
getSup() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.QualityMeasures: Method to return the value of the support
getSupport() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset: It returns the support of the antecedent of the itemset.
getSupport() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: It returns the Support of the rule.
getSupport() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset: It returns the support of the antecedent of the itemset
getSupport() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: It returns the Support of the rule
getSupport() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Itemset: It returns the support of the antecedent of the itemset
getSupport() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule: It returns the support of the rule
getSupport() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Itemset: It returns the support of the antecedent of the itemset
getSupport() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Rule: It returns the support of the rule
getSupport() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Itemset: It returns the support of the antecedent of the itemset
getSupport() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Rule: It returns the support of the rule
getSupport() - Method in class keel.Algorithms.Discretizers.UCPD.Itemset: It returns the support of this itemset
getSupport() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Itemset
getSupport() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule
getSupport() - Method in class keel.Algorithms.Rule_Learning.ART.Rule
getSupport() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Item: Returns the support value for the item.
getSupport() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Returns the support value of the rule.
getSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Itemset: It returns the support of an itemset
getSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams
getSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Itemset: It returns the support of an itemset
getSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Itemset: It returns the support of an itemset
getSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Itemset: It returns the support of an itemset
getSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Itemset: It returns the support of an itemset
getSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AssociationRule: It returns the support of an association rule
getSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.Item: It returns the support of an item
getSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule: It returns the support of an association rule
getSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.Item: It returns the support of an item
getSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
getSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
getSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule: It returns the support of an association rule
getSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome: It returns the support of the association rule represented by a chromosome
getSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule: It returns the support of an association rule
getSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome: It returns the support of the association rule represented by a chromosome
getSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule: It returns the support of an association rule
getSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome: It returns the support of the association rule represented by a chromosome
getSupport_Ant() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
getSupport_Ant() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule
getSupport_cons() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
getSupport_cons() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule: It returns the consequent support of an association rule
getSupport_cons() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
getSupport_consq() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
getSupport_consq() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
getSupportAll() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
getSupportAll() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
getSupportAnt() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
getSupportAnt() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
getSupportClass() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset: It returns the support of the itemset for its related output class.
getSupportClass() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset: It returns the support of the itemset for its related output class
getSupportClass() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Itemset: It returns the support of the itemset for its related output class
getSupportClass() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Itemset: It returns the support of the itemset for its related output class
getSupportClass() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Itemset: It returns the support of the itemset for its related output class
getSupportClass() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Itemset
getSupportCon() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
getSupportCon() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
getSupportForItemSetInTtree(short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Commences process for finding the support value for the given item set in the T-tree.
getSupportForItemSetInTtree(short[]) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TotalSupportTree: Commences process for finding the support value for the given item set in the T-tree (which is know to exist in the T-tree).
getSupportForItemSetInTtree(short[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TotalSupportTree: Commences process for finding the support value for the given item set in the T-tree (which is know to exist in the T-tree).
getSupportVectors() - Method in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: This method returns the support vectors
getSV() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_model
getSV() - Method in class org.libsvm.svm_model
getSxx() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the statistical Sxx.
getSxy() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the statistical Sxy.
getSyy() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the statistical Syy.
getT() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
getT0() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the statistical T0.
getTablaDataset() - Method in class keel.GraphInterKeel.datacf.editData.EditDataPanel: Return tablaDataset
getTable() - Static method in class keel.GraphInterKeel.statistical.tests.WilcoxonDistribution: Get the inner table
getTableCellEditorComponent(JTable, Object, boolean, int, int) - Method in class keel.GraphInterKeel.datacf.util.EachRowEditor
getTableCellEditorComponent(JTable, Object, boolean, int, int) - Method in class keel.GraphInterKeel.statistical.StatCellEditor: Gets the cell editor component used
gettableVector() - Method in class keel.GraphInterKeel.experiments.Joint
getTabSize(int) - Static method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
getTabSize(int) - Static method in class keel.Dataset.SimpleCharStream
getTags() - Method in class keel.Algorithms.Decision_Trees.M5.SelectedAssociation: Gets the set of all valid Tags.
getTags() - Method in class keel.Algorithms.SVM.SMO.core.SelectedTag: Gets the set of all valid Tags.
getTarget() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.Link: Returns the target neuron of the link
getTechnicalInformation() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in interface keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformationHandler: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in interface keel.Algorithms.SVM.SMO.core.TechnicalInformationHandler: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class keel.Algorithms.SVM.SMO.SMO: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class keel.Algorithms.SVM.SMO.supportVector.Puk: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMO: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class keel.Algorithms.SVM.SMO.SVMreg: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTemperExponent() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Returns the temperature exponent to be used in the mutations
getTemperExponent() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Returns the temperature exponent to be used in the mutations
getTest() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.SMOTE_RSB
getTestAt(int) - Method in class keel.GraphInterKeel.experiments.DataSet: Get the test file at the indicated position
getTestAUC() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.C45CS: Calculates the AUC for the test set
getTestAUC() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svmClassifierCost: Calculates the AUC for the test set
getTestClassificationBehaviorArray(IClassifier) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.ClassificationProblemEvaluator: Returns an array with the bevavior of the classifier with test dataset
getTestClassificationBehaviorMatrix(IClassifier, IClassifier) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.ClassificationProblemEvaluator: Returns a matrix with the comparative bevavior of two classifiers in the test dataset
getTestClassificationError(IClassifier, IErrorFunction<byte[][]>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.ClassificationProblemEvaluator: Returns the test error value of a neural net with an specified error function
getTestData() - Method in class keel.Algorithms.Decision_Trees.CART.ResultPrinter: It gets the test data
getTestData() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.problem.IProblem: Returns the test data associated to this problem
getTestData() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Returns the test data associated to this evaluator
getTestFiles() - Method in class keel.GraphInterKeel.experiments.Parameters: return original test files
getTestInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.parseParameters
getTestInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.parseParameters: It returns the test input file
getTestInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.parseParameters: It returns the test input file
getTestInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.parseParameters: It returns the test input file
getTestInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.parseParameters: It returns the test input file
getTestInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.parseParameters: It returns the test input file
getTestInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.parseParameters: It returns the test input file
getTestInputFile() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.parseParameters: It returns the test input file
getTestInputFile() - Method in class keel.Algorithms.Decision_Trees.DT_GA.parseParameters: It returns the test input file
getTestInputFile() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.parseParameters: It returns the test input file
getTestInputFile() - Method in class keel.Algorithms.Decision_Trees.Target.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.parseParameters: It returns the test input file
getTestInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.parseParameters: It returns the test input file
getTestInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.parseParameters: It returns the test input file
getTestInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.ParametersParser: It returns the test input file
getTestInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.parseParameters: It returns the test input file
getTestInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.parseParameters: Gets the path of the test input file
getTestInputFile() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.PSO_Learning.CPSO.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.PSO_Learning.REPSO.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Rule_Learning.C45Rules.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Rule_Learning.LEM1.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Rule_Learning.LEM2.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Rule_Learning.PART.parseParameters: It returns the test input file
getTestInputFile() - Method in class keel.Algorithms.Rule_Learning.Ripper.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Rule_Learning.Ritio.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Rule_Learning.Rules6.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Rule_Learning.Slipper.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Rule_Learning.SRI.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.parseParameters: It returns the name of the file containing the test data
getTestInputFile() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.parseParameters: Gets the name of the test file
getTestInputFile() - Method in class keel.Algorithms.SVM.C_SVM.parseParameters: Gets the path of the test input file
getTestInputFile() - Method in class keel.Algorithms.SVM.EPSILON_SVR.parseParameters: Gets the path of the test input file
getTestInputFile() - Method in class keel.Algorithms.SVM.NU_SVM.parseParameters: Gets the path of the test input file
getTestInputFile() - Method in class keel.Algorithms.SVM.NU_SVR.parseParameters: Gets the path of the test input file
getTestOutputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.parseParameters: It returns the test output file
getTestOutputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.parseParameters: It returns the test output file
getTestOutputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.parseParameters: It returns the test output file
getTestOutputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.parseParameters: It returns the test output file
getTestOutputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.parseParameters: It returns the test output file
getTestOutputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.parseParameters: It returns the test output file
getTestOutputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.parseParameters: It returns the test output file
getTestOutputFile() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.parseParameters: It returns the test output file
getTestOutputFile() - Method in class keel.Algorithms.Decision_Trees.DT_GA.parseParameters: It returns the test output file
getTestOutputFile() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.parseParameters: It returns the test output file
getTestOutputFile() - Method in class keel.Algorithms.Decision_Trees.Target.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.parseParameters: It returns the test output file
getTestOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.parseParameters: It returns the test output file
getTestOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.parseParameters: It returns the test output file
getTestOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.ParametersParser: It returns the test output file
getTestOutputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.parseParameters: It returns the test output file
getTestOutputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.parseParameters: Gets the path of the test output file for printing the results (from the test file)
getTestOutputFile() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.PSO_Learning.CPSO.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.PSO_Learning.REPSO.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Rule_Learning.C45Rules.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Rule_Learning.LEM1.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Rule_Learning.LEM2.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Rule_Learning.PART.parseParameters: It returns the test output file
getTestOutputFile() - Method in class keel.Algorithms.Rule_Learning.Ripper.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Rule_Learning.Ritio.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Rule_Learning.Rules6.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Rule_Learning.Slipper.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Rule_Learning.SRI.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.parseParameters: It returns the name of the file containing the output for the test data
getTestOutputFile() - Method in class keel.Algorithms.SVM.C_SVM.parseParameters: Gets the path of the test output file for printing the results (from the test file)
getTestOutputFile() - Method in class keel.Algorithms.SVM.EPSILON_SVR.parseParameters: Gets the path of the test output file for printing the results (from the test file)
getTestOutputFile() - Method in class keel.Algorithms.SVM.NU_SVM.parseParameters: Gets the path of the test output file for printing the results (from the test file)
getTestOutputFile() - Method in class keel.Algorithms.SVM.NU_SVR.parseParameters: Gets the path of the test output file for printing the results (from the test file)
getTestOutputFiles() - Method in class keel.GraphInterKeel.experiments.Parameters: return .tst output files
getTestPartition(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.PartitionScheme: It returns the test partition specified
getTestPartition(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.PartitionScheme: It returns the test partition specified
getTestPartition(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.PartitionScheme: It returns the test partition specified
getTestPartition(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.PartitionScheme: It returns the test partition specified
getTestPartition(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.PartitionScheme: It returns the test partition specified
getTestPartition(int) - Method in class keel.GraphInterKeel.datacf.partitionData.PartitioningSchemes.DistanceBased_best: It returns the test partition specified
getTestQStatistic(IClassifier, IClassifier) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.ClassificationProblemEvaluator: Returns the diversity measure Q statistic of two classifiers in test data set
getTestQStatistic(List<IClassifier>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.ClassificationProblemEvaluator: Returns the diversity measure Q statistic of two classifiers in test data set
getTestRegressionError(IRegressor, IErrorFunction<double[]>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.problem.regression.RegressionProblemEvaluator: Returns the test error value of a neural net with an specified error function
getTestResultFile() - Method in class keel.Algorithms.Decision_Trees.CART.ResultPrinter: It gets the test result file
getTestResultFile() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlusReporterClas: Returns file name where the testing results of best model obtained will be written
getTestResultFile() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.IRPropPlusReporterRegr: Returns file name where the testing results of best model obtained will be written
getTestResultFile() - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.listener.NeuralNetReporterClas: Returns file name where the testing results of best model obtained will be written
getTestResultFile() - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.listener.NeuralNetReporterRegr: Returns file name where the testing results of best model obtained will be written
getTestTime() - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.Timer: Get test time
getTestTime() - Static method in class keel.Algorithms.RST_Learning.Timer: Get test time
getTextRules() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Returns the matrix that stores the text of all the rules.
getTheoryCost(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Ruleset: The description length of the theory for the ruleset.
getTheoryCost(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset: The description length of the theory for the ruleset.
getTheoryCost(MyDataset) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset: The description length of the theory for the ruleset.
getTheoryLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
getTheoryLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
getTheoryLength() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
getTHRESHOLD() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
getTHRESHOLD() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
getTHRESHOLD() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getTHRESHOLD() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
getTHRESHOLD() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
getTHRESHOLD() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
getTHRESHOLD() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getTHRESHOLD() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
getThreshold() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess
getTHRESHOLDR() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getTHRESHOLDR() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getTIDList() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It outputs an array of attribute values with their corresponding TIDs
getTIDList() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: It outputs an array of attribute values with their corresponding TIDs
getTIDList() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: It outputs an array of attribute values with their corresponding TIDs
getTIDList() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: It outputs an array of attribute values with their corresponding TIDs
getTime() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: It returns the time of the rule.
getTime() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: It returns the time of the rule
getTime() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule: It returns the time of the rule
getTime() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Returns the time stamp of the classifier.
getTime() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters: Returns the time of the classifier.
getTime() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Returns the time stamp of the classifier.
getTime() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Returns the time of the classifier.
getTime() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Tell the time elapsed.
getTime() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Tell the time elapsed.
gettimes() - Method in class keel.GraphInterKeel.experiments.Joint
getTipifiedProbability(double, boolean) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.NormalDistribution: Computes cumulative N(0,1) distribution.
getTipifiedProbability(double, boolean) - Method in class keel.GraphInterKeel.statistical.tests.NormalDistribution: Computes cumulative N(0,1) distribution.
getTipo(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Returns the type of the attribute with index passed as argument.
getTipo(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It returns the type of an attribute
getTipo(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It returns the type of an attribute
getTipo(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It returns the type of an attribute
getTipo(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It returns the type of an attribute
getTipo(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Returns the type of the attribute with index passed as argument.
getTipo(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It returns the type of an attribute
getTipo(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It returns the type of an attribute
getTipo(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It returns the type of an attribute
getTipo(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It returns the type of the attribute specified
getTipo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Atributo: Returns the attribute type.
getTipo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Atributo: Returns the attribute type.
getTipo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Atributo: Returns the attribute type.
getTipo(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It returns the type of an attribute
getTipo(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It returns the type of an attribute
getTipo(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It returns the type of an attribute
getTipo(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It returns the type of the attribute specified
getTipo() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Atributo: Returns the attribute type.
getTipo(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It returns the type of an attribute
getTipo(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It returns the type of the attribute specified
getTipo(int) - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It returns the type of the attribute specified
getTipos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
getTipos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Returns the types of every attribute.
getTiposAt(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.OpenDataset: Return type (0 nominal, 1 integer, 2 float,..) of attribute at index.
getTiposAt(int) - Method in class keel.Algorithms.Neural_Networks.gann.OpenDataset: Return type (0 nominal, 1 integer, 2 float,..) of the attribute at index
getTiposAt(int) - Method in class keel.Algorithms.Neural_Networks.gmdh.OpenDataset: Return type (0 nominal, 1 integer, 2 float,..) of attribute at index.
getTiposAt(int) - Method in class keel.Algorithms.Neural_Networks.net.OpenDataset: Return type (0 nominal, 1 integer, 2 float,..) of attribute at index.
getTiposIndex(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos: Returns the type of the given attribute index.
getTiposIndex(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Returns the type of the attribute given.
getTiposIndex2(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Returns the type of the attribute given.
getTMtablaAtributos() - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelAttribute: Get the Attributes Table
getTodo() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Individual: It returns the whole chromosome
getToken(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
getToken(int) - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
getToken(int) - Static method in class keel.Dataset.DataParser
getTolerance() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: returns the current tolerance
getToleranceParameter() - Method in class keel.Algorithms.SVM.SMO.SMO: Get the value of tolerance parameter.
getToleranceParameter() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Get the value of tolerance parameter.
getTotal() - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Returns total weight of itemsets.
getTotal() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Returns total weight of itemsets.
getTotal() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Returns total weight of itemsets.
getTotal() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Returns total weight of itemsets.
getTotal() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Returns total weight of itemsets.
getTotal() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Returns total weight of itemsets.
getTotal() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Returns total weight of itemsets.
getTotal() - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Returns total weight of itemsets.
getTotal(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Score: Returns the total number of instances of the dataset that contains the value at a given position of the vector.
getTotal(double) - Method in class keel.Algorithms.Rule_Learning.Ripper.Score: Returns the total number of instances that contains the given value.
getTotal(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Score: Returns the total number of instances of the dataset that contains the value at a given position of the vector.
getTotal(double) - Method in class keel.Algorithms.Rule_Learning.Slipper.Score: Returns the total number of instances that contains the given value.
getTotal() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Returns total weight of itemsets.
getTotalClass() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual: Returns the number of the class of the invividual
getTotalClass() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual: Returns the number of the class of the invividual
getTotalClass() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual: Returns the number of the class of the invividual
getTotalErrors() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Selected: It returns the total of errors in the structure.
getTotalErrors() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Selected: It returns the total of errors in the structure
getTotalexperiments() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Chc
getTotalexperiments() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Chc
getTotalexperiments() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Chc
getTotalexperiments() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Chc
getTotalexperiments() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Chc
getTotalexperiments() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Chc
getTotalexperiments() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Chc
getTotalexperiments() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Chc
getTotalFiringDegrees() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Returns the Vector that contains the total firing degrees of rules.
getTotalFractions() - Method in class keel.GraphInterKeel.datacf.partitionData.HoldOutOptionsJDialog: Gets the number of fractions of the hold-out process.
getTotalNumberClass() - Method in class keel.Algorithms.Statistical_Tests.Shared.StatTest.InformationAboutClass: Returns the total number of classes of the problem.
getTotalPartitions() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: This methos return the partition total number
getTotaltrials() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Chc
getTotaltrials() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Chc
getTotaltrials() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Chc
getTotaltrials() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Chc
getTotaltrials() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Chc
getTotaltrials() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Chc
getTotaltrials() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Chc
getTotaltrials() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Chc
getTotalWeight() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.PRM: It returns the total weight of the positive classification
getTP() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Return the value of TP
getTrainAUC() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.C45CS: Calculates the AUC for the training set
getTrainAUC() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svmClassifierCost: Calculates the AUC for the training set
getTrainClassificationBehaviorArray(IClassifier) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.ClassificationProblemEvaluator: Returns an array with the bevavior of the classifier with train dataset
getTrainClassificationBehaviorMatrix(IClassifier, IClassifier) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.ClassificationProblemEvaluator: Returns a matrix with the comparative bevavior of two classifiers in the train dataset
getTrainClassificationError(IClassifier, IErrorFunction<byte[][]>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.ClassificationProblemEvaluator: Returns the train error value of a neural net with an specified error function
getTrainData() - Method in class keel.Algorithms.Decision_Trees.CART.ResultPrinter: Ir returns the training data
getTrainData() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.problem.IProblem: Returns the train data associated to this problem
getTrainData() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Returns the train data associated to this evaluator
getTrainingAt(int) - Method in class keel.GraphInterKeel.experiments.DataSet: Get the training file at the indicated position
getTrainingInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.parseParameters: It returns the training input file
getTrainingInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.parseParameters: It returns the training input file
getTrainingInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.parseParameters: It returns the training input file
getTrainingInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.parseParameters: It returns the training input file
getTrainingInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.parseParameters: It returns the training input file
getTrainingInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.parseParameters: It returns the training input file
getTrainingInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.parseParameters: It returns the training input file
getTrainingInputFile() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.parseParameters: It returns the training input file
getTrainingInputFile() - Method in class keel.Algorithms.Decision_Trees.DT_GA.parseParameters: It returns the training input file
getTrainingInputFile() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.parseParameters: It returns the training input file
getTrainingInputFile() - Method in class keel.Algorithms.Decision_Trees.Target.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.parseParameters: It returns the training input file
getTrainingInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.parseParameters: It returns the training input file
getTrainingInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.parseParameters: It returns the training input file
getTrainingInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.ParametersParser: It returns the training input file
getTrainingInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.parseParameters: It returns the training input file
getTrainingInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.parseParameters: Gets the path of the training input file
getTrainingInputFile() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.PSO_Learning.CPSO.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.PSO_Learning.REPSO.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Rule_Learning.C45Rules.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Rule_Learning.LEM1.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Rule_Learning.LEM2.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Rule_Learning.PART.parseParameters: It returns the training input file
getTrainingInputFile() - Method in class keel.Algorithms.Rule_Learning.Ripper.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Rule_Learning.Ritio.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Rule_Learning.Rules6.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Rule_Learning.Slipper.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Rule_Learning.SRI.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.parseParameters: It returns the name of the file containing the training data
getTrainingInputFile() - Method in class keel.Algorithms.SVM.C_SVM.parseParameters: Gets the path of the training input file
getTrainingInputFile() - Method in class keel.Algorithms.SVM.EPSILON_SVR.parseParameters: Gets the path of the training input file
getTrainingInputFile() - Method in class keel.Algorithms.SVM.NU_SVM.parseParameters: Gets the path of the training input file
getTrainingInputFile() - Method in class keel.Algorithms.SVM.NU_SVR.parseParameters: Gets the path of the training input file
getTrainingOutputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.parseParameters: It returns the training output file
getTrainingOutputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.parseParameters: It returns the training output file
getTrainingOutputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.parseParameters: It returns the training output file
getTrainingOutputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.parseParameters: It returns the training output file
getTrainingOutputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.parseParameters: It returns the training output file
getTrainingOutputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.parseParameters: It returns the training output file
getTrainingOutputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.parseParameters: It returns the training output file
getTrainingOutputFile() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.parseParameters: It returns the training output file
getTrainingOutputFile() - Method in class keel.Algorithms.Decision_Trees.DT_GA.parseParameters: It returns the training output file
getTrainingOutputFile() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.parseParameters: It returns the training output file
getTrainingOutputFile() - Method in class keel.Algorithms.Decision_Trees.Target.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.parseParameters: It returns the training output file
getTrainingOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.parseParameters: It returns the training output file
getTrainingOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.parseParameters: It returns the training output file
getTrainingOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.ParametersParser: It returns the training output file
getTrainingOutputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.parseParameters: It returns the training output file
getTrainingOutputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.parseParameters: Gets the path of the training output file for printing the results (from the validation file)
getTrainingOutputFile() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.PSO_Learning.CPSO.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.PSO_Learning.REPSO.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Rule_Learning.C45Rules.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Rule_Learning.LEM1.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Rule_Learning.LEM2.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Rule_Learning.PART.parseParameters: It returns the training output file
getTrainingOutputFile() - Method in class keel.Algorithms.Rule_Learning.Ripper.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Rule_Learning.Ritio.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Rule_Learning.Rules6.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Rule_Learning.Slipper.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Rule_Learning.SRI.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.parseParameters: It returns the name of the file containing the output for the training data
getTrainingOutputFile() - Method in class keel.Algorithms.SVM.C_SVM.parseParameters: Gets the path of the training output file for printing the results (from the validation file)
getTrainingOutputFile() - Method in class keel.Algorithms.SVM.EPSILON_SVR.parseParameters: Gets the path of the training output file for printing the results (from the validation file)
getTrainingOutputFile() - Method in class keel.Algorithms.SVM.NU_SVM.parseParameters: Gets the path of the training output file for printing the results (from the validation file)
getTrainingOutputFile() - Method in class keel.Algorithms.SVM.NU_SVR.parseParameters: Gets the path of the training output file for printing the results (from the validation file)
getTrainingOutputFiles() - Method in class keel.GraphInterKeel.experiments.Parameters: return .tra output files
getTrainingSetDocumentsID() - Static method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.WrapperManager
getTrainingTime() - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.Timer: Get training time
getTrainingTime() - Static method in class keel.Algorithms.RST_Learning.Timer: Get training time
getTrainingValidationFiles() - Method in class keel.GraphInterKeel.experiments.Parameters: return training files to validate
getTrainInstances() - Method in class keel.Algorithms.MIL.AbstractMIAlgorithm
getTrainPartition(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.PartitionScheme: It returns the training partition specified
getTrainPartition(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.PartitionScheme: It returns the training partition specified
getTrainPartition(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.PartitionScheme: It returns the training partition specified
getTrainPartition(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.PartitionScheme: It returns the training partition specified
getTrainPartition(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.PartitionScheme: It returns the training partition specified
getTrainPartition(int) - Method in class keel.GraphInterKeel.datacf.partitionData.PartitioningSchemes.DistanceBased_best: It returns the training partition specified
getTrainQStatistic(IClassifier, IClassifier) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.ClassificationProblemEvaluator: Returns the diversity measure Q statistic of two classifiers in train dataset
getTrainQStatistic(List<IClassifier>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.ClassificationProblemEvaluator: Returns the diversity measure Q statistic of a list of classifiers in train dataset
getTrainRegressionError(IRegressor, IErrorFunction<double[]>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.problem.regression.RegressionProblemEvaluator: Returns the train error value of a neural net with an specified error function
getTrainResultFile() - Method in class keel.Algorithms.Decision_Trees.CART.ResultPrinter: It returns the training results file
getTrainResultFile() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlusReporterClas: Returns file name where the training results of best model obtained will be written
getTrainResultFile() - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.IRPropPlusReporterRegr: Returns file name where the training results of best model obtained will be written
getTrainResultFile() - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.listener.NeuralNetReporterClas: Returns file name where the training results of best model obtained will be written
getTrainResultFile() - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.listener.NeuralNetReporterRegr: Returns file name where the training results of best model obtained will be written
getTransactionsInputFile() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.parseParameters: Gets the name of the train file
getTransactionsInputFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.parseParameters: It returns the name of the transactions input file
getTransactionsInputFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.parseParameters: It returns the name of the transactions input file
getTransactionsInputFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.parseParameters: It returns the name of the transactions input file
getTransactionsInputFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.parseParameters: It returns the name of the transactions input file
getTransactionsInputFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.parseParameters: It returns the name of the transactions input file
getTransactionsInputFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.parseParameters: It returns the name of the transactions input file
getTransactionsInputFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.parseParameters: It returns the name of the transactions input file
getTransactionsInputFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.parseParameters: It returns the name of the transactions input file
getTransactionsInputFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.parseParameters: It returns the name of the transactions input file
getTransactionsInputFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.parseParameters: It returns the name of the transactions input file
getTransactionsInputFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.parseParameters: It returns the name of the transactions input file
getTransactionsInputFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.parseParameters: It returns the name of the transactions input file
getTransactionsInputFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.parseParameters: It returns the name of the transactions input file
getTransactionsInputFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.parseParameters: It returns the name of the transactions input file
getTransactionsInputFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.parseParameters: It returns the name of the transactions input file
getTransactionsInputFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.parseParameters: It returns the name of the transactions input file
getTransactionsInputFile() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.parseParameters: It returns the name of the transactions input file
getTransAt(int) - Method in class keel.GraphInterKeel.experiments.DataSet: Get the transductive file file at the indicated position
getTree() - Method in class keel.Algorithms.Decision_Trees.CART.CART: It returns the decision tree
getTree() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5
getTree() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.C45: Returns the C4.5 tree
getTree() - Method in class keel.Algorithms.Rule_Learning.C45Rules.C45: Returns the C4.5 tree
getTree() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.C45: Returns the C4.5 tree
getTree() - Method in class keel.Algorithms.Rule_Learning.PART.C45: Returns the C4.5 tree
getTreeCellRendererComponent(JTree, Object, boolean, boolean, boolean, int, boolean) - Method in class keel.GraphInterKeel.experiments.KeelTreeCellRenderer: Get the base component of the tree
getTreeError() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Obtains the general error of the tree from all its leaves in a two number array
getTrials() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
getTrials() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
getTrials() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getTrials() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
getTrials() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
getTrials() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
getTrials() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getTrials() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
getTrials() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Gets the number of trials in the algorithm
getTrueTransactions() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: Outputs an array of transactions with their corresponding attribute values.
getTrueTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: Outputs an array of transactions with their corresponding attribute values.
getTrueTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: Outputs an array of transactions with their corresponding attribute values.
getTrueTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: Outputs an array of transactions with their corresponding attribute values.
getTrueTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: Outputs an array of transactions with their corresponding attribute values.
getTrueTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: Outputs an array of transactions with their corresponding attribute values.
getTrueTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: Outputs an array of transactions with their corresponding attribute values.
getTrueTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: Outputs an array of transactions with their corresponding attribute values.
getTrueTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: Outputs an array of transactions with their corresponding attribute values.
getTrueTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: Outputs an array of transactions with their corresponding attribute values.
getTrueTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: Outputs an array of transactions with their corresponding attribute values.
getTrueTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: Outputs an array of transactions with their corresponding attribute values.
getTrueTransactions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: Outputs an array of transactions with their corresponding attribute values.
getType(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It returns the type of the variable
getType(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It returns the type of an attribute
getType(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It returns the type for the attribute "variable"
getType(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It returns the type for the attribute "variable"
getType(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It returns the type for the attribute "variable"
getType(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It returns the type for the attribute "variable"
getType(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It returns the type for the attribute "variable"
getType(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It returns the type for the attribute "variable"
getType() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: returns the type of attribute that is located
getType() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Condition: Returns the type of the condition (gene)
getType(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It returns the type for the attribute "variable"
getType() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Gets the type of the attribute (CONTINUOUS or DISCRET).
getType() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Ruleset: Returns the common output (consecuent) of the rules in the ruleset.
getType() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule: It returns the right side (class) of the rule.
getType() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Ruleset: Returns the common output (consecuent) of the rules in the ruleset.
getType() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Condition: It gets the type of the condition
getType() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractAttribute: Access to the attribute type
getType() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.CategoricalAttribute: Access to the attribute type
getType() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IAttribute: Access to the attribute type
getType() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.IntegerNumericalAttribute: Access to the attribute type
getType() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.RealNumericalAttribute: Access to the attribute type
getType() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Returns the type of this layer
getType(int) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It returns the type for the attribute "variable"
getType(int) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It returns the type for the attribute "variable"
getType() - Method in class keel.Algorithms.Rule_Learning.AQ.Instance: It returns the type of the instance
getType() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Rule: It returns the right side (class) of the rule.
getType() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Ruleset: Returns the common output (consecuent) of the rules in the ruleset.
getType() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule: It returns the right side (class) of the rule.
getType() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset: Returns the common output (consecuent) of the rules in the ruleset.
getType() - Method in class keel.Algorithms.Rule_Learning.PART.Rule: It returns the right side (class) of the rule.
getType() - Method in class keel.Algorithms.Rule_Learning.PART.Ruleset: Returns the common output (consecuent) of the rules in the ruleset.
getType(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It returns the type of the attribute specified
getType() - Method in class keel.Algorithms.Rule_Learning.Ripper.Rule: It returns the right side (class) of the rule.
getType() - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset: Returns the common output (consecuent) of the rules in the ruleset.
getType() - Method in class keel.Algorithms.Rule_Learning.Slipper.Rule: It returns the right side (class) of the rule.
getType() - Method in class keel.Algorithms.Rule_Learning.Slipper.Ruleset: Returns the common output (consecuent) of the rules in the ruleset.
getType() - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It does return the type of the attribute
getType() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation: returns the type of this technical information
getType(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Returns the type of the variable "pos"
getType() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeVar: Gets the char with the type used in the variable
getType(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Returns the type of the variable "pos"
getType() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeVar: Gets the char with the type used in the variable
getType(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Returns the type of the variable "pos"
getType() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeVar: Gets the char with the type used in the variable
getType() - Method in class keel.Algorithms.SVM.SMO.core.TechnicalInformation: returns the type of this technical information
getType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It returns the type of the attribute specified
getType() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene
getType() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
getType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: This function return the type of the attribute
getType() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
getType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: It returns the type of the attribute specified
getType() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Gene
getType() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Gene
getType() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene
getType() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene
getType() - Method in class keel.Dataset.Attribute: It does return the type of the attribute
getType() - Method in class keel.GraphInterKeel.experiments.Graph: Gets the type of this graph
getType() - Method in class keel.GraphInterKeel.experiments.Node: Gets the type of the node
getType() - Method in class keel.GraphInterKeel.experiments.UseCase
getTypeInference() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: Returns the type of inference used to build the rules.
getTypeInference() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
gettypelqd() - Method in class keel.GraphInterKeel.experiments.Joint
getTypelqd() - Method in class keel.GraphInterKeel.experiments.Node: Gets the type of the node
getTypeOfAttribute(int) - Static method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Return the type of the attribute.
getTypeOfAttribute(int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Return the type of the attribute.
getTypeRelation() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Returns the Relation value.
getTypes() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: It returns the attribute type
getTypes() - Method in class keel.GraphInterKeel.datacf.util.Dataset: Return a vector with variable types (integer, real, nominal)
getU() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Returns the U part of the matrix.
getU() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.LUDecomposition: Return upper triangular factor
getU() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.SingularValueDecomposition: Return the left singular vectors
getU() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Gene
getU() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Gene
getU() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene
getUID(String) - Static method in class keel.Algorithms.SVM.SMO.core.SerializationHelper: reads or creates the serialVersionUID for the given class
getUID(Class) - Static method in class keel.Algorithms.SVM.SMO.core.SerializationHelper: reads or creates the serialVersionUID for the given class
getUltimaRegla() - Method in class keel.Algorithms.Rule_Learning.Prism.ConjReglas: Returns the last rule(normally the one with best weight)
getUltimaRegla() - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjReglas: Returns the last rule(normally the one with best weight)
getUltimaRegla() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjReglas: Returns the last rule(normally the one with best weight)
getUltimaRegla() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjReglas: Returns the last rule(normally the one with best weight)
getUltimaRegla() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetRules: Return the last rule
getUncover() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: Returns the number of examples uncovered by the rules
getUncover() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
getUndefinedAttribute(int) - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It returns the undefined attribute being int the position passed as an argument.
getUndefinedAttribute(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It returns the undefined attribute being int the position passed as an argument.
getUndefinedAttribute(int) - Static method in class keel.Dataset.Attributes: It returns the undefined attribute being int the position passed as an argument.
getUndefinedAttribute(int) - Method in class keel.Dataset.InstanceAttributes: It returns the undefined attribute being int the position passed as an argument.
getUndefinedAttributes() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It does return all the undefined attributes
getUndefinedAttributes() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It does return all the undefined attributes
getUndefinedAttributes() - Static method in class keel.Dataset.Attributes: It does return all the undefined attributes
getUndefinedAttributes() - Method in class keel.Dataset.InstanceAttributes: It does return all the undefined attributes
getUndefinedAttributesHeader() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It does return a String with all the undefined attributes definition in keel format.
getUndefinedAttributesHeader() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It does return a String with all the undefined attributes definition in keel format.
getUndefinedAttributesHeader() - Static method in class keel.Dataset.Attributes: It does return a String with all the undefined attributes definition in keel format.
getUndefinedAttributesHeader() - Method in class keel.Dataset.InstanceAttributes: It does return a String with all the undefined attributes definition in keel format.
getUndefinedNumAttributes() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It return the number of undefined attributes in the API
getUndefinedNumAttributes() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It return the number of undefined attributes in the API
getUndefinedNumAttributes() - Static method in class keel.Dataset.Attributes: It return the number of undefined attributes in the API
getUndefinedNumAttributes() - Method in class keel.Dataset.InstanceAttributes: It return the number of undefined attributes in the API
getUniformFuzzyAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.AlcalaetalProcess: It returns the uniform fuzzy attributes before running the genetic learning process
getUnscaledMax() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Returns the array of maximum values in datasets
getUnscaledMin() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Returns the array of minimum values in datasets
getUnscaledTestData() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Returns the DataSet associated to the evaluator as unscaled test data
getUnscaledTrainData() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Returns the DataSet associated to the evaluator as unscaled train data
getUnsmoothedFunction() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode
getUnus() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Retuns the value of UNUS
getUpper_aproximation(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
getUpperAllele() - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Attribute: It returns the upper allele.
getUpperAllele() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep: Returns the upper real value of the representation
getUpperAllele() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep
getUpperAllele() - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute: It returns the upper allele.
getUpperAllele() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: Returns the upper real value of the representation
getUpperAllele() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: Returns the upper real value of the representation
getUpperAllele(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: Returns the value of the alelle
getUpperAllele() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep
getUpperBound() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Condition: It returns the upper bound
getUpperBound() - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.Gcvfctn: Gets the upper bound of the evaluation
getUpperBound(int) - Method in interface keel.Algorithms.Preprocess.Missing_Values.EM.util.MultivariateFunction: get upper bound of argument n
getUpperBound() - Method in interface keel.Algorithms.Preprocess.Missing_Values.EM.util.UnivariateFunction
getUpperBound() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene: It returns the upper bound of the interval stored in a gene
getUpperBound() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene: It returns the upper bound of the interval stored in a gene
getUpperBound() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene: It returns the upper bound of the interval stored in a gene
getUpperNumericBound() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Returns the upper bound of a numeric attribute.
getUpperQuartile() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Histogram: Returns the upper quartile.
getUpperrealBound() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Returns the upper bound of a real attribute.
getUpperValues() - Method in class keel.Algorithms.Hyperrectangles.EACH.Hyperrectangle
getURL(String, String) - Static method in class keel.Algorithms.SVM.SMO.core.ClassDiscovery: If the given package can be found in this part of the classpath then an URL object is returned, otherwise null.
getUsedConstants() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGPRegSym: This method returns the list of used constants.
getUsedConsts() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.RegSymFuzzyGP: This method returns information about used constant
getUsedElite() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Method to get the number of different individuals in the final elite population
getUseful() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Gets the useful parameter of the classifier.
getUseful() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Gets the useful parameter of the classifier.
getUsefulTimes() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Gets the useful times parameter of the classifier.
getUsefulTimes() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Returns the useful time parameter of the classifier
getUseLowerOrder() - Method in class keel.Algorithms.SVM.SMO.supportVector.PolyKernel: Gets whether lower-order terms are used.
getUseNormalization() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Returns whether normalization is used.
getUserOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.KernelEvaluation: returns the options the user supplied for the kernel
getUseRuleStretching() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Gets whether pruning is performed
getUseUnsmoothed() - Method in class keel.Algorithms.Decision_Trees.M5.M5: Get the value of UseUnsmoothed.
getUseUnsmoothed() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: Get the value of UseUnsmoothed.
getV() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.EigenvalueDecomposition: Return the eigenvector matrix
getV() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.SingularValueDecomposition: Return the right singular vectors
getVA(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.VAList: this method returns a object with the frequencies list associated to the value passed as parameter
getVA(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.VAList: this method returns a object with the frequencies list associated to the value passed as parameter
getVA(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.VAList: this method returns a object with the frequencies list associated to the value passed as parameter
getVA(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.VAList: this method returns a object with the frequencies list associated to the value passed as parameter
getVA(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.VAList: this method returns a object with the frequencies list associated to the value passed as parameter
getVA(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.VAList: this method returns a object with the frequencies list associated to the value passed as parameter
getVA(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.VAList: this method returns a object with the frequencies list associated to the value passed as parameter
getVA(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.VAList: this method returns a object with the frequencies list associated to the value passed as parameter
getVal3() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.QualityMeasures: Method to return the value of the objective 3
getValEnum() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
getValid() - Method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.Chromosome: Tests if the chromosome is valid
getValidationInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.parseParameters: It returns the validation input file
getValidationInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.parseParameters: It returns the validation input file
getValidationInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.parseParameters: It returns the validation input file
getValidationInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.parseParameters: It returns the validation input file
getValidationInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.parseParameters: It returns the validation input file
getValidationInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.parseParameters: It returns the validation input file
getValidationInputFile() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.parseParameters: It returns the validation input file
getValidationInputFile() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.parseParameters: It returns the validation input file
getValidationInputFile() - Method in class keel.Algorithms.Decision_Trees.DT_GA.parseParameters: It returns the validation input file
getValidationInputFile() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.parseParameters: It returns the validation input file
getValidationInputFile() - Method in class keel.Algorithms.Decision_Trees.Target.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.parseParameters: It returns the validation input file
getValidationInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.parseParameters: It returns the validation input file
getValidationInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.parseParameters: It returns the validation input file
getValidationInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.ParametersParser: It returns the validation input file
getValidationInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.parseParameters: It returns the validation input file
getValidationInputFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.parseParameters: Gets the path of the validation input file
getValidationInputFile() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.PSO_Learning.CPSO.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.PSO_Learning.REPSO.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Rule_Learning.C45Rules.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Rule_Learning.LEM1.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Rule_Learning.LEM2.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Rule_Learning.PART.parseParameters: It returns the validation input file
getValidationInputFile() - Method in class keel.Algorithms.Rule_Learning.Ripper.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Rule_Learning.Ritio.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Rule_Learning.Rules6.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Rule_Learning.Slipper.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Rule_Learning.SRI.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.parseParameters: It returns the name of the file containing the validation data
getValidationInputFile() - Method in class keel.Algorithms.SVM.C_SVM.parseParameters: Gets the path of the validation input file
getValidationInputFile() - Method in class keel.Algorithms.SVM.EPSILON_SVR.parseParameters: Gets the path of the validation input file
getValidationInputFile() - Method in class keel.Algorithms.SVM.NU_SVM.parseParameters: Gets the path of the validation input file
getValidationInputFile() - Method in class keel.Algorithms.SVM.NU_SVR.parseParameters: Gets the path of the validation input file
getValor() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Atributo: Returns the assigned value of the attribute.
getValor() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Condicion: Returns the value for the attribute of this condition.
getValor(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Muestra: Returns the value for the attribute/condition in the given position.
getValor() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Atributo: Returns the assigned value of the attribute.
getValor() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Condicion: Returns the value for the attribute of this condition.
getValor(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Muestra: Returns the value for the attribute/condition in the given position.
getValor() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Atributo: Returns the assigned value of the attribute.
getValor(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Muestra: Returns the value for the attribute/condition in the given position.
getValor() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Atributo: Returns the assigned value of the attribute.
getValor() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Condicion: Returns the value for the attribute of this condition.
getValor(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Muestra: Returns the value for the attribute/condition in the given position.
getValor() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Entero
getValor() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Atributo: Returns the assigned value of the attribute.
getValor() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Condicion: Returns the value for the attribute of this condition.
getValor(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Muestra: Returns the value for the attribute/condition in the given position.
getValor() - Method in class keel.Algorithms.Rule_Learning.LEM2.Atributo_valor
getValor() - Method in class keel.Algorithms.Rule_Learning.Prism.Selector: Return the attribute's value associated
getValor() - Method in class keel.Algorithms.Rule_Learning.Rules6.Atributo_valor: Returns the value of the attribute stored.
getValor() - Method in class keel.Algorithms.Rule_Learning.SRI.Atributo_valor: Returns the value of the attribute stored.
getValor() - Method in class keel.Algorithms.Rule_Learning.UnoR.Selector: Return the attribute's value associated
getValor() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Selector: Return the attribute's value associated
getValor() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Selector: Return the value associated with the attribute
getValorConstante() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
getValores() - Method in class keel.Algorithms.Rule_Learning.Prism.Selector: Returns the set of values of the selector
getValores() - Method in class keel.Algorithms.Rule_Learning.UnoR.Selector: Returns the set of values of the selector
getValores() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Selector: Returns the set of values of the selector
getValores() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Selector: Return the set of values for the selector
getValoresInvalidos() - Method in class keel.Algorithms.Rule_Learning.Rules6.Regla: Returns the list with the invalid values for the rule.
getValoresInvalidos() - Method in class keel.Algorithms.Rule_Learning.SRI.Regla: Returns the list with the invalid values for the rule.
getValoresN() - Method in class keel.Algorithms.Rule_Learning.Prism.Selector: Return the set of nominal values of the selector
getValoresN() - Method in class keel.Algorithms.Rule_Learning.UnoR.Selector: Return the set of nominal values of the selector
getValorN() - Method in class keel.Algorithms.Rule_Learning.Prism.Selector: Returns the nominal value of the associated value
getValorN() - Method in class keel.Algorithms.Rule_Learning.UnoR.Selector: Returns the nominal value of the associated value
getValorOperacion1() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
getValorOperacion2() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
getValTrueTransactions(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: Outputs an array of transactions with their corresponding attribute values.
getValue() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Item: It returns the value of the item.
getValue() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Item: It returns the value of the item
getValue() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.Literal: It returns the value of the attribute stored in the literal
getValue() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Item: It returns the value of the item
getValue() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Item: It returns the value of the item
getValue() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Item: It returns the value of the item
getValue(int) - Method in class keel.Algorithms.Decision_Trees.C45.Itemset: Returns the value of the given attribute.
getValue() - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode
getValue(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset: Returns the value of the given attribute.
getValue(int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute: Gets the value of a nominal attribute from the position in the list of possible values
getValue() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.Split: Gets the value of the attribute for the split
getValue(int) - Method in class keel.Algorithms.Decision_Trees.ID3.Itemset: Returns the value of the given attribute.
getValue(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute: Gets the value of a nominal attribute from the position in the list of possible values
getValue() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Split: Gets the value of the attribute for the split
getValue(int) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Itemset: Returns the value of the given attribute.
getValue() - Method in class keel.Algorithms.Discretizers.OneR.Opt: Gets the explanatory value associated
GetValue(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger
GetValue(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationinteger
GetValue(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationinteger
getValue() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Item
getValue() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.Condition
getValue(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Returns the value of the given attribute.
getValue(MyAttribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Returns the value of the given attribute.
getValue() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleRule: Returns the value of the attribute
getValue(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset: Returns the value of the given attribute.
getValue(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset: Returns the value of the given attribute.
getValue() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.SimpleRule: Returns the value of the attribute
getValue() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Condition: It returns the value for the attribute
getValue(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset: Returns the value of the given attribute.
getValue(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset: Returns the value of the given attribute.
getValue(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset.Instance: Returns vector's attribute value in internal format
getValue(IAttribute) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset.Instance: Returns value at specified attribute
getValue(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset.Instance: Returns value at specified attribute name
getValue(int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IDataset.IInstance: Returns vector's attribute value in internal format
getValue(IAttribute) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IDataset.IInstance: Returns value at specified attribute
getValue(String) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IDataset.IInstance: Returns value at specified attribute name
getValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.ValueFreq: Returns the value of this object (the string)
getValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.valueAssociations: The reference value of this object
getValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.ValueFreq: Returns the value of this object (the string)
getValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.valueAssociations: The reference value of this object
getValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.ValueFreq: Returns the value of this object (the string)
getValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.valueAssociations: The reference value of this object
getValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValueFreq: Returns the value of this object (the string)
getValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.valueAssociations: The reference value of this object
getValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.ValueFreq: Returns the value of this object (the string)
getValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.valueAssociations: The reference value of this object
getValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.ValueFreq: Returns the value of this object (the string)
getValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.valueAssociations: The reference value of this object
getValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.ValueFreq: Returns the value of this object (the string)
getValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.ValueFreq: Returns the value of this object (the string)
getValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.valueAssociations: The reference value of this object
getValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.ValueFreq: Returns the value of this object (the string)
getValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.valueAssociations: The reference value of this object
getValue() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.ValueFreq: Returns the value of this object (the string)
getValue() - Method in class keel.Algorithms.Rule_Learning.AQ.Selector: It returns the value of the associated attribute
getValue(int) - Method in class keel.Algorithms.Rule_Learning.ART.Itemset: Returns the value of the given attribute.
getValue(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Itemset: Returns the value of the given attribute.
getValue() - Method in class keel.Algorithms.Rule_Learning.C45Rules.SimpleRule: Returns the value of the attribute
getValue(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset: Returns the value of the given attribute.
getValue() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.SimpleRule: Returns the value of the attribute
getValue() - Method in class keel.Algorithms.Rule_Learning.CN2.Selector: It returns the value of the associated attribute
getValue(int) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset: Returns the value of the given attribute.
getValue(int) - Method in class keel.Algorithms.Rule_Learning.PART.Itemset: Returns the value of the given attribute.
getValue() - Method in class keel.Algorithms.Rule_Learning.PART.SimpleRule: Returns the value of the attribute
getValue() - Method in class keel.Algorithms.Rule_Learning.Ripper.SimpleRule: Returns the value of the attribute
getValue() - Method in class keel.Algorithms.Rule_Learning.Slipper.SimpleRule: Returns the value of the attribute
getValue(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset: Returns the value of the given attribute.
getValue(TechnicalInformation.Field) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation: returns the value associated with the given field, or empty if field is not currently stored.
getValue(TechnicalInformation.Field) - Method in class keel.Algorithms.SVM.SMO.core.TechnicalInformation: returns the value associated with the given field, or empty if field is not currently stored.
getValue() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Item: It returns the ID of the label involved in the item
getValue() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
getValue(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
getValue() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
getValue(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
getValue() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene: It returns the value stored in a gene
getValue(int) - Method in class keel.GraphInterKeel.experiments.Parameters: return actual value for parameter at index position
getValue1() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValuesFreq: Returns the first value of this object (the first string)
getValue2() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValuesFreq: Returns the second value of the object (the second string)
getValueAt(int, int) - Method in class keel.GraphInterKeel.datacf.util.AttributeTable: Returns the value of an element of the table
getValueAt(int, int) - Method in class keel.GraphInterKeel.datacf.util.DatasetTable
getValueAt(int, int) - Method in class keel.GraphInterKeel.datacf.util.VariableTable: Returns the value of a cell
getValueAt(int, int) - Method in class keel.GraphInterKeel.experiments.ParametersTable: Returns the value of the parameter especified
getValueAt(int, int) - Method in class keel.GraphInterKeel.statistical.statTableModel: Gets the value of a cell
GetValueBinary(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
GetValueBinary(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
GetValueBinary(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
GetValueInteger(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
GetValueInteger(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
GetValueInteger(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
getValueKFoldCross() - Method in class keel.GraphInterKeel.experiments.SelectExp: Gets number of folds
getValueObj(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Method to get the value of the objective indicated
getValueObj(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Method to get the value of the objective with the name indicated
GetValueReal(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
GetValueReal(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
GetValueReal(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
getValues() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute: Gets an array of possible values if it is a nominal attribute
getValues() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute: Gets an array of possible values if it is a nominal attribute
getValues() - Method in class keel.Algorithms.Hyperrectangles.EACH.Selector: Returns the set of values of the selector
getValues() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset.Instance: Get value array of the this instance
getValues() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IDataset.IInstance: Get value array of the this instance
getValues() - Method in class keel.Algorithms.Rule_Learning.AQ.Selector: It returns the set of values of the selector
getValues() - Method in class keel.Algorithms.Rule_Learning.ART.Rule
getValues() - Method in class keel.Algorithms.Rule_Learning.CN2.Selector: It returns the set of values of the selector
getValues() - Method in class keel.Algorithms.Rule_Learning.Riona.Selector: Returns the set of values of the selector
getValues() - Method in class keel.Algorithms.Rule_Learning.UnoR.Interval: Returns the values of the interval.
getValues() - Method in class keel.GraphInterKeel.experiments.Parameters: return actual values
getValueSparse(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Returns an instance's attribute value in internal format.
getVar(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Obtains the ith variable used in this rule antecedent
getVar(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Obtains the ith variable used in this rule antecedent
getVarbValues() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization: Get the variable values.
getVariable() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Item: It returns the variable of the item.
getVariable() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Item: It returns the variable of the item
getVariable() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.Literal: It returns the attribute stored in the literal
getVariable() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Item: It returns the variable of the item
getVariable() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Item: It returns the variable of the item
getVariable() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Item: It returns the variable of the item
getVariable() - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode
getVariable() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Item
getVariable() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Item: It returns the ID of the attribute involved in the item
getVariableTable() - Method in class keel.GraphInterKeel.datacf.editData.EditVariablePanel: Gets the current VariableTable
getVariance(int, int) - Method in class keel.Algorithms.Complexity_Metrics.Statistics: It returns the variance of the given attribute within the given class
getVariance() - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster: Get variance of the cluster
getVariance() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Sample: Returns the variance.
getVariance(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Sample: Returns a Confidence Interval for the variance value with the given confidence.
getVarValues() - Method in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
getVecino(int, int) - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma: Get the neighbour with the given indeces.
getVecino(int, int) - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma: Get the neighbour with the given indeces.
getVecino(int, int) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma: Get the neighbour with the given indeces.
getVecino(int, int) - Method in class keel.Algorithms.Instance_Selection.SSMA.Cromosoma: Get the j-neighbour of a given instance
getVecino(int, int) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma: Get the j-neighbour of a given instance
getVectorsNeighbors() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
getVectorsNeighbors(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
getVerbosity() - Method in class keel.Algorithms.Decision_Trees.M5.M5: Get the value of Verbosity.
getVerbosity() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: Get the value of Verbosity.
getVerbosity() - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils: Returns verbosity value
getVerbosity() - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils: Returns verbosity value
getVerbosity() - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils: Returns verbosity value
getVerbosity() - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils: Returns verbosity value
getVerbosity() - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils: Returns verbosity value
getVerbosity() - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils: Returns verbosity value
getVerbosity() - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils: Returns verbosity value
getVolume() - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Returns the volume value of the complex object.
getVolume() - Method in class keel.Algorithms.Hyperrectangles.EACH.Hyperrectangle
getVolume() - Method in class keel.Algorithms.Rule_Learning.Riona.Complex
getVotingValue() - Method in class keel.Algorithms.ImbalancedClassification.Auxiliar.AUC.PredPair: Provides the value of the voting procedure associated to a given instance
getW(MyDataset, Mask, double[]) - Method in class keel.Algorithms.Rule_Learning.Slipper.Rule: Computes W+ or W- for this rule, according to the function W=sum(Di) i e R
getW(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Methods to get the value for the weight of the objective in a position
getW() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.MembershipFunction: It returns the spread of an isosceles-triangle
getW() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.MembershipFunction: It returns the spread of an isosceles-triangle
getW1() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Methods to get the value for the weight of the objective 1
getW2() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Methods to get the value for the weight of the objective 2
getW3() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Methods to get the value for the weight of the objective 3
getWeight(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It returns the weight of the given example
getWeight() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.ExampleWeight: Returns the weight given to the pattern.
getWeight() - Method in class keel.Algorithms.Decision_Trees.C45.Itemset: Returns the itemset weight.
getWeight() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset: Returns the itemset weight.
getWeight() - Method in class keel.Algorithms.Decision_Trees.ID3.Itemset: Returns the itemset weight.
getWeight() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Itemset: Returns the itemset weight.
getWeight(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It returns the weight of the given class
getWeight() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Obtains the weight associated to this rule
getWeight() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.ExampleWeight
getWeight() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Returns the itemset weight.
getWeight() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset: Returns the itemset weight.
getWeight() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset: Returns the itemset weight.
getWeight() - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Returns the weight value of the complex object.
getWeight() - Method in class keel.Algorithms.Hyperrectangles.EACH.Hyperrectangle
getWeight() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset: Returns the itemset weight.
getWeight() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset: Returns the itemset weight.
getWeight() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Obtains the weight associated to this rule
getWeight(int) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbf: Gets the i-th weight of a neuron
getWeight() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset.Instance: Returns the weight of this instance
getWeight() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IDataset.IInstance: Returns the weight of this instance
getWeight() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.Link: Returns the weight associated to this link, used to obtain the output value of the destiny neuron
getWeight(int) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbf: Gets the ith weight of a neuron
getWeight(int) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbf: Gets the ith weight of a neuron
getWeight(int) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbf: Gets the ith weight of a neuron
getWeight(int) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf: Gets the ith weight of a neuron
getWeight(int) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbf: Gets the ith weight of a neuron
getWeight(int) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf: Gets the ith weight of a neuron
getWeight() - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.pnPair: It returns the weight value
getWeight() - Method in class keel.Algorithms.Rule_Learning.AQ.Complex: It returns the weight of the complex (LEF function)
getWeight() - Method in class keel.Algorithms.Rule_Learning.ART.Itemset: Returns the itemset weight.
getWeight() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Itemset: Returns the itemset weight.
getWeight() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset: Returns the itemset weight.
getWeight() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset: Returns the itemset weight.
getWeight() - Method in class keel.Algorithms.Rule_Learning.PART.Itemset: Returns the itemset weight.
getWeight() - Method in class keel.Algorithms.Rule_Learning.Riona.Complex
getWeight() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset: Returns the itemset weight.
getWeight() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Informs of the weight of the prototype.
getWeightAttributes() - Method in class keel.Algorithms.Hyperrectangles.EACH.HyperrectangleSet: Return the weight for each attribute
getWeightRange() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron: Returns the weight range associated to the links
getWeights() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbf: Gets the weights of a neuron
getWeights() - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbf: Gets the weights of a neuron
getWeights() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbf: Gets the weights of a neuron
getWeights() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbf: Gets the weights of a neuron
getWeights() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf: Gets the weights of a neuron
getWeights() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbf: Gets the weights of a neuron
getWeights() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf: Gets the weights of a neuron
getWeights(double[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ConjGradNN: Returns a copy of weights in vector p.
getWeights(double[]) - Method in class keel.Algorithms.Shared.ClassicalOptim.ConjGradNN: Returns a copy of weights in vector p.
getWeightsAsString() - Method in class keel.Algorithms.Shared.ClassicalOptim.ConjGradNN: Return the weights as a String to print them
getWeightVector() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
getWeightVector(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
getWk() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess
getWorst(Classifier[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper: Obtains the worst classifier of population.
getWorst(Classifier[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper: Obtains the worst classifier of population.
getWorst() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
getWorst() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
getWorst() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
getWorst() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
getWorst() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
getWorst() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
getWorst() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
getWorst() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
getWorstPopulation() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.populationWrapper
getWracc() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Rule: It returns the Wracc of the rule
getWracc() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule
getWrongN() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule: It returns the number of wrong of the rule
getwRule() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Structure: It returns the position of the first rule that wrongly classifies the example stored in the structure
getwRule() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Structure: It returns the position of the first rule that wrongly classifies the example stored in the structure
getX() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: It returns the values of the input attributes
getX() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: It returns the values of the input attributes
getX() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Return the values of the in-put attributes
getX(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Return the values of the in-put attributes for an instance
getX() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.IndMichigan
getX() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.IndMichigan
getX() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.IndMichigan
getX() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.IndMichigan
getX() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.IndMichigan
getX() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Returns the whole input data.
getX() - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Returns the whole input data.
getX() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Returns the whole input data.
getX() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Returns the whole input data.
getX() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Returns the whole input data.
getX() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Returns the whole input data.
getX() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Returns the whole input data.
getX() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the summation of X samples values.
getX() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: Returns input examples.
getX() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Rule_Learning.AQ.Dataset: It returns the values of the input attributes
getX() - Method in class keel.Algorithms.Rule_Learning.CN2.Dataset: It returns the values of the input attributes
getX() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Return the values of the in-put attributes
getX(int) - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Return the values of the in-put attributes for an instance
getX() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Return the values of in-put attributes
getX(int) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Return the values of the in-put attributes for an instance
getX() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: It returns the values of the input attributes
getX(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Return the values of the in-put attributes for an instance
getX() - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Returns input examples.
getX() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: Outputs an array of examples with their corresponding attribute values.
getX() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: It returns the values of the input attributes
getX() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset: It returns the values of the input attributes
getX() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset: It returns the values of the input attributes
getX0() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Fuzzy: Methods to get the value of x0
getX0(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Method to return the value of the cut points X0
getX0() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Fuzzy: Methods to get the value of x0
getX0(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Method to return the value of the cut points X0
getX0() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Fuzzy: Methods to get the value of x0
getX0(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Method to return the value of the cut points X0
getX0() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyRegion: It returns the X0 value of a fuzzy region
getX0() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyRegion: It returns the X0 value of a fuzzy region
getX0() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyRegion: It returns the X0 value of a fuzzy region
getX0() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyRegion: It returns the X0 value of a fuzzy region
getX1() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Fuzzy: Methods to get the value of x1
getX1(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Method to return the value of the cut points X1
getX1() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Fuzzy: Methods to get the value of x1
getX1(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Method to return the value of the cut points X1
getX1() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Fuzzy: Methods to get the value of x1
getX1(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Method to return the value of the cut points X1
getX1() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyRegion: It returns the X1 value of a fuzzy region
getX1() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyRegion: It returns the X1 value of a fuzzy region
getX1() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyRegion: It returns the X1 value of a fuzzy region
getX1() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyRegion: It returns the X1 value of a fuzzy region
getX2() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Return the values of the in-put attributes
getX2() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Return the string values of the in-put attributes
getX2() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Return the values of the in-put attributes
getX2() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Return the values of the in-put attributes
getX3() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Fuzzy: Methods to get the value of x3
getX3(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Method to return the value of the cut points X3
getX3() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Fuzzy: Methods to get the value of x3
getX3(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Method to return the value of the cut points X3
getX3() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Fuzzy: Methods to get the value of x3
getX3(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Method to return the value of the cut points X3
getX3() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyRegion: It returns the X3 value of a fuzzy region
getX3() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyRegion: It returns the X3 value of a fuzzy region
getX3() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyRegion: It returns the X3 value of a fuzzy region
getX3() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyRegion: It returns the X3 value of a fuzzy region
getXavg() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the X values mean
getXNor(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Return the normalized values of the in-put attributes for an instance
getXNor(int) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Return the normalized values of the in-put attributes for an instance
getXOver() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
getXOverRate() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
getXs() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: Outputs an array of examples with their corresponding attribute values.
getXX() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the summation of the X samples squares.
getXXavg() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the mean of the X samples squares.
getXY() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the summation of the X-Y products.
getXYavg() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the mean of the X-Y products.
gety() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Replace: It returns the class for the example stored in the structure.
gety() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Structure: It returns the class for the example stored in the structure
gety() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Replace: It returns the class for the example stored in the structure
gety() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Structure: It returns the class for the example stored in the structure
getY() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.IndMichigan
getY() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.IndMichigan
getY() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.IndMichigan
getY() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.IndMichigan
getY() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.IndMichigan
getY() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Returns the outputs of each example (regression).
getY() - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Returns the outputs of each example (regression).
getY() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Returns the outputs of each example (regression).
getY() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Returns the outputs of each example (regression).
getY() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Returns the outputs of each example (regression).
getY() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Returns the outputs of each example (regression).
getY() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Returns the outputs of each example (regression).
getY() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the summation of Y samples values.
getY() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: Returns input examples.
getY() - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Returns input examples.
getY() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyRegion: It returns the Y value of a fuzzy region
getY() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyRegion: It returns the Y value of a fuzzy region
getY() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyRegion: It returns the Y value of a fuzzy region
getY() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyRegion: It returns the Y value of a fuzzy region
getYavg() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the Y values mean
getYo() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForModels: This method return the result of the model
getYo() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForSymbRegr: This method obtain a crips output that we can compare to punctual models
getyulesQ() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AssociationRule: It returns the yulesQ of an association rule
getYulesQ() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule: It returns the yulesQ of an association rule
getYulesQ() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
getYulesQ() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
getYulesQ() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule: It returns the yulesQ of an association rule
getYulesQ() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule: It returns the yulesQ of an association rule
getYulesQ() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
getYulesQ() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
getYulesQ() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
getYulesQ() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
getYulesQ() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
getYulesQ() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
getYulesQ() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
getYulesQ() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
getYY() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the summation of the Y samples squares.
getYYavg() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Returns the mean of the Y samples squares.
getZero() - Method in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
getZeroValue() - Method in class keel.Algorithms.Hyperrectangles.EACH.Selector: Return the attribute's value associated
getZeroValue() - Method in class keel.Algorithms.Rule_Learning.Riona.Selector: Returns the value or less extrem of the associated attribute
GFS_AllPositive_APR - Class in keel.Algorithms.MIL.APR.GFS_AllPositive_APR
GFS_AllPositive_APR() - Constructor for class keel.Algorithms.MIL.APR.GFS_AllPositive_APR.GFS_AllPositive_APR
GFS_ElimCount_APR - Class in keel.Algorithms.MIL.APR.GFS_ElimCount_APR
GFS_ElimCount_APR() - Constructor for class keel.Algorithms.MIL.APR.GFS_ElimCount_APR.GFS_ElimCount_APR
GFS_Kde_APR - Class in keel.Algorithms.MIL.APR.GFS_Kde_APR
GFS_Kde_APR() - Constructor for class keel.Algorithms.MIL.APR.GFS_Kde_APR.GFS_Kde_APR
GFS_RB_MF - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF: Title: GFS_RB_MF Description: It contains the implementation of the GFS RB MF algorithm Company: KEEL
GFS_RB_MF() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.GFS_RB_MF: Default constructor
GFS_RB_MF(parseParameters) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.GFS_RB_MF: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
GG - Class in keel.Algorithms.Instance_Selection.GG: File: GG.java The GG Instance Selection algorithm.
GG(String) - Constructor for class keel.Algorithms.Instance_Selection.GG.GG: Default constructor.
GG - Class in keel.Algorithms.Preprocess.Instance_Selection.GG: File: GG.java The GG Instance Selection algorithm.
GG(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.GG.GG: Default constructor.
GGA - Class in keel.Algorithms.Instance_Selection.GGA: File: GGA.java Generational Genetic algorithm for Instance Selection.
GGA(String) - Constructor for class keel.Algorithms.Instance_Selection.GGA.GGA: Default builder.
GGA - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GGA: File: GGA.java A Generational Genetic algorithm for Feature Selection
GGA(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GGA.GGA: Builder.
GGA - Class in keel.Algorithms.Preprocess.Instance_Selection.GGA: File: GGA.java Generational Genetic algorithm for Instance Selection.
GGA(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.GGA.GGA: Default builder.
GGABinaryIncon - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_BinCod.filter: Main class of Generational Genetic Algorithm for feature selection using inconsistency ratio as evaluation measure This class implements a Generational GA with binary representation for feature selection Inconsistency ratio (FILTER) Inputs: nearest neighbours for K-NN, k value for k-Tournamentvalor, crossover probability, mutation probability, seed, population size, number of evaluations, alfa value for fitness balancing Encoding: Binary Selection: k-Tournament Replacement: Descendants always replaces parents Crossover and mutation in one point Fitness: (1-alfa)*%hits + alfa*features_selected Stopping criteria: number of evaluations
GGABinaryIncon(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_BinCod.filter.GGABinaryIncon: Creates a new instance of GGABinaryIncon
GGABinaryLVO - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_BinCod.wrapper: MAIN CLASS OF GENERATIONAL GENETIC ALGORITHM FOR FEATURES SELECTION USING LVO WRAPPER METHOD
GGABinaryLVO(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_BinCod.wrapper.GGABinaryLVO: Creates a new instance of GGABinaryLVO
GGAIntegerIncon - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_IntCod.filter: MAIN CLASS OF GENERATIONAL GENETIC ALGORITHM FOR FEATURES SELECTION USING INCONSISTENCY RATIO AS EVALUATION MEASURE
GGAIntegerIncon(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_IntCod.filter.GGAIntegerIncon: Creates a new instance of GGAIntegerIncon
GGAIntegerLVO - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_IntCod.wrapper: MAIN CLASS OF GENERATIONAL GENETIC ALGORITHM FOR FEATURES SELECTION USING LVO AS WRAPPER ALGORITHM
GGAIntegerLVO(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_IntCod.wrapper.GGAIntegerLVO: Creates a new instance of GGAIntegerLVO
GI_CUSTOM_CESAR - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OperatorIdent: Identifier for local optimization (GI_CUSTOM_CESAR).
GI_CUSTOM_CESAR - Static variable in class keel.Algorithms.Shared.Parsing.OperatorIdent: Identifier for local optimization (GI_CUSTOM_CESAR).
GI_STANDARD - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OperatorIdent: Identifier for local optimization (GI_STANDARD).
GI_STANDARD - Static variable in class keel.Algorithms.Shared.Parsing.OperatorIdent: Identifier for local optimization (GI_STANDARD).
GIL - Class in keel.Algorithms.Genetic_Rule_Learning.GIL: Title: Algorithm Description: It contains the implementation of the algorithm Company: KEEL
GIL() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GIL.GIL: Default constructor
GIL(parseParameters) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GIL.GIL: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Gini - Class in keel.Algorithms.Decision_Trees.CART.impurities: Implementation of GINI impurity Function
Gini() - Constructor for class keel.Algorithms.Decision_Trees.CART.impurities.Gini
giveClasses() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Returns teh value of the classes
giveClasses() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Returns the value of the classes
giveNames() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Returns the name of the problem variables
global - Static variable in class keel.Algorithms.Decision_Trees.C45.Tree: To compute the average attributes per rule
global - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: To compute the average attributes per rule
globalInfo() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets: Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Arrays: Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Returns a string describing classifier
globalInfo() - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Returns a string describing classifier
globalInfo() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Returns a string describing classifier
globalInfo() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Returns a string describing this classifier
globalInfo() - Method in class keel.Algorithms.SVM.SMO.SMO: Returns a string describing classifier
globalInfo() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Returns a string describing classifier
globalInfo() - Method in class keel.Algorithms.SVM.SMO.supportVector.Kernel: Returns a string describing the kernel
globalInfo() - Method in class keel.Algorithms.SVM.SMO.supportVector.NormalizedPolyKernel: Returns a string describing the kernel
globalInfo() - Method in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: Returns a string describing the kernel
globalInfo() - Method in class keel.Algorithms.SVM.SMO.supportVector.PolyKernel: Returns a string describing the kernel
globalInfo() - Method in class keel.Algorithms.SVM.SMO.supportVector.Puk: Returns a string describing the kernel
globalInfo() - Method in class keel.Algorithms.SVM.SMO.supportVector.RBFKernel: Returns a string describing the kernel
globalInfo() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMO: Returns a string describing classifier
globalInfo() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: Returns a string describing the object
globalInfo() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Returns a string describing the kernel
globalInfo() - Method in class keel.Algorithms.SVM.SMO.SVMreg: Returns a string describing classifier
globalIterationsSinceBest - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.timerEvolutionStats
Globals_ADI - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist: Computes and maintains global information for the ADI KR
Globals_ADI() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_ADI
Globals_ADI - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS: Computes and maintains global information for the ADI KR
Globals_ADI() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_ADI
Globals_DefaultC - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist: Computes and maintains global information for the Default C
Globals_DefaultC() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_DefaultC
Globals_DefaultC - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS: Computes and maintains global information for the DefaultC KR
Globals_DefaultC() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_DefaultC
Globals_GABIL - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist: Computes and maintains global information for the GABIL KR
Globals_GABIL() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_GABIL
Globals_GABIL - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS: Computes and maintains global information for the GABIL KR
Globals_GABIL() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_GABIL
Globals_MDL - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist: Computes and maintains global information for the MDL KR
Globals_MDL() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_MDL
Globals_MDL - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS: Computes and maintains global information for the MDL KR
Globals_MDL() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_MDL
Globals_UBR - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist: Computes and maintains global information for the UBR KR
Globals_UBR() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_UBR
globalWilcoxonC - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Classification Wilcoxon Stat-test identifier.
globalWilcoxonI - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Imbalanced Wilcoxon Stat-test identifier.
globalWilcoxonR - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Regression Wilcoxon Stat-test identifier.
GMCAAlgorithm - Class in keel.Algorithms.Instance_Generation.GMCA: GMCA algorithm calling.
GMCAAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.GMCA.GMCAAlgorithm
GMCAGenerator - Class in keel.Algorithms.Instance_Generation.GMCA: Implements GMCAGenerator algorithm.
GMCAGenerator(PrototypeSet) - Constructor for class keel.Algorithms.Instance_Generation.GMCA.GMCAGenerator: Basic constructor
GMCAGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.GMCA.GMCAGenerator: Constructor
gmdh - Class in keel.Algorithms.Neural_Networks.gmdh: Class representing the gmdh algorithm
gmdh() - Constructor for class keel.Algorithms.Neural_Networks.gmdh.gmdh: Empty constructor
GMEAN - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Geometric mean measurement identifier.
goout(String) - Static method in class keel.Algorithms.Instance_Generation.utilities.Debug: Shows a message and ends the program.
goout(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Debug: Shows a message and ends the program.
GP_COACH - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH: Title: GP-COACH Description: It contains the implementation of the GP-COACH algorithm Company: KEEL
GP_COACH() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.GP_COACH: Default constructor
GP_COACH(parseParameters) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.GP_COACH: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
GP_COACH_H - Class in keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H: Title: GP-COACH Description: It contains the implementation of the GP-COACH algorithm Company: KEEL
GP_COACH_H() - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.GP_COACH_H: Default constructor
GP_COACH_H(parseParameters) - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.GP_COACH_H: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
gr(double, double) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Tests if a is greater than b.
gr(double, double) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Tests if a is greater than b.
gr(double, double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Tests if a is smaller than b.
gr(double, double) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Tests if a is greater than b.
gr(double, double) - Static method in class keel.Algorithms.Rule_Learning.Slipper.Utilities: Tests if a is greater than b.
gr(double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Tests if a is greater than b.
gr(double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Tests if a is greater than b.
gr(double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Tests if a is greater than b.
gr(double, double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Tests if a is greater than b.
gr(double, double) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Tests if a is greater than b.
Grade_Is_Covered(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
Grade_Is_Covered(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set
Grade_Is_Covered(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set
Grade_Is_Negative_Covered(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
Grade_Is_Positive_Covered(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
Grade_Is_Positive_Covered(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set
gradient(double[]) - Method in class keel.Algorithms.MIL.Diverse_Density.Optimization.DDoptimization
gradient(double[]) - Method in class keel.Algorithms.MIL.Diverse_Density.Optimization.EMDDoptimization
gradient(double[]) - Method in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
gradient(double[][], double[][]) - Method in interface keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IOptimizableFunc: Returns the gradient vector of the derivative of an error function (E) with respect to each coefficient of the model, using an input observation matrix (x[]) and an expected output matrix (y[]).
gradient(double[][], double[][]) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.MSEOptimizablePUNeuralNetClassifier: Returns the gradient vector of the derivative of MSE error function with respect to each coefficient of the model, using an input observation matrix (x[]) and an expected output matrix (y[])
gradient(double[][], double[][]) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.MSEOptimizableSigmNeuralNetClassifier: Returns the gradient vector of the derivative of MSE error function with respect to each coefficient of the model, using an input observation matrix (x[]) and an expected output matrix (y[])
gradient(double[][], double[][]) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.MSEOptimizablePUNeuralNetRegressor: Returns the gradient vector of the derivative of MSE error function with respect to each coefficient of the model, using an input observation matrix (x[]) and an expected output matrix (y[])
gradient(double[][], double[][]) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.MSEOptimizableSigmNeuralNetRegressor: Returns the gradient vector of the derivative of MSE error function with respect to each coefficient of the model, using an input observation matrix (x[]) and an expected output matrix (y[])
gradient(MultivariateFunction, double[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.util.NumericalDerivative: determine gradient
gradient(MultivariateFunction, double[], double[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.util.NumericalDerivative: determine gradient
GradoEmp - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Sel: Empirical Grade for each rule.
GradoEmp - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Tun: Empirical Grade for each rule.
Graph - Class in keel.GraphInterKeel.experiments
Graph() - Constructor for class keel.GraphInterKeel.experiments.Graph: Builder
graphDiagramINNER - Variable in class keel.GraphInterKeel.experiments.Experiments
GraphInterKeel - Class in keel.GraphInterKeel.menu: GraphInterKeel
GraphInterKeel() - Constructor for class keel.GraphInterKeel.menu.GraphInterKeel: Builder
GraphPanel - Class in keel.GraphInterKeel.experiments
GraphPanel(Experiments, Graph) - Constructor for class keel.GraphInterKeel.experiments.GraphPanel: Builder
GraphPanel() - Constructor for class keel.GraphInterKeel.experiments.GraphPanel: Builder
GraspIncon - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.filter.inconsistency: MAIN CLASS OF GRASP ALGORITHM USING INCONSISTENCY RATIO AS EVALUATION MEASURE
GraspIncon(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.filter.inconsistency.GraspIncon: Creates a new instance of GraspIncon
GraspLVO - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.wrapper: MAIN CLASS OF GRASP ALGORITHM USING LVO CLASSIFIER AS WRAPPER METHOD
GraspLVO(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.wrapper.GraspLVO: Creates a new instance of GraspLVO
GraspMI - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.filter.im: Main class of Grasp Algorithm using mutual information as evaluation mesure
GraspMI(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.filter.im.GraspMI: Creates a new instance of GraspMI
GREATER - Static variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Selector: Number to represent greater.
GREATER - Static variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule: number to indentify the operator >.
GREATER - Static variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleRule: number to indentify the operator >.
GREATER - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule: number to indentify the operator >.
GREATER - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.SimpleRule: number to indentify the operator >.
GREATER - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Selector: Number to represent greater.
GREATER - Static variable in class keel.Algorithms.Rule_Learning.C45Rules.Rule: Flag for greater operator
GREATER - Static variable in class keel.Algorithms.Rule_Learning.C45Rules.SimpleRule: Flag for greater operator
GREATER - Static variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule: Flag for greater operator
GREATER - Static variable in class keel.Algorithms.Rule_Learning.C45RulesSA.SimpleRule: Flag for greater operator
GREATER - Static variable in class keel.Algorithms.Rule_Learning.PART.Rule: Flag for greater operator
GREATER - Static variable in class keel.Algorithms.Rule_Learning.PART.SimpleRule: Flag for greater operator
GREATER - Static variable in class keel.Algorithms.Rule_Learning.Ripper.Rule: Flag for greater operator
GREATER - Static variable in class keel.Algorithms.Rule_Learning.Ripper.SimpleRule: Flag for greater operator
GREATER - Static variable in class keel.Algorithms.Rule_Learning.Slipper.Rule: Flag for greater operator
GREATER - Static variable in class keel.Algorithms.Rule_Learning.Slipper.SimpleRule: Flag for greater operator
greedyFeatureSelection(double[][], int) - Method in class keel.Algorithms.MIL.APR.AbstractAPR
greedyMODL(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.MODL.MODL: This method implements the greedy version of the MODL discretizer.
grOrEq(double, double) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Tests if a is greater or equal to b.
grOrEq(double, double) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Tests if a is greater or equal to b.
grOrEq(double, double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Tests if a is greater or equal to b.
grOrEq(double, double) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Tests if a is greater or equal to b.
grOrEq(double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Tests if a is greater or equal to b.
grOrEq(double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Tests if a is greater or equal to b.
grOrEq(double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Tests if a is greater or equal to b.
grOrEq(double, double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Tests if a is greater or equal to b.
grOrEq(double, double) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Tests if a is greater or equal to b.
grouping(boolean) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FlexibleDecimalFormat: Sets the grouping flag with the value given.
grow(Instances) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule: Build one rule using the growing data
grow(Instances) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Rule: Build this rule
grow(int, double, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule: Adds a simple rule to this rule.
grow(SimpleRule) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule: Adds a simple rule to this rule.
grow(int, double, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule: Adds a simple rule to this rule.
grow(SimpleRule) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule: Adds a simple rule to this rule.
grow(int, double, int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Rule: Adds a simple rule to this rule.
grow(SimpleRule) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Rule: Adds a simple rule to this rule.
grow(int, double, int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule: Adds a simple rule to this rule.
grow(SimpleRule) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule: Adds a simple rule to this rule.
grow(int, double, int) - Method in class keel.Algorithms.Rule_Learning.PART.Rule: Adds a simple rule to this rule.
grow(SimpleRule) - Method in class keel.Algorithms.Rule_Learning.PART.Rule: Adds a simple rule to this rule.
grow(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ripper: It grows a rule maximizing the following heuristic: h= p*(log(p/t)-log(P/T)) p/t: number of positive/total instances covered by the current rule P/T: number of positive/total instances
grow(Rule, MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ripper: It expands a rule, greedily adding simple rules, maximizing the following heuristic: h= p*(log(p/t)-log(P/T)) p/t: number of positive/total instances covered by the current rule P/T: number of positive/total instances
grow(int, double, int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Rule: Adds a simple rule to this rule.
grow(SimpleRule) - Method in class keel.Algorithms.Rule_Learning.Ripper.Rule: Adds a simple rule to this rule.
grow(int, double, int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Rule: Adds a simple rule to this rule.
grow(SimpleRule) - Method in class keel.Algorithms.Rule_Learning.Slipper.Rule: Adds a simple rule to this rule.
grow(MyDataset, Mask, Mask, double[]) - Static method in class keel.Algorithms.Rule_Learning.Slipper.Slipper: It expands a rule, greedily adding simple rules, maximizing the following heuristic: Z=sqrt(W+)-sqrt(W_) W+: sum of the weights of the positive instances that are covered by the current rule W_: sum of the weights of the negative instances that are covered by the current rule
growTree(TreeNode) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.FunctionalTrees: Builds the tree from a tree node that functions as a root node, with all the data stored in the class
GWS - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters

H

hacerGenetico() - Method in class keel.Algorithms.Decision_Trees.Target.Poblacion: Executes the genetic algorithm over the population.
HALF - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Fuzzy_Ish: Configuration flags.
hammingDistance(CHC_Chromosome, int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC_Chromosome: Obtains the Hamming distance between this and another chromosome
HandlerCSVM - Class in keel.Algorithms.Semi_Supervised_Learning.Basic: HandlerCSVM for C support vector machine algorithm.
HandlerCSVM(int, int, String) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerCSVM: Parameter constructor.
HandlerCSVM(InstanceSet, InstanceSet, int, String) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerCSVM: Parameter constructor.
HandlerNB - Class in keel.Algorithms.Semi_Supervised_Learning.Basic: Handler for Naive Bayes algorithm.
HandlerNB() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerNB: Default constructor.
HandlerNB(String, String, int, int) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerNB: Parameter constructor.
HandlerNB(InstanceSet, InstanceSet) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerNB: Parameter constructor.
HandlerNB(double[][], int[], double[][], int[], int) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerNB: Parameter constructor.
HandlerSMO - Class in keel.Algorithms.Semi_Supervised_Learning.Basic: Handler for SMO algorithm.
HandlerSMO(int, int, String) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerSMO: Parameter constructor.
HandlerSMO(InstanceSet, InstanceSet, int, String) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerSMO: Parameter constructor.
hardCorrect(Prototype, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQ2_1: Corrects a prototype of a set.
hasAdditional() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation: returns true if there are further technical informations stored in this
hasAdditional() - Method in class keel.Algorithms.SVM.SMO.core.TechnicalInformation: returns true if there are further technical informations stored in this
hasAntds() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule: Whether this rule has antecedents, i.e. whether it is a default rule
hasAntds() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Rule: Whether this rule has antecedents, i.e. whether it is a default rule
hasChildren() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.Item: It returns whether an item has children items
hasChildren() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.Item: It returns whether an item has children items
hasClassUncovered(int[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: Indentifies how many classes are uncovered with a selection of rules.
hasClassUncovered(int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
hasDefaultClass() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_ADI
hasDefaultClass() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_GABIL
hasDefaultClass() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_UBR
hasDefaultClass() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_ADI
hasDefaultClass() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_GABIL
hasEnumAttr(M5Instances) - Static method in class keel.Algorithms.Decision_Trees.M5.M5: Tests if enumerated attribute(s) exists in the instances
hasEnumAttr(MyDataset) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: Tests if enumerated attribute(s) exists in the itemsets
hashCode() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute: Hash-code function for the class that is used when object is inserted in a structure like a hashtable
hashCode() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset: Hash-code function for the class that is used when object is inserted in a structure like a hashtable
hashCode() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.Split: Hash-code function for the class that is used when object is inserted in a structure like a hashtable
hashCode() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Hash-code function for the class that is used when object is inserted in a structure like a hashtable
hashCode() - Method in class keel.Algorithms.Decision_Trees.M5.SerializedObject: Returns a hashcode for this object.
hashCode() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute: Hash-code function for the class that is used when object is inserted in a structure like a hashtable
hashCode() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset: Hash-code function for the class that is used when object is inserted in a structure like a hashtable
hashCode() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Node: Hash-code function for the class that is used when object is inserted in a structure like a hashtable
hashCode() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Register: Hash-code function for the class that is used when object is inserted in a structure like a hashtable
hashCode() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Split: Hash-code function for the class that is used when object is inserted in a structure like a hashtable
hashCode() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode: Hash-code function for the class that is used when object is inserted in a structure like a hashtable
hashCode() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.SerializedObject: Returns a hashcode for this object.
hashCode() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Fuzzy: Computes the hash code associated to the current fuzzy label
hashCode() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Computes the hash code associated to the current FuzzyAntecedent
hashCode() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Computes the hash code associated to the current rule
hashCode() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SerializedObject: Returns a hashcode for this object.
hashCode() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Returns an integer number that identifies the neural net
hashCode() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.ILayer: Returns an integer number that identifies the layer
hashCode() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuralNet: Returns an integer number that identifies the neural net
hashCode() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuron: Returns an integer number that identifies the neuron
hashCode() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputLayer: Returns an integer number that identifies the layer
hashCode() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputNeuron: Returns an integer number that identifies the neuron
hashCode() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.Link: Returns an integer number that identifies the link
hashCode() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Returns an integer number that identifies the layer
hashCode() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron: Returns an integer number that identifies the neuron
hashCode() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.util.random.RanNnep: Returns a hashcode identifier of the random generator
hashCode() - Method in class keel.Algorithms.SVM.SMO.core.SerializedObject: Returns a hashcode for this object.
HashSparseMatrix<IndexType,ElementType> - Class in keel.Algorithms.Instance_Generation.utilities
HashSparseMatrix() - Constructor for class keel.Algorithms.Instance_Generation.utilities.HashSparseMatrix
HashSparseMatrix<IndexType,ElementType> - Class in keel.Algorithms.Semi_Supervised_Learning.utilities: Hash Sparse Matrix implementation.
HashSparseMatrix() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.utilities.HashSparseMatrix
hasIntegerAttributes() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: The function returns if there is any integer attribute.
hasIntegerAttributes() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: The function returns if there is any integer attribute.
hasIntegerAttributes() - Static method in class keel.Dataset.Attributes: The function returns if there is any integer attribute.
hasIntegerAttributes() - Method in class keel.Dataset.InstanceAttributes: The function returns if there is any integer attribute.
hasInterface(String, String) - Static method in class keel.Algorithms.SVM.SMO.core.ClassDiscovery: Checks whether the given class implements the given interface.
hasInterface(Class, Class) - Static method in class keel.Algorithms.SVM.SMO.core.ClassDiscovery: Checks whether the given class implements the given interface.
hasMissing(M5Instances) - Static method in class keel.Algorithms.Decision_Trees.M5.M5: Tests if missing value(s) exists in the instances
hasMissing(MyDataset) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: Tests if missing value(s) exists in the itemsets
hasMissingAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: It checks if the data-set has any missing value.
hasMissingAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: It checks if the data-set has any missing value
hasMissingAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: It checks if the data-set has any missing value
hasMissingValue() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Tests whether an instance has a missing value.
hasMissingValue() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Tests whether an instance has a missing value.
hasMissingValue() - Method in class keel.Algorithms.SVM.SMO.core.Instance: Tests whether an instance has a missing value.
hasMissingValues() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: The function returns if there is any missing value
hasMissingValues() - Static method in class keel.Dataset.Attributes: The function returns if there is any missing value
hasMoreElements() - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector.FastVectorEnumeration: Tests if there are any more elements to enumerate.
hasMoreElements() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector.FastVectorEnumeration: Tests if there are any more elements to enumerate.
hasMoreElements() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector.FastVectorEnumeration: Tests if there are any more elements to enumerate.
hasMoreElements() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector.FastVectorEnumeration: Tests if there are any more elements to enumerate.
hasMoreElements() - Method in class keel.Algorithms.SVM.SMO.core.FastVector.FastVectorEnumeration: Tests if there are any more elements to enumerate.
hasNominalAttributes() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It checks if the data-set has any nominal value
hasNominalAttributes() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: The function returns if there is any nominal attribute
hasNominalAttributes() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: The function returns if there is any nominal attribute
hasNominalAttributes() - Static method in class keel.Dataset.Attributes: The function returns if there is any nominal attribute
hasNominalAttributes() - Method in class keel.Dataset.InstanceAttributes: The function returns if there is any nominal attribute
hasNumericalAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Checks if the data-set has any numeric value.
hasNumericalAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Checks if the data-set has any numeric value.
hasNumericalAttributes() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It checks if the data-set has any numerical (real or integer) value
hasNumericalAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It checks if the data-set has any numerical (real or integer) value
hasNumericalAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It checks if the data-set has any numerical (real or integer) value
hasNumericalAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It checks if the data-set has any numerical (real or integer) value
hasNumericalAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It checks if the data-set has any numerical (real or integer) value
hasNumericalAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It checks if the data-set has any numerical (real or integer) value
hasNumericalAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It checks if the data-set has any numerical (real or integer) value
hasNumericalAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It checks if the data-set has any numerical (real or integer) value
hasNumericalAttributes() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It checks if the data-set has any numerical (real or integer) value
hasNumericalAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It checks if the data-set has any missing value
hasNumericalAttributes() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It checks if the data-set has any numerical value
hasNumericalAttributes() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It checks if the data-set has any numerical value (real or integer)
hasNumericalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It checks if the data-set has any numerical value (real or integer)
hasNumericalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It checks if the data-set has any numerical value (real or integer)
hasNumericalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: It checks if the data-set has any numerical value (real or integer)
hasNumericalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: It checks if the data-set has any numerical value (real or integer)
hasNumericalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: It checks if the data-set has any numerical value (real or integer)
hasNumericalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: It checks if the data-set has any numerical value (real or integer)
hasNumericalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: It checks if the data-set has any numerical value (real or integer)
hasNumericalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: It checks if the data-set has any numerical value (real or integer)
hasNumericalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: It checks if the data-set has any numerical value (real or integer)
hasNumericalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: It checks if the data-set has any numerical value (real or integer)
hasNumericalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: It checks if the data-set has any numerical value (real or integer)
hasNumericalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: It checks if the data-set has any numerical value (real or integer)
hasNumericalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: It checks if the data-set has any numerical value (real or integer)
hasNumericalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: It checks if the data-set has any numerical value (real or integer)
hasNumericalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: It checks if the data-set has any numerical value (real or integer)
hasNumericalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: It checks if the data-set has any numerical value (real or integer)
hasNumericalAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: It checks if the data-set has any numerical value (real or integer)
hasRealAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: It checks if the data-set has any real value.
hasRealAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: The function returns if there is any real attribute.
hasRealAttributes() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: The function returns if there is any real attribute.
hasRealAttributes() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: It checks if the data-set has any real value
hasRealAttributes() - Static method in class keel.Dataset.Attributes: The function returns if there is any real attribute.
hasRealAttributes() - Method in class keel.Dataset.InstanceAttributes: The function returns if there is any real attribute.
hasRuleUncoveredExamples() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams
hasRuleUncoveredExamples() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams
hasUID(String) - Static method in class keel.Algorithms.SVM.SMO.core.SerializationHelper: checks whether the given class contains a serialVersionUID
hasUID(Class) - Static method in class keel.Algorithms.SVM.SMO.core.SerializationHelper: checks whether the given class contains a serialVersionUID
hasUncover() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: Checks if there are examples uncovered by the rules.
hasUncover() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
hasUncoverClass(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Apriori: Indentifies how many times a class has been uncovered.
hasUncoverClass(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Apriori
hasZeropoint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Returns whether the attribute has a zeropoint and may be added meaningfully.
hasZeropoint() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Returns whether the attribute has a zeropoint and may be added meaningfully.
HausdorffMaxDistance - Variable in class keel.Algorithms.MIL.Nearest_Neighbour.AbstractNearestNeighbour
HausdorffMaxDistance(ArrayList<IInstance>, ArrayList<IInstance>) - Method in class keel.Algorithms.MIL.Nearest_Neighbour.AbstractNearestNeighbour
HausdorffMinDistance(ArrayList<IInstance>, ArrayList<IInstance>) - Method in class keel.Algorithms.MIL.Nearest_Neighbour.AbstractNearestNeighbour
hayAtributosContinuos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: It checks if the data-set has any real value
hayAtributosContinuos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: It checks if the data-set has any real value
hayAtributosContinuos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: It checks if the data-set has any real value
hayAtributosContinuos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: It checks if the data-set has any real value
hayAtributosContinuos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: It checks if in the data-set there is any continous input
hayAtributosContinuos() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: It checks if the data-set has any real value
hayAtributosContinuos() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: It checks if in the data-set there is any continous input
hayAtributosContinuos() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Checks if in the data base there is a in-put type real or continous
hayAtributosContinuos() - Method in class keel.Algorithms.Rule_Learning.AQ.Dataset: It checks if in the data-set there is any continous input
hayAtributosContinuos() - Method in class keel.Algorithms.Rule_Learning.CN2.Dataset: It checks if in the data-set there is any continous input
hayAtributosContinuos() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Checks if in the data base there is a in-put type real or continous
hayAtributosContinuos() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Checks if in the data base there is a in-put type real or continous
hayAtributosContinuos() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: It checks if in the data-set there is any continous input
hayAtributosContinuos() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset: It checks if in the data-set there is any continous input
hayAtributosContinuos() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset: It checks if in the data-set there is any continous input
hayAtributosDiscretos() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Checks if in the data base there is an in-put type integer
hayInstanciasDeClaseC(int) - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Checks if in the instance set the are instances of a one determinet class
hayInstanciasDeClaseC(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Checks if in the instances set left instances of a determined class
hazUniforme(Dataset) - Method in class keel.Algorithms.Rule_Learning.Prism.ConjDatos: Adapt the examples to the [0,1] interval
hazUniforme(Dataset) - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjDatos: Adapt the examples to the [0,1] interval
hazUniforme(Dataset) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjDatos: Adapt the examples to the [0,1] interval
HDD - Class in keel.Algorithms.Discretizers.HDD: This class implements the HDD discretizer.
HDD(double) - Constructor for class keel.Algorithms.Discretizers.HDD.HDD: Builder
header() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It returns the header.
HeaderFormatException - Exception in keel.Dataset: HeaderFormatException Exception thrown when the header is not in the correct format
HeaderFormatException() - Constructor for exception keel.Dataset.HeaderFormatException: Creates a new instance of HeaderFormatException
HeaderFormatException(String) - Constructor for exception keel.Dataset.HeaderFormatException: Creates a new instance of HeaderFormatException with a message given.
headerParse(String, boolean) - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser: It's the parser main method.
headerParse(String, boolean) - Static method in class keel.Dataset.DataParser: It's the parser main method.
headerTable - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.FPtree: Start reference for header table.
headerTable - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.FPtree: Start reference for header table.
headLegend(int, String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.AdministrativeStaff
headToString() - Static method in class keel.Algorithms.Decision_Trees.M5.M5: Prints the head lines of the output
heapSize - Variable in class keel.GraphInterKeel.experiments.Experiments
HellingerBD - Class in keel.Algorithms.Discretizers.HellingerBD: This class implements the HellingerBD discretizer
HellingerBD() - Constructor for class keel.Algorithms.Discretizers.HellingerBD.HellingerBD: Constructor of the class
help() - Method in class keel.GraphInterKeel.datacf.DataCFFrame: Shows help
help_mouseEntered(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Enter in help button
help_mouseExited(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Exit from help button
help_mouseReleased(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Show main help
HelpContent - Class in keel.GraphInterKeel.datacf.help
HelpContent() - Constructor for class keel.GraphInterKeel.datacf.help.HelpContent: Constructor that initializes the panel
helpContent - Variable in class keel.GraphInterKeel.experiments.Experiments
HelpContent - Class in keel.GraphInterKeel.help: File: HelpFrame.java A class for managing the contents of the help
HelpContent() - Constructor for class keel.GraphInterKeel.help.HelpContent: Builder
HelpContent - Class in keel.GraphInterKeel.statistical.help: File: HelpContent.java This class shows the help panel of the module
HelpContent() - Constructor for class keel.GraphInterKeel.statistical.help.HelpContent: Constructor that initializes the panel
HelpFrame - Class in keel.GraphInterKeel.datacf.help
HelpFrame() - Constructor for class keel.GraphInterKeel.datacf.help.HelpFrame: Constructor that initializes the frame
HelpFrame - Class in keel.GraphInterKeel.help: File: HelpFrame.java A class for managing the help frame
HelpFrame() - Constructor for class keel.GraphInterKeel.help.HelpFrame: Builder
HelpOptions - Class in keel.GraphInterKeel.datacf.help
HelpOptions(HelpFrame) - Constructor for class keel.GraphInterKeel.datacf.help.HelpOptions: Constructor that initializes the frame
HelpOptions - Class in keel.GraphInterKeel.help: File: HelpOptions.java A class for managing options for help
HelpOptions(HelpFrame) - Constructor for class keel.GraphInterKeel.help.HelpOptions: Builder
HelpSheet - Class in keel.GraphInterKeel.datacf.help
HelpSheet(String, String) - Constructor for class keel.GraphInterKeel.datacf.help.HelpSheet: Constructor that initializes the sheet using a file
HelpSheet(String, URL) - Constructor for class keel.GraphInterKeel.datacf.help.HelpSheet: Constructor that initializes the sheet using an URL address
HelpSheet - Class in keel.GraphInterKeel.help: File: HelpSheet.java A class for managing help sheets
HelpSheet(String, String) - Constructor for class keel.GraphInterKeel.help.HelpSheet: Builder
HelpSheet(String, URL) - Constructor for class keel.GraphInterKeel.help.HelpSheet: Builder
HeterDisc - Class in keel.Algorithms.Discretizers.HeterDisc: This class implements the Heter-Disc discretizer
HeterDisc() - Constructor for class keel.Algorithms.Discretizers.HeterDisc.HeterDisc: Constructor of the class
HeterDisc.DiscretizationScheme - Class in keel.Algorithms.Discretizers.HeterDisc: This class lets to manipulate discretization schemes
hidden - Variable in class keel.GraphInterKeel.experiments.Parameters
HIDDEN_LAYER - Static variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Defines hidden layer type
hiddenLayers - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Hidden layer
hiddenLinksPercentage - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Percentage of links to remove in the mutations
hideImportButton() - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Hides import button
hideImportButton() - Method in class keel.GraphInterKeel.experiments.SelectData: Hide import button
Hider - Class in keel.Algorithms.Genetic_Rule_Learning.Hider
Hider() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider: Empty constructor
Hider(String) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider: Constructor with configuration file name parameter
hierarchicalSelectionThreshold - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
hierarchicalSelectionThreshold - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
Higher(double[], double[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
Higher(double[], double[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
Higher(double[], double[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
HIGHEST_FIRST - Static variable in class keel.Algorithms.Discretizers.HellingerBD.Quicksort: Configuration tag (Lowest first).
HIGHEST_FIRST - Static variable in class keel.Algorithms.Discretizers.UCPD.Quicksort: Configuration tags.
HIGHEST_FIRST - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Quicksort: HIGHEST_FIRST tag.
HIGHEST_FIRST - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Quicksort: Configuration tag (HIGHEST_FIRST).
HIGHEST_FIRST - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Quicksort: Configuration tag (HIGHEST_FIRST).
HIGHEST_FIRST - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Quicksort: Configuration tag (HIGHEST_FIRST).
Histogram - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat: Histomgram, Statistics class that extends Sample and computes statistical values as percentiles.
Histogram(int, double, double) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Histogram: Parameter constructor.
Histogram(int, double) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Histogram: Parameter constructor. when base is equal to 0.
Histograma - Class in keel.Algorithms.Decision_Trees.SLIQ: This class implements the histogram associated with the tree nodes.
HMNEI - Class in keel.Algorithms.Instance_Selection.HMNEI: File: HMNEI.java The HMNEI Instance Selection algorithm.
HMNEI(String) - Constructor for class keel.Algorithms.Instance_Selection.HMNEI.HMNEI: Default constructor.
HMNEI - Class in keel.Algorithms.Preprocess.Instance_Selection.HMNEI: File: HMNEI.java The HMNEI Instance Selection algorithm.
HMNEI(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.HMNEI.HMNEI: Default constructor.
Hoch - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Hoch boolean
Hoch - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Apply Hoch flag.
hoja - Variable in class keel.Algorithms.Decision_Trees.SLIQ.ListaClases: Associated leaf.
holdOutOptions - Variable in class keel.GraphInterKeel.datacf.partitionData.PartitionPanel: An option dialog for obtaining the options of the holdout partition proccess
HoldOutOptionsJDialog - Class in keel.GraphInterKeel.datacf.partitionData
HoldOutOptionsJDialog(Frame, boolean) - Constructor for class keel.GraphInterKeel.datacf.partitionData.HoldOutOptionsJDialog: Constructor that initializes the dialog
Holland - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Apply Holland flag.
Holm - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Holm boolean
Holm - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Apply Holm flag.
Hommel - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Hommel boolean
Hommel - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Apply Hommel flag.
homogeneity() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet
homogeneity() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet
HomogenousPair<T> - Class in keel.Algorithms.Instance_Generation.utilities: Implements a homogenous pair.
HomogenousPair(T, T) - Constructor for class keel.Algorithms.Instance_Generation.utilities.HomogenousPair: Contructor.
HomogenousPair<T> - Class in keel.Algorithms.Semi_Supervised_Learning.utilities: Homogenous Pair Class.
HomogenousPair(T, T) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.utilities.HomogenousPair: Constructs a pair with the elements given.
horner(double[], double[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.Interpolation: implements Horners-s method to compute the polynomial a0+(t-x0)(a1+(t-x1)(a2+(t-x2)(a3+(...
howManyBestActions() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.PredictionArray: Returns the number of "best actions" in the prediction array.
howManyBestActions() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.PredictionArray: Returns the number of "best actions" in the prediction array.
htDoubleValue - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.CategoricalAttribute: Association between double and real values
HtmlToKeel - Class in keel.Algorithms.Preprocess.Converter: HtmlToKeel This class extends from the Importer class.
HtmlToKeel(String) - Constructor for class keel.Algorithms.Preprocess.Converter.HtmlToKeel: HtmlToKeel class Constructor.
htRealValue - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.CategoricalAttribute: Association between real and double values
Hux(Individual) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual: Crosses the individuals using the HUX operator.
Hux(Individual) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual
HUX(int, int, int) - Method in class keel.Algorithms.Instance_Selection.CoCoIS.Subpopulation: Crosses two selectors of the subpopulation and generates two new childs
HUX(int, int, int) - Method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.Subpopulation: Crosses two selectors of the subpopulation and generates two new childs
HYBAlgorithm - Class in keel.Algorithms.Instance_Generation.HYB: HYB algorithm calling.
HYBAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.HYB.HYBAlgorithm
HYBGenerator - Class in keel.Algorithms.Instance_Generation.HYB: Hybrid algorithm
HYBGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.HYB.HYBGenerator: Constructor of HYBGenerator algorithm.
HYBGenerator(PrototypeSet, int, int, double, double, double, double, double, double, double, double, double, String, String[]) - Constructor for class keel.Algorithms.Instance_Generation.HYB.HYBGenerator: Construct a HYBGenerator algorithm.
HYBGenerator(PrototypeSet, int, int, double, double, double, double, double, double, double, double, double, String, String) - Constructor for class keel.Algorithms.Instance_Generation.HYB.HYBGenerator: Construct a HYBGenerator algorithm.
Hyper - Class in keel.Algorithms.Hyperrectangles.EHS_CHC: Hyper class.
Hyper(double[], double[], boolean[][], int) - Constructor for class keel.Algorithms.Hyperrectangles.EHS_CHC.Hyper
HyperbolicSecant - Static variable in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: Type of Positive Definite Functions supported (Hyperbolic secant)
hyperbolicSecant(double) - Method in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: Computes the result of the Hyperbolic Secant PDRF
HyperMutation(String[], double, String[], double, int) - Method in class keel.Algorithms.Instance_Generation.PSCSA.PSCSAGenerator: PROLIFERATION-I: HyperMutacion of the m match.
Hyperrectangle - Class in keel.Algorithms.Hyperrectangles.EACH: Structure to store a hyperrectangle.
Hyperrectangle() - Constructor for class keel.Algorithms.Hyperrectangles.EACH.Hyperrectangle: Constructor
Hyperrectangle(int, double[], int, int) - Constructor for class keel.Algorithms.Hyperrectangles.EACH.Hyperrectangle: Constructor
HyperrectanglesAlgorithm - Class in keel.Algorithms.Hyperrectangles.Basic: File: HyperrectanglesAlgorithm.java A general framework for Hyperrectangles Algorithms.
HyperrectanglesAlgorithm() - Constructor for class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm
HyperrectangleSet - Class in keel.Algorithms.Hyperrectangles.EACH: Structure to store a set of hyperrectangle.
HyperrectangleSet(Hyperrectangle[], int, int, int, double) - Constructor for class keel.Algorithms.Hyperrectangles.EACH.HyperrectangleSet: Constructor
hypot(double, double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Returns sqrt(a^2 + b^2) without under/overflow.
hypot(double, double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: sqrt(a^2 + b^2) without under/overflow.

I

I(int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
I(int) - Static method in class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
i - Variable in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Relation: first element
i - Variable in class keel.GraphInterKeel.statistical.tests.Relation: first element
I0 - Static variable in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: Configuration flags
I0a - Static variable in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: Configuration flags
I0b - Static variable in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: Configuration flags
I1 - Static variable in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: Configuration flags
I2 - Static variable in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: Configuration flags
I3 - Static variable in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: Configuration flags
IALPHA - Static variable in class keel.Algorithms.Instance_Generation.LVQ.LVQ1: Index of the alpha parameter.
IAttribute - Interface in keel.Algorithms.Neural_Networks.NNEP_Common.data: Dataset Attributes.
IB2 - Class in keel.Algorithms.Instance_Selection.IB2: File: IB2.java The IB2 Instance Selection algorithm.
IB2(String) - Constructor for class keel.Algorithms.Instance_Selection.IB2.IB2: Default constructor.
IB2 - Class in keel.Algorithms.Preprocess.Instance_Selection.IB2: File: IB2.java The IB2 Instance Selection algorithm.
IB2(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.IB2.IB2: Default constructor.
IB3 - Class in keel.Algorithms.Instance_Selection.IB3: File: IB3.java The IB3 Instance Selection algorithm.
IB3(String) - Constructor for class keel.Algorithms.Instance_Selection.IB3.IB3: Default constructor.
IB3 - Class in keel.Algorithms.Preprocess.Instance_Selection.IB3: File: IB3.java The IB3 Instance Selection algorithm.
IB3(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.IB3.IB3: Default constructor.
ICF - Class in keel.Algorithms.Instance_Selection.ICF: File: ICF.java The ICF Instance Selection algorithm.
ICF(String) - Constructor for class keel.Algorithms.Instance_Selection.ICF.ICF: Default constructor.
ICF - Class in keel.Algorithms.Preprocess.Instance_Selection.ICF: File: ICF.java The ICF Instance Selection algorithm.
ICF(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.ICF.ICF: Default constructor.
ICFLVQ3 - Class in keel.Algorithms.Instance_Generation.ICFLVQ3
ICFLVQ3(String) - Constructor for class keel.Algorithms.Instance_Generation.ICFLVQ3.ICFLVQ3
ICFPSO - Class in keel.Algorithms.Instance_Generation.ICFPSO: Hybridization of ICF with PSO.
ICFPSO(String) - Constructor for class keel.Algorithms.Instance_Generation.ICFPSO.ICFPSO: Builder.
ICFSFLSDE - Class in keel.Algorithms.Instance_Generation.ICFSFLSDE
ICFSFLSDE(String) - Constructor for class keel.Algorithms.Instance_Generation.ICFSFLSDE.ICFSFLSDE
IClassifier - Interface in keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification: Generic classifier.
iClassIndex - Variable in class keel.Algorithms.Discretizers.Basic.Discretizer: Class index in the dataset.
icpl1_or_3(int) - Method in class keel.Algorithms.Instance_Generation.ICPL.ICPLGenerator: ICPL1_Pseudo-code: ICPL1( T training) 1) C1= abstraccion T 2) C2 = Filtrar T. 3) S = C1 4) Para cada prototipo P en C2 Tmp = S U P.
icpl2_or_4(int) - Method in class keel.Algorithms.Instance_Generation.ICPL.ICPLGenerator: ICPL2 - pseudo-code ICPL2( T training) 1) C1= abstraccion T 2) C2 = Filtrar T. 3) S = C1 4) Para cada prototipo P en C2 Tmp = S U P.
ICPLAlgorithm - Class in keel.Algorithms.Instance_Generation.ICPL: PSO algorithm calling.
ICPLAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.ICPL.ICPLAlgorithm
ICPLGenerator - Class in keel.Algorithms.Instance_Generation.ICPL
ICPLGenerator(PrototypeSet, int, String, int, int) - Constructor for class keel.Algorithms.Instance_Generation.ICPL.ICPLGenerator: Build a new ICPLGenerator Algorithm
ICPLGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.ICPL.ICPLGenerator: Build a new RSPGenerator Algorithm
id - Variable in class keel.Algorithms.Instance_Generation.GMCA.Cluster: Id of the cluster
id - Variable in class keel.Algorithms.Instance_Selection.CPruner.Trio: Element id.
id - Variable in class keel.Algorithms.Preprocess.Instance_Selection.CPruner.Trio: Element id.
id - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: To check the number of itemsets it covers when is created
id - Variable in class keel.GraphInterKeel.experiments.Node
ID3 - Class in keel.Algorithms.Decision_Trees.ID3: A Java implementation of the ID3 algorithm.
ID3(String) - Constructor for class keel.Algorithms.Decision_Trees.ID3.ID3: Constructor.
Id3Discretizer - Class in keel.Algorithms.Discretizers.Id3_Discretizer: This class implements the Id3Discretizer discretizer.
Id3Discretizer() - Constructor for class keel.Algorithms.Discretizers.Id3_Discretizer.Id3Discretizer
Id3Discretizer - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Id3_Discretizer
Id3Discretizer() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Id3_Discretizer.Id3Discretizer
IDataset - Interface in keel.Algorithms.Neural_Networks.NNEP_Common.data: Dataset Interface
IDataset.IInstance - Interface in keel.Algorithms.Neural_Networks.NNEP_Common.data: Dataset instance
IDD - Class in keel.Algorithms.Discretizers.IDD: This class implements the IDD
IDD() - Constructor for class keel.Algorithms.Discretizers.IDD.IDD: Constructor of the class
ideal() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Computes the number of training samples that match this rule.
ideal() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual: It returns the number of examples that the individual matches
ideal() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Computes the number of training samples that match this rule and are correctly classified.
iden_node - Variable in class keel.GraphInterKeel.experiments.GraphPanel
IDENT - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
IDENT - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for indentation.
IDENT - Static variable in interface keel.Dataset.DataParserConstants
identifyBorder(PrototypeSet[]) - Method in class keel.Algorithms.Instance_Generation.ICPL.ICPLGenerator: Identify a border by typicaly.
identity(int, int) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Generate identity matrix
identList() - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
identList() - Static method in class keel.Dataset.DataParser
identNum() - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
identNum() - Static method in class keel.Dataset.DataParser
IDIBL - Class in keel.Algorithms.Lazy_Learning.IDIBL: File: IDIBL.java The Integrated Decremental Instance Based Learning algorithm.
IDIBL(String) - Constructor for class keel.Algorithms.Lazy_Learning.IDIBL.IDIBL: The main method of the class
idInputDataOrdering(DataBase) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Reorders input data according to frequency of single attributes but excluding classifiers which are left unordered at the end of the attribute list.
idInputDataOrdering(DataBase) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Reorders input data according to frequency of single attributes.
idInputDataOrdering() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Reorders input data according to frequency of single attributes.
idInputDataOrdering() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Reorders input data according to frequency of single attributes.
idRbf - Variable in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf: RBF id.
idRbf - Variable in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf: RBF id.
IEducationalRunkeelListener<A extends EducationalRunKeelTxt> - Interface in keel.GraphInterKeel.experiments
IEducationalRunListener<A extends EducationalRun> - Interface in keel.GraphInterKeel.experiments
IErrorFunction<T> - Interface in keel.Algorithms.Neural_Networks.NNEP_Common.problem.errorfunctions: Interface of an error function
IF_KNN - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.IF_KNN: File: IF_KNN.java The IF-KNN algorithm.
IF_KNN(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.IF_KNN.IF_KNN: Main builder.
IFreeMutation - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: This class implements the free mutation.
IFreeMutation() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.IFreeMutation
IFS_COCO - Class in keel.Algorithms.Coevolution.IFS_COCO: File: IFS_COCO.java The IFS_COCO Algorithm.
IFS_COCO(String) - Constructor for class keel.Algorithms.Coevolution.IFS_COCO.IFS_COCO: The main method of the class
IFSKNN - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.IFSKNN: File: IFSKNN.java The IFSKNN algorithm.
IFSKNN(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFSKNN.IFSKNN: Main builder.
IFV_NP - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.IFV_NP: File: IFV_NP.java The IFV_NP algorithm.
IFV_NP(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFV_NP.IFV_NP: Main builder.
IGA(SEM, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Ilga
IGA(SEM) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Oiga: Applies the Incremental Genetic Algorithm strategy.
iga(RuleSet) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.RuleSet: Incremental Genetic Algorithm, which increases the size of the actual rule set appending all the attributes from rs to the rules of the actual rule set.
IGA - Class in keel.Algorithms.Instance_Selection.IGA: File: IGA.java The IGA Instance Selection algorithm.
IGA(String) - Constructor for class keel.Algorithms.Instance_Selection.IGA.IGA: Default constructor.
IGA - Class in keel.Algorithms.Preprocess.Instance_Selection.IGA: File: IGA.java The IGA Instance Selection algorithm.
IGA(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.IGA.IGA: Default constructor.
ignore_missing - Class in keel.Algorithms.Preprocess.Missing_Values.ignore_missing: This class delete all instances with at least one missing value from the data set
ignore_missing(String) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.ignore_missing.ignore_missing: Creates a new instance of ignore_missing
IGUAL - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.Selector: Identifier for the equal condition operator.
IGUAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Condition: Tag (EQUAL).
IImpurityFunction - Interface in keel.Algorithms.Decision_Trees.CART.impurities: This interface must be followed by any impurity function
IInitiator - Interface in keel.Algorithms.Neural_Networks.NNEP_Common.initiators: Initiate links of a linked layer
IKNN - Class in keel.Algorithms.Instance_Selection.IKNN: File: IKNN.java The IKNN Instance Selection algorithm.
IKNN(String) - Constructor for class keel.Algorithms.Instance_Selection.IKNN.IKNN: Default constructor.
IKNN - Class in keel.Algorithms.Preprocess.Instance_Selection.IKNN: File: IKNN.java The IKNN Instance Selection algorithm.
IKNN(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.IKNN.IKNN: Default constructor.
ILAS - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
ilas - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper
ilas - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper
ILayer<N extends INeuron> - Interface in keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet: Represents a layer in the neural net
Ilga - Class in keel.Algorithms.Genetic_Rule_Learning.ILGA: This class contains the main body of the ILGA algorithm, presented by: Guan, S.
Ilga() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.ILGA.Ilga: Default constructor
Ilga(String) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.ILGA.Ilga: Constructor for the KEEL parameter file
image - Variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Token: The string image of the token.
image - Variable in class keel.Algorithms.Rule_Learning.Swap1.Token: The string image of the token.
image - Variable in class keel.Dataset.Token: The string image of the token.
image - Variable in class keel.GraphInterKeel.experiments.Node
Iman - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Iman boolean
Iman - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Apply Iman flag.
imbalance_mouseEntered(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Enter in imbalanced button
imbalance_mouseExited(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Exit from imbalanced button
imbalance_mouseReleased(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Entering in Imbalanced module
IMBALANCED - Static variable in class keel.GraphInterKeel.experiments.Experiments
Imbalanced_General - Class in keel.Algorithms.Statistical_Tests.Classification.Imbalanced_General: This class has only a main method that calls Clasif_General output method for classification problems, defined in StatTest
Imbalanced_General() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.Imbalanced_General.Imbalanced_General
Imbalanced_Summary - Class in keel.Algorithms.Statistical_Tests.Classification.Imbalanced_Summary: This class has only a main method that calls Model_Summary output method for classification problems, defined in StatTest
Imbalanced_Summary() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.Imbalanced_Summary.Imbalanced_Summary
Imbalanced_Tabular - Class in keel.Algorithms.Statistical_Tests.Classification.Imbalanced_Tabular: This class has only a main method that calls Model_Tabular output method for classification problems, defined in StatTest
Imbalanced_Tabular() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.Imbalanced_Tabular.Imbalanced_Tabular
imbalancedMeasure - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Type of imbalanced measurement used.
IMetadata - Interface in keel.Algorithms.Neural_Networks.NNEP_Common.data: Dataset spectification
ImplicatorLukasiewicz(double, double) - Static method in class keel.Algorithms.RST_Learning.Operators: Lukasiewicz implicator
import_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Import button
importanceSampling(myDataset, int, boolean[], double) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Importance undersampling of the dataset given.
importar_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectData: Import user data sets button control
Importer - Class in keel.Algorithms.Preprocess.Converter: Importer Abstract class that contains the methods to import different format files to files with KEEL format.
Importer() - Constructor for class keel.Algorithms.Preprocess.Converter.Importer
importOptionsDialog - Variable in class keel.GraphInterKeel.datacf.importData.ImportPanel: An option dialog for obtaining the options of the import proccess
ImportPanel - Class in keel.GraphInterKeel.datacf.importData
ImportPanel() - Constructor for class keel.GraphInterKeel.datacf.importData.ImportPanel: Constructor that initializes the panel
imprime(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Prints on the standard output the rule.
imprime(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Prints on the standard output the rule.
imprime() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Prints on the standard output the rule.
imprime(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Prints on the standard output the rule.
imprime(Vector, String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion
imprime(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Atributo: Prints the information of the attribute.
imprime() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Condicion: Prints in the standard output the condition.
imprime(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Prints on the standard output the rule.
imprimeCortes(Vector, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Discretizacion: Prints the discretization cuts guven as parameter.
imprimeFichero(PrintStream, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Prints on the given file (PrintStream) the rule.
imprimeFichero(PrintStream, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Prints on the given file (PrintStream) the rule.
imprimeFichero(PrintStream) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Prints on the given file (PrintStream) the rule.
imprimeFichero(PrintStream, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Prints on the given file (PrintStream) the rule.
imprimeFichero(int, PrintStream) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Prints on the given file (PrintStream) the rule.
imprimePosicion(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Prints on the standard output the actual position.
imprimir(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Prints the dataset on the standard output.
imprimir() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Muestra: Prints on the standard output the example as rule (conditions ---> class).
imprimir(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Prints the dataset on the standard output.
imprimir() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Muestra: Prints on the standard output the example as rule (conditions ---> class).
imprimir(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ConjuntoDatos: Prints the dataset on the standard output.
imprimir() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Muestra: Prints on the standard output the example as rule (conditions ---> class).
imprimir(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Prints the dataset on the standard output.
imprimir() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Muestra: Prints on the standard output the example as rule (conditions ---> class).
imprimir(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Prints the dataset on the standard output.
imprimir() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Muestra: Prints on the standard output the example as rule (conditions ---> class).
improve - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters: Problem coefficients
improve - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters: Problem coefficients
improve - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters: Problem coefficients
improve - Variable in class keel.Algorithms.Neural_Networks.net.Parameters: Problem coefficients
impureza(double[][], int[], int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.Individuo: Returns the impurity used as fitness of each individual.
impurities(int[], double) - Method in class keel.Algorithms.Decision_Trees.CART.impurities.Gini: It compute the impurity value associated
impurities(int[], double) - Method in interface keel.Algorithms.Decision_Trees.CART.impurities.IImpurityFunction: It compute the impurity value associated
impurities(int[], double) - Method in class keel.Algorithms.Decision_Trees.CART.impurities.LeastSquaresDeviation: It compute the impurity value associated
Impurity - Class in keel.Algorithms.Decision_Trees.M5: Class for handling the impurity values when spliting the instances
Impurity(int, int, M5Instances, int) - Constructor for class keel.Algorithms.Decision_Trees.M5.Impurity: Constructs an Impurity object containing the impurity values of partitioning the instances using an attribute
Impurity - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules: Class for handling the impurity values when spliting the instances
Impurity(int, int, MyDataset, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Impurity: Constructs an Impurity object containing the impurity values of partitioning the instances using an attribute
impurityFunction - Static variable in class keel.Algorithms.Decision_Trees.CART.RunCART: Impurity function to use.
impute_llsq_l2_blind(DenseMatrix, InstanceSet) - Method in class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.LLSImpute: Function that applies the Local Least Squares Imputation to a given array
impute_rowavg(DenseMatrix, int, InstanceSet) - Method in class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.LLSImpute: Perform the row-average of given matrix
in(int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion: Down
in(int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion: Down
inBuf - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
inBuf - Static variable in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
inBuf - Static variable in class keel.Dataset.SimpleCharStream
incCount() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.ExampleWeight: Increments in 1 the number of times that it has been covered.
incCount() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.ExampleWeight
incFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.ValueFreq: Increases this object frequency by one
incFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.ValueFreq: Increases this object frequency by one
incFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.ValueFreq: Increases this object frequency by one
incFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValueFreq: Increases this object frequency by one
incFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValuesFreq: Increases this object frequency by one
incFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.ValueFreq: Increases this object frequency by one
incFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.ValueFreq: Increases this object frequency by one
incFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.ValueFreq: Increases this object frequency by one
incFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.ValueFreq: Increases this object frequency by one
incFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.ValueFreq: Increases this object frequency by one
incFreq() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.ValueFreq: Increases this object frequency by one
incIteration(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.timersManagement
incluir(Poblacion) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.BaseR
inclusionDegree(Rule, int) - Method in class keel.Algorithms.Hyperrectangles.INNER.Rule: Computes the inclussion degree of two rules in a given attribute
incluyeSelector(Selector) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Rule: Includes a selector given as an argument into the list of antecedents.
incluyeSelector(Selector) - Method in class keel.Algorithms.Decision_Trees.DT_GA.Regla: Adds a Selector (antedent of the rule, attribute-condition).
incluyeSelector(Selector) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Rule: Includes a selector given as an argument into the list of antecedents.
incompleteBeta(double, double, double) - Static method in class keel.Algorithms.Lazy_Learning.Statistics: Returns the Incomplete Beta Function evaluated from zero to xx.
incompleteBeta(double, double, double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics: Returns the Incomplete Beta Function evaluated from zero to xx.
inconsistencyCheck(Instance[]) - Method in class keel.Algorithms.Discretizers.Chi2_Discretizer.Discretizer: Returns the inconsistency percentage of the given instances.
inconsistencyCheck(Instance[]) - Method in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Discretizer: Returns the inconsistency percentage of the given instances.
inconsistencyCheck(Instance[]) - Method in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Discretizer: Returns the inconsistency percentage of the given instances.
inconsistencyThreshold - Variable in class keel.Algorithms.Discretizers.Chi2_Discretizer.Discretizer: Inconsistency Threshold.
inconsistencyThreshold - Variable in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Discretizer: Inconsistency Threshold.
inconsistencyThreshold - Variable in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Discretizer: Inconsistency Threshold.
inconsistencyThreshold - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
incorrect() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Gets the number of instances incorrectly classified (that is, for which an incorrect prediction was made).
incorrectlyClassifiedSamples(Cluster) - Method in class keel.Algorithms.Instance_Generation.VQ.AVQGenerator: Performs Q - R for a cluster.
incrCovered(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: Function to increase in 1 the number of examples whose output class is the given class "clas" and are covered by this rule.
incrCovered(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: Function to increase in 1 the number of examples whose output class is the given class "clas" and are covered by this rule
increaseMicroClSum(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Increases the number of micro classifiers in the set.
increaseMicroClSum(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Increases the number of micro classifiers in the set.
increaseNumberMatches(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Increases the number of matches
increaseNumberMatches(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Increases the number of matches
increaseNumerosity(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Increases the numerosity of the classifier.
increaseNumerosity(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters: Increases the numerosity of the classifier.
increaseNumerosity(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Increases the numerosity of the classifier.
increaseNumerosity(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Increases the numerosity of the classifier.
incremDistribClase(int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Add one to the n of the rule for the class
incrementaCubierta() - Method in class keel.Algorithms.Rule_Learning.Prism.Muestra: Adds one to the number of times that the example has been matched
incrementaCubierta() - Method in class keel.Algorithms.Rule_Learning.UnoR.Muestra: Adds one to the number of times that the example has been matched
incrementaCubierta() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Muestra: Adds one to the number of times that the example has been matched
incrementaCubierta() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Muestra: Adds one to the number of times that the example has been matched
incrementaDistrib(int) - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Adds 1 to the n of the example for the class 'clase' matched for the example
incrementaDistrib(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Add one to the n of the complex for the class
incrementaDistrib(int) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Add one to the n of the complex for the class
incremental(double, int) - Method in class keel.Algorithms.Decision_Trees.M5.Impurity: Incrementally computes the impurirty values
incremental(Measures) - Method in class keel.Algorithms.Decision_Trees.M5.Measures: Adds up performance measures for cross-validation
incremental(double, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Impurity: Incrementally computes the impurity values
incremental(Measures) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Measures: Adds up performance measures for cross-validation
incrementar(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Attribute
incrementCovered() - Method in class keel.Algorithms.Hyperrectangles.EACH.Sample: Adds one to the number of times that the example has been matched
incrementDistrib(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Increments the number of example for the class cover for the complex
incrementDistrib(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.AssociationRule: Increments the number of example for the class cover for the complex
incrementDistribution(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Increases by 1 the number of covered examples of the given class
incrementDistribution(int) - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Add one to the n of the complex for the class
incrPositive(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.PNArray: It increases the positive value P of the PNArray by adding the weight of a training example given by its position in the training dataset
incrRightN() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule: It increases the number of right
incrWrongN() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule: It increases the number of wrong
incStep() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ProbabilityManagement
incStep() - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.ProbabilityManagement
incStep() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.ProbabilityManagement
incStep() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ProbabilityManagement
incSupport() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.Item: It increments the support of an item
ind - Variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.rank
IndCAN - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF: Defines an individual composed by a Canonical cromosome.
IndCAN(Genetic, int, int, int) - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndCAN: Creates new instance of IndCAN
IndCAN - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD: Defines the individual of the population
IndCAN() - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndCAN: Creates new instance of Canonical individual
IndCAN(int, int, int) - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndCAN: Creates new instance of Canonical individual
IndCAN - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA: Defines an individual composed by a Canonical cromosome.
IndCAN(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndCAN: Creates new instance of IndCAN
IndDNF - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF: Defines an individual composed by a DNF cromosome.
IndDNF(Genetic, int, TableVar, int, int) - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndDNF: Creates new instance of IndDNF
IndDNF - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD: Defines the DNF individual of the population
IndDNF(int, int, int, TableVar) - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndDNF: Creates new instance of Individual
IndDNF - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA: Defines an individual composed by a DNF cromosome.
IndDNF(int, TableVar) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndDNF: Creates new instance of IndDNF
index() - Method in class keel.Algorithms.Decision_Trees.M5.M5Attribute: Returns the index of this attribute.
index(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Returns the index of the attribute stored at the given position.
index(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5SparseInstance: Returns the index of the attribute stored at the given position.
index - Variable in class keel.Algorithms.Discretizers.Khiops.DeltaValue: index of the first element in the LEFT interval in the global sorted real values
index - Variable in class keel.Algorithms.Discretizers.MODL.DeltaValue: index of the first element in the LEFT interval in the global sorted real values
index() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Returns the index of this attribute.
index(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Returns the index of the attribute stored at the given position.
index(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Returns the index of the attribute stored at the given position.
index() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Returns the index of the attribute.
index - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_node
index() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Returns the index of this attribute.
index(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance: Returns the index of the attribute stored at the given position.
index - Variable in class keel.Algorithms.Instance_Generation.Basic.Prototype: Index of the prototype in the set, used as an identifier.
index - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputNeuron: Index of the neuron
index - Variable in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.FreqList: Iterator index.
index - Variable in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.FreqList: Iterator index.
index - Variable in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.FreqList: Iterator index.
index - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqList: Iterator index.
index - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqListPair: Iterator index.
index - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.InstanceP: The index of the instance.
index - Variable in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.FreqList: Iterator index.
index - Variable in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.FreqList: Iterator index.
index - Variable in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.FreqList: Iterator index.
index - Variable in class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.IndexValuePair: Index of the value stored.
index - Variable in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.FreqList: Iterator index.
index - Variable in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.FreqList: Iterator index.
index - Variable in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.FreqList: Iterator index.
index - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Index of the prototype in the set, used as an identifier.
index(int) - Method in class keel.Algorithms.SVM.SMO.core.Instance: Returns the index of the attribute stored at the given position.
index - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Lists: Current element of the list.
index - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Lists: Current element of the list.
index - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Lists: Current element of the list.
index - Variable in class org.libsvm.svm_node
indexExtendedArg - Static variable in class keel.Algorithms.Instance_Generation.utilities.Parameters: Extended argument index.
indexExtendedArg - Static variable in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Index of the extended argument.
indexLiterals(boolean[][], int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.pnPair: It computes the indexes of the literals that cover the pnPair
IndexNearestTo(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Return the INDEX off nearest prototype to another in the set.
IndexNearestTo(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Return the INDEX off nearest prototype to another in the set.
indexOf(Object) - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector: Searches for the first occurence of the given argument, testing for equality using the equals method.
indexOf(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector: Searches for the first occurence of the given argument, testing for equality using the equals method.
indexOf(Object) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector: Searches for the first occurence of the given argument, testing for equality using the equals method.
indexOf(InputNeuron) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputLayer: Returns the index of a neuron in the layer
indexOf(Rule) - Method in class keel.Algorithms.Rule_Learning.Slipper.Ruleset: It returns the position of a rule (if it is belongs to the ruleset)
indexOf(Object) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector: Searches for the first occurence of the given argument, testing for equality using the equals method.
indexOf(Object) - Method in class keel.Algorithms.SVM.SMO.core.FastVector: Searches for the first occurence of the given argument, testing for equality using the equals method.
indexOf(Attribute) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns the index of the attribute given.
indexOfMax(int[]) - Method in class keel.Algorithms.Discretizers.OneR.OneR: Looks for the index of the maximum element in the array
indexOfMax() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Returns the index of the maximum.
indexOfMinElement(ArrayList<Double>) - Static method in class keel.Algorithms.Instance_Generation.LVQ.LVQPRU: Returns the index of the minimum element of an array.
indexOfValue(String) - Method in class keel.Algorithms.Decision_Trees.M5.M5Attribute: Returns the index of a given attribute value.
indexOfValue(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Returns the index of a given attribute value.
indexOfValue(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Returns the index of a given attribute value.
indexOfValue(Attribute, String) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns the index of the value given of the attribute given.
IndexSecondNearestTo(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Return the INDEX of the Second closest prototype to another in the set.
IndexSecondNearestTo(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Return the INDEX of the Second closest prototype to another in the set.
IndexTest - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: It ignores the repetition of train file
IndexTest - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: It ignores the repetition of train file
IndexTestKMeans - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Test file in clustering algorithms
IndexTestKMeans - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Test file in clustering algorithms
indexToString(int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.SingleIndex: Creates a string representation of the given index.
IndexTrain - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: First file in inputData
IndexTrain - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: First file in inputData (training)
IndexValuePair - Class in keel.Algorithms.Preprocess.Missing_Values.LLSImpute: Index value pair.
IndexValuePair(double, int) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.IndexValuePair: Parameter construnctor.
indice - Variable in class keel.Algorithms.Decision_Trees.SLIQ.ListaAtributos: Index on the classes list
indice - Variable in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.MultiplePair: first element
indice - Variable in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Pair: first element
indice - Variable in class keel.GraphInterKeel.statistical.tests.MultiplePair: first element
indice - Variable in class keel.GraphInterKeel.statistical.tests.Pair: first element
indicesToRangeList(int[]) - Static method in class keel.Algorithms.Decision_Trees.M5.Interval: Creates a string representation of the indices in the supplied array.
indicesToRangeList(int[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Range: Creates a string representation of the indices in the supplied array.
Individual - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD: Title: Individual Description: This class contains the representation of the individuals of the population (CHC Algorithm) Copyright: Copyright KEEL (c) 2007 Company: KEEL
Individual() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual: Default constructor.
Individual(RuleBase, DataBase, double) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual: Builder
Individual - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA: This class contains the representation of the individuals of the population
Individual() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Individual: Default Constructor
Individual(int[], double, double, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Individual: Constructor with parameters
Individual(double, double, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Individual: Constructor with parameters.
Individual - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99: An individual of the population
Individual(int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.Individual: Creates an individual containing with "variables" input variables
Individual(Individual) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.Individual: Creates an individual as a copy of another individual
Individual(int, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary
Individual(int, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger
Individual(int, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal
Individual - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen
Individual(int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.Individual: Creates an individual containing with a legth equal to "longitud" and having "max_n_reglas" rules
Individual(int, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
Individual(int, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationinteger
Individual(int, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal
Individual(int, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
Individual(int, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationinteger
Individual(int, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationreal
Individual - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS: Title: Individual Description: This class contains the representation of the individuals of the population (CHC Algorithm) Copyright: Copyright KEEL (c) 2007 Company: KEEL
Individual() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual: Default constructor.
Individual(RuleBase, DataBase, double, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual: Builder
Individual - Class in keel.Algorithms.Genetic_Rule_Learning.Corcoran: Title: Individual Description: Chromosome Definition
Individual(int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Individual: Builder
Individual - Class in keel.Algorithms.Genetic_Rule_Learning.LogenPro
Individual(myDataset, double, double, double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual: Constructor
Individual(Individual) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual: Constructor (create the individual as a copy of another individual)
individual(int) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation: Returns the _num-th individuals
Individual - Class in keel.Algorithms.Neural_Networks.gann: Class which represents an individual
Individual(int) - Constructor for class keel.Algorithms.Neural_Networks.gann.Individual: Constructor that sets individual's size
Individual(SetupParameters) - Constructor for class keel.Algorithms.Neural_Networks.gann.Individual: Constructor that initializes all the attributes of an individual instance, given the SetupParameters 'global' object.
Individual - Class in keel.Algorithms.RE_SL_Methods.P_FCS1
Individual(int, int) - Constructor for class keel.Algorithms.RE_SL_Methods.P_FCS1.Individual: Creates an individual containing "num" fuzzy rules, each one then with "tam" gaussian fuzzy sets
Individual(Individual) - Constructor for class keel.Algorithms.RE_SL_Methods.P_FCS1.Individual: Creates an individual as a copy of another individual
Individual - Class in keel.Algorithms.RE_SL_Methods.SEFC
Individual(int) - Constructor for class keel.Algorithms.RE_SL_Methods.SEFC.Individual: Creates an individual containing "entradas" gaussian membership functions
Individual(Individual) - Constructor for class keel.Algorithms.RE_SL_Methods.SEFC.Individual: Creates an individual as a copy of another individual
Individual - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate: Defines an individual, composed by a cromosome.
Individual(int, TypeVar[]) - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual: Creates new instance of Individual
Individual - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF: Individual abstract class for the different types of genetic individuals.
Individual() - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Default Constructor.
Individual - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate: Defines an individual, composed by a chromosome.
Individual(int, TypeVar[]) - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual: Creates new instance of Individual
Individual - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD
Individual() - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Default Constructor.
Individual - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate: Defines an individual, composed by a cromosome.
Individual(int, TypeVar[]) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual: Creates new instance of Individual
Individual - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA: Individual abstract class for the different types of genetic individuals.
Individual() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual: Default Constructor.
individualMutation() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.geneticAlgorithm
individualsComparator - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Comparator of individuals
Individuo - Class in keel.Algorithms.Decision_Trees.DT_GA: Title: Individuo (Individual).
Individuo() - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.Individuo: Default Constructor.
Individuo(boolean[], String, myDataset, int) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.Individuo: Paramenter constructor.
Individuo(myDataset, int) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.Individuo: Paramenter constructor.
Individuo(Individuo, Individuo, int) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.Individuo: Create a new individual by crossing other two at the position given.
Individuo - Class in keel.Algorithms.Decision_Trees.DT_oblicuo: Title: Individuo (Individual).
Individuo(int) - Constructor for class keel.Algorithms.Decision_Trees.DT_oblicuo.Individuo: Constructor of the class
Individuo(double[], double) - Constructor for class keel.Algorithms.Decision_Trees.DT_oblicuo.Individuo: Constructor of the class
Individuo(Individuo, Individuo, int) - Constructor for class keel.Algorithms.Decision_Trees.DT_oblicuo.Individuo: Create a new individual by crossing other two at the position given.
Individuo(Individuo, Individuo, boolean[]) - Constructor for class keel.Algorithms.Decision_Trees.DT_oblicuo.Individuo: Create a new individual by crossing other two using a mask given as an argument.
Individuo - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec: Title: Description: Copyright: Copyright (c) 2007 Company:
Individuo() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Individuo
Individuo(BaseR, double, double, double) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Individuo
Individuo(Individuo, Individuo, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Individuo
Individuo - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF
Individuo() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Individuo
Individuo(int, int, int, BaseD) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Individuo
Individuo(int, int, int, double[], double[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Individuo
Individuo(Individuo, Individuo, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Individuo
Individuo - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift: Title: Description: Copyright: Copyright (c) 2007 Company:
Individuo() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Individuo
Individuo(int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Individuo
Individuo - Class in keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM
Individuo() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Individuo
Individuo(MatrizR, double[], double[]) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Individuo
IndividuoComparator - Class in keel.Algorithms.Genetic_Rule_Learning.Hider
IndividuoComparator(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.IndividuoComparator
IndMichigan - Class in keel.Algorithms.LQD.methods.FGFS_costInstances: File: fuzzy.java Properties and functions of individual of the population
IndMichigan - Class in keel.Algorithms.LQD.methods.FGFS_Minimum_Risk: File: fuzzy.java Properties and functions of individual of the population
IndMichigan - Class in keel.Algorithms.LQD.methods.FGFS_Original: File: fuzzy.java Properties and functions of individual of the population
IndMichigan - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight: File: fuzzy.java Properties and functions of individual of the population
IndMichigan - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty: File: fuzzy.java Properties and functions of individual of the population
induce_One_Rule(int, myDataset, int, int, int) - Static method in class keel.Algorithms.Rule_Learning.Rules6.InduceOneRule: Returns the best rule posible for an example of the given dataset.
induce_One_Rule(Instances, int, int, int, int) - Static method in class keel.Algorithms.Rule_Learning.SRI.InduceOneRule: Returns the best rule posible for an example of the given dataset.
InduceOneRule - Class in keel.Algorithms.Rule_Learning.Rules6: Title: Induce One Rule Description: This class finds the best rule posible for an example of the given dataset.
InduceOneRule() - Constructor for class keel.Algorithms.Rule_Learning.Rules6.InduceOneRule
InduceOneRule - Class in keel.Algorithms.Rule_Learning.SRI: Title: Induce One Rule Description: This class finds the best rule posible for an example of the given dataset.
InduceOneRule() - Constructor for class keel.Algorithms.Rule_Learning.SRI.InduceOneRule
INeuralNet - Interface in keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet: Represents a neural net
INeuralNetSpecies - Interface in keel.Algorithms.Neural_Networks.NNEP_Common: Species for Individuals that contains a NeuralNet as genotype.
INeuron - Interface in keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet: Represents a neuron in the neural net
INeuronParametricMutator<N extends LinkedNeuron> - Interface in keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric: Parametric Mutator of a specific neuron
INeuronStructuralMutator<N extends LinkedNeuron> - Interface in keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural: Structural Mutator of a specific neuron
Inf_Range() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
Inf_Range() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
Inf_Range(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Inf_Range() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Returns the lower value for all the labels in the domain.
Inf_Range() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t: Returns the lower value of the definition interval of variable's domain.
Inf_Range(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Returns the lower value of the definition interval of variable in position "var" of the list
Inf_Range() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
Inf_Range() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
Inf_Range(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
Inf_Range() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
Inf_Range() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
Inf_Range(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
InfBound(int[][], int[][]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets
InferenceC(vectordouble, Double_t, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.ruleset: Returns the class of the rule that better adapts to the example "v", the adaptation degree "grado" and the ordinal of the fired rule "regla_disparada".
InferenceC(vectordouble, int, double[], double[], double[], double[], int[], int[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.ruleset
InferenceC(vectordouble, Double_t, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.ruleset: Returns the class of the rule that better adapts to the example "v", the adaptation degree "grado" and the ordinal of the fired rule "regla_disparada".
InferenceC(vectordouble, Double_t, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.ruleset: Returns the class of the rule that better adapts to the example "v", the adaptation degree "grado" and the ordinal of the fired rule "regla_disparada".
InferenciaBanco(int, double[][][]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
inferior - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets: Aprox Inferior
INFFC_2STEPS - Class in keel.Algorithms.Preprocess.NoiseFilters.INFFC
INFFC_2STEPS() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.INFFC.INFFC_2STEPS: It initializes the partitions from training set
info(int[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Computes entropy for an array of integers.
info(int[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Computes entropy for an array of integers.
info(int[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Computes entropy for an array of integers.
info(int[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Computes entropy for an array of integers.
info(int[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Computes entropy for an array of integers.
info(int[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Computes entropy for an array of integers.
info(int[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Computes entropy for an array of integers.
info(int[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Computes entropy for an array of integers.
info(int[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Computes entropy for an array of integers.
infoGainCutCrit(Classification, double, double) - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Function to compute the information gain.
infoGainCutCrit(Classification, double, double) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to compute the information gain.
infoGainCutCrit(Classification, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Function to compute the information gain.
infoGainCutCrit(Classification, double, double) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Function to compute the information gain.
infoGainCutCrit(Classification, double, double) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to compute the information gain.
infoGainCutCrit(Classification, double, double) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Function to compute the information gain.
infoGainCutCrit(Classification, double, double) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Function to compute the information gain.
infoGainCutCrit(Classification, double, double) - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Function to compute the information gain.
infoGainCutCrit(Classification, double, double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Function to compute the information gain.
InfoNode - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams: InfoNode Class.
InfoNode() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.InfoNode: Default Constructor.
InfoNode(String, String, double, String, String, double, String) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.InfoNode: Parameter Constructor.
Information - Class in keel.Algorithms.Decision_Trees.M5: Class to store information about an option.
Information(String, String, int, String) - Constructor for class keel.Algorithms.Decision_Trees.M5.Information: Creates new option with the given parameters.
information() - Method in class keel.GraphInterKeel.experiments.Joint
InformationAboutClass() - Constructor for class keel.Algorithms.Statistical_Tests.Shared.StatTest.InformationAboutClass: Default Constructor
InformationAboutClass(String) - Constructor for class keel.Algorithms.Statistical_Tests.Shared.StatTest.InformationAboutClass: Parameter Constructor.
InformationHandler - Class in keel.Algorithms.Decision_Trees.M5: Class for handing options.
InformationHandler(M5Instances) - Constructor for class keel.Algorithms.Decision_Trees.M5.InformationHandler: Constructor.
InformationHandler(String[]) - Constructor for class keel.Algorithms.Decision_Trees.M5.InformationHandler: Constructs an object to store command line options and other necessary information
ini(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.PNArray: It initializes a PNArray with a given class
ini(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.PNArray: It initializes a PNArray from a given rule
inic_vector(int[]) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Sets each position of the vector given as parameter with the i-th number.
inic_vector(int[]) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Initiates the vector given.
inic_vector_sin(int[], int) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Sets each position of the vector given as parameter with the i-th, skipping the position given as argument.
inic_vector_sin(int[], int) - Method in class keel.Algorithms.Instance_Generation.DROP3SFLSDE.DROP3SFLSDE
inic_vector_sin(int[], int) - Method in class keel.Algorithms.Instance_Generation.ICFSFLSDE.ICFSFLSDE
inic_vector_sin(int[], int) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE
inic_vector_sin(int[], int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Initiates the vector given without the index given.
INichedMutation - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: This class applies the niched mutation.
INichedMutation() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.INichedMutation
inicializa() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoosting
inicializa() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoostingMaxMin
inicializa() - Method in class keel.Algorithms.PSO_Learning.CPSO.Crono: Initialization.
inicializa() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.Crono: Initialization.
inicializa() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.Crono: Initialization.
inicializa() - Method in class keel.Algorithms.PSO_Learning.REPSO.Crono: Initialization.
inicializaCortesAtributo(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Discretizacion: Initializes the discretization cuts.
inicializaFeromona(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Initializes the particle pheromone values for the given list of conditions.
inicializaMejorPosicionContinua(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Initializes the best continuous particle position with the given conditions vector
inicializaMejorPosicionNominal(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Initializes the best nominal particle position with the given conditions vector
inicializaPoblacion(int, Vector, Vector) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion
inicializaPosicionActualContinua(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Initializes the actual continuous particle position with the given conditions vector
inicializaPosicionActualNominal(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Initializes the actual nominal particle position with the given conditions vector
inicializaSiTieneCondicionVacia() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ACO: Initializes a vector that indicates if the corresponding attribute of the rule can be assigned with a null/empty condition.
inicializaSiTieneCondicionVacia() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ACO: Initializes a vector that indicates if the corresponding attribute of the rule can be assigned with a null/empty condition.
inicializaSiTieneCondicionVacia() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ACO: Initializes a vector that indicates if the corresponding attribute of the rule can be assigned with a null/empty condition.
inicializaVelocidad(int, float) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Initializes the particle velocity with the given value for all the conditions considered.
inicializaVelocidad(float[][], int, Randomize) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Initializes the particle velocity using the given conditions intervals.
iniNObjectives() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Initialises the structure for the name of the objectives
iniPN(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.PRM: It initializes positive and negative values for each example for a given class
init() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.DiscretizationManager
init(boolean) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_DefaultC
init() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.DiscretizationManager
init(boolean) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_DefaultC
init(Population) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RouletteSelection: It creates and initializes the roulette with the fitness of all classifiers in the population.
init(Population) - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Selection: Initializes the system for selection.
init(Population) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TournamentSelection: Initializes the tournament selection.
INIT - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
init(Population) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RouletteSelection: It creates and initializes the roulette with the fitness of all classifiers in the population.
init(Population) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Selection: Initializes the system for selection.
init(Population) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TournamentSelection: Initializes the tournament selection.
init() - Method in class keel.Algorithms.Neural_Networks.gmdh.PrintfApplet
init() - Method in class keel.Algorithms.Rule_Learning.Swap1.FormatErrorKeeper: Initializes the error vector
Init() - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Gain: Computes and stores the info gain values
Init() - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Gain: Computes and stores the info gain values
Init() - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Gain: Computes and stores the info gain values
init(Instances) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: initializes the algorithm
init(Instances) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMO: initialize various variables before starting the actual optimizer
init(Instances) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: initialize various variables before starting the actual optimizer
init() - Method in class keel.Dataset.FormatErrorKeeper: Initializes the error vector
init() - Static method in class keel.GraphInterKeel.experiments.Layer: Initialize the layers
init_genrand(long) - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.MTwister
init_genrand(long) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.MTwister
init_genrand(long) - Method in class org.core.MTwister
initCache() - Static method in class keel.Algorithms.SVM.SMO.core.ClassDiscovery: initializes the cache for the classnames
initClusters(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.GMCA.GMCAGenerator: Initializes the clusters for the given prototype set.
initComponents() - Method in class keel.GraphInterKeel.statistical.statTableModel: Initializes the table model
initCounterOf(Prototype) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQTC
initCrom(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.CromCAN: Random initialization of an existing chromosome
initCrom() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.CromDNF: Random initialization of an existing chromosome
initCromBsd(TableVar, float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.CromCAN: Biased Random initialization of an existing chromosome The random inicializacion is biased by generating chromosomes with a maximum number or participating variables and for an indicated % of the population (the rest is random)
initCromBsd(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.CromDNF: Biased Random initialization of an existing chromosome The random inicializacion is biased by generating chromosomes with a maximum number or participating variables and for an indicated % of the population (the rest is random)
InitCromEmp() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Chromosome: Empty initialization of an existing chromosome We denote that the variable does not take part by setting the value to n_etiq (valid class are from 0 to n_etiq-1)
InitCromEmp() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Chromosome: Empty initialization of an existing chromosome We denote that the variable does not take part by setting the value to n_etiq (valid class are from 0 to n_etiq-1)
InitCromEmp() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Chromosome: Empty initialization of an existing chromosome We denote that the variable does not take part by setting the value to n_etiq (valid class are from 0 to n_etiq-1)
initCromRnd(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.CromCAN: Random initialization of an existing chromosome
initCromRnd() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.CromDNF: Random initialization of an existing chromosome
initDat(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeDat: Initialise the structure for the examples
initDat(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeDat: Initialise the structure for the examples
initDat(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeDat: Initialise the structure for the examples
initDataSet() - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQ1: Initialize the output data set
initDataSet() - Method in class keel.Algorithms.Instance_Generation.MSE.MSEGenerator: Initialize the output data set
initDataSetRandomMode() - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQ1: Initialize the output data set ignoring the a priority probabilities
initGA() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.GA: Prepares GA for a new run.
initGA() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.GA: Prepares GA for a new run.
InitGeneRnd() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Gene: Random initialization of an existing gene
initial - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.FUN: A set of initial rules
initial - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQGenerator: Initial Prototype set.
initial_dataset(Point, ExternalObjectDescription, Vector, Vector, ExternalObjectDescription, Vector, int) - Method in class keel.GraphInterKeel.experiments.GraphPanel: Initializes data set
initialAlphaInput - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Initial alpha coeficient for the input weigthts
initialAlphaOutput - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Initial alpha coeficient for the output weigthts
initialFitness - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It is the inital fitness for the new classifiers.
INITIALFITNESS - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
Initialise() - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Semantics: This method generates the semantics of the linguistic variables using a partition consisting of triangle simetrics fuzzy sets.
Initialise() - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Semantics: This method generates the semantics of the linguistic variables using a partition consisting of triangle simetrics fuzzy sets.
Initialise() - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Semantics: This method generates the semantics of the linguistic variables using a partition consisting of triangle simetrics fuzzy sets.
initialization() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase: It generates the initial rule set for each partition of the input space (2...L)
initializationMethod - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
initializationTypeTipText() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
initialize() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.BTS: It initilizes the root node of the tree
initialize(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.InformationHandler: Initializes for constucting model trees
initialize(int, int, int) - Method in class keel.Algorithms.Decision_Trees.M5.SplitInfo: Resets the object of split information
initialize(Instances, int, int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: initializes the AttributeLocator
initialize(Instances, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: initializes with the header information of the given dataset and sets the capacity of the set of instances.
initialize() - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Cogin
initialize() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Core: Initialize all populations (both Michigan and Pitts approaches) randomly
initialize() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Factory
initialize() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_ADI
initialize() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_GABIL
initialize() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_UBR
initialize(int, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SplitInfo: Resets the object of split information
initialize() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Factory
initialize() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_ADI
initialize() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_GABIL
Initialize(int, double[][], double[], int, double[][], double[], int, int, int, double, double[], long) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion: It initialize the population
Initialize(int, double[][], double[], int, double[][], double[], int, int, int, double, double[], long) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion: It initialize the population
Initialize(int, double[][], double[], int, double[][], double[], int, int, int, double, double[], long) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion: It initialize the population
Initialize(int, double[][], double[], int, double[][], double[], int, int, int, double, double[], long) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion: It initialize the population
Initialize(int, double[][], double[], int, double[][], double[], int, int, int, double, double[], long) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion: It initialize the population
Initialize(int, double[][], double[], int, double[][], double[], int, int, int, double, double[], long) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion: It initialize the population
Initialize(int, double[][], double[], int, double[][], double[], int, int, int, double, double[], long) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion: It initialize the population
Initialize(int, double[][], double[], int, double[][], double[], int, int, int, double, double[], long) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion: It initialize the population
initialize(Instances, int) - Method in class keel.Algorithms.SVM.SMO.core.Instances: initializes with the header information of the given dataset and sets the capacity of the set of instances.
initializeChromosome() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list
initializeChromosome() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
initializeHelpOptions() - Method in class keel.GraphInterKeel.help.HelpOptions: Initialize help options
InitializeParameters(double, double, double) - Static method in class keel.Algorithms.PSO_Learning.CPSO.Particle
InitializeParameters(double, double, double) - Static method in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
InitializeParameters(double, double, double, double) - Static method in class keel.Algorithms.PSO_Learning.PSOLDA.Particle
InitializeParameters(double, double, double, double, double, double, double, double, int) - Static method in class keel.Algorithms.PSO_Learning.REPSO.Particle
initializePopulation() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.geneticAlgorithm
initializeWindowing(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.instanceSet
initialMaxNofneurons - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Initial number of neurons of each LinkedLayer of the neural nets
initialmaxnofneurons - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Initial maximum number of neurons for the net
initialNumberOfRules - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
initialNumberOfRules - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
initialPError - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It represents the initial system error for the new classifiers.
INITIALPERROR - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
InitialPopulation(int[][], int, int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary
InitialPopulation(double[][], int, int, int, int[][], int, int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation: Generates an initial population
InitialPopulation(int[][], int, int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
InitialPopulation(double[][], int, int, int, int[][], int, int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation: Generates an initial population
InitialPopulation(int[][], int, int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
InitialPopulation_4L(double[][], int, int, int[][], int, int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation: Generates an initial population
InitialPopulationClassFixed(double[][], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal
InitialPopulationValue(char) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary
InitialPopulationValue(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal
INITIALPREDICITON - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
initialPrediction - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Is the initial prediction for the new classifiers.
initialReduction() - Method in class keel.Algorithms.Instance_Generation.HYB.HYBGenerator: Makes the initial reduction of the prototype set
initialset - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQGenerator: If the initialset is set.
initialStepSize - Variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus: Initial Delta for RpropPlus Algorithm
initialTheoryLengthRatio - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
initialTheoryLengthRatio - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
initialTheoryLengthRatio - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
initialTime - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Initial time.
initialTime - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Initial time.
initialTime - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Initial time.
initialTime - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm: Initial time.
initiate(LinkedLayer, ILayer<? extends INeuron>, int, int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.initiators.IInitiator: Initiation method of a linked layer
initiate(LinkedLayer, ILayer<? extends INeuron>, int, int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.initiators.PureLayerInitiator: Initiation method of a linked layer
initiateWeights(LinkedLayer) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.initiators.PureLayerInitiator: Initiate the weights of all the links of a neural net
initiateWeights(LinkedLayer) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.initiators.RandomInitiator: Initiate the weights of all the links of a neural net
initiator - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Initiator of each LinkedLayer of the neural nets
initiatorNeuronTypes - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Initiator of neurons of each HibridLayer of the neural nets
initiators - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetCreator: Initiators of weights
InitIndBsd(TableVar, float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndCAN: Creates biased random instance of Canonical individual
InitIndBsd(TableVar, float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndDNF: Creates biased random instance of Canonical individual
InitIndBsd(TableVar, float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Creates biased random instance of individual
InitIndEmp() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual: Empty initialization of the individual.
initIndEmp(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population: Individual of the Population initialisation
InitIndEmp() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual: Empty initialization of the individual.
initIndEmp(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population: Individual of the Population initialisation
InitIndEmp() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual: Empty initialization of the individual.
initIndEmp(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population: Individual of the Population initialisation
InitIndRnd(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndCAN: Creates random instance of Canonical individual
InitIndRnd(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndDNF: Creates random instance of DNF individual
InitIndRnd(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Creates random instance of individual
initInput() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.ExpNeuron: Init the input of the neuron (0 or 1 depending on the kind of neuron)
initInput() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinearNeuron: Init the input of the neuron (0 or 1 depending on the kind of neuron)
initInput() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron: Init the input of the neuron (0 or 1 depending on the kind of neuron)
initInput() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.SigmNeuron: Init the input of the neuron (0 or 1 depending on the kind of neuron)
initInstanceLists() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.instanceSet
initInstancesEvaluation() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper
initInstancesEvaluation() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper
initIteration() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper
initIteration() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper
initLogManager() - Static method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.LogManager
initLogManager() - Static method in class keel.Algorithms.Genetic_Rule_Learning.Globals.LogManager
initLogManager() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.LogManager
initMethod - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
initMethod - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
initMethod - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
initPlantilla(Cromosoma, double) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.Cromosoma: this method is used in CHC method.
initPlantilla(Cromosoma, double) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaBinario: this method is used in CHC method.
initPlantilla(Cromosoma, double) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaEntero: this method is used in CHC method.
initPopEmp() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population: Population initialisation (calling individual inicialisation)
initPopEmp() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population: Population initialisation (calling individual inicialisation)
initPopEmp() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population: Population initialisation (calling individual inicialisation)
initRand() - Static method in class keel.Algorithms.Genetic_Rule_Learning.Globals.Rand: Generates a new instance of Random
initRand() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Rand: Generates a new instance of Random
initRand() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.Cromosoma: random initialization of a chromosome
initRand() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaBinario: random initialization of a chromosome (two values: 0 or 1)
initRand() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaEntero: random initialization of a chromosome.
initRandomClassifier() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
initRandomClassifier() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierADI
initRandomClassifier() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierGABIL
initRandomClassifier() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierUBR
initRandomClassifier() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
initRandomClassifier() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
initRandomClassifier() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
initRead(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.FileManagement: Init a file for reading.
initRead(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.FileManagement: Init a file for reading.
initReductionIteration - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It indicates the iteration where the reduction specified has to be applied for the first time.
INITREDUCTIONITERATION - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
InitSemantics(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Defined to manage de semantics of the linguistic variables Generates the semantics of the linguistic variables using a partition consisting of triangle simetrics fuzzy sets.
InitSemantics(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Defined to manage de semantics of the linguistic variables Generates the semantics of the linguistic variables using a partition consisting of triangle simetrics fuzzy sets.
InitSemantics(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Defined to manage de semantics of the linguistic variables Generates the semantics of the linguistic variables using a partition consisting of triangle simetrics fuzzy sets.
initStatistics() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Statistics
initStatistics() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Statistics
initTimeAverages() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TimeControl: It initilizes the time averages to zero.
initTimeAverages() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TimeControl: It initilizes the time averages to zero.
initTokenizer(StreamTokenizer) - Method in class keel.Algorithms.Decision_Trees.C45.Algorithm: Function to initialize the stream tokenizer.
initTokenizer(StreamTokenizer) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm: Function to initialize the stream tokenizer.
initTokenizer(StreamTokenizer) - Method in class keel.Algorithms.Decision_Trees.ID3.Algorithm: Function to initialize the stream tokenizer.
initTokenizer(StreamTokenizer) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm: Function to initialize the stream tokenizer.
initTokenizer(StreamTokenizer) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm: Function to initialize the stream tokenizer.
initTokenizer(StreamTokenizer) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm: Function to initialize the stream tokenizer.
initTokenizer(StreamTokenizer) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm: Function to initialize the stream tokenizer.
initTokenizer(StreamTokenizer) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Initializes the StreamTokenizer used for reading the ARFF file.
initTokenizer(StreamTokenizer) - Method in class keel.Algorithms.Rule_Learning.ART.Algorithm: Function to initialize the stream tokenizer.
initTokenizer(StreamTokenizer) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Algorithm: Function to initialize the stream tokenizer.
initTokenizer(StreamTokenizer) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Algorithm: Function to initialize the stream tokenizer.
initTokenizer(StreamTokenizer) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm: Function to initialize the stream tokenizer.
initTokenizer(StreamTokenizer) - Method in class keel.Algorithms.Rule_Learning.PART.Algorithm: Function to initialize the stream tokenizer.
initValues(Vector) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeVar: Initialize private "valores" with the values of the vector
initValues(Vector) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeVar: Initialize private "valores" with the values of the vector
initValues(Vector) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeVar: Initialize private "valores" with the values of the vector
initVars(Instances) - Method in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: initializes variables etc.
initVars(Instances) - Method in class keel.Algorithms.SVM.SMO.supportVector.Kernel: initializes variables etc.
initVars(Instances) - Method in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: initializes variables etc.
initVars(Instances) - Method in class keel.Algorithms.SVM.SMO.supportVector.Puk: initializes variables etc.
initVars(Instances) - Method in class keel.Algorithms.SVM.SMO.supportVector.RBFKernel: initializes variables etc.
initVars(Instances) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: initializes variables etc.
initWrite(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.FileManagement: Init a file for reading.
initWrite(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.FileManagement: Init a file for reading.
iniWeight() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It initializes the weights for each example to 1.
INNER - Class in keel.Algorithms.Hyperrectangles.INNER: File: INNER.java The INNER Algorithm.
INNER(String) - Constructor for class keel.Algorithms.Hyperrectangles.INNER.INNER: The main method of the class
innerProduct(DoubleVector) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Returns the inner product of two DoubleVectors
input(M5Instance) - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Input an instance for filtering.
input(M5Instance) - Method in class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter: Input an instance for filtering.
INPUT - Static variable in class keel.Algorithms.Rule_Learning.Swap1.Attribute: Label to identify INPUT attributes
INPUT - Static variable in class keel.Dataset.Attribute: Label to identify INPUT attributes
input_file_tra - Variable in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Param: Training Input mandatory file
input_file_tra - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Param: Training Input mandatory file
input_file_tra - Variable in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Param: Training Input mandatory file
input_file_tst - Variable in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Param: Test Input mandatory file
input_file_tst - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Param: Test Input mandatory file
input_file_tst - Variable in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Param: Test Input mandatory file
input_stream - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserTokenManager
input_stream - Static variable in class keel.Algorithms.Rule_Learning.Swap1.DataParserTokenManager
input_stream - Static variable in class keel.Dataset.DataParserTokenManager
input_test_name - Variable in class keel.Algorithms.SVM.SMO.SMO: Test dataset filename.
input_train_name - Variable in class keel.Algorithms.SVM.SMO.SMO: Training dataset filename.
input_validation_name - Variable in class keel.Algorithms.SVM.SMO.SMO: Validation dataset filename.
inputAtt - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Number of input attributes
inputAtt - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: Number of input attributes.
inputAtt - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Number of input attributes
inputAtt - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Number of input attributes.
inputAtt - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm: Number of input attributes
inputAtt - Variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: Number of input attributes
inputAtt - Variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: Number of input attributes
inputAtt - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm: Number of input attributes
INPUTDATA - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
inputDataSet(myDataset, DataBase) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Commences process of getting inout data (GUI version also exists).
inputFiles - Static variable in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm: Name of input files.
inputFiles - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm: Name of input files.
inputFilesPath - Static variable in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm: Complete path of input files.
inputFilesPath - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm: Complete path of input files.
inputFormat(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Deprecated.
use setInputFormat(Instances) instead.
inputFormatOkFlag - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Input format OK flag( default = true).
inputFunction(double, double, double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.ExpNeuron: Input function of the neuron.
inputFunction(double, double, double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinearNeuron: Input function of the neuron.
inputFunction(double, double, double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron: Input function of the neuron.
inputFunction(double, double, double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.SigmNeuron: Input function of the neuron.
inputInterval - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Normalization input interval
inputLayer - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Input layer
InputLayer - Class in keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet: Input layer of a neural net
InputLayer() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputLayer: Empty constructor
InputNeuron - Class in keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet: Input Neuron of a neural net
InputNeuron() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputNeuron: Empty Constructor
inputs - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Inputs attributes
inputs - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: Training size.
inputs - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Inputs attributes
inputs - Variable in class keel.Algorithms.Instance_Generation.Basic.Prototype: Normalized inputs of the prototype (values in [0,1]).
inputs - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Inputs attributes.
inputs - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm: Inputs attributes
inputs - Variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: Inputs attributes
inputs - Variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: Inputs attributes
inputs - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm: Inputs attributes
INPUTS - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for inputs line.
inputs - Variable in class keel.Algorithms.Rule_Learning.Swap1.swap1
inputs - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Normalized inputs of the prototype (values in [0,1]).
INPUTS - Static variable in interface keel.Dataset.DataParserConstants
inputs - Variable in class keel.GraphInterKeel.experiments.Multiplexor
inputs_list() - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
inputs_list() - Static method in class keel.Dataset.DataParser
InputsInTestNotEquals - Static variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo
InputsInTestNotEquals - Static variable in class keel.Dataset.ErrorInfo
inputsLine - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
inputsLine - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
inputStream - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
inputStream - Static variable in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
inputStream - Static variable in class keel.Dataset.SimpleCharStream
InputTestAttributeNotDefined - Static variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo
InputTestAttributeNotDefined - Static variable in class keel.Dataset.ErrorInfo
InputTrainAttributeNotDefined - Static variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo
InputTrainAttributeNotDefined - Static variable in class keel.Dataset.ErrorInfo
Insert(int, genetcode[], double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.ruleset
insert(Neighbour) - Method in class keel.Algorithms.Lazy_Learning.IDIBL.NQueue: Insert a new neighbor
insert(int) - Method in class keel.Algorithms.SVM.SMO.supportVector.SMOset: Inserts an element into the set.
insert(Lists, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Lists: Insert an element X into the list at location specified by NODE
insert(Lists, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Lists: Insert an element X into the list at location specified by NODE
insert(Lists, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Lists: Insert an element X into the list at location specified by NODE
insert(DatasetXML, String) - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Insert a new data set in the list, from an External Object Description
insert(Vector<String>) - Method in class keel.GraphInterKeel.experiments.DinamicParameter
insert(DatasetXML, String) - Method in class keel.GraphInterKeel.experiments.SelectData: Insert a new External Object Description (of a data set) in the list
insertaAtributos(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Adds all the conditions given.
insertaAtributos(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Adds all the conditions given.
insertaAtributos(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Adds all the conditions given.
insertaAtributos(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Adds all the conditions given.
insertaCondicionesContinuos(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Adds all the continuous conditions given.
insertaCondicionesNominales(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Adds all the nominal conditions given.
insertaMuestra(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Adds the given example to the dataset.
insertaMuestra(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Adds the given example to the dataset.
insertaMuestra(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ConjuntoDatos: Adds the given example to the dataset.
insertaMuestra(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Adds the given example to the dataset.
insertaMuestra(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Adds the given example to the dataset.
insertaNumCondicionesReales(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Sets the number of real conditions.
insertaNumCondicionesReales(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Sets the number of real conditions.
insertaNumCondicionesReales(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Sets the number of real conditions.
insertaPosicion(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Muestra: Sets tha position of the example in the file (example id).
insertaPosicion(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Muestra: Sets tha position of the example in the file (example id).
insertaPosicion(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Muestra: Sets tha position of the example in the file (example id).
insertaPosicion(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Muestra: Sets tha position of the example in the file (example id).
insertaPosicion(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Muestra: Sets tha position of the example in the file (example id).
insertarAtributo(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Muestra: Adds an attribute with its value to the example.
insertarAtributo(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Muestra: Adds an attribute with its value to the example.
insertarAtributo(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Muestra: Adds an attribute with its value to the example.
insertarAtributo(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Adds a condition (an attribute with its value and an operator).
insertarAtributo(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Muestra: Adds an attribute with its value to the example.
insertarAtributo(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Muestra: Adds an attribute with its value to the example.
insertArc(Arc) - Method in class keel.GraphInterKeel.experiments.Graph: Inserts a new arc in the graph
insertArc(Arc, int) - Method in class keel.GraphInterKeel.experiments.Graph: Inserts a new arc at the specified position
insertarClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Muestra: Adds a class that identifies the example/rule with all its attributes.
insertarClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Adds a class that identifies the rule with all its attributes.
insertarClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Muestra: Adds a class that identifies the example/rule with all its attributes.
insertarClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Adds a class that identifies the rule with all its attributes.
insertarClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Muestra: Adds a class that identifies the example/rule with all its attributes.
insertarClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Adds a class that identifies the rule with all its attributes.
insertarClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Muestra: Adds a class that identifies the example/rule with all its attributes.
insertarClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Adds a class that identifies the rule with all its attributes.
insertarClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Muestra: Adds a class that identifies the example/rule with all its attributes.
insertarClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Adds a class that identifies the rule with all its attributes.
insertarCondicion(Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Adds a condition (an attribute with its value and an operator).
insertarCondicion(Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Adds a condition (an attribute with its value and an operator).
insertarCondicion(Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Adds a condition (an attribute with its value and an operator).
insertarCondicionContinua(Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Adds a condition (a continuous attribute with its value and an operator).
insertarCondicionNominal(Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Adds a condition (a nominal attribute with its value and an operator).
insertAttributeAt(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Inserts an attribute at the given position (0 to numAttributes()).
insertAttributeAt(M5Attribute, int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Inserts an attribute at the given position (0 to numAttributes()) and sets all values to be missing.
insertAttributeAt(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Inserts an attribute at the given position (0 to numAttributes()).
insertAttributeAt(AttributeWeka, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Inserts an attribute at the given position (0 to numAttributes()) and sets all values to be missing.
insertAttributeAt(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Inserts an attribute at the given position (0 to numAttributes()).
insertAttributeAt(MyAttribute, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Inserts an attribute at the given position (0 to numAttributes()) and sets all values to be missing.
insertAttributeAt(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Inserts an attribute at the given position (0 to numAttributes()).
insertAttributeAt(Attribute, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Inserts an attribute at the given position (0 to numAttributes()) and sets all values to be missing.
insertAttributeAt(int) - Method in class keel.Algorithms.SVM.SMO.core.Instance: Inserts an attribute at the given position (0 to numAttributes()).
insertAttributeAt(Attribute, int) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Inserts an attribute at the given position (0 to numAttributes()) and sets all values to be missing.
insertC(DatasetXML, String) - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Insert a new C data set in the list, from an External Object Description
insertC(DatasetXML, String) - Method in class keel.GraphInterKeel.experiments.SelectData: Insert a new External Object Description (of a data set) in the list
insertC_LQD(DatasetXML, String) - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Insert a new LQD data set in the list, from an External Object Description
insertC_LQD(DatasetXML, String) - Method in class keel.GraphInterKeel.experiments.SelectData: Insert a new External Object Description (of a data set) in the list
insertDataflowItem - Variable in class keel.GraphInterKeel.experiments.Experiments
insertDataSelected(String) - Method in class keel.GraphInterKeel.experiments.Joint
insertDirectoryData(URL, String) - Method in class keel.GraphInterKeel.experiments.Experiments: Read XML files
insertElementAt(Object, int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector: Inserts an element at the given position.
insertElementAt(Object, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector: Inserts an element at the given position.
insertElementAt(Object, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector: Inserts an element at the given position.
insertElementAt(Object, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector: Inserts an element at the given position.
insertElementAt(Object, int) - Method in class keel.Algorithms.SVM.SMO.core.FastVector: Inserts an element at the given position.
insertInPopulation(Classifier, Population) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Inserts the classifier in the population.
insertInPopulation(Classifier, Population) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Inserts the classifier in the population.
insertInPSubsumingCl(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Inserts the classifier into the population.
insertInPSubsumingCl(Classifier, Population) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Inserts the classifier into the population.
insertion(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.ruleSet: Insertion operator
insertLQD_C(DatasetXML, String) - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Insert a new LQD data set in the list, from an External Object Description
insertLQD_C(DatasetXML, String) - Method in class keel.GraphInterKeel.experiments.SelectData: Insert a new External Object Description (of a data set) in the list
insertNode(Node) - Method in class keel.GraphInterKeel.experiments.Graph: Insert a new node in the graph
insertNode(Node, int) - Method in class keel.GraphInterKeel.experiments.Graph: Insert a node at the specified position
insertParameter(DinamicParameter) - Method in class keel.GraphInterKeel.experiments.Joint
insertproblem(String) - Method in class keel.GraphInterKeel.experiments.Joint
insertRbf(Rbf) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Adds a neuron to the net, assigning an automatic name
insertRbf(Rbf, String) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Adds a neuron to the net with a given name
insertRbf(Rbf) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbfn: Adds a neuron to the net
insertRbf(Rbf) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn: Adds a neuron to the net
insertRbf(Rbf) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn: Adds a neuron to the net
insertRbf(Rbf) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn: Adds a neuron to the net
insertRbf(Rbf) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn: Adds a neuron to the net
insertRbf(Rbf) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn: Adds a neuron to the net
insertRinRlistCMARranking(short[], short[], double, double, double, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList: Inserts an (association/classification) rule into the linkedlist of rules pointed at by startRulelist.
insertRow(Vector, Attribute) - Method in class keel.GraphInterKeel.datacf.util.AttributeTable: Inserts a row in the table
insertRule(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.RuleSet
insertRule(Rule, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Ruleset: Inserts a new rule in a given position of the ruleset.
insertRule(int, matchProfileAgent) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ruleOrderAgent
insertRule(Rule, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Ruleset: Inserts a new rule in a given position of the ruleset.
insertRule(Rule, int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Ruleset: Inserts a new rule in a given position of the ruleset.
insertRule(Rule, int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset: Inserts a new rule in a given position of the ruleset.
insertRule(Rule, int) - Method in class keel.Algorithms.Rule_Learning.PART.Ruleset: Inserts a new rule in a given position of the ruleset.
insertRule(Rule, int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset: Inserts a new rule in a given position of the ruleset.
insertRule(Rule, int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Ruleset: Inserts a new rule in a given position of the ruleset.
insertRuleintoRulelist(short[], short[], double, double, double) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Inserts an (association/classification) rule into the linkedlist of rules pointed at by startRulelist.
insertRuleintoRulelist(short[], short[], double, double, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Inserts an (association/classification) rule into the linkedlist of rules pointed at by startRulelist.
InsertVector(double[], double, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Arrays
InsertVector(int[], int, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Arrays
inside(double[]) - Method in class keel.Algorithms.Hyperrectangles.INNER.Rule: Tests if an instance is covered by the rule
inside(double[]) - Method in class keel.Algorithms.Hyperrectangles.RISE.Rule: Tests if an instance is covered by the rule
insignificant(double, M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.Function: Detects the most insignificant variable in the funcion
insignificant(double, MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Function: Detects the most insignificant variable in the funcion
inst - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.InstanceP: Proper instance (referenced)
instance(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Returns the instance at the given position.
Instance - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: Class for handling an instance.
Instance(Instance) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Constructor that copies the attribute values and the weight from the given instance.
Instance(double, double[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Constructor that inititalizes instance variable with given values.
Instance(int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.
Instance() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Private constructor for subclasses.
instance(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns the instance at the given position.
Instance - Class in keel.Algorithms.Genetic_Rule_Learning.Corcoran: Title: Instance Description: Structure for an instance used in the data-set
Instance(double[], int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Instance: Builder
Instance(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Instance: Builder without data
Instance - Class in keel.Algorithms.Genetic_Rule_Learning.SIA: Title: Instance Description: Structure for an instance used in the data-set
Instance(double[], int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.SIA.Instance: Builder
Instance(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.SIA.Instance: Builder without data
Instance - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets: Class for handling an instance.
Instance(Instance) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Constructor that copies the attribute values and the weight from the given instance.
Instance(double, double[]) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Constructor that inititalizes instance variable with given values.
Instance(int) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.
Instance() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Private constructor for subclasses.
instance(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns the instance at the given position.
Instance() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset.Instance: Empty constructor.
Instance - Class in keel.Algorithms.Rule_Learning.AQ: Title: Instance Description: Structure for an instance used in the data-set
Instance(double[], int, int) - Constructor for class keel.Algorithms.Rule_Learning.AQ.Instance: Builder
Instance(int) - Constructor for class keel.Algorithms.Rule_Learning.AQ.Instance: Builder without data
Instance - Class in keel.Algorithms.Rule_Learning.CN2: Title: Instance Description: Structure for an instance used in the data-set
Instance(double[], int, int) - Constructor for class keel.Algorithms.Rule_Learning.CN2.Instance: Builder
Instance(int) - Constructor for class keel.Algorithms.Rule_Learning.CN2.Instance: Builder without data
Instance - Class in keel.Algorithms.Rule_Learning.Swap1: Instance This class keeps all the information of an instance.
Instance(String, boolean, int) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.Instance: It parses a new attribute line.
Instance(Instance) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.Instance: Creates a deep copy of the Instance
Instance(double[], InstanceAttributes) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.Instance: Creates an instance from a set of given values.
Instance - Class in keel.Algorithms.Subgroup_Discovery.SDAlgorithm
Instance(double[], int, int) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Instance: Builder
Instance(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Instance: Builder without data
Instance - Class in keel.Algorithms.SVM.SMO.core: Class for handling an instance.
Instance(Instance) - Constructor for class keel.Algorithms.SVM.SMO.core.Instance: Constructor that copies the attribute values and the weight from the given instance.
Instance(double, double[]) - Constructor for class keel.Algorithms.SVM.SMO.core.Instance: Constructor that inititalizes instance variable with given values.
Instance(int) - Constructor for class keel.Algorithms.SVM.SMO.core.Instance: Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.
Instance() - Constructor for class keel.Algorithms.SVM.SMO.core.Instance: Private constructor for subclasses.
instance(int) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns the instance at the given position.
Instance - Class in keel.Dataset: Instance This class keeps all the information of an instance.
Instance(String, boolean, int) - Constructor for class keel.Dataset.Instance: It parses a new attribute line.
Instance(Instance) - Constructor for class keel.Dataset.Instance: Creates a deep copy of the Instance
Instance(double[], InstanceAttributes) - Constructor for class keel.Dataset.Instance: Creates an instance from a set of given values.
InstanceAttributes - Class in keel.Algorithms.Rule_Learning.Swap1: InstanceAttributes This class contains the information of all the attributes in the dataset.
InstanceAttributes() - Constructor for class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: InstanceAttributes Class constructor.
InstanceAttributes(InstanceAttributes) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes
InstanceAttributes - Class in keel.Dataset: InstanceAttributes This class contains the information of all the attributes in the dataset.
InstanceAttributes() - Constructor for class keel.Dataset.InstanceAttributes: InstanceAttributes Class constructor.
InstanceAttributes(InstanceAttributes) - Constructor for class keel.Dataset.InstanceAttributes
instanceCoveredByRule(Instance) - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Checks if the rule gets the parameter instance
instanceNum - Variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo: It stores the instance number where the error has appeared.
instanceNum - Variable in class keel.Dataset.ErrorInfo: It stores the instance number where the error has appeared.
InstanceP - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering: This class stores an instance with a P(x) value associated
InstanceP() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.InstanceP: Creates a new instance of InstanceP
InstanceP(Instance, double, int) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.InstanceP: Creates a new InstanceP with the arguments passed
InstanceParser - Class in keel.Algorithms.Rule_Learning.Swap1: InstanceParser This class is a parser for the instances.
InstanceParser(String, boolean) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.InstanceParser: It does create a new instance of ParserARFF.
InstanceParser - Class in keel.Dataset: InstanceParser This class is a parser for the instances.
InstanceParser(String, boolean) - Constructor for class keel.Dataset.InstanceParser: It does create a new instance of ParserARFF.
Instances - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: Class for handling an ordered set of weighted instances.
Instances(Instances) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Constructor copying all instances and references to the header information from the given set of instances.
Instances(Instances, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Constructor creating an empty set of instances.
Instances(Instances, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Creates a new set of instances by copying a subset of another set.
Instances(String, FastVector, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Creates an empty set of instances.
instances - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
Instances - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets: Class for handling an ordered set of weighted instances.
Instances(Reader) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Reads an ARFF file from a reader, and assigns a weight of one to each instance.
Instances(Vector<Instance>) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances
Instances(Reader, int) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Reads the header of an ARFF file from a reader and reserves space for the given number of instances.
Instances(Instances) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Constructor copying all instances and references to the header information from the given set of instances.
Instances(Instances, int) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Constructor creating an empty set of instances.
Instances(String, FastVector, FastVector, int) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances
Instances(Instances, int, int) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Creates a new set of instances by copying a subset of another set.
Instances(String, FastVector, int) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Creates an empty set of instances.
instances - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArrayDataset: Instances list
instances - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters: Instances.
Instances - Class in keel.Algorithms.Rule_Learning.SRI: Title: Intances class Description: it stores the dataset in a way that this Rule learning algorithm can understand.
Instances() - Constructor for class keel.Algorithms.Rule_Learning.SRI.Instances: Default constructor.
Instances(double[][], String[], int, int) - Constructor for class keel.Algorithms.Rule_Learning.SRI.Instances: Parameter constructor.
Instances - Class in keel.Algorithms.SVM.SMO.core: Class for handling an ordered set of weighted instances.
Instances(Instances) - Constructor for class keel.Algorithms.SVM.SMO.core.Instances: Constructor copying all instances and references to the header information from the given set of instances.
Instances(Instances, int) - Constructor for class keel.Algorithms.SVM.SMO.core.Instances: Constructor creating an empty set of instances.
Instances(Instances, int, int) - Constructor for class keel.Algorithms.SVM.SMO.core.Instances: Creates a new set of instances by copying a subset of another set.
Instances(String, FastVector, int) - Constructor for class keel.Algorithms.SVM.SMO.core.Instances: Creates an empty set of instances.
instancesAndWeights() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns string including all instances, their weights and their indices in the original dataset.
instancesAndWeights() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns string including all instances, their weights and their indices in the original dataset.
instancesAndWeights() - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns string including all instances, their weights and their indices in the original dataset.
instancesByClass - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper
instancesByClass - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper
instancesByClass - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
instancesByClass - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal
instanceSet - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
instanceSet(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.instanceSet
InstanceSet - Class in keel.Algorithms.Rule_Learning.Swap1: InstanceSet The instance set class mantains a pool of instances read from the keel formated data file.
InstanceSet() - Constructor for class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: It instances a new instance of InstanceSet
InstanceSet(boolean) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: InstanceSet This constructor permit define if the attribute's definition need to be stored as non-static (nonStaticAttributes = true).
InstanceSet(InstanceSet) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: Creates a new InstanceSet with the header and Instances from the passed object It performs a deep (new allocated) copy.
InstanceSet - Class in keel.Dataset: InstanceSet The instance set class mantains a pool of instances read from the keel formated data file.
InstanceSet() - Constructor for class keel.Dataset.InstanceSet: It instances a new instance of InstanceSet
InstanceSet(boolean) - Constructor for class keel.Dataset.InstanceSet: InstanceSet This constructor permit define if the attribute's definition need to be stored as non-static (nonStaticAttributes = true).
InstanceSet(InstanceSet) - Constructor for class keel.Dataset.InstanceSet: Creates a new InstanceSet with the header and Instances from the passed object It performs a deep (new allocated) copy.
instanceSimilarity(int, int) - Static method in class keel.Algorithms.RST_Learning.RSTData
InstancesKEEL2Weka(InstanceSet, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Creates a new allocated WEKA's set of Instances (i.e.
InstancesKEEL2Weka(InstanceSet, int, boolean) - Method in class keel.Algorithms.SVM.SMO.SMO: Creates a new allocated WEKA's set of Instances (i.e.
InstancesOfClass - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
Instancetest - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Original test data set to be condensed
Instancetrain - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Original training data set to be condensed
InstanceWrapper - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist: Wrapper for the global KEEL Instance class tailored to the needs of GAssist
InstanceWrapper(Instance) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.InstanceWrapper
InstanceWrapper - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS: Wrapper for the global KEEL Instance class tailored to the needs of GAssist
InstanceWrapper(Instance) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.InstanceWrapper
instOfAttAndValue(int, int, int[], int, int[], int, int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
INT_CONST - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
INT_CONST - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for integer constant.
INT_CONST - Static variable in interface keel.Dataset.DataParserConstants
Int_t - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV: Integer wrapper.
Int_t - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2: Integer wrapper.
Int_t - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0: Integer wrapper.
intCount - Variable in class keel.Algorithms.Decision_Trees.M5.M5AttrStats: The number of int-like values
intCount - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.AttributeStats: The number of int-like values
IntDouble - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: Display the consequent and the weight of the rule as a string.
IntDouble() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.IntDouble
INTEG - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for integer data.
INTEG - Static variable in interface keel.Dataset.DataParserConstants
INTEGER - Static variable in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Decision_Trees.Target.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
INTEGER - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Instance_Generation.Basic.Prototype: Integer type of attributes.
INTEGER - Static variable in class keel.Algorithms.PSO_Learning.CPSO.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.PSO_Learning.REPSO.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Rule_Learning.LEM1.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Rule_Learning.LEM2.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Rule_Learning.Ritio.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Rule_Learning.Rules6.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Rule_Learning.SRI.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.Rule_Learning.Swap1.Attribute: Label for INTEGER values.
INTEGER - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Integer type of attributes.
INTEGER - Static variable in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Gene: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Gene
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: Number to represent type of variable integer.
INTEGER - Static variable in class keel.Dataset.Attribute: Label for INTEGER values.
integerBoundaries(Attribute) - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
integerBoundaries(Attribute) - Static method in class keel.Dataset.DataParser
IntegerMutation - Interface in keel.Algorithms.Genetic_Rule_Learning.XCS: IntegerMutation.
IntegerNumericalAttribute - Class in keel.Algorithms.Neural_Networks.NNEP_Common.data: Integer attributes
IntegerNumericalAttribute() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.data.IntegerNumericalAttribute: Empty constructor
IntegerRep - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: This class represents the integer representation of a gene.
IntegerRep(double, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: Does create a Integer representation for de environmental value given as a parameter
IntegerRep(int, int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: It is the default constructor of the class.
IntegerRep(int, int, int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: It is the constructor of the class.
IntegerRep(Attribute) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: It is a constructor for the class.
IntegerSet - Class in keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter: This class implements a set of integers and its basic operations
IntegerSet() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.IntegerSet: Constructor without parameters
IntegerSet(int) - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.IntegerSet: Constructor with initial size
integerValid(String) - Method in class keel.GraphInterKeel.datacf.util.Attribute: Return a boolean for a given int value, true is valid value, false invalid value.
interchangeValues(Individual, int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Individual: It interchanges the values between the position pointCross1 and pointCross2
internalCacheSizeTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Returns the tip text for this property
internalClassifier(double[]) - Method in class keel.Algorithms.Statistical_Classifiers.ClassifierKernel.Kernel: This methods computes the class with maximum kernel value for the input value
Interpolation - Class in keel.Algorithms.Preprocess.Missing_Values.EM: Title: Polynomial Interpolation Description: Polynomial interpoaltion using Newton's and Lagrange's methods Copyright: Copyright Byrge Birkeland(c) 2002 Company: Agder University College
Interpolation() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EM.Interpolation
intersect(Rule) - Method in class keel.Algorithms.Hyperrectangles.INNER.Rule: Test if two rules intersects
Interval - Class in keel.Algorithms.Decision_Trees.M5: Class representing a range of cardinal numbers.
Interval() - Constructor for class keel.Algorithms.Decision_Trees.M5.Interval: Default constructor.
Interval(String) - Constructor for class keel.Algorithms.Decision_Trees.M5.Interval: Constructor to set initial range.
Interval - Class in keel.Algorithms.Discretizers.Chi2_Discretizer: Interval class.
Interval(int, int[], int, int, int[]) - Constructor for class keel.Algorithms.Discretizers.Chi2_Discretizer.Interval: Compute the interval ratios.
Interval - Class in keel.Algorithms.Discretizers.ExtendedChi2_Discretizer
Interval(int, int[], int, int, int[]) - Constructor for class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Interval: Compute the interval ratios.
Interval - Class in keel.Algorithms.Discretizers.ModifiedChi2_Discretizer: Interval class.
Interval(int, int[], int, int, int[]) - Constructor for class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Interval: Compute the interval ratios.
Interval - Class in keel.Algorithms.Discretizers.MVD: Simple class to codify an interval for a numerical attribute
Interval(double, double, int) - Constructor for class keel.Algorithms.Discretizers.MVD.Interval: Parametrized constructor
Interval - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyRegressor: the type for fuzzy regressors based on interval sets (interval fuzzy sets).
Interval - Class in keel.Algorithms.LQD.methods.FGFS_costInstances: File: interval.java Properties and functions of the interval.
Interval() - Constructor for class keel.Algorithms.LQD.methods.FGFS_costInstances.Interval
Interval(float, float) - Constructor for class keel.Algorithms.LQD.methods.FGFS_costInstances.Interval
Interval(float) - Constructor for class keel.Algorithms.LQD.methods.FGFS_costInstances.Interval
Interval - Class in keel.Algorithms.LQD.methods.FGFS_Minimum_Risk: File: interval.java Properties and functions of the interval.
Interval() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.Interval
Interval(float, float) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.Interval
Interval(float) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.Interval
Interval - Class in keel.Algorithms.LQD.methods.FGFS_Original: File: interval.java Properties and functions of the interval.
Interval() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Original.Interval
Interval(float, float) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Original.Interval
Interval - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight
Interval() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.Interval
Interval(float, float) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.Interval
Interval - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty: File: interval.java Properties and functions of the interval.
Interval() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.Interval
Interval(float, float) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.Interval
interval - Class in keel.Algorithms.LQD.preprocess.Expert: File: interval.java Properties and functions of the interval.
interval() - Constructor for class keel.Algorithms.LQD.preprocess.Expert.interval
interval(float, float) - Constructor for class keel.Algorithms.LQD.preprocess.Expert.interval
interval(float) - Constructor for class keel.Algorithms.LQD.preprocess.Expert.interval
interval - Class in keel.Algorithms.LQD.preprocess.Prelabelling: File: interval.java Properties and functions of the interval.
interval() - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling.interval
interval(float, float) - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling.interval
interval(float) - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling.interval
interval - Class in keel.Algorithms.LQD.preprocess.Prelabelling_Expert: File: interval.java Properties and functions of the interval.
interval() - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.interval
interval(float, float) - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.interval
interval(float) - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.interval
interval - Class in keel.Algorithms.LQD.tests.IntermediateBoost
interval() - Constructor for class keel.Algorithms.LQD.tests.IntermediateBoost.interval
interval(float, float) - Constructor for class keel.Algorithms.LQD.tests.IntermediateBoost.interval
Interval - Class in keel.Algorithms.Rule_Learning.UnoR
Interval(int, int) - Constructor for class keel.Algorithms.Rule_Learning.UnoR.Interval: Constructor
Interval - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA
Interval() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Interval: Default constructor
intervalCost(ArrayList<Double>, int, int[], int) - Method in class keel.Algorithms.Discretizers.MODL.MODL: Computes the cost of the interval in the current discretization scheme
intervalEntropy(int, int) - Method in class keel.Algorithms.Discretizers.HellingerBD.HellingerBD: It computes the interval entropy
Intervals - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA: Title: Description: Copyright: Copyright (c) 2007 Company:
Intervals() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Intervals
Intervals - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA: Title: Description: Copyright: Copyright (c) 2007 Company:
Intervals() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Intervals
intervalValues() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.CategoricalAttribute: Return an interval with the allowed values
intervalValues() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.IntegerNumericalAttribute: Return an interval with the allowed values
intervalValues() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.RealNumericalAttribute: Return an interval with the allowed values
into(int[]) - Method in class keel.Algorithms.Discretizers.UCPD.Itemset: It checks if the example given follows the patron of the itemset
IntVector - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix: A vector specialized on integers.
IntVector() - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector: Constructs a null vector.
IntVector(int) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector: Constructs an n-vector of zeros.
IntVector(int, int) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector: Constructs an n-vector of a constant
IntVector(int[]) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector: Constructs a vector given an int array
inv(double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
inv(DenseMatrix) - Static method in class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.LLSImpute: Computes the inverse of a square non-singular Matrix
inv(double[][]) - Static method in class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
inv_prot(double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
inv_prot(double[][]) - Static method in class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
invalidCrossover - Exception in keel.Algorithms.Shared.Exceptions: This exception is to report an invalid crossover operator.
invalidCrossover() - Constructor for exception keel.Algorithms.Shared.Exceptions.invalidCrossover: Creates an new invalidCrossover object calling the super() method();
invalidCrossover(String) - Constructor for exception keel.Algorithms.Shared.Exceptions.invalidCrossover: Creates an new invalidCrossover object calling the super(s) method() with the report string s.
invalidFitness - Exception in keel.Algorithms.Shared.Exceptions: This exception is to report an invalid fitness function.
invalidFitness() - Constructor for exception keel.Algorithms.Shared.Exceptions.invalidFitness: Creates an new invalidFitness object calling the super() method();
invalidFitness(String) - Constructor for exception keel.Algorithms.Shared.Exceptions.invalidFitness: Creates an new invalidFitness object calling the super(s) method() with the report string s.
invalidMutation - Exception in keel.Algorithms.Shared.Exceptions: This exception is to report an invalid mutation operator.
invalidMutation() - Constructor for exception keel.Algorithms.Shared.Exceptions.invalidMutation: Creates an new invalidMutation object calling the super() method();
invalidMutation(String) - Constructor for exception keel.Algorithms.Shared.Exceptions.invalidMutation: Creates an new invalidMutation object calling the super(s) method() with the report string s.
invalidOptim - Exception in keel.Algorithms.Shared.Exceptions: This exception is to report an invalid optimization method.
invalidOptim() - Constructor for exception keel.Algorithms.Shared.Exceptions.invalidOptim: Creates an new invalidMutation object calling the super() method();
invalidOptim(String) - Constructor for exception keel.Algorithms.Shared.Exceptions.invalidOptim: Creates an new invalidOptim object calling the super(s) method() with the report string s.
inverse() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Matrix inverse or pseudoinverse
inverseNormalDistribution(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.NormalDistribution: Returns the value with the probability given as parameter (inverse normal distribution function).
inverseNormalDistribution(double) - Method in class keel.GraphInterKeel.statistical.tests.NormalDistribution: Computes inverse cumulative distribution.
inverseOfNumberOfIterations - Variable in class keel.Algorithms.Instance_Generation.VQ.VQGenerator: Inverse of the number of iterations which performs the algorithm.
invert() - Method in class keel.Algorithms.Instance_Generation.utilities.Pair: Invert the pair.
invert() - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Pair: Invert the pair.
invert(int, double, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene: It does inversions for each part of a gene which were specifically designed for this algorithm
invert(int, double, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene: It does inversions for each part of a gene which were specifically designed for this algorithm
invert(int, double, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene: It does inversions for each part of a gene which were specifically designed for this algorithm
invertedBList - Variable in class keel.Algorithms.Instance_Generation.PNN.MatrixOfDistances: For each B-prototype, there is a hashset of all the others prototypes which point to it.
InvertMatrix(double[][], double[][], int) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Matrix: Inversion method
InvertMatrix(double[][], double[][], int) - Static method in class keel.Algorithms.Neural_Networks.net.Matrix: Inversion method
invertSelection_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Invert button
invertSelection_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectData: Invert button
invertSelectionC_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Invert button
invertSelectionC_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectData: Invert button
invertSelectionC_LQD_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Invert button
invertSelectionC_LQD_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectData: Invert button
invertSelectionLQD_C_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Invert button
invertSelectionLQD_C_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectData: Invert button
invertSelectionUser_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Invert button
invertSelectionUser_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectData: Invert button
INVESTIGATION - Static variable in class keel.GraphInterKeel.experiments.Experiments
invisible - Variable in class keel.GraphInterKeel.experiments.Experiments
invScale(double[], double[], double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method is for inverse scale of a vector
invScale(double[][], double[][], double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method is for inverse scale a mtrix
invScale(double[][][], double[][][], double[][][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method is for inverse scale a cubic mtrix
invScale(double[], double[], double[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV: Returns a inverted scaled copy of vector a.
invScale(double[][], double[][], double[][]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV: Returns a inverted scaled copy of matrix a.
invScale(double[][][], double[][][], double[][][]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV: Returns a inverted scaled copy of matrix a.
invScale(double[], double[], double[]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV: Returns a inverted scaled copy of vector a.
invScale(double[][], double[][], double[][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV: Returns a inverted scaled copy of matrix a.
invScale(double[][][], double[][][], double[][][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV: Returns a inverted scaled copy of matrix a.
IOptimizableFunc - Interface in keel.Algorithms.Neural_Networks.IRPropPlus_Clas
IPLDEAlgorithm - Class in keel.Algorithms.Instance_Generation.IPLDE: IPLDE algorithm calling.
IPLDEAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.IPLDE.IPLDEAlgorithm
IPLDEGenerator - Class in keel.Algorithms.Instance_Generation.IPLDE
IPLDEGenerator(PrototypeSet, int, int, int, int, double, double, int) - Constructor for class keel.Algorithms.Instance_Generation.IPLDE.IPLDEGenerator: Build a new IPLDEGenerator Algorithm
IPLDEGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.IPLDE.IPLDEGenerator: Build a new IPLDEGenerator Algorithm
IProblem - Interface in keel.Algorithms.Neural_Networks.NNEP_Common.problem: Represents a problem with training and test data
ir() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It computes the Imbalance Ratio
irandom(double, double) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.MLPerceptronBackpropCS: Generates a random integer number between min and max
irandom(double, double) - Static method in class keel.Algorithms.Neural_Networks.ensemble.Genesis: Generate random int between min and max using r seed
irandom(double, double) - Static method in class keel.Algorithms.Neural_Networks.gann.Genesis: Generate integer random number between min and max
irandom(double, double) - Static method in class keel.Algorithms.Neural_Networks.gann.Rand: Method that returns a random integer value between min and max values
irandom(double, double) - Static method in class keel.Algorithms.Neural_Networks.gmdh.Genesis: Generate random integer number between min and max
irandom(double, double) - Static method in class keel.Algorithms.Neural_Networks.net.Genesis: Generates a random integer number between min and max
IRegressor - Interface in keel.Algorithms.Neural_Networks.NNEP_Regr.problem.regression: Generic regressor.
IREPstar(Ruleset, MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ripper: It implements the Ripper2's Build Phase: Iteratively, it grows and prunes rules until the descrition length (DL) of the ruleset and examples is 64 bits greater than the smallest DL met so far, or there are no positive examples, or the error rate >= 50%.
IRIDGE - Static variable in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: IRIDGE flag.
iridge(DenseMatrix, DenseMatrix, DenseMatrix, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: MATLAB - Individual ridge regressions with generalized cross-validation.
IRPropPlus - Class in keel.Algorithms.Neural_Networks.IRPropPlus_Clas
IRPropPlus() - Constructor for class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus: Empty constructor.
IRPropPlusReporterClas - Class in keel.Algorithms.Neural_Networks.IRPropPlus_Clas: Reporter for iRProp+ algorithm
IRPropPlusReporterClas() - Constructor for class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlusReporterClas: Empty constructor
IRPropPlusReporterRegr - Class in keel.Algorithms.Neural_Networks.IRPropPlus_Regr: Reporter for iRProp+ algorithm for regression
IRPropPlusReporterRegr() - Constructor for class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.IRPropPlusReporterRegr: Empty constructor
IRunkeelListener<A extends EducationalRunKeelTxt> - Interface in keel.GraphInterKeel.experiments
IS - Variable in class keel.Algorithms.Decision_Trees.C45.Dataset: Keel dataset InstanceSet
IS - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Keel dataset InstanceSet
IS - Variable in class keel.Algorithms.Decision_Trees.ID3.Dataset: Keel dataset InstanceSet
IS - Variable in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Keel dataset InstanceSet
IS - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyDataset: Dataset (InstanceSet Object).
IS - Variable in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: The whole instance set.
IS - Variable in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: The whole instance set.
IS - Variable in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: The whole instance set.
IS - Variable in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: The whole instance set.
is - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
is - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper
IS - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Keel dataset InstanceSet
is - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper
IS - Variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Keel dataset InstanceSet
IS - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Keel dataset InstanceSet
IS - Variable in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: The whole instance set.
IS - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Keel dataset InstanceSet
IS - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Keel dataset InstanceSet
is - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters: Instance Set.
IS - Variable in class keel.Algorithms.RE_SL_Methods.LEL_TSK.MiDataset
IS - Variable in class keel.Algorithms.RE_SL_Methods.MamWM.MiDataset
IS - Variable in class keel.Algorithms.RE_SL_Methods.mogulHC.MiDataset
IS - Variable in class keel.Algorithms.RE_SL_Methods.mogulIRL.MiDataset
IS - Variable in class keel.Algorithms.RE_SL_Methods.mogulSC.MiDataset
IS - Variable in class keel.Algorithms.RE_SL_Methods.TSK_IRL.MiDataset
IS - Variable in class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.MiDataset
IS - Variable in class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.MiDataset
IS - Variable in class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.MiDataset
IS - Variable in class keel.Algorithms.RE_SL_Postprocess.MamSelect.MiDataset
IS - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWSelect.MiDataset
IS - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWTuning.MiDataset
IS - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.MiDataset
IS - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKSelect.MiDataset
IS - Variable in class keel.Algorithms.Rule_Learning.ART.Dataset: Keel dataset InstanceSet
IS - Variable in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Keel dataset InstanceSet
IS - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Keel dataset InstanceSet
IS - Variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Keel dataset InstanceSet
IS - Variable in class keel.Algorithms.Rule_Learning.PART.MyDataset: Keel dataset InstanceSet
IS - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Keel dataset InstanceSet
IS3(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.RuleSet: Incremental Genetic Algorithm, which increases the size of the actual rule set appending all the attributes from rs to the rules of the actual rule set.
IS4(RuleSet) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.RuleSet: Incremental Genetic Algorithm, which increases the size of the actual rule set appending all the attributes from rs to the rules of the actual rule set.
Is_Covered(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set: Returns whether the individual "i" is covered or not
Is_Covered(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set: Returns if the example in position "i" is covered
Is_Covered(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set: Returns whether the individual "i" is covered or not
Is_Covered(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set: Returns whether the individual "i" is covered or not
Is_Number(String) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Is_Number(String) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
Is_Test_Example(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
Is_Test_Example(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set: Returns if the example in position "ejemplo" belongs to the partition "particion"
Is_Test_Example(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set
Is_Test_Example(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set
Is_Training_Example(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
Is_Training_Example(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set: Returns if the example in position "ejemplo" belongs to the partition "particion"
Is_Training_Example(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set
Is_Training_Example(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set
Is_Valid(String, double[], double, Double_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Is_Valid(String, double[], double, ArrayList<Double>) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Returns if the set rules encoded in the String "regla" is valid or not.
Is_Valid(String, Double_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
Is_Valid(String, Double_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
isActive() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.ExampleWeight: Checks if the pattern is active.
isActive() - Method in class keel.Algorithms.Decision_Trees.C45.Attribute: Returns true if this attribute used in output or input clause.
isActive() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Attribute: Returns true if this attribute used in output or input clause.
isActive() - Method in class keel.Algorithms.Decision_Trees.ID3.Attribute: Returns true if this attribute used in output or input clause.
isActive() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Attribute: Returns true if this attribute used in output or input clause.
isActive(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule: It returns if a given condition is active or not
IsActive(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
IsActive() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t: Returns if the variable is considered in the learning process.
IsActive(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Returns if the variable in position "variable" is considered in the learning process.
IsActive(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
IsActive(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
isActive() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.ExampleWeight
isActive(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask: Returns wether a given entry is active or not
isActive() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Returns true if this attribute used in output or input clause.
isActive() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA_Attribute: Returns true if this attribute used in output or input clause.
isActive(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Returns wether a given entry is active or not
isActive() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyAttribute: Returns true if this attribute used in output or input clause.
isActive() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Attribute: Returns true if this attribute used in output or input clause.
isActive() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Attribute: Returns true if this attribute used in output or input clause.
isActive() - Method in class keel.Algorithms.Rule_Learning.ART.Attribute: Returns true if this attribute used in output or input clause.
isActive(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask: Returns wether a given entry is active or not
isActive() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyAttribute: Returns true if this attribute used in output or input clause.
isActive(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Returns wether a given entry is active or not
isActive() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyAttribute: Returns true if this attribute used in output or input clause.
isActive() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Attribute: Returns true if this attribute used in output or input clause.
isActive(int) - Method in class keel.Algorithms.Rule_Learning.PART.Mask: Returns wether a given entry is active or not
isActive() - Method in class keel.Algorithms.Rule_Learning.PART.MyAttribute: Returns true if this attribute used in output or input clause.
isActive(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Mask: Returns wether a given entry is active or not
isActive(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Mask: Returns wether a given entry is active or not
IsAntecedent(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
IsAntecedent() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t: Retuns if the variable is an antecedent of the rule.
IsAntecedent(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Retuns if the variable in position "variable" is an antecedent of the rule.
IsAntecedent(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
IsAntecedent(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
isAny() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.FuzzyAntecedent: Checks if the fuzzy antecedent represents an any condition.
isAny(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual: It checks is the condition for the variable in position "atributo" is equal to ANY
isAny() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Checks if the fuzzy antecedent represents an any condition.
isAnySelected() - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Checks if any of the data sets are selected in the list
isAnySelected() - Method in class keel.GraphInterKeel.experiments.SelectData: Test if any of the data sets in the list are selected by their correspondent check button
isAnySelectedC() - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Checks if any of the data sets are selected in the list
isAnySelectedC() - Method in class keel.GraphInterKeel.experiments.SelectData: Test if any of the data sets in the list are selected by their correspondent check button
isAnySelectedC_LQD() - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Checks if any of the data sets are selected in the list
isAnySelectedC_LQD() - Method in class keel.GraphInterKeel.experiments.SelectData: Test if any of the data sets in the list are selected by their correspondent check button
isAnySelectedLQD_C() - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Checks if any of the data sets are selected in the list
isAnySelectedLQD_C() - Method in class keel.GraphInterKeel.experiments.SelectData: Test if any of the data sets in the list are selected by their correspondent check button
isAttribute(int) - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
isAttribute(int) - Static method in class keel.Algorithms.RST_Learning.RSTData
isAveragable() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Returns whether the attribute can be averaged meaningfully.
isAveragable() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Returns whether the attribute can be averaged meaningfully.
isBefore(short[], short[]) - Static method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Checks whether one item set is lexicographically before a second item set.
isBergman() - Static method in class keel.GraphInterKeel.statistical.Configuration: Tests if Bergman test is used
isBetter(Particle) - Method in class keel.Algorithms.PSO_Learning.CPSO.Particle
isBetter(Particle) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
isBetter(Particle) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.Particle
isBetter(Particle) - Method in class keel.Algorithms.PSO_Learning.REPSO.Particle
isBetter(Chromosome) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
isBetter(Chromosome) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
isBiased() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Returns true if the layer has a bias neuron
isBiased() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron: Returns a boolean indicating if the layer has a bias neuron
isBonferroni() - Static method in class keel.GraphInterKeel.statistical.Configuration: Tests if Bonferroni test is used
isBroken() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.Link: Returns a boolean indicating if the link is or not broken
isCellEditable(int, int) - Method in class keel.GraphInterKeel.datacf.util.DatasetTable
isCellEditable(EventObject) - Method in class keel.GraphInterKeel.datacf.util.EachRowEditor: Is a cell editable?
isCellEditable(int, int) - Method in class keel.GraphInterKeel.datacf.util.VariableTable: Is a cell editable?
isCellEditable(int, int) - Method in class keel.GraphInterKeel.experiments.ParametersTable: Check if the cell is editable
isCellEditable(int, int) - Method in class keel.GraphInterKeel.statistical.statTableModel: Tets if a cell is editable
isCentroidItsNearestPrototoype(Prototype) - Method in class keel.Algorithms.Instance_Generation.VQ.Cluster: Informs if the centroid of the cluster is its nearest prototype.
isComplete() - Method in class keel.GraphInterKeel.experiments.DataSet: Test if the data set node has all the partitions (are available from disk)
isConsistent(Cluster, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.GMCA.GMCAGenerator: Checks if the given cluster is consistent with the modified prototypeset.
isConsistent(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.MCA.MCAGenerator: Informs if a modified prototype set is consistent (is as well as original or is better).
isConsistent(Complex, int[], int) - Method in class keel.Algorithms.Rule_Learning.Riona.Riona: Inidcates if a rule is consistent with a determined set of examples
isContinous() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleRule: Returns true if the attribute is continous
isContinous() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.SimpleRule: Returns true if the attribute is continous
isContinous() - Method in class keel.Algorithms.Rule_Learning.C45Rules.SimpleRule: Returns true if the attribute is continous
isContinous() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.SimpleRule: Returns true if the attribute is continous
isContinous() - Method in class keel.Algorithms.Rule_Learning.PART.SimpleRule: Returns true if the attribute is continous
isContinous() - Method in class keel.Algorithms.Rule_Learning.Ripper.SimpleRule: Returns true if the attribute is continous
isContinous() - Method in class keel.Algorithms.Rule_Learning.Slipper.SimpleRule: Returns true if the attribute is continous
isContinuous() - Method in class keel.Algorithms.Decision_Trees.C45.Attribute: Returns if the attribute is continuous or not.
isContinuous() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Attribute: Returns if the attribute is continuous or not.
isContinuous() - Method in class keel.Algorithms.Decision_Trees.ID3.Attribute: Returns if the attribute is continuous or not.
isContinuous() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Attribute: Returns if the attribute is continuous or not.
isContinuous() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Returns if the attribute is continuous or not.
isContinuous() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA_Attribute: Returns if the attribute is continuous or not.
isContinuous() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyAttribute: Returns if the attribute is continuous or not.
isContinuous() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Attribute: Returns if the attribute is continuous or not.
isContinuous() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Attribute: Returns if the attribute is continuous or not.
isContinuous() - Method in class keel.Algorithms.Rule_Learning.ART.Attribute: Returns if the attribute is continuous or not.
isContinuous() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyAttribute: Returns if the attribute is continuous or not.
isContinuous() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyAttribute: Returns if the attribute is continuous or not.
isContinuous() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Attribute: Returns if the attribute is continuous or not.
isContinuous() - Method in class keel.Algorithms.Rule_Learning.PART.MyAttribute: Returns if the attribute is continuous or not.
isContinuous() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.FPgrowth: Checks if the dataset has continuous variables
isContinuous() - Method in class keel.GraphInterKeel.experiments.UseCase
isCost_instance() - Method in class keel.GraphInterKeel.experiments.Parameters: Is cost instance
isCover(int, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene
isCover(int, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene
isCover(int, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene
isCovered(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual: It checks is the example in position "idEjemplo" is covered by the individual
isCovered(Sample) - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Check if the complex gets the given data
isCovered(boolean[][], int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.pnPair: It checks if only the literal lit covers the pnPair and the other do not
isCovered(int, int, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.DataB
isCovered(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Intervals
isCovered(int, int, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.DataB
isCovered(int, int, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.DiscreteDataset: It checks whether a value is covered by an interval for an attribute
isCovered(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Interval: It checks whether a value is covered by an interval
isCovered(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Intervals
isCovered(double[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
isCovered(double[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome: It adds a dataset records to the list of records being covered by a chromosome
isCovered(double[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
isCovered(double[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome: It adds a dataset records to the list of records being covered by a chromosome
isCovered(double[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
isCovered(double[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome: It adds a dataset records to the list of records being covered by a chromosome
isCoveringExamples(byte[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams
isCrisp() - Method in class keel.GraphInterKeel.experiments.Parameters: Is crisp
isDataNormalized() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.problem.IProblem: Returns a boolean value indicating if the DataSets are going to be normalized
isDataNormalized() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Returns a boolean value indicating if the DataSets are going to be normalized
isDate() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Tests if the attribute is a date type.
isDate() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Tests if the attribute is a date type.
isDiscret() - Method in class keel.Algorithms.Decision_Trees.C45.Attribute: Returns if the attribute is discret or not.
isDiscret() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Attribute: Returns if the attribute is discret or not.
isDiscret() - Method in class keel.Algorithms.Decision_Trees.ID3.Attribute: Returns if the attribute is discret or not.
isDiscret() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Attribute: Returns if the attribute is discret or not.
isDiscret() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Returns if the attribute is discret or not.
isDiscret() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleRule: Returns true if the attribute is discret
isDiscret() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA_Attribute: Returns if the attribute is discret or not.
isDiscret() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyAttribute: Returns if the attribute is discret or not.
isDiscret() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.SimpleRule: Returns true if the attribute is discret
isDiscret() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Attribute: Returns if the attribute is discret or not.
isDiscret() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Attribute: Returns if the attribute is discret or not.
isDiscret() - Method in class keel.Algorithms.Rule_Learning.ART.Attribute: Returns if the attribute is discret or not.
isDiscret() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyAttribute: Returns if the attribute is discret or not.
isDiscret() - Method in class keel.Algorithms.Rule_Learning.C45Rules.SimpleRule: Returns true if the attribute is discret
isDiscret() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyAttribute: Returns if the attribute is discret or not.
isDiscret() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.SimpleRule: Returns true if the attribute is discret
isDiscret() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Attribute: Returns if the attribute is discret or not.
isDiscret() - Method in class keel.Algorithms.Rule_Learning.PART.MyAttribute: Returns if the attribute is discret or not.
isDiscret() - Method in class keel.Algorithms.Rule_Learning.PART.SimpleRule: Returns true if the attribute is discret
isDiscret() - Method in class keel.Algorithms.Rule_Learning.Ripper.SimpleRule: Returns true if the attribute is discret
isDiscret() - Method in class keel.Algorithms.Rule_Learning.Slipper.SimpleRule: Returns true if the attribute is discret
IsDiscrete() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
IsDiscrete() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.fuzzy_t
IsDiscrete() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
IsDiscrete(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
IsDiscrete() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Returns if the domain_t object is formed by labels with all their domain been crisp.
IsDiscrete() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.fuzzy_t: Returns if the fuzzy label represents a crisp value
IsDiscrete() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t: Returns if the domain associated to the variable is only formed by crisp values.
IsDiscrete(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Returns if the domain associated to the variable in position "var" of the list is only formed by crisp values.
IsDiscrete() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
IsDiscrete() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.fuzzy_t
IsDiscrete() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
IsDiscrete(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
IsDiscrete() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
IsDiscrete() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.fuzzy_t
IsDiscrete() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
IsDiscrete(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
isDiscretized() - Method in class keel.GraphInterKeel.experiments.UseCase
isDontCareSymbol() - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Attribute: If it's a ternary representation it returns 1 if is # and 0 otherwise.
isDontCareSymbol() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep: Returns if the real representation is a don't care symbol.
isDontCareSymbol() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep: Returns if the character of the representation is a don't care symbol
isDontCareSymbol() - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute: If it's a ternary representation it returns 1 if is # and 0 otherwise.
isDontCareSymbol() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: Return if the integer of the representation.
isDontCareSymbol() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: Returns if the real representation is a don't care symbol.
isDontCareSymbol() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep: Returns if the character of the representation is a don't care symbol
isEmpty - Variable in class keel.Algorithms.Decision_Trees.C45.Tree: Is this node empty or not.
isEmpty - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Tree: Is this node empty or not.
isEmpty - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree: Is this node empty or not.
isEmpty - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Tree: Is this node empty or not.
isEmpty - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree: Is this node empty or not.
isEmpty() - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster: See if the cluster is empty
isEmpty - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Tree: Is this node empty or not.
isEmpty - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree: Is this node empty or not.
isEmpty - Variable in class keel.Algorithms.Rule_Learning.PART.Tree: Is this node empty or not.
isEmpty() - Method in class keel.Algorithms.Rule_Learning.SRI.Instances: Checks if the dataset is empty.
isEmpty - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: Is this node empty or not.
isEmpty() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Checks if it is an empty vector
isEmpty() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector: Returns true if the vector is empty
isEnumerate() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Returns true if this attribute has been synthetizied from an enumerate one.
isEqual(Item) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Item: Function to check if an item is equal to another given.
isEqual(Itemset) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset: Function to check if an itemset is equal to another given.
isEqual(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: Function to check if a rule is equal to another given.
isEqual(Item) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Item: Function to check if an item is equal to another given
isEqual(Itemset) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset: Function to check if an itemset is equal to another given
isEqual(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: Function to check if a rule is equal to another given
isEqual(short[], short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Checks whether two item sets are the same.
isEqual(Item) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Item: Function to check if an item is equal to another given
isEqual(Itemset) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Itemset: Function to check if an itemset is equal to another given
isEqual(Item) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Item: Function to check if an item is equal to another given
isEqual(Itemset) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Itemset: Function to check if an itemset is equal to another given
isEqual(Item) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Item: Function to check if an item is equal to another given
isEqual(Itemset) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Itemset: Function to check if an itemset is equal to another given
isEqual(Item) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Item
isEqual(Itemset) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Itemset
isEqual(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule: Return wether this rule is equal to another given rule
isEqual(SimpleRule) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleRule: Return wether this simple rule is equal to another given simple rule
isEqual(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule: Return wether this rule is equal to another given rule
isEqual(SimpleRule) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.SimpleRule: Return wether this simple rule is equal to another given simple rule
isEqual(Condition) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Condition: It checks if the condition is equal to other
isEqual(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Rule: It checks if the rule is equal to another
isEqual(Complex) - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Check is two complex are equals
isEqual(Cromosoma) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.Cromosoma: this boolean method return true if two chromosomes are equal in all of its genes
isEqual(Rule) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Rule: Return wether this rule is equal to another given rule
isEqual(SimpleRule) - Method in class keel.Algorithms.Rule_Learning.C45Rules.SimpleRule: Return wether this simple rule is equal to another given simple rule
isEqual(Rule) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule: Return wether this rule is equal to another given rule
isEqual(SimpleRule) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.SimpleRule: Return wether this simple rule is equal to another given simple rule
isEqual(Rule) - Method in class keel.Algorithms.Rule_Learning.PART.Rule: Return wether this rule is equal to another given rule
isEqual(SimpleRule) - Method in class keel.Algorithms.Rule_Learning.PART.SimpleRule: Return wether this simple rule is equal to another given simple rule
isEqual(Complex) - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Check if two complex are equals (represent the same)
isEqual(Rule) - Method in class keel.Algorithms.Rule_Learning.Ripper.Rule: Return wether this rule is equal to another given rule
isEqual(SimpleRule) - Method in class keel.Algorithms.Rule_Learning.Ripper.SimpleRule: Return wether this simple rule is equal to another given simple rule
isEqual(Rule) - Method in class keel.Algorithms.Rule_Learning.Slipper.Rule: Return wether this rule is equal to another given rule
isEqual(SimpleRule) - Method in class keel.Algorithms.Rule_Learning.Slipper.SimpleRule: Return wether this simple rule is equal to another given simple rule
isEqual(Complex) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Check if two complex are equal
isEqual(Chromosome) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
isEqualAnt(Itemset) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset: Function to check if the antecedent of our itemset is equal to another given.
isEqualAnt(Itemset) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset: Function to check if the antecedent of our itemset is equal to another given
isEqualValue(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
isEvaluated() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome: Gets the evaluation status
isEvaluated() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Cochromosome: Gets the evaluation condition
isEvaluated - Variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
isEvaluated() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.RuleSet: Test if the rule set is currently evaluated
isEvaluated - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
isEvaluated() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.RuleSet: Test if the rule set is currently evaluated
isEvaluated() - Method in class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome: Checks if the chromosome has been evaluated.
isEvaluated() - Method in class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome: Checks if the chromosome has been evaluated.
isFinished() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: This method return the state of the experiment.
isFinner() - Static method in class keel.GraphInterKeel.statistical.Configuration: Tests if Finner test is used
isFullRank() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.QRDecomposition: Is the matrix full rank?
IsFuzzy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
IsFuzzy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.fuzzy_t
IsFuzzy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
IsFuzzy(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
IsFuzzy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Returns if the domain_t object is formed by labels with all their domain been fuzzy.
IsFuzzy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.fuzzy_t: Returns if the fuzzy label represents a fuzzy set
IsFuzzy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t: Returns if the domain associated to the variable is only formed by fuzzy sets.
IsFuzzy(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Returns if the domain associated to the variable in position "var" of the list is only formed by fuzzy sets.
IsFuzzy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
IsFuzzy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.fuzzy_t
IsFuzzy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
IsFuzzy(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
IsFuzzy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
IsFuzzy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.fuzzy_t
IsFuzzy() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
IsFuzzy(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
isFuzzy() - Method in class keel.GraphInterKeel.experiments.Parameters: Is fuzzy
isHidden(int) - Method in class keel.GraphInterKeel.experiments.Parameters: Checks if the parameter at position i is hidden
isHiddenLayerBiased(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Returns a boolean indicating if a hidden layer is biased
isHiddenLayerBiased(int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Returns a boolean indicating if a hidden layer is biased
isHighConfidence(Prototype, int) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ADE_CoForestGenerator: To judege whether the confidence for a given instance of H* is high enough, which is affected by the onfidence threshold.
isHighConfidence(Prototype, int) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCGenerator: To judege whether the confidence for a given instance of H* is high enough, which is affected by the onfidence threshold.
isHighConfidence(Prototype, int) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.CoForestGenerator: To judege whether the confidence for a given instance of H* is high enough, which is affected by the onfidence threshold.
isHochberg() - Static method in class keel.GraphInterKeel.statistical.Configuration: Tests if Hochberg test is used
isHolland() - Static method in class keel.GraphInterKeel.statistical.Configuration: Tests if Holland test is used
isHolm() - Static method in class keel.GraphInterKeel.statistical.Configuration: Tests if Holm test is used
isHommel() - Static method in class keel.GraphInterKeel.statistical.Configuration: Tests if Hommel test is used
isIman() - Static method in class keel.GraphInterKeel.statistical.Configuration: Tests if Iman-Davenport test is used
isIn(String, String[]) - Static method in class keel.Algorithms.Instance_Generation.Basic.AccuracyMeter: Informs if a string is equal to someone in an array
isIn(String, String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.AccuracyMeter: Informs if a string is equal to someone in an array
isInBounds(double) - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It does check if the value passed as an argument is bounded by the [min, max] interval.
isInBounds(double) - Method in class keel.Dataset.Attribute: It does check if the value passed as an argument is bounded by the [min, max] interval.
isInput() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute: Answer if the attribute is an input attribute or not
isInput() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute: Answer if the attribute is an input attribute or not
isInRange(int) - Method in class keel.Algorithms.Decision_Trees.M5.Interval: Gets whether the supplied cardinal number is included in the current range.
isInRange(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Determines whether a value lies within the bounds of the attribute.
isInRange(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Range: Gets whether the supplied cardinal number is included in the current range.
isInRange(double) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Determines whether a value lies within the bounds of the attribute.
isInside(Point, Point, Point) - Method in class keel.GraphInterKeel.experiments.Arc: Test if the provided point is inside
isInside(Point) - Method in class keel.GraphInterKeel.experiments.Node: Test if the provided point is inside of this node
isInteger(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: This function checks if the attribute value is integer
isInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: This function checks if the attribute value is integer
isInteger(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: This function checks if the attribute value is integer
isInteger(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: Checks if the type of the attribute with the given id is integer.
isInteger() - Method in class keel.GraphInterKeel.experiments.UseCase
isInterrumpted() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: This methos return if a partition is interrupted for the extern user True, interrupted False, no interrupted
IsInterval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
IsInterval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.fuzzy_t
IsInterval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
IsInterval(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
IsInterval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Returns if the domain_t object is formed by labels with all their domain been intervals.
IsInterval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.fuzzy_t: Returns if the fuzzy label represents an interval
IsInterval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t: Returns if the domain associated to the variable is only formed by intervals.
IsInterval(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Returns if the domain associated to the variable in position "var" of the list is only formed by intervals.
IsInterval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
IsInterval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.fuzzy_t
IsInterval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
IsInterval(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
IsInterval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
IsInterval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.fuzzy_t
IsInterval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
IsInterval(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
isLeaf - Variable in class keel.Algorithms.Decision_Trees.C45.Tree: Is this node leaf or not.
isLeaf - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Tree: Is this node leaf or not.
isLeaf() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Answers if the node is a leaf node or not
isLeaf() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode: Answers if the node is a leaf node or not
isLeaf() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode
isLeaf - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree: Is this node leaf or not.
isLeaf - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Tree: Is this node leaf or not.
isLeaf - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree: Is this node leaf or not.
isLeaf - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Tree: Is this node leaf or not.
isLeaf - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree: Is this node leaf or not.
isLeaf - Variable in class keel.Algorithms.Rule_Learning.PART.Tree: Is this node leaf or not.
isLeaf - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: Is this node leaf or not.
isLi() - Static method in class keel.GraphInterKeel.statistical.Configuration: Tests if Li test is used
isLogTransformation() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.problem.IProblem: Returns a boolean value indicating if the DataSets are going to be log transformated
isLogTransformation() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Returns a boolean value indicating if the DataSets are going to be log transformated
isMark() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: It returns if the rule is marked.
isMark() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: It returns if the rule is marked
isMissing(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: This function checks if the attribute value is missing.
isMissing(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: This function checks if the attribute value is missing
isMissing(int) - Method in class keel.Algorithms.Decision_Trees.C45.Itemset: Function to check if a value is missing.
isMissing(int, int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: This function checks if the attribute value is missing
isMissing(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset: Function to check if a value is missing.
isMissing(int, int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: This function checks if the attribute value is missing
isMissing(int) - Method in class keel.Algorithms.Decision_Trees.ID3.Itemset: Function to check if a value is missing.
isMissing(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Tests if a specific value is "missing".
isMissing(M5Attribute) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Tests if a specific value is "missing".
isMissing(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5SparseInstance: Tests if a specific value is "missing".
isMissing(int) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Itemset: Function to check if a value is missing.
isMissing(int, int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: This function checks if the attribute value is missing
isMissing(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Tests if a specific value is "missing".
isMissing(AttributeWeka) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Tests if a specific value is "missing".
isMissing(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: This function checks if the attribute value is missing
isMissing(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Function to check if a value is missing.
isMissing(MyAttribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Tests if a specific value is "missing".
isMissing(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: It returns wether the value for an attribute in a given exemple is missing
isMissing(Mask, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: It returns wether the value for an attribute in a given exemple is missing
isMissing(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: This function checks if the attribute value is missing
isMissing(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset: Function to check if a value is missing.
isMissing(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset: Function to check if a value is missing.
isMissing(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: It returns wether the value for an attribute in a given exemple is missing
isMissing(Mask, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: It returns wether the value for an attribute in a given exemple is missing
isMissing(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: Comprueba si un atributo está "perdido" o no
isMissing(int, int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Checks if one attribute is lost or not
isMissing(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset: Function to check if a value is missing.
isMissing(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset: Function to check if a value is missing.
isMissing(int, int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: This function checks if the attribute value is missing
isMissing(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Tests if a specific value is "missing".
isMissing(Attribute) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Tests if a specific value is "missing".
isMissing(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance: Tests if a specific value is "missing".
isMissing(int, int) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Returns True if the value of j-th attribute of the i-th instance is missing and false, otherwise.
isMissing(int, int) - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Returns True if the value of j-th attribute of the i-th instance is missing and false, otherwise.
isMissing(int, int) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Returns True if the value of j-th attribute of the i-th instance is missing and false, otherwise.
isMissing(int, int) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Returns True if the value of j-th attribute of the i-th instance is missing and false, otherwise.
isMissing(int, int) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Returns True if the value of j-th attribute of the i-th instance is missing and false, otherwise.
isMissing(int, int) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Returns True if the value of j-th attribute of the i-th instance is missing and false, otherwise.
isMissing(int, int) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Returns True if the value of j-th attribute of the i-th instance is missing and false, otherwise.
isMissing(int, int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: Returns if an example is missing.
isMissing(int, int) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Rule_Learning.AQ.Dataset: Comprueba si un atributo está "perdido" o no
isMissing(int) - Method in class keel.Algorithms.Rule_Learning.ART.Itemset: Function to check if a value is missing.
isMissing(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Itemset: Function to check if a value is missing.
isMissing(int, int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: It returns wether the value for an attribute in a given exemple is missing
isMissing(Mask, int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: It returns wether the value for an attribute in a given exemple is missing
isMissing(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset: Function to check if a value is missing.
isMissing(int, int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: It returns wether the value for an attribute in a given exemple is missing
isMissing(Mask, int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: It returns wether the value for an attribute in a given exemple is missing
isMissing(int, int) - Method in class keel.Algorithms.Rule_Learning.CN2.Dataset: Comprueba si un atributo está "perdido" o no
isMissing(int) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset: Function to check if a value is missing.
isMissing(int, int) - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: This function checks if the attribute value is missing
isMissing(int) - Method in class keel.Algorithms.Rule_Learning.PART.Itemset: Function to check if a value is missing.
isMissing(int, int) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: It returns wether the value for an attribute in a given exemple is missing
isMissing(Mask, int) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: It returns wether the value for an attribute in a given exemple is missing
isMissing(int, int) - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Checks if one attribute is lost or not
isMissing(int, int) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Checks if an attribute is lost or not
isMissing(int, int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: This function checks if the attribute value is missing
isMissing(Mask, int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: This function checks if the attribute value is missing
isMissing(Mask, int) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Checks if one attribute is missing or not
isMissing(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset: Function to check if a value is missing.
isMissing(int, int) - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Returns if an example is missing.
isMissing(int, int) - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset: Comprueba si un atributo esta "perdido" o no
isMissing(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: This function checks if the attribute value is missing
isMissing(int) - Method in class keel.Algorithms.SVM.SMO.core.Instance: Tests if a specific value is "missing".
isMissing(Attribute) - Method in class keel.Algorithms.SVM.SMO.core.Instance: Tests if a specific value is "missing".
isMissing(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: This function checks if the attribute value is missing
isMissing(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: This function checks if the attribute value is missing
isMissingSparse(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Tests if a specific value is "missing".
isMissingSparse(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Tests if a specific value is "missing".
isMissingSparse(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Tests if a specific value is "missing".
isMissingSparse(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Tests if a specific value is "missing".
isMissingSparse(int) - Method in class keel.Algorithms.SVM.SMO.core.Instance: Tests if a specific value is "missing".
isMissingValue(double) - Static method in class keel.Algorithms.Decision_Trees.C45.Itemset: Function to check if the value given is the missing value.
isMissingValue(double) - Static method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset: Function to check if the value given is the missing value.
isMissingValue(double) - Static method in class keel.Algorithms.Decision_Trees.ID3.Itemset: Function to check if the value given is the missing value.
isMissingValue(double) - Static method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Tests if the given value codes "missing".
isMissingValue(double) - Static method in class keel.Algorithms.Decision_Trees.SLIQ.Itemset: Function to check if the value given is the missing value.
isMissingValue(double) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Tests if the given value codes "missing".
isMissingValue(double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Function to check if the value given is the missing value.
isMissingValue(double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset: Function to check if the value given is the missing value.
isMissingValue(double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset: Function to check if the value given is the missing value.
isMissingValue(double) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset: Function to check if the value given is the missing value.
isMissingValue(double) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset: Function to check if the value given is the missing value.
isMissingValue(double) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Tests if the given value codes "missing".
isMissingValue(double) - Static method in class keel.Algorithms.Rule_Learning.ART.Itemset: Function to check if the value given is the missing value.
isMissingValue(double) - Static method in class keel.Algorithms.Rule_Learning.C45Rules.Itemset: Function to check if the value given is the missing value.
isMissingValue(double) - Static method in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset: Function to check if the value given is the missing value.
isMissingValue(double) - Static method in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset: Function to check if the value given is the missing value.
isMissingValue(double) - Static method in class keel.Algorithms.Rule_Learning.PART.Itemset: Function to check if the value given is the missing value.
isMissingValue(double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Tests if the given value codes "missing".
isMissingValue(double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset: Function to check if the value given is the missing value.
isMissingValue(double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Tests if the given value codes "missing".
isMissingValue(double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Tests if the given value codes "missing".
isMissingValue(double) - Static method in class keel.Algorithms.SVM.SMO.core.Instance: Tests if the given value codes "missing".
isModified() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
isMoreGeneral(Attribute) - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Attribute: Returns if the current interval is more general than the interval given as a parameter
isMoreGeneral(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep: Returns if the current interval is more general than the interval given as a parameter
isMoreGeneral(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep: Indicates if the classifier is more general than the classifier passed as a parameter.
isMoreGeneral(Attribute) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute: Returns if the current interval is more general than the interval given as a parameter
isMoreGeneral(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Indicates if the classifier is more general than the classifier passed as a parameter.
isMoreGeneral(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: Returns if the current interval is more general than the interval given as a parameter
isMoreGeneral(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: Returns if the current interval is more general than the interval given as a parameter
isMoreGeneral(Representation) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: Indicate if the current representation is more general than the representation passed as a parameter.
isMoreGeneral(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep: Indicates if the classifier is more general than the classifier passed as a parameter.
isNemenyi() - Static method in class keel.GraphInterKeel.statistical.Configuration: Tests if Nemenyi test is used
isNew() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual: Function to return if this individual is new in the population
isNew() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Individual: Function to return if this individual is new in the population
isNew() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule: It returns if the rule is new or not
isNew() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual
isNew() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
isNew() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
isNew() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
isNominal(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: This function checks if the attribute value is nominal.
isNominal(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: This function checks if the attribute value is nominal
isNominal(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: This function checks if the attribute value is nominal
isNominal(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: This function checks if the attribute value is nominal
isNominal(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: This function checks if the attribute value is nominal
isNominal(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: This function checks if the attribute value is nominal
isNominal(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: This function checks if the attribute value is nominal
isNominal() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute: Answers if the attribute is nominal or not
isNominal() - Method in class keel.Algorithms.Decision_Trees.M5.M5Attribute: Test if the attribute is nominal.
isNominal() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute: Answers if the attribute is nominal or not
isNominal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: This function checks if the attribute value is nominal
isNominal() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Test if the attribute is nominal.
isNominal() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Test if the attribute is nominal.
isNominal() - Method in class keel.Algorithms.Rule_Learning.Riona.Selector: Returns nominal attribute
isNominal(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: Checks if the attribute with the given id is nominal.
isNominal(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: This function checks if the attribute value is nominal
isNominal(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: Checks if the given attribute is nominal.
isNominal(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: Checks if the given attribute is nominal.
isNominal(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: Checks if the attribute with the index given is nominal
isNominal(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: This function checks if the attribute value is missing
isNominal(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: This function checks if the attribute is nominal
isNominal(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: Checks if the type of the attribute with the given id is nominal.
isNominal(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: This function checks if the attribute is nominal
isNominal() - Method in class keel.GraphInterKeel.experiments.UseCase
isNominalValue(String) - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It returns if the value passed is in the list of nominal values
isNominalValue(String) - Method in class keel.Dataset.Attribute: It returns if the value passed is in the list of nominal values
isNonsingular() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.LUDecomposition: Is the matrix nonsingular?
isNumeric() - Method in class keel.Algorithms.Decision_Trees.M5.M5Attribute: Tests if the attribute is numeric.
isNumeric() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Tests if the attribute is numeric.
ISoftmaxClassifier - Interface in keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax: Generic softmax classifier.
isOk() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SD: Checks if the algorithm is working fine.
isOk() - Method in class keel.GraphInterKeel.datacf.util.OptionsDialog: Returns if Ok button has been pressed
isolatedNodes() - Method in class keel.GraphInterKeel.experiments.Experiments: checks if are nodes not connected to a dataset
isolatedNodesLQD() - Method in class keel.GraphInterKeel.experiments.Experiments: checks if are nodes LQD not connected to a dataset
isOrderedFlag - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Flag to indicate whether input data has been sorted or not.
isOrderedFlag - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Flag to indicate whether input data has been sorted or not.
isOutputFormatDefined() - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Returns whether the output format is ready to be collected
isOutputInfered() - Method in class keel.Dataset.InstanceSet: Test if the output attribute has been infered.
isOutputLayerBiased() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Returns a boolean indicating if output layer is biased
isOutputLayerBiased() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.INeuralNetSpecies: Returns a boolean indicating if output layer is biased
isPartitionable(int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Decides if a node is partitionable or not.
isPerformed() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.DECEnvironment: Returns if the class has been performed.
isPerformed() - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Environment: Returns if the class has been performed.
isPerformed() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MPEnvironment: Returns if the action has been performed.
isPerformed() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MSEnvironment: Returns if the class has been performed.
isPerformed() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.PAREnvironment: Returns if the class has been performed.
isPerformed() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.POSEnvironment: Returns if the class has been performed.
isPerformed() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.SSFileEnvironment: Does return if the class has been executed.
isPositiveInstance() - Method in class keel.Algorithms.ImbalancedClassification.Auxiliar.AUC.PosProb: Checks if a given probability related to an instance is positive or not
isPrecedence(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: Function to know whether our rule has more precedence than another given or not.
isPrecedence(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: Function to know whether our rule has more precedence than another given or not.
isProbabilistic() - Method in class keel.GraphInterKeel.experiments.Parameters: returns if algorithm use seeds
isPrototypeConsistent(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.GMCA.GMCAGenerator: Hard-checking consistency method.
isPrunedFlag - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Flag to indicate whether input data has been sorted and pruned or not.
isPrunedFlag - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Flag to indicate whether input data has been sorted and pruned or not.
isPure() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Node: Check is a node is pure or a node isn't pure
isReal() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Tests if the attribute is numeric.
isReal(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: Checks if the type of the attribute with the given id is real.
isRegression() - Method in class keel.Algorithms.Decision_Trees.CART.CART: Returns if we are dealing with a regression problem
isRegular() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Returns whether the attribute values are equally spaced.
isRegular() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Returns whether the attribute values are equally spaced.
isRelated(Genotype) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.Genotype: abstract method for determining if the given Genotype is similar to current one.
isRelated(Genotype) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGAP: This method determines if the given Genotype is of the same type thant the current object.
isRelated(Genotype) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGP: This method determines if the given Genotype is of the same type thant the current object.
isRelated(Genotype) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGPRegSym: This method determines if the given Genotype is of the same type thant the current object.
isRelated(Genotype) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypePitts: This method determines if the given Genotype is of the same type thant the current object.
isRelationValued() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Tests if the attribute is relation valued.
isRelationValued() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Tests if the attribute is relation valued.
isRom() - Static method in class keel.GraphInterKeel.statistical.Configuration: Tests if Rom test is used
isSeized(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Checks if a determined value of the training set is seized by this rule or not
isSeized(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Checks if a determined value of the training set is seized by this rule or not
isSelective() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Returns a boolean indicating if the mutator is selective
isSerializable(String) - Static method in class keel.Algorithms.SVM.SMO.core.SerializationHelper: checks whether a class is serializable
isSerializable(Class) - Static method in class keel.Algorithms.SVM.SMO.core.SerializationHelper: checks whether a class is serializable
isShaffer() - Static method in class keel.GraphInterKeel.statistical.Configuration: Tests if Shaffer test is used
isSPD() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.CholeskyDecomposition: Is the matrix symmetric and positive definite?
isSquare() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: returns whether the matrix is a square matrix or not.
isString() - Method in class keel.Algorithms.Decision_Trees.M5.M5Attribute: Tests if the attribute is a string.
isString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Tests if the attribute is a string.
isString() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Tests if the attribute is a string.
isSub(Chromosome) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
isSub(Chromosome) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
isSubChromo(Chromosome) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome
isSubChromo(Chromosome) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
isSubChromo(Chromosome) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
isSubclass(String, String) - Static method in class keel.Algorithms.SVM.SMO.core.ClassDiscovery: Checks whether the "otherclass" is a subclass of the given "superclass".
isSubclass(Class, Class) - Static method in class keel.Algorithms.SVM.SMO.core.ClassDiscovery: Checks whether the "otherclass" is a subclass of the given "superclass".
isSubGen(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene
isSubGen(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene
isSubGen(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene
isSubItemset(Itemset) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset: Function to check if our itemset is Subitemset (can be contained) of a given itemset.
isSubItemset(Itemset) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset: Function to check if our itemset is Subitemset (can be contained) of a given itemset
isSubset(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: Function to check whether our rule is subset of a given rule "a".
isSubset(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: Function to check whether our rule is subset of a given rule "a"
isSubset(short[], short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Checks whether one item set is subset of a second item set.
isSubset(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule: Function to check if a given rule is a subrule of this rule
isSubset(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Rule: Function to check if a given rule is a subrule of this rule
isSubset(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Rule: Function to check if a given rule is a subrule of this rule
isSubset(Rule) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule
isSubValue(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
isSubValue(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
isSymmetric() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Returns true if the matrix is symmetric.
isSymmetric() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Returns true if the matrix is symmetric.
isTerminal() - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode
isTheNearPrototype(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Return all the prototypes in (this) that has "other" as the nearest neighbour
isTheNearPrototype(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Return all the prototype in (this) that has other like the nearest neighbor
isTheNearPrototypeWithClass(Prototype, double) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Return all the prototypes in (this) that has "other" as the nearest neighbour with the class given.
isTheNearPrototypeWithClass(Prototype, double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Return all the prototype in (this) that has other like the nearest neighbor with the class given.
isThereClassifier(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Returns the position of the classifier in the set.
isThereClassifier(Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Returns the position of the classifier in the set.
isUnexplored - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree: Is this node unexplored or not.
isUnexplored() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree: Returns whether the node is unexplore or not.
isUnexplored - Variable in class keel.Algorithms.Rule_Learning.PART.Tree: Is this node unexplored or not.
isUnexplored() - Method in class keel.Algorithms.Rule_Learning.PART.Tree: Returns whether the node is unexplore or not.
isUsed(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
isUsed(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
isUsed(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
isUsed(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
isUseVariant1() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: Whether variant 1 is used
isValid() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.FuzzyAntecedent: Checks if the fuzzy antecedent is a valid and representative antecedent for a rule or not.
isValid() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Checks if the fuzzy antecedent is a valid and representative antecedent for a rule or not.
isValid(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractAttribute: Check if this internal attribute value is valid
isValid(Object) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractAttribute: Check if this external attribute value is valid
isValid(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.CategoricalAttribute: Check if this internal attribute value is valid
isValid(Object) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.CategoricalAttribute: Check if this external attribute value is valid
isValid(double) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IAttribute: Check if this internal attribute value is valid
isValid(Object) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IAttribute: Check if this external attribute value is valid
isValid(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.IntegerNumericalAttribute: Check if this internal attribute value is valid
isValid(Object) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.IntegerNumericalAttribute: Check if this external attribute value is valid
isValid(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.RealNumericalAttribute: Check if this internal attribute value is valid
isValid(Object) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.RealNumericalAttribute: Check if this external attribute value is valid
isValid(String) - Method in class keel.GraphInterKeel.datacf.util.Attribute: Return a boolean for a given nominal values, true is valid value, false is an invalid value.
isValidRange(String) - Method in class keel.Algorithms.Decision_Trees.M5.Interval: Determines if a string represents a valid index or simple range.
isValidRange(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Range: Determines if a string represents a valid index or simple range.
ISW - Class in keel.Algorithms.RST_Learning.EFS_RPS: File: ISW.java A implementation of a rough set based Instance Selection Wrapper class for EFS_RPS.
ISW() - Constructor for class keel.Algorithms.RST_Learning.EFS_RPS.ISW
isWhitoutValues() - Method in class keel.GraphInterKeel.experiments.UseCase
isWindowingEnabled() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.instanceSet
isWindowsPlatform() - Static method in class keel.GraphInterKeel.menu.BrowserControl: Try to determine whether this application is running under Windows or some other platform by examing the "os.name" property.
isWithImprecise() - Method in class keel.GraphInterKeel.experiments.UseCase
isYes(String) - Static method in class keel.Algorithms.Instance_Generation.Basic.AccuracyMeter: Informs if a string is yes.
isYes(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.AccuracyMeter: Informs if a string is yes.
iszero(Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.AlgGenetic
iszero(Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.AlgGenetic
IT2FKNN - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.IT2FKNN: File: IT2FKNN.java The IT2FKNN algorithm.
IT2FKNN(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.IT2FKNN.IT2FKNN: Main builder.
Item - Class in keel.Algorithms.Associative_Classification.ClassifierCBA: This class stores an item representation for classification by association algorithms.
Item() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.Item: Default Constructor.
Item(int, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.Item: Parameters Constructor.
Item - Class in keel.Algorithms.Associative_Classification.ClassifierCBA2: This class stores an item representation for classification by association algorithms.
Item() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.Item: Default Constructor
Item(int, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.Item: Parameters Constructor.
Item - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR: This class contains the representation of a item
Item() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Item: Default Constructor
Item(int, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Item: Parameters Constructor
Item - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD: Title: Item Description: This class contains the representation of a item Copyright: Copyright KEEL (c) 2007 Company: KEEL
Item() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Item: Default constructor.
Item(int, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Item: Builder
Item - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA: This class contains the representation of a item
Item() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Item: Default Constructor
Item(int, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Item: Parameters Constructor
Item - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS: Title: Item Description: This class contains the representation of a item Copyright: Copyright KEEL (c) 2007 Company: KEEL
Item() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Item: Default constructor.
Item(int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Item: Builder
Item - Class in keel.Algorithms.Subgroup_Discovery.aprioriSD: Title: Item Implements an item of a dataset, with their values and their attributes/columns references.
Item() - Constructor for class keel.Algorithms.Subgroup_Discovery.aprioriSD.Item: Default constructor.
Item(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.aprioriSD.Item: Creates an item with k values but empty.
Item(int, int, int) - Constructor for class keel.Algorithms.Subgroup_Discovery.aprioriSD.Item: Creates an item with one value.
Item - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal: It represents an item throughout the execution of the algorithm
Item() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Item: Default constructor
Item(int, int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Item: It creates a new item by setting up its parameters
Item - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams
Item() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Item: Default constructor
Item(int, int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Item: It creates a new item by setting up its parameters
Item - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori
Item() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Item: Default constructor
Item(int, int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Item: It creates a new item by setting up its parameters
Item - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori
Item() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Item: Default constructor
Item(int, int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Item: It creates a new item by setting up its parameters
Item - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC
Item() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Item: Default constructor
Item(int, int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Item: It creates a new item by setting up its parameters
Item - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori: It represents an item throughout the execution of the algorithm
Item(int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.Item: It creates a new item by setting up its label
Item - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat: It represents an item throughout the execution of the algorithm
Item(int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.Item: It creates a new item by setting up its label
itemName - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.FPtree.FPgrowthHeaderTable: The 1-itemset (attribute) identifier.
itemName - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.FPtree.FPgrowthHeaderTable: The 1-itemset (attribute) identifier.
Itemset - Class in keel.Algorithms.Associative_Classification.ClassifierCBA: This class stores an itemset representation for classification by association algorithms.
Itemset() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset: Default Constructor.
Itemset(int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset: Parameters Constructor.
Itemset - Class in keel.Algorithms.Associative_Classification.ClassifierCBA2: This class stores an itemset representation for classification by association algorithms.
Itemset() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset: Default Constructor
Itemset(int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset: Parameters Constructor
itemSet - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PtreeNode: Array of short (16 bit) integers describing the row.
Itemset - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR: This class contains the representation of a itemset
Itemset() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Itemset: Default Constructor
Itemset(int, double) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Itemset: Parameters Constructor
Itemset - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD: Title: Itemset Description: This class contains the representation of a itemset Copyright: Copyright KEEL (c) 2007 Company: KEEL
Itemset() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Itemset: Default constructor.
Itemset(int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Itemset: Builder
Itemset - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA: This class contains the representation of a itemset
Itemset() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Itemset: Default Constructor
Itemset(int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Itemset: Parameters Constructor
itemset(int) - Method in class keel.Algorithms.Decision_Trees.C45.Dataset: Returns the itemset at the given position.
Itemset - Class in keel.Algorithms.Decision_Trees.C45: Class to manipulate an itemset.
Itemset(Itemset) - Constructor for class keel.Algorithms.Decision_Trees.C45.Itemset: Constructor that copies the values and the weight.
Itemset(double, double[]) - Constructor for class keel.Algorithms.Decision_Trees.C45.Itemset: Constructor that sets the values and the weight.
itemset(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Returns the itemset at the given position.
Itemset - Class in keel.Algorithms.Decision_Trees.DT_GA.C45: Class to manipulate an itemset.
Itemset(Itemset) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset: Constructor that copies the values and the weight.
Itemset(double, double[]) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset: Constructor that sets the values and the weight.
itemset(int) - Method in class keel.Algorithms.Decision_Trees.ID3.Dataset: Returns the itemset at the given position.
Itemset - Class in keel.Algorithms.Decision_Trees.ID3: Class to manipulate an itemset.
Itemset(Itemset) - Constructor for class keel.Algorithms.Decision_Trees.ID3.Itemset: Constructor that copies the values and the weight.
Itemset(double, double[]) - Constructor for class keel.Algorithms.Decision_Trees.ID3.Itemset: Constructor that sets the values and the weight.
itemset(int) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Returns the itemset at the given position.
Itemset - Class in keel.Algorithms.Decision_Trees.SLIQ: Class to manipulate an itemset.
Itemset(Itemset) - Constructor for class keel.Algorithms.Decision_Trees.SLIQ.Itemset: Constructor that copies the values and the weight.
Itemset(double, double[]) - Constructor for class keel.Algorithms.Decision_Trees.SLIQ.Itemset: Constructor that sets the values and the weight.
Itemset - Class in keel.Algorithms.Discretizers.UCPD: This class lets to manipulate itemsets
Itemset(int[], int, int) - Constructor for class keel.Algorithms.Discretizers.UCPD.Itemset: Constructor for creating a Itemset object
Itemset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS: Title: Itemset Description: This class contains the representation of a itemset Copyright: Copyright KEEL (c) 2007 Company: KEEL
Itemset() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Itemset: Default constructor.
Itemset(int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Itemset: Builder
Itemset - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules: Class to manipulate an itemset.
Itemset(Itemset) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Constructor that copies the values and the weight.
Itemset(double, double[]) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Constructor that sets the values and the weight.
Itemset(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.
Itemset() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Default Constructor.
itemset(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns the itemset at the given position.
itemset(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Returns the itemset at the given position.
Itemset - Class in keel.Algorithms.Genetic_Rule_Learning.olexGA: Class to manipulate an itemset.
Itemset(Itemset) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset: Constructor that copies the values and the weight.
Itemset(double, double[]) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset: Constructor that sets the values and the weight.
Itemset - Class in keel.Algorithms.Genetic_Rule_Learning.PART: Class to manipulate an itemset.
Itemset(Itemset) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset: Constructor that copies the values and the weight.
Itemset(double, double[]) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset: Constructor that sets the values and the weight.
itemset(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Returns the itemset at the given position.
itemset(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Returns the itemset at the given position.
Itemset - Class in keel.Algorithms.ImbalancedClassification.CSMethods.C45CS: Class to manipulate an itemset.
Itemset(Itemset) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset: Constructor that copies the values and the weight.
Itemset(double, double[]) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset: Constructor that sets the values and the weight.
itemset(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Returns the itemset at the given position.
Itemset - Class in keel.Algorithms.ImbalancedClassification.Ensembles.C45: Class to manipulate an itemset.
Itemset(Itemset) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset: Constructor that copies the values and the weight.
Itemset(double, double[]) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset: Constructor that sets the values and the weight.
itemset(int) - Method in class keel.Algorithms.Rule_Learning.ART.Dataset: Returns the itemset at the given position.
Itemset - Class in keel.Algorithms.Rule_Learning.ART: Class to manipulate an itemset.
Itemset(Itemset) - Constructor for class keel.Algorithms.Rule_Learning.ART.Itemset: Constructor that copies the values and the weight.
Itemset(double, double[]) - Constructor for class keel.Algorithms.Rule_Learning.ART.Itemset: Constructor that sets the values and the weight.
Itemset - Class in keel.Algorithms.Rule_Learning.C45Rules: Class to manipulate an itemset.
Itemset(Itemset) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.Itemset: Constructor that copies the values and the weight.
Itemset(double, double[]) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.Itemset: Constructor that sets the values and the weight.
itemset(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Returns the itemset at the given position.
Itemset - Class in keel.Algorithms.Rule_Learning.C45RulesSA: Class to manipulate an itemset.
Itemset(Itemset) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset: Constructor that copies the values and the weight.
Itemset(double, double[]) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset: Constructor that sets the values and the weight.
itemset(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Returns the itemset at the given position.
itemset(int) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Returns the itemset at the given position.
Itemset - Class in keel.Algorithms.Rule_Learning.DataSqueezer: Class to manipulate an itemset.
Itemset(Itemset) - Constructor for class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset: Constructor that copies the values and the weight.
Itemset(double, double[]) - Constructor for class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset: Constructor that sets the values and the weight.
Itemset - Class in keel.Algorithms.Rule_Learning.PART: Class to manipulate an itemset.
Itemset(Itemset) - Constructor for class keel.Algorithms.Rule_Learning.PART.Itemset: Constructor that copies the values and the weight.
Itemset(double, double[]) - Constructor for class keel.Algorithms.Rule_Learning.PART.Itemset: Constructor that sets the values and the weight.
itemset(int) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Returns the itemset at the given position.
itemset(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Returns the itemset at the given position.
Itemset - Class in keel.Algorithms.Semi_Supervised_Learning.Basic.C45: Class to manipulate an itemset.
Itemset(Itemset) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset: Constructor that copies the values and the weight.
Itemset(double, double[]) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset: Constructor that sets the values and the weight.
Itemset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal: It represents an itemset throughout the execution of the algorithm
Itemset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Itemset: Default constructor
Itemset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams: It represents an itemset throughout the execution of the algorithm
Itemset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Itemset: Default constructor
Itemset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori: It represents an itemset throughout the execution of the algorithm
Itemset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Itemset: Default constructor
Itemset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori: It represents an itemset throughout the execution of the algorithm
Itemset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Itemset: Default constructor
Itemset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC: It represents an itemset throughout the execution of the algorithm
Itemset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Itemset: Default constructor
itemsets - Variable in class keel.Algorithms.Decision_Trees.C45.Dataset: The itemsets.
itemsets - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: The itemsets.
itemsets - Variable in class keel.Algorithms.Decision_Trees.ID3.Dataset: The itemsets.
itemsets - Variable in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: The itemsets.
itemsets - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: The itemsets.
itemsets - Variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: The itemsets.
itemsets - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: The itemsets.
itemsets - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: The itemsets.
itemsets - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: The itemsets.
itemsets - Variable in class keel.Algorithms.Rule_Learning.ART.Dataset: The itemsets.
itemsets - Variable in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: The itemsets.
itemsets - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: The itemsets.
itemsets - Variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: The itemsets.
itemsets - Variable in class keel.Algorithms.Rule_Learning.PART.MyDataset: The itemsets.
itemsets - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: The itemsets.
itemsetsPerLeaf - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters: Minimun items per leaf.
itemsetsPerLeaf - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Parameters: Minimun items per leaf.
itemsetsPerLeaf - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters: Minimun items per leaf.
itemsetsPerLeaf - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Parameters: Minimun items per leaf.
itemsetsPerLeaf - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.ParametersC45: Minimum number of items per leaf.
itemsetsPerLeaf - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.ParametersC45: Minimum number of items per leaf.
itemStateChanged(ItemEvent) - Method in class keel.GraphInterKeel.experiments.Experiments: Change state
iter_no_changes - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.Parameters: number of iterations.
iter_no_changes - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.Parameters
iter_no_changes - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.Parameters
itera(FUN, double[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.Ameba: It makes the iterations of the algorithm
ITERACIONES - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.Fitness
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.FreqList: Iterates to the next element in the list
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.FreqList: Iterates to the next element in the list
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.valueAssociations: Iterates to the next element in the list
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.FreqList: Iterates to the next element in the list
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.valueAssociations: Iterates to the next element in the list
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqList: Iterates to the next element in the list
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqListPair: Iterates to the next element in the list
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.valueAssociations: Iterates to the next element in the list
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.FreqList: Iterates to the next element in the list
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.valueAssociations: Iterates to the next element in the list
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.FreqList: Iterates to the next element in the list
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.valueAssociations: Iterates to the next element in the list
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.FreqList: Iterates to the next element in the list
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.valueAssociations: Iterates to the next element in the list
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.FreqList: Iterates to the next element in the list
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.FreqList: Iterates to the next element in the list
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.valueAssociations: Iterates to the next element in the list
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.FreqList: Iterates to the next element in the list
iterate() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.valueAssociations: Iterates to the next element in the list
IteratedDiscrimination - Class in keel.Algorithms.MIL.APR.IteratedDiscrimination
IteratedDiscrimination() - Constructor for class keel.Algorithms.MIL.APR.IteratedDiscrimination.IteratedDiscrimination
iterationCompleted(<any>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.listener.NeuralNetReporterClas: This event is fired when the algorithm has finished a generation.
iterationCompleted(<any>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.listener.NeuralNetReporterRegr: This event is fired when the algorithm has finished a generation.
iterationHierarchicalSelection - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
iterationHierarchicalSelection - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
iterationMDL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
iterationMDL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
iterationRuleDeletion - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
iterationRuleDeletion - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
iterations - Variable in class keel.Algorithms.Instance_Generation.HYB.HYBGenerator: Iterations of the final optimal LVQ3.
iterations - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQGenerator: Number of iterations of the algorithm.
iterationsSinceBest - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.timerEvolutionStats
iterationsSinceBest - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Statistics
iterationsSinceBest - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Statistics
IterativePartitioningFilter - Class in keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter: The Ensemble Filter...
IterativePartitioningFilter() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.IterativePartitioningFilter: It initializes the partitions from training set
Itest - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm: Test instances set.
iVectorCombine(int[], int[]) - Static method in class keel.Algorithms.Decision_Trees.M5.Function: Combine two vectors in one maintaining an ascending order.
iVectorCombine(int[], int[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Function
iVectorCopy(int[], int) - Static method in class keel.Algorithms.Decision_Trees.M5.Function: Copy the first n elements of the array a.
iVectorCopy(int[], int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Function
IVTURS - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS: Title: IVTURS Description: It contains the implementation of the Farchd algorithm Company: KEEL
IVTURS() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.IVTURS: Default constructor
IVTURS(parseParameters) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.IVTURS: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
iz(int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion: Left
iz(int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion: Left

J

j - Variable in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Relation: second element
j - Variable in class keel.GraphInterKeel.statistical.tests.Relation: second element
jacobi(double[][], double[], double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
jacobi(double[][], double[], double[][]) - Static method in class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
JADEAlgorithm - Class in keel.Algorithms.Instance_Generation.JADE: JADE algorithm calling.
JADEAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.JADE.JADEAlgorithm
JADEGenerator - Class in keel.Algorithms.Instance_Generation.JADE
JADEGenerator(PrototypeSet, int, int, int, int, double, double, int) - Constructor for class keel.Algorithms.Instance_Generation.JADE.JADEGenerator: Build a new JADEGenerator Algorithm
JADEGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.JADE.JADEGenerator: Build a new JADEGenerator Algorithm
jarFile - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
Jclec - Class in keel.GraphInterKeel.experiments
Jclec(ExternalObjectDescription, Point, GraphPanel) - Constructor for class keel.GraphInterKeel.experiments.Jclec: Builder
Jclec(ExternalObjectDescription, Point, GraphPanel, Parameters, int) - Constructor for class keel.GraphInterKeel.experiments.Jclec: Builder
JFKNN - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN: File: JFKNN.java The JFKNN algorithm.
JFKNN(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN.JFKNN: Main builder.
jj_nt - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
jj_nt - Static variable in class keel.Algorithms.Rule_Learning.Swap1.DataParser
jj_nt - Static variable in class keel.Dataset.DataParser
jjFillToken() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserTokenManager
jjFillToken() - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParserTokenManager
jjFillToken() - Static method in class keel.Dataset.DataParserTokenManager
jjstrLiteralImages - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserTokenManager
jjstrLiteralImages - Static variable in class keel.Algorithms.Rule_Learning.Swap1.DataParserTokenManager
jjstrLiteralImages - Static variable in class keel.Dataset.DataParserTokenManager
join(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Join a prototype to a set
join(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Join a prototype to a set
joinOptions(String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Joins all the options in an option array into a single string, as might be used on the command line.
joinOptions(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Joins all the options in an option array into a single string, as might be used on the command line.
joinOptions(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Joins all the options in an option array into a single string, as might be used on the command line.
joinOptions(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Joins all the options in an option array into a single string, as might be used on the command line.
joinOptions(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Joins all the options in an option array into a single string, as might be used on the command line.
joinOptions(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Joins all the options in an option array into a single string, as might be used on the command line.
joinOptions(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Joins all the options in an option array into a single string, as might be used on the command line.
joinOptions(String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Joins all the options in an option array into a single string, as might be used on the command line.
joinOptions(String[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Joins all the options in an option array into a single string, as might be used on the command line.
Joint - Class in keel.GraphInterKeel.experiments
Joint() - Constructor for class keel.GraphInterKeel.experiments.Joint
JoinTemp() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: JoinTemp Joins Elite population with Population into temporal population Previous contents are lost
JoinTemp(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Joins two populations
jRadioButton1_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectExp: Classification button
jRadioButton2_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectExp: Regression button
jRadioButton3_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectExp: Unsupervised button
jRadioButton4_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectExp: Select k-folds
jRadioButton5_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectExp: Enable k-fold spinner
jRadioButton6_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectExp: Enable k-fold spinner

K

k - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN.Triplet: Number of neightbours.
k - Variable in class keel.Algorithms.Instance_Generation.BasicMethods.CNN: Neighborhood size in KNN
k - Variable in class keel.Algorithms.Instance_Generation.GENN.GENNGenerator: Number of neighbors selected in the underlying KNN.
k - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQPRU: Size of the neighborhood used in KNN
K - Static variable in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN: Number of neighbor-prototypes to be searched in the KNN.
k() - Static method in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN: Returns the current value of K.
k - Variable in class keel.Algorithms.Instance_Generation.VQ.VQGenerator: Nearest-neighbors selected to assign class to each prototype of the selected data set.
K - Static variable in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Number of neighbor-prototypes to be searched in the KNN.
k() - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Returns the current value of K.
k - Variable in class keel.GraphInterKeel.datacf.partitionData.KFoldOptionsJDialog: Number of folds (k)
k_value - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters: Number of nearest neighbours considered.
kappa() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Returns value of kappa statistic if class is nominal.
KBInformation() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Return the total Kononenko & Bratko Information score in bits
KBMeanInformation() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Return the Kononenko & Bratko Information score in bits per instance.
KBRelativeInformation() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Return the Kononenko & Bratko Relative Information score
keel.Algorithms.Associative_Classification.ClassifierCBA - package keel.Algorithms.Associative_Classification.ClassifierCBA
keel.Algorithms.Associative_Classification.ClassifierCBA2 - package keel.Algorithms.Associative_Classification.ClassifierCBA2
keel.Algorithms.Associative_Classification.ClassifierCMAR - package keel.Algorithms.Associative_Classification.ClassifierCMAR
keel.Algorithms.Associative_Classification.ClassifierCPAR - package keel.Algorithms.Associative_Classification.ClassifierCPAR
keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR - package keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR
keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD - package keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD
keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA - package keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA
keel.Algorithms.Clustering_Algorithms.ClusterKMeans - package keel.Algorithms.Clustering_Algorithms.ClusterKMeans
keel.Algorithms.Coevolution - package keel.Algorithms.Coevolution
keel.Algorithms.Coevolution.CIW_NN - package keel.Algorithms.Coevolution.CIW_NN
keel.Algorithms.Coevolution.IFS_COCO - package keel.Algorithms.Coevolution.IFS_COCO
keel.Algorithms.Complexity_Metrics - package keel.Algorithms.Complexity_Metrics
keel.Algorithms.Decision_Trees.C45 - package keel.Algorithms.Decision_Trees.C45
keel.Algorithms.Decision_Trees.C45_Binarization - package keel.Algorithms.Decision_Trees.C45_Binarization
keel.Algorithms.Decision_Trees.CART - package keel.Algorithms.Decision_Trees.CART
keel.Algorithms.Decision_Trees.CART.classification - package keel.Algorithms.Decision_Trees.CART.classification
keel.Algorithms.Decision_Trees.CART.dataset - package keel.Algorithms.Decision_Trees.CART.dataset
keel.Algorithms.Decision_Trees.CART.impurities - package keel.Algorithms.Decision_Trees.CART.impurities
keel.Algorithms.Decision_Trees.CART.regression - package keel.Algorithms.Decision_Trees.CART.regression
keel.Algorithms.Decision_Trees.CART.tree - package keel.Algorithms.Decision_Trees.CART.tree
keel.Algorithms.Decision_Trees.DT_GA - package keel.Algorithms.Decision_Trees.DT_GA
keel.Algorithms.Decision_Trees.DT_GA.C45 - package keel.Algorithms.Decision_Trees.DT_GA.C45
keel.Algorithms.Decision_Trees.DT_oblicuo - package keel.Algorithms.Decision_Trees.DT_oblicuo
keel.Algorithms.Decision_Trees.FunctionalTrees - package keel.Algorithms.Decision_Trees.FunctionalTrees
keel.Algorithms.Decision_Trees.ID3 - package keel.Algorithms.Decision_Trees.ID3
keel.Algorithms.Decision_Trees.M5 - package keel.Algorithms.Decision_Trees.M5
keel.Algorithms.Decision_Trees.PUBLIC - package keel.Algorithms.Decision_Trees.PUBLIC
keel.Algorithms.Decision_Trees.SLIQ - package keel.Algorithms.Decision_Trees.SLIQ
keel.Algorithms.Decision_Trees.Target - package keel.Algorithms.Decision_Trees.Target
keel.Algorithms.Discretizers.Ameva_Discretizer - package keel.Algorithms.Discretizers.Ameva_Discretizer
keel.Algorithms.Discretizers.Basic - package keel.Algorithms.Discretizers.Basic
keel.Algorithms.Discretizers.Bayesian_Discretizer - package keel.Algorithms.Discretizers.Bayesian_Discretizer
keel.Algorithms.Discretizers.CACC - package keel.Algorithms.Discretizers.CACC
keel.Algorithms.Discretizers.CADD_Discretizer - package keel.Algorithms.Discretizers.CADD_Discretizer
keel.Algorithms.Discretizers.CAIM_Discretizer - package keel.Algorithms.Discretizers.CAIM_Discretizer
keel.Algorithms.Discretizers.Chi2_Discretizer - package keel.Algorithms.Discretizers.Chi2_Discretizer
keel.Algorithms.Discretizers.ChiMerge_Discretizer - package keel.Algorithms.Discretizers.ChiMerge_Discretizer
keel.Algorithms.Discretizers.Cluster_Analysis - package keel.Algorithms.Discretizers.Cluster_Analysis
keel.Algorithms.Discretizers.DIBD - package keel.Algorithms.Discretizers.DIBD
keel.Algorithms.Discretizers.ExtendedChi2_Discretizer - package keel.Algorithms.Discretizers.ExtendedChi2_Discretizer
keel.Algorithms.Discretizers.Fayyad_Discretizer - package keel.Algorithms.Discretizers.Fayyad_Discretizer
keel.Algorithms.Discretizers.FixedFrequency_Discretizer - package keel.Algorithms.Discretizers.FixedFrequency_Discretizer
keel.Algorithms.Discretizers.FUSINTER - package keel.Algorithms.Discretizers.FUSINTER
keel.Algorithms.Discretizers.HDD - package keel.Algorithms.Discretizers.HDD
keel.Algorithms.Discretizers.HellingerBD - package keel.Algorithms.Discretizers.HellingerBD
keel.Algorithms.Discretizers.HeterDisc - package keel.Algorithms.Discretizers.HeterDisc
keel.Algorithms.Discretizers.Id3_Discretizer - package keel.Algorithms.Discretizers.Id3_Discretizer
keel.Algorithms.Discretizers.IDD - package keel.Algorithms.Discretizers.IDD
keel.Algorithms.Discretizers.Khiops - package keel.Algorithms.Discretizers.Khiops
keel.Algorithms.Discretizers.MantarasDist_Discretizer - package keel.Algorithms.Discretizers.MantarasDist_Discretizer
keel.Algorithms.Discretizers.ModifiedChi2_Discretizer - package keel.Algorithms.Discretizers.ModifiedChi2_Discretizer
keel.Algorithms.Discretizers.MODL - package keel.Algorithms.Discretizers.MODL
keel.Algorithms.Discretizers.MVD - package keel.Algorithms.Discretizers.MVD
keel.Algorithms.Discretizers.OneR - package keel.Algorithms.Discretizers.OneR
keel.Algorithms.Discretizers.Proportional_Discretizer - package keel.Algorithms.Discretizers.Proportional_Discretizer
keel.Algorithms.Discretizers.Random_Discretizer - package keel.Algorithms.Discretizers.Random_Discretizer
keel.Algorithms.Discretizers.UCPD - package keel.Algorithms.Discretizers.UCPD
keel.Algorithms.Discretizers.UniformFrequency_Discretizer - package keel.Algorithms.Discretizers.UniformFrequency_Discretizer
keel.Algorithms.Discretizers.UniformWidth_Discretizer - package keel.Algorithms.Discretizers.UniformWidth_Discretizer
keel.Algorithms.Discretizers.USD_Discretizer - package keel.Algorithms.Discretizers.USD_Discretizer
keel.Algorithms.Discretizers.Zeta_Discretizer - package keel.Algorithms.Discretizers.Zeta_Discretizer
keel.Algorithms.Fuzzy_Instance_Based_Learning - package keel.Algorithms.Fuzzy_Instance_Based_Learning
keel.Algorithms.Fuzzy_Instance_Based_Learning.CFKNN - package keel.Algorithms.Fuzzy_Instance_Based_Learning.CFKNN
keel.Algorithms.Fuzzy_Instance_Based_Learning.D_SKNN - package keel.Algorithms.Fuzzy_Instance_Based_Learning.D_SKNN
keel.Algorithms.Fuzzy_Instance_Based_Learning.FCMKNN - package keel.Algorithms.Fuzzy_Instance_Based_Learning.FCMKNN
keel.Algorithms.Fuzzy_Instance_Based_Learning.FENN - package keel.Algorithms.Fuzzy_Instance_Based_Learning.FENN
keel.Algorithms.Fuzzy_Instance_Based_Learning.FRKNNA - package keel.Algorithms.Fuzzy_Instance_Based_Learning.FRKNNA
keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN - package keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN
keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_FRS - package keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_FRS
keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_VQRS - package keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_VQRS
keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyKNN - package keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyKNN
keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyNPC - package keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyNPC
keel.Algorithms.Fuzzy_Instance_Based_Learning.GAFuzzyKNN - package keel.Algorithms.Fuzzy_Instance_Based_Learning.GAFuzzyKNN
keel.Algorithms.Fuzzy_Instance_Based_Learning.IF_KNN - package keel.Algorithms.Fuzzy_Instance_Based_Learning.IF_KNN
keel.Algorithms.Fuzzy_Instance_Based_Learning.IFSKNN - package keel.Algorithms.Fuzzy_Instance_Based_Learning.IFSKNN
keel.Algorithms.Fuzzy_Instance_Based_Learning.IFV_NP - package keel.Algorithms.Fuzzy_Instance_Based_Learning.IFV_NP
keel.Algorithms.Fuzzy_Instance_Based_Learning.IT2FKNN - package keel.Algorithms.Fuzzy_Instance_Based_Learning.IT2FKNN
keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN - package keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN
keel.Algorithms.Fuzzy_Instance_Based_Learning.PFKNN - package keel.Algorithms.Fuzzy_Instance_Based_Learning.PFKNN
keel.Algorithms.Fuzzy_Instance_Based_Learning.PosIBL - package keel.Algorithms.Fuzzy_Instance_Based_Learning.PosIBL
keel.Algorithms.Fuzzy_Instance_Based_Learning.VWFuzzyKNN - package keel.Algorithms.Fuzzy_Instance_Based_Learning.VWFuzzyKNN
keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW - package keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW
keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.ClassifierFuzzyWangMendel - package keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.ClassifierFuzzyWangMendel
keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted - package keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyAdaBoost - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyAdaBoost
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGAP - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGAP
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGP - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGP
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyLogitBoost - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyLogitBoost
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyMaxLogitBoost - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyMaxLogitBoost
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyPittsBurgh - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyPittsBurgh
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySAP - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySAP
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99 - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2 - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0 - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzyGAP - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzyGAP
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzyGP - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzyGP
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzyPittsBurgh - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzyPittsBurgh
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzySAP - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzySAP
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal
keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift - package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift
keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA - package keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA
keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core - package keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core
keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS - package keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS
keel.Algorithms.Fuzzy_Rule_Learning.Random_Sets.FSS98 - package keel.Algorithms.Fuzzy_Rule_Learning.Random_Sets.FSS98
keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy - package keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy
keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner - package keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner
keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus - package keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus
keel.Algorithms.Genetic_Rule_Learning.Ant_Miner - package keel.Algorithms.Genetic_Rule_Learning.Ant_Miner
keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus - package keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus
keel.Algorithms.Genetic_Rule_Learning.BioHEL - package keel.Algorithms.Genetic_Rule_Learning.BioHEL
keel.Algorithms.Genetic_Rule_Learning.Bojarczuk_GP - package keel.Algorithms.Genetic_Rule_Learning.Bojarczuk_GP
keel.Algorithms.Genetic_Rule_Learning.COGIN - package keel.Algorithms.Genetic_Rule_Learning.COGIN
keel.Algorithms.Genetic_Rule_Learning.Corcoran - package keel.Algorithms.Genetic_Rule_Learning.Corcoran
keel.Algorithms.Genetic_Rule_Learning.CORE - package keel.Algorithms.Genetic_Rule_Learning.CORE
keel.Algorithms.Genetic_Rule_Learning.DMEL - package keel.Algorithms.Genetic_Rule_Learning.DMEL
keel.Algorithms.Genetic_Rule_Learning.Falco_GP - package keel.Algorithms.Genetic_Rule_Learning.Falco_GP
keel.Algorithms.Genetic_Rule_Learning.GAssist - package keel.Algorithms.Genetic_Rule_Learning.GAssist
keel.Algorithms.Genetic_Rule_Learning.GIL - package keel.Algorithms.Genetic_Rule_Learning.GIL
keel.Algorithms.Genetic_Rule_Learning.Globals - package keel.Algorithms.Genetic_Rule_Learning.Globals
keel.Algorithms.Genetic_Rule_Learning.Hider - package keel.Algorithms.Genetic_Rule_Learning.Hider
keel.Algorithms.Genetic_Rule_Learning.ILGA - package keel.Algorithms.Genetic_Rule_Learning.ILGA
keel.Algorithms.Genetic_Rule_Learning.LogenPro - package keel.Algorithms.Genetic_Rule_Learning.LogenPro
keel.Algorithms.Genetic_Rule_Learning.M5Rules - package keel.Algorithms.Genetic_Rule_Learning.M5Rules
keel.Algorithms.Genetic_Rule_Learning.MPLCS - package keel.Algorithms.Genetic_Rule_Learning.MPLCS
keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Basic - package keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Basic
keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.ChiMerge_Discretizer - package keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.ChiMerge_Discretizer
keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Fayyad_Discretizer - package keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Fayyad_Discretizer
keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Id3_Discretizer - package keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Id3_Discretizer
keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.UniformFrequency_Discretizer - package keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.UniformFrequency_Discretizer
keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.UniformWidth_Discretizer - package keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.UniformWidth_Discretizer
keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.USD_Discretizer - package keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.USD_Discretizer
keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals - package keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals
keel.Algorithms.Genetic_Rule_Learning.OCEC - package keel.Algorithms.Genetic_Rule_Learning.OCEC
keel.Algorithms.Genetic_Rule_Learning.OIGA - package keel.Algorithms.Genetic_Rule_Learning.OIGA
keel.Algorithms.Genetic_Rule_Learning.olexGA - package keel.Algorithms.Genetic_Rule_Learning.olexGA
keel.Algorithms.Genetic_Rule_Learning.PART - package keel.Algorithms.Genetic_Rule_Learning.PART
keel.Algorithms.Genetic_Rule_Learning.PSO_ACO - package keel.Algorithms.Genetic_Rule_Learning.PSO_ACO
keel.Algorithms.Genetic_Rule_Learning.RMini - package keel.Algorithms.Genetic_Rule_Learning.RMini
keel.Algorithms.Genetic_Rule_Learning.SIA - package keel.Algorithms.Genetic_Rule_Learning.SIA
keel.Algorithms.Genetic_Rule_Learning.Tan_GP - package keel.Algorithms.Genetic_Rule_Learning.Tan_GP
keel.Algorithms.Genetic_Rule_Learning.UCS - package keel.Algorithms.Genetic_Rule_Learning.UCS
keel.Algorithms.Genetic_Rule_Learning.XCS - package keel.Algorithms.Genetic_Rule_Learning.XCS
keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser - package keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser
keel.Algorithms.Hyperrectangles.Basic - package keel.Algorithms.Hyperrectangles.Basic
keel.Algorithms.Hyperrectangles.BNGE - package keel.Algorithms.Hyperrectangles.BNGE
keel.Algorithms.Hyperrectangles.EACH - package keel.Algorithms.Hyperrectangles.EACH
keel.Algorithms.Hyperrectangles.EHS_CHC - package keel.Algorithms.Hyperrectangles.EHS_CHC
keel.Algorithms.Hyperrectangles.INNER - package keel.Algorithms.Hyperrectangles.INNER
keel.Algorithms.Hyperrectangles.RISE - package keel.Algorithms.Hyperrectangles.RISE
keel.Algorithms.ImbalancedClassification.Auxiliar.AUC - package keel.Algorithms.ImbalancedClassification.Auxiliar.AUC
keel.Algorithms.ImbalancedClassification.CSMethods.C45CS - package keel.Algorithms.ImbalancedClassification.CSMethods.C45CS
keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost - package keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost
keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS - package keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS
keel.Algorithms.ImbalancedClassification.Ensembles - package keel.Algorithms.ImbalancedClassification.Ensembles
keel.Algorithms.ImbalancedClassification.Ensembles.Basic - package keel.Algorithms.ImbalancedClassification.Ensembles.Basic
keel.Algorithms.ImbalancedClassification.Ensembles.C45 - package keel.Algorithms.ImbalancedClassification.Ensembles.C45
keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic - package keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic
keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat - package keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat
keel.Algorithms.ImbalancedClassification.Ensembles.SMOTE - package keel.Algorithms.ImbalancedClassification.Ensembles.SMOTE
keel.Algorithms.ImbalancedClassification.Ensembles.SPIDER - package keel.Algorithms.ImbalancedClassification.Ensembles.SPIDER
keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H - package keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H
keel.Algorithms.ImbalancedClassification.Resampling.ADASYN - package keel.Algorithms.ImbalancedClassification.Resampling.ADASYN
keel.Algorithms.ImbalancedClassification.Resampling.ADOMS - package keel.Algorithms.ImbalancedClassification.Resampling.ADOMS
keel.Algorithms.ImbalancedClassification.Resampling.AHCClustering - package keel.Algorithms.ImbalancedClassification.Resampling.AHCClustering
keel.Algorithms.ImbalancedClassification.Resampling.Borderline_SMOTE - package keel.Algorithms.ImbalancedClassification.Resampling.Borderline_SMOTE
keel.Algorithms.ImbalancedClassification.Resampling.CNN - package keel.Algorithms.ImbalancedClassification.Resampling.CNN
keel.Algorithms.ImbalancedClassification.Resampling.CNN_TomekLinks - package keel.Algorithms.ImbalancedClassification.Resampling.CNN_TomekLinks
keel.Algorithms.ImbalancedClassification.Resampling.CPM - package keel.Algorithms.ImbalancedClassification.Resampling.CPM
keel.Algorithms.ImbalancedClassification.Resampling.NCL - package keel.Algorithms.ImbalancedClassification.Resampling.NCL
keel.Algorithms.ImbalancedClassification.Resampling.OSS - package keel.Algorithms.ImbalancedClassification.Resampling.OSS
keel.Algorithms.ImbalancedClassification.Resampling.RandomOverSampling - package keel.Algorithms.ImbalancedClassification.Resampling.RandomOverSampling
keel.Algorithms.ImbalancedClassification.Resampling.RandomUnderSampling - package keel.Algorithms.ImbalancedClassification.Resampling.RandomUnderSampling
keel.Algorithms.ImbalancedClassification.Resampling.Safe_Level_SMOTE - package keel.Algorithms.ImbalancedClassification.Resampling.Safe_Level_SMOTE
keel.Algorithms.ImbalancedClassification.Resampling.SBC - package keel.Algorithms.ImbalancedClassification.Resampling.SBC
keel.Algorithms.ImbalancedClassification.Resampling.SMOTE - package keel.Algorithms.ImbalancedClassification.Resampling.SMOTE
keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_ENN - package keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_ENN
keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB - package keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB
keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets - package keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets
keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_TomekLinks - package keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_TomekLinks
keel.Algorithms.ImbalancedClassification.Resampling.SPIDER - package keel.Algorithms.ImbalancedClassification.Resampling.SPIDER
keel.Algorithms.ImbalancedClassification.Resampling.SPIDER2 - package keel.Algorithms.ImbalancedClassification.Resampling.SPIDER2
keel.Algorithms.ImbalancedClassification.Resampling.TomekLinks - package keel.Algorithms.ImbalancedClassification.Resampling.TomekLinks
keel.Algorithms.Instance_Generation.AMPSO - package keel.Algorithms.Instance_Generation.AMPSO
keel.Algorithms.Instance_Generation.Basic - package keel.Algorithms.Instance_Generation.Basic
keel.Algorithms.Instance_Generation.BasicMethods - package keel.Algorithms.Instance_Generation.BasicMethods
keel.Algorithms.Instance_Generation.BTS3 - package keel.Algorithms.Instance_Generation.BTS3
keel.Algorithms.Instance_Generation.Chen - package keel.Algorithms.Instance_Generation.Chen
keel.Algorithms.Instance_Generation.DE - package keel.Algorithms.Instance_Generation.DE
keel.Algorithms.Instance_Generation.DEGL - package keel.Algorithms.Instance_Generation.DEGL
keel.Algorithms.Instance_Generation.Depur - package keel.Algorithms.Instance_Generation.Depur
keel.Algorithms.Instance_Generation.DROP3LVQ3 - package keel.Algorithms.Instance_Generation.DROP3LVQ3
keel.Algorithms.Instance_Generation.DROP3PSO - package keel.Algorithms.Instance_Generation.DROP3PSO
keel.Algorithms.Instance_Generation.DROP3SFLSDE - package keel.Algorithms.Instance_Generation.DROP3SFLSDE
keel.Algorithms.Instance_Generation.DSM - package keel.Algorithms.Instance_Generation.DSM
keel.Algorithms.Instance_Generation.ENPC - package keel.Algorithms.Instance_Generation.ENPC
keel.Algorithms.Instance_Generation.GENN - package keel.Algorithms.Instance_Generation.GENN
keel.Algorithms.Instance_Generation.GMCA - package keel.Algorithms.Instance_Generation.GMCA
keel.Algorithms.Instance_Generation.HYB - package keel.Algorithms.Instance_Generation.HYB
keel.Algorithms.Instance_Generation.ICFLVQ3 - package keel.Algorithms.Instance_Generation.ICFLVQ3
keel.Algorithms.Instance_Generation.ICFPSO - package keel.Algorithms.Instance_Generation.ICFPSO
keel.Algorithms.Instance_Generation.ICFSFLSDE - package keel.Algorithms.Instance_Generation.ICFSFLSDE
keel.Algorithms.Instance_Generation.ICPL - package keel.Algorithms.Instance_Generation.ICPL
keel.Algorithms.Instance_Generation.IPLDE - package keel.Algorithms.Instance_Generation.IPLDE
keel.Algorithms.Instance_Generation.JADE - package keel.Algorithms.Instance_Generation.JADE
keel.Algorithms.Instance_Generation.LVQ - package keel.Algorithms.Instance_Generation.LVQ
keel.Algorithms.Instance_Generation.MCA - package keel.Algorithms.Instance_Generation.MCA
keel.Algorithms.Instance_Generation.MixtGauss - package keel.Algorithms.Instance_Generation.MixtGauss
keel.Algorithms.Instance_Generation.MSE - package keel.Algorithms.Instance_Generation.MSE
keel.Algorithms.Instance_Generation.OBDE - package keel.Algorithms.Instance_Generation.OBDE
keel.Algorithms.Instance_Generation.PNN - package keel.Algorithms.Instance_Generation.PNN
keel.Algorithms.Instance_Generation.POC - package keel.Algorithms.Instance_Generation.POC
keel.Algorithms.Instance_Generation.PSCSA - package keel.Algorithms.Instance_Generation.PSCSA
keel.Algorithms.Instance_Generation.PSO - package keel.Algorithms.Instance_Generation.PSO
keel.Algorithms.Instance_Generation.RSP - package keel.Algorithms.Instance_Generation.RSP
keel.Algorithms.Instance_Generation.SADE - package keel.Algorithms.Instance_Generation.SADE
keel.Algorithms.Instance_Generation.SFLSDE - package keel.Algorithms.Instance_Generation.SFLSDE
keel.Algorithms.Instance_Generation.SGP - package keel.Algorithms.Instance_Generation.SGP
keel.Algorithms.Instance_Generation.SSMALVQ3 - package keel.Algorithms.Instance_Generation.SSMALVQ3
keel.Algorithms.Instance_Generation.SSMAPSO - package keel.Algorithms.Instance_Generation.SSMAPSO
keel.Algorithms.Instance_Generation.SSMASFLSDE - package keel.Algorithms.Instance_Generation.SSMASFLSDE
keel.Algorithms.Instance_Generation.Trivial - package keel.Algorithms.Instance_Generation.Trivial
keel.Algorithms.Instance_Generation.utilities - package keel.Algorithms.Instance_Generation.utilities
keel.Algorithms.Instance_Generation.utilities.KNN - package keel.Algorithms.Instance_Generation.utilities.KNN
keel.Algorithms.Instance_Generation.VQ - package keel.Algorithms.Instance_Generation.VQ
keel.Algorithms.Instance_Selection.AllKNN - package keel.Algorithms.Instance_Selection.AllKNN
keel.Algorithms.Instance_Selection.BSE - package keel.Algorithms.Instance_Selection.BSE
keel.Algorithms.Instance_Selection.CCIS - package keel.Algorithms.Instance_Selection.CCIS
keel.Algorithms.Instance_Selection.CHC - package keel.Algorithms.Instance_Selection.CHC
keel.Algorithms.Instance_Selection.CNN - package keel.Algorithms.Instance_Selection.CNN
keel.Algorithms.Instance_Selection.CoCoIS - package keel.Algorithms.Instance_Selection.CoCoIS
keel.Algorithms.Instance_Selection.CPruner - package keel.Algorithms.Instance_Selection.CPruner
keel.Algorithms.Instance_Selection.DROP1 - package keel.Algorithms.Instance_Selection.DROP1
keel.Algorithms.Instance_Selection.DROP2 - package keel.Algorithms.Instance_Selection.DROP2
keel.Algorithms.Instance_Selection.DROP3 - package keel.Algorithms.Instance_Selection.DROP3
keel.Algorithms.Instance_Selection.ENN - package keel.Algorithms.Instance_Selection.ENN
keel.Algorithms.Instance_Selection.ENNRS - package keel.Algorithms.Instance_Selection.ENNRS
keel.Algorithms.Instance_Selection.ENNTh - package keel.Algorithms.Instance_Selection.ENNTh
keel.Algorithms.Instance_Selection.ENRBF - package keel.Algorithms.Instance_Selection.ENRBF
keel.Algorithms.Instance_Selection.Explore - package keel.Algorithms.Instance_Selection.Explore
keel.Algorithms.Instance_Selection.FCNN - package keel.Algorithms.Instance_Selection.FCNN
keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM - package keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM
keel.Algorithms.Instance_Selection.GCNN - package keel.Algorithms.Instance_Selection.GCNN
keel.Algorithms.Instance_Selection.GG - package keel.Algorithms.Instance_Selection.GG
keel.Algorithms.Instance_Selection.GGA - package keel.Algorithms.Instance_Selection.GGA
keel.Algorithms.Instance_Selection.HMNEI - package keel.Algorithms.Instance_Selection.HMNEI
keel.Algorithms.Instance_Selection.IB2 - package keel.Algorithms.Instance_Selection.IB2
keel.Algorithms.Instance_Selection.IB3 - package keel.Algorithms.Instance_Selection.IB3
keel.Algorithms.Instance_Selection.ICF - package keel.Algorithms.Instance_Selection.ICF
keel.Algorithms.Instance_Selection.IGA - package keel.Algorithms.Instance_Selection.IGA
keel.Algorithms.Instance_Selection.IKNN - package keel.Algorithms.Instance_Selection.IKNN
keel.Algorithms.Instance_Selection.MCNN - package keel.Algorithms.Instance_Selection.MCNN
keel.Algorithms.Instance_Selection.MCS - package keel.Algorithms.Instance_Selection.MCS
keel.Algorithms.Instance_Selection.MENN - package keel.Algorithms.Instance_Selection.MENN
keel.Algorithms.Instance_Selection.MNV - package keel.Algorithms.Instance_Selection.MNV
keel.Algorithms.Instance_Selection.ModelCS - package keel.Algorithms.Instance_Selection.ModelCS
keel.Algorithms.Instance_Selection.MSS - package keel.Algorithms.Instance_Selection.MSS
keel.Algorithms.Instance_Selection.Multiedit - package keel.Algorithms.Instance_Selection.Multiedit
keel.Algorithms.Instance_Selection.NCNEdit - package keel.Algorithms.Instance_Selection.NCNEdit
keel.Algorithms.Instance_Selection.NRMCS - package keel.Algorithms.Instance_Selection.NRMCS
keel.Algorithms.Instance_Selection.PBIL - package keel.Algorithms.Instance_Selection.PBIL
keel.Algorithms.Instance_Selection.POP - package keel.Algorithms.Instance_Selection.POP
keel.Algorithms.Instance_Selection.PSC - package keel.Algorithms.Instance_Selection.PSC
keel.Algorithms.Instance_Selection.PSRCG - package keel.Algorithms.Instance_Selection.PSRCG
keel.Algorithms.Instance_Selection.Reconsistent - package keel.Algorithms.Instance_Selection.Reconsistent
keel.Algorithms.Instance_Selection.RENN - package keel.Algorithms.Instance_Selection.RENN
keel.Algorithms.Instance_Selection.RMHC - package keel.Algorithms.Instance_Selection.RMHC
keel.Algorithms.Instance_Selection.RNG - package keel.Algorithms.Instance_Selection.RNG
keel.Algorithms.Instance_Selection.RNN - package keel.Algorithms.Instance_Selection.RNN
keel.Algorithms.Instance_Selection.SGA - package keel.Algorithms.Instance_Selection.SGA
keel.Algorithms.Instance_Selection.Shrink - package keel.Algorithms.Instance_Selection.Shrink
keel.Algorithms.Instance_Selection.SNN - package keel.Algorithms.Instance_Selection.SNN
keel.Algorithms.Instance_Selection.SSMA - package keel.Algorithms.Instance_Selection.SSMA
keel.Algorithms.Instance_Selection.SVBPS - package keel.Algorithms.Instance_Selection.SVBPS
keel.Algorithms.Instance_Selection.TCNN - package keel.Algorithms.Instance_Selection.TCNN
keel.Algorithms.Instance_Selection.TRKNN - package keel.Algorithms.Instance_Selection.TRKNN
keel.Algorithms.Instance_Selection.VSM - package keel.Algorithms.Instance_Selection.VSM
keel.Algorithms.Instance_Selection.ZhangTS - package keel.Algorithms.Instance_Selection.ZhangTS
keel.Algorithms.Lazy_Learning - package keel.Algorithms.Lazy_Learning
keel.Algorithms.Lazy_Learning.CamNN - package keel.Algorithms.Lazy_Learning.CamNN
keel.Algorithms.Lazy_Learning.CenterNN - package keel.Algorithms.Lazy_Learning.CenterNN
keel.Algorithms.Lazy_Learning.CPW - package keel.Algorithms.Lazy_Learning.CPW
keel.Algorithms.Lazy_Learning.CW - package keel.Algorithms.Lazy_Learning.CW
keel.Algorithms.Lazy_Learning.Deeps - package keel.Algorithms.Lazy_Learning.Deeps
keel.Algorithms.Lazy_Learning.DeepsNN - package keel.Algorithms.Lazy_Learning.DeepsNN
keel.Algorithms.Lazy_Learning.IDIBL - package keel.Algorithms.Lazy_Learning.IDIBL
keel.Algorithms.Lazy_Learning.KNN - package keel.Algorithms.Lazy_Learning.KNN
keel.Algorithms.Lazy_Learning.KNNAdaptive - package keel.Algorithms.Lazy_Learning.KNNAdaptive
keel.Algorithms.Lazy_Learning.KSNN - package keel.Algorithms.Lazy_Learning.KSNN
keel.Algorithms.Lazy_Learning.KStar - package keel.Algorithms.Lazy_Learning.KStar
keel.Algorithms.Lazy_Learning.LazyDT - package keel.Algorithms.Lazy_Learning.LazyDT
keel.Algorithms.Lazy_Learning.LBR - package keel.Algorithms.Lazy_Learning.LBR
keel.Algorithms.Lazy_Learning.NM - package keel.Algorithms.Lazy_Learning.NM
keel.Algorithms.Lazy_Learning.NSC - package keel.Algorithms.Lazy_Learning.NSC
keel.Algorithms.Lazy_Learning.PW - package keel.Algorithms.Lazy_Learning.PW
keel.Algorithms.LQD.methods.FGFS_costInstances - package keel.Algorithms.LQD.methods.FGFS_costInstances
keel.Algorithms.LQD.methods.FGFS_Minimum_Risk - package keel.Algorithms.LQD.methods.FGFS_Minimum_Risk
keel.Algorithms.LQD.methods.FGFS_Original - package keel.Algorithms.LQD.methods.FGFS_Original
keel.Algorithms.LQD.methods.FGFS_Rule_Weight - package keel.Algorithms.LQD.methods.FGFS_Rule_Weight
keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty - package keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty
keel.Algorithms.LQD.preprocess.Expert - package keel.Algorithms.LQD.preprocess.Expert
keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE - package keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE
keel.Algorithms.LQD.preprocess.Prelabelling - package keel.Algorithms.LQD.preprocess.Prelabelling
keel.Algorithms.LQD.preprocess.Prelabelling_Expert - package keel.Algorithms.LQD.preprocess.Prelabelling_Expert
keel.Algorithms.LQD.tests.IntermediateBoost - package keel.Algorithms.LQD.tests.IntermediateBoost
keel.Algorithms.LQD.tests.Results - package keel.Algorithms.LQD.tests.Results
keel.Algorithms.MIL - package keel.Algorithms.MIL
keel.Algorithms.MIL.APR - package keel.Algorithms.MIL.APR
keel.Algorithms.MIL.APR.GFS_AllPositive_APR - package keel.Algorithms.MIL.APR.GFS_AllPositive_APR
keel.Algorithms.MIL.APR.GFS_ElimCount_APR - package keel.Algorithms.MIL.APR.GFS_ElimCount_APR
keel.Algorithms.MIL.APR.GFS_Kde_APR - package keel.Algorithms.MIL.APR.GFS_Kde_APR
keel.Algorithms.MIL.APR.IteratedDiscrimination - package keel.Algorithms.MIL.APR.IteratedDiscrimination
keel.Algorithms.MIL.Diverse_Density.DD - package keel.Algorithms.MIL.Diverse_Density.DD
keel.Algorithms.MIL.Diverse_Density.EMDD - package keel.Algorithms.MIL.Diverse_Density.EMDD
keel.Algorithms.MIL.Diverse_Density.Optimization - package keel.Algorithms.MIL.Diverse_Density.Optimization
keel.Algorithms.MIL.G3PMI - package keel.Algorithms.MIL.G3PMI
keel.Algorithms.MIL.Nearest_Neighbour - package keel.Algorithms.MIL.Nearest_Neighbour
keel.Algorithms.MIL.Nearest_Neighbour.CKNN - package keel.Algorithms.MIL.Nearest_Neighbour.CKNN
keel.Algorithms.MIL.Nearest_Neighbour.KNN - package keel.Algorithms.MIL.Nearest_Neighbour.KNN
keel.Algorithms.Neural_Networks.ClassifierMLPerceptron - package keel.Algorithms.Neural_Networks.ClassifierMLPerceptron
keel.Algorithms.Neural_Networks.ensemble - package keel.Algorithms.Neural_Networks.ensemble
keel.Algorithms.Neural_Networks.EvRBF_CL - package keel.Algorithms.Neural_Networks.EvRBF_CL
keel.Algorithms.Neural_Networks.gann - package keel.Algorithms.Neural_Networks.gann
keel.Algorithms.Neural_Networks.gmdh - package keel.Algorithms.Neural_Networks.gmdh
keel.Algorithms.Neural_Networks.IRPropPlus_Clas - package keel.Algorithms.Neural_Networks.IRPropPlus_Clas
keel.Algorithms.Neural_Networks.IRPropPlus_Regr - package keel.Algorithms.Neural_Networks.IRPropPlus_Regr
keel.Algorithms.Neural_Networks.LVQ - package keel.Algorithms.Neural_Networks.LVQ
keel.Algorithms.Neural_Networks.ModelMLPerceptron - package keel.Algorithms.Neural_Networks.ModelMLPerceptron
keel.Algorithms.Neural_Networks.net - package keel.Algorithms.Neural_Networks.net
keel.Algorithms.Neural_Networks.NNEP_Clas - package keel.Algorithms.Neural_Networks.NNEP_Clas
keel.Algorithms.Neural_Networks.NNEP_Clas.algorithm.classification - package keel.Algorithms.Neural_Networks.NNEP_Clas.algorithm.classification
keel.Algorithms.Neural_Networks.NNEP_Clas.listener - package keel.Algorithms.Neural_Networks.NNEP_Clas.listener
keel.Algorithms.Neural_Networks.NNEP_Clas.neuralnet - package keel.Algorithms.Neural_Networks.NNEP_Clas.neuralnet
keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification - package keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification
keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax - package keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax
keel.Algorithms.Neural_Networks.NNEP_Clas.problem.errorfunctions - package keel.Algorithms.Neural_Networks.NNEP_Clas.problem.errorfunctions
keel.Algorithms.Neural_Networks.NNEP_Common - package keel.Algorithms.Neural_Networks.NNEP_Common
keel.Algorithms.Neural_Networks.NNEP_Common.algorithm - package keel.Algorithms.Neural_Networks.NNEP_Common.algorithm
keel.Algorithms.Neural_Networks.NNEP_Common.data - package keel.Algorithms.Neural_Networks.NNEP_Common.data
keel.Algorithms.Neural_Networks.NNEP_Common.initiators - package keel.Algorithms.Neural_Networks.NNEP_Common.initiators
keel.Algorithms.Neural_Networks.NNEP_Common.mutators - package keel.Algorithms.Neural_Networks.NNEP_Common.mutators
keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric - package keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric
keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural - package keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural
keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet - package keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet
keel.Algorithms.Neural_Networks.NNEP_Common.problem - package keel.Algorithms.Neural_Networks.NNEP_Common.problem
keel.Algorithms.Neural_Networks.NNEP_Common.problem.errorfunctions - package keel.Algorithms.Neural_Networks.NNEP_Common.problem.errorfunctions
keel.Algorithms.Neural_Networks.NNEP_Common.util.normalizer - package keel.Algorithms.Neural_Networks.NNEP_Common.util.normalizer
keel.Algorithms.Neural_Networks.NNEP_Common.util.random - package keel.Algorithms.Neural_Networks.NNEP_Common.util.random
keel.Algorithms.Neural_Networks.NNEP_Regr - package keel.Algorithms.Neural_Networks.NNEP_Regr
keel.Algorithms.Neural_Networks.NNEP_Regr.listener - package keel.Algorithms.Neural_Networks.NNEP_Regr.listener
keel.Algorithms.Neural_Networks.NNEP_Regr.neuralnet - package keel.Algorithms.Neural_Networks.NNEP_Regr.neuralnet
keel.Algorithms.Neural_Networks.NNEP_Regr.problem.errorfunctions - package keel.Algorithms.Neural_Networks.NNEP_Regr.problem.errorfunctions
keel.Algorithms.Neural_Networks.NNEP_Regr.problem.regression - package keel.Algorithms.Neural_Networks.NNEP_Regr.problem.regression
keel.Algorithms.Neural_Networks.RBFN - package keel.Algorithms.Neural_Networks.RBFN
keel.Algorithms.Neural_Networks.RBFN_CL - package keel.Algorithms.Neural_Networks.RBFN_CL
keel.Algorithms.Neural_Networks.RBFN_decremental - package keel.Algorithms.Neural_Networks.RBFN_decremental
keel.Algorithms.Neural_Networks.RBFN_decremental_CL - package keel.Algorithms.Neural_Networks.RBFN_decremental_CL
keel.Algorithms.Neural_Networks.RBFN_incremental - package keel.Algorithms.Neural_Networks.RBFN_incremental
keel.Algorithms.Neural_Networks.RBFN_incremental_CL - package keel.Algorithms.Neural_Networks.RBFN_incremental_CL
keel.Algorithms.Preprocess.Basic - package keel.Algorithms.Preprocess.Basic
keel.Algorithms.Preprocess.Converter - package keel.Algorithms.Preprocess.Converter
keel.Algorithms.Preprocess.Feature_Selection - package keel.Algorithms.Preprocess.Feature_Selection
keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms - package keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms
keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CHC.filter - package keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CHC.filter
keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CHC.wrapper - package keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CHC.wrapper
keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_BinCod.filter - package keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_BinCod.filter
keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_BinCod.wrapper - package keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_BinCod.wrapper
keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_IntCod.filter - package keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_IntCod.filter
keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_IntCod.wrapper - package keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_IntCod.wrapper
keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_BinCod.filter - package keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_BinCod.filter
keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_BinCod.wrapper - package keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_BinCod.wrapper
keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_IntCod.filter - package keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_IntCod.filter
keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_IntCod.wrapper - package keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_IntCod.wrapper
keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GGA - package keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GGA
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_IEP - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_IEP
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_LIU - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_LIU
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_MI - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_MI
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.BACKWARD.filter - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.BACKWARD.filter
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.BACKWARD.wrapper - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.BACKWARD.wrapper
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FOCUS - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FOCUS
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FORWARD.filter - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FORWARD.filter
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FORWARD.wrapper - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FORWARD.wrapper
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_IEP - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_IEP
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_LIU - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_LIU
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_MI - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_MI
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.filter.im - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.filter.im
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.filter.inconsistency - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.filter.inconsistency
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.wrapper - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.wrapper
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVF - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVF
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVF_IEP - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVF_IEP
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVW - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVW
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.RELIEF - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.RELIEF
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.RELIEF_F - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.RELIEF_F
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_IEP_FS - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_IEP_FS
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_LIU_FS - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_LIU_FS
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_MI_FS - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_MI_FS
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_IEP_FS - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_IEP_FS
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_LIU_FS - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_LIU_FS
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_MI_FS - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_MI_FS
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_IEP_FS - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_IEP_FS
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_LIU_FS - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_LIU_FS
keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_MI_FS - package keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_MI_FS
keel.Algorithms.Preprocess.Feature_Selection.Shared - package keel.Algorithms.Preprocess.Feature_Selection.Shared
keel.Algorithms.Preprocess.Instance_Selection.AllKNN - package keel.Algorithms.Preprocess.Instance_Selection.AllKNN
keel.Algorithms.Preprocess.Instance_Selection.BSE - package keel.Algorithms.Preprocess.Instance_Selection.BSE
keel.Algorithms.Preprocess.Instance_Selection.CCIS - package keel.Algorithms.Preprocess.Instance_Selection.CCIS
keel.Algorithms.Preprocess.Instance_Selection.CHC - package keel.Algorithms.Preprocess.Instance_Selection.CHC
keel.Algorithms.Preprocess.Instance_Selection.CNN - package keel.Algorithms.Preprocess.Instance_Selection.CNN
keel.Algorithms.Preprocess.Instance_Selection.CoCoIS - package keel.Algorithms.Preprocess.Instance_Selection.CoCoIS
keel.Algorithms.Preprocess.Instance_Selection.CPruner - package keel.Algorithms.Preprocess.Instance_Selection.CPruner
keel.Algorithms.Preprocess.Instance_Selection.DROP1 - package keel.Algorithms.Preprocess.Instance_Selection.DROP1
keel.Algorithms.Preprocess.Instance_Selection.DROP2 - package keel.Algorithms.Preprocess.Instance_Selection.DROP2
keel.Algorithms.Preprocess.Instance_Selection.DROP3 - package keel.Algorithms.Preprocess.Instance_Selection.DROP3
keel.Algorithms.Preprocess.Instance_Selection.ENN - package keel.Algorithms.Preprocess.Instance_Selection.ENN
keel.Algorithms.Preprocess.Instance_Selection.ENNRS - package keel.Algorithms.Preprocess.Instance_Selection.ENNRS
keel.Algorithms.Preprocess.Instance_Selection.ENNTh - package keel.Algorithms.Preprocess.Instance_Selection.ENNTh
keel.Algorithms.Preprocess.Instance_Selection.ENRBF - package keel.Algorithms.Preprocess.Instance_Selection.ENRBF
keel.Algorithms.Preprocess.Instance_Selection.Explore - package keel.Algorithms.Preprocess.Instance_Selection.Explore
keel.Algorithms.Preprocess.Instance_Selection.FCNN - package keel.Algorithms.Preprocess.Instance_Selection.FCNN
keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM - package keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM
keel.Algorithms.Preprocess.Instance_Selection.GCNN - package keel.Algorithms.Preprocess.Instance_Selection.GCNN
keel.Algorithms.Preprocess.Instance_Selection.GG - package keel.Algorithms.Preprocess.Instance_Selection.GG
keel.Algorithms.Preprocess.Instance_Selection.GGA - package keel.Algorithms.Preprocess.Instance_Selection.GGA
keel.Algorithms.Preprocess.Instance_Selection.HMNEI - package keel.Algorithms.Preprocess.Instance_Selection.HMNEI
keel.Algorithms.Preprocess.Instance_Selection.IB2 - package keel.Algorithms.Preprocess.Instance_Selection.IB2
keel.Algorithms.Preprocess.Instance_Selection.IB3 - package keel.Algorithms.Preprocess.Instance_Selection.IB3
keel.Algorithms.Preprocess.Instance_Selection.ICF - package keel.Algorithms.Preprocess.Instance_Selection.ICF
keel.Algorithms.Preprocess.Instance_Selection.IGA - package keel.Algorithms.Preprocess.Instance_Selection.IGA
keel.Algorithms.Preprocess.Instance_Selection.IKNN - package keel.Algorithms.Preprocess.Instance_Selection.IKNN
keel.Algorithms.Preprocess.Instance_Selection.MCNN - package keel.Algorithms.Preprocess.Instance_Selection.MCNN
keel.Algorithms.Preprocess.Instance_Selection.MCS - package keel.Algorithms.Preprocess.Instance_Selection.MCS
keel.Algorithms.Preprocess.Instance_Selection.MENN - package keel.Algorithms.Preprocess.Instance_Selection.MENN
keel.Algorithms.Preprocess.Instance_Selection.MNV - package keel.Algorithms.Preprocess.Instance_Selection.MNV
keel.Algorithms.Preprocess.Instance_Selection.ModelCS - package keel.Algorithms.Preprocess.Instance_Selection.ModelCS
keel.Algorithms.Preprocess.Instance_Selection.MSS - package keel.Algorithms.Preprocess.Instance_Selection.MSS
keel.Algorithms.Preprocess.Instance_Selection.Multiedit - package keel.Algorithms.Preprocess.Instance_Selection.Multiedit
keel.Algorithms.Preprocess.Instance_Selection.NCNEdit - package keel.Algorithms.Preprocess.Instance_Selection.NCNEdit
keel.Algorithms.Preprocess.Instance_Selection.NRMCS - package keel.Algorithms.Preprocess.Instance_Selection.NRMCS
keel.Algorithms.Preprocess.Instance_Selection.PBIL - package keel.Algorithms.Preprocess.Instance_Selection.PBIL
keel.Algorithms.Preprocess.Instance_Selection.POP - package keel.Algorithms.Preprocess.Instance_Selection.POP
keel.Algorithms.Preprocess.Instance_Selection.PSC - package keel.Algorithms.Preprocess.Instance_Selection.PSC
keel.Algorithms.Preprocess.Instance_Selection.PSRCG - package keel.Algorithms.Preprocess.Instance_Selection.PSRCG
keel.Algorithms.Preprocess.Instance_Selection.Reconsistent - package keel.Algorithms.Preprocess.Instance_Selection.Reconsistent
keel.Algorithms.Preprocess.Instance_Selection.RENN - package keel.Algorithms.Preprocess.Instance_Selection.RENN
keel.Algorithms.Preprocess.Instance_Selection.RMHC - package keel.Algorithms.Preprocess.Instance_Selection.RMHC
keel.Algorithms.Preprocess.Instance_Selection.RNG - package keel.Algorithms.Preprocess.Instance_Selection.RNG
keel.Algorithms.Preprocess.Instance_Selection.RNN - package keel.Algorithms.Preprocess.Instance_Selection.RNN
keel.Algorithms.Preprocess.Instance_Selection.SGA - package keel.Algorithms.Preprocess.Instance_Selection.SGA
keel.Algorithms.Preprocess.Instance_Selection.Shrink - package keel.Algorithms.Preprocess.Instance_Selection.Shrink
keel.Algorithms.Preprocess.Instance_Selection.SNN - package keel.Algorithms.Preprocess.Instance_Selection.SNN
keel.Algorithms.Preprocess.Instance_Selection.SSMA - package keel.Algorithms.Preprocess.Instance_Selection.SSMA
keel.Algorithms.Preprocess.Instance_Selection.SVBPS - package keel.Algorithms.Preprocess.Instance_Selection.SVBPS
keel.Algorithms.Preprocess.Instance_Selection.TCNN - package keel.Algorithms.Preprocess.Instance_Selection.TCNN
keel.Algorithms.Preprocess.Instance_Selection.TRKNN - package keel.Algorithms.Preprocess.Instance_Selection.TRKNN
keel.Algorithms.Preprocess.Instance_Selection.VSM - package keel.Algorithms.Preprocess.Instance_Selection.VSM
keel.Algorithms.Preprocess.Instance_Selection.ZhangTS - package keel.Algorithms.Preprocess.Instance_Selection.ZhangTS
keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues - package keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues
keel.Algorithms.Preprocess.Missing_Values.BPCA - package keel.Algorithms.Preprocess.Missing_Values.BPCA
keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues - package keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues
keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue - package keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue
keel.Algorithms.Preprocess.Missing_Values.EM - package keel.Algorithms.Preprocess.Missing_Values.EM
keel.Algorithms.Preprocess.Missing_Values.EM.util - package keel.Algorithms.Preprocess.Missing_Values.EM.util
keel.Algorithms.Preprocess.Missing_Values.EventCovering - package keel.Algorithms.Preprocess.Missing_Values.EventCovering
keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat - package keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat
keel.Algorithms.Preprocess.Missing_Values.fkmeans - package keel.Algorithms.Preprocess.Missing_Values.fkmeans
keel.Algorithms.Preprocess.Missing_Values.ignore_missing - package keel.Algorithms.Preprocess.Missing_Values.ignore_missing
keel.Algorithms.Preprocess.Missing_Values.kmeansImpute - package keel.Algorithms.Preprocess.Missing_Values.kmeansImpute
keel.Algorithms.Preprocess.Missing_Values.knnImpute - package keel.Algorithms.Preprocess.Missing_Values.knnImpute
keel.Algorithms.Preprocess.Missing_Values.LLSImpute - package keel.Algorithms.Preprocess.Missing_Values.LLSImpute
keel.Algorithms.Preprocess.Missing_Values.MostCommonValue - package keel.Algorithms.Preprocess.Missing_Values.MostCommonValue
keel.Algorithms.Preprocess.Missing_Values.SVDimpute - package keel.Algorithms.Preprocess.Missing_Values.SVDimpute
keel.Algorithms.Preprocess.Missing_Values.svmImpute - package keel.Algorithms.Preprocess.Missing_Values.svmImpute
keel.Algorithms.Preprocess.Missing_Values.wknnImpute - package keel.Algorithms.Preprocess.Missing_Values.wknnImpute
keel.Algorithms.Preprocess.NoiseFilters.ANR - package keel.Algorithms.Preprocess.NoiseFilters.ANR
keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter - package keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter
keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter - package keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter
keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter - package keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter
keel.Algorithms.Preprocess.NoiseFilters.INFFC - package keel.Algorithms.Preprocess.NoiseFilters.INFFC
keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter - package keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter
keel.Algorithms.Preprocess.NoiseFilters.PANDA - package keel.Algorithms.Preprocess.NoiseFilters.PANDA
keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter - package keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter
keel.Algorithms.Preprocess.Transformations.CleanAttributes - package keel.Algorithms.Preprocess.Transformations.CleanAttributes
keel.Algorithms.Preprocess.Transformations.decimal_scaling - package keel.Algorithms.Preprocess.Transformations.decimal_scaling
keel.Algorithms.Preprocess.Transformations.min_max - package keel.Algorithms.Preprocess.Transformations.min_max
keel.Algorithms.Preprocess.Transformations.Nominal2Binary - package keel.Algorithms.Preprocess.Transformations.Nominal2Binary
keel.Algorithms.Preprocess.Transformations.z_score - package keel.Algorithms.Preprocess.Transformations.z_score
keel.Algorithms.PSO_Learning.CPSO - package keel.Algorithms.PSO_Learning.CPSO
keel.Algorithms.PSO_Learning.LDWPSO - package keel.Algorithms.PSO_Learning.LDWPSO
keel.Algorithms.PSO_Learning.PSOLDA - package keel.Algorithms.PSO_Learning.PSOLDA
keel.Algorithms.PSO_Learning.REPSO - package keel.Algorithms.PSO_Learning.REPSO
keel.Algorithms.RE_SL_Methods.LEL_TSK - package keel.Algorithms.RE_SL_Methods.LEL_TSK
keel.Algorithms.RE_SL_Methods.MamWM - package keel.Algorithms.RE_SL_Methods.MamWM
keel.Algorithms.RE_SL_Methods.mogulHC - package keel.Algorithms.RE_SL_Methods.mogulHC
keel.Algorithms.RE_SL_Methods.mogulIRL - package keel.Algorithms.RE_SL_Methods.mogulIRL
keel.Algorithms.RE_SL_Methods.mogulSC - package keel.Algorithms.RE_SL_Methods.mogulSC
keel.Algorithms.RE_SL_Methods.P_FCS1 - package keel.Algorithms.RE_SL_Methods.P_FCS1
keel.Algorithms.RE_SL_Methods.SEFC - package keel.Algorithms.RE_SL_Methods.SEFC
keel.Algorithms.RE_SL_Methods.TSK_IRL - package keel.Algorithms.RE_SL_Methods.TSK_IRL
keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM - package keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM
keel.Algorithms.RE_SL_Postprocess.Mam2TSK - package keel.Algorithms.RE_SL_Postprocess.Mam2TSK
keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB - package keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB
keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules - package keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules
keel.Algorithms.RE_SL_Postprocess.MamSelect - package keel.Algorithms.RE_SL_Postprocess.MamSelect
keel.Algorithms.RE_SL_Postprocess.MamWSelect - package keel.Algorithms.RE_SL_Postprocess.MamWSelect
keel.Algorithms.RE_SL_Postprocess.MamWTuning - package keel.Algorithms.RE_SL_Postprocess.MamWTuning
keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs - package keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs
keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs - package keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs
keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs - package keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs
keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs - package keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs
keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs - package keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs
keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs - package keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs
keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs - package keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs
keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs - package keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs
keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules - package keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules
keel.Algorithms.RE_SL_Postprocess.TSKSelect - package keel.Algorithms.RE_SL_Postprocess.TSKSelect
keel.Algorithms.RST_Learning - package keel.Algorithms.RST_Learning
keel.Algorithms.RST_Learning.EFS_RPS - package keel.Algorithms.RST_Learning.EFS_RPS
keel.Algorithms.RST_Learning.EIS_RFS - package keel.Algorithms.RST_Learning.EIS_RFS
keel.Algorithms.Rule_Learning.AQ - package keel.Algorithms.Rule_Learning.AQ
keel.Algorithms.Rule_Learning.ART - package keel.Algorithms.Rule_Learning.ART
keel.Algorithms.Rule_Learning.C45Rules - package keel.Algorithms.Rule_Learning.C45Rules
keel.Algorithms.Rule_Learning.C45RulesSA - package keel.Algorithms.Rule_Learning.C45RulesSA
keel.Algorithms.Rule_Learning.CN2 - package keel.Algorithms.Rule_Learning.CN2
keel.Algorithms.Rule_Learning.DataSqueezer - package keel.Algorithms.Rule_Learning.DataSqueezer
keel.Algorithms.Rule_Learning.LEM1 - package keel.Algorithms.Rule_Learning.LEM1
keel.Algorithms.Rule_Learning.LEM2 - package keel.Algorithms.Rule_Learning.LEM2
keel.Algorithms.Rule_Learning.PART - package keel.Algorithms.Rule_Learning.PART
keel.Algorithms.Rule_Learning.Prism - package keel.Algorithms.Rule_Learning.Prism
keel.Algorithms.Rule_Learning.Riona - package keel.Algorithms.Rule_Learning.Riona
keel.Algorithms.Rule_Learning.Ripper - package keel.Algorithms.Rule_Learning.Ripper
keel.Algorithms.Rule_Learning.Ritio - package keel.Algorithms.Rule_Learning.Ritio
keel.Algorithms.Rule_Learning.Rules6 - package keel.Algorithms.Rule_Learning.Rules6
keel.Algorithms.Rule_Learning.Slipper - package keel.Algorithms.Rule_Learning.Slipper
keel.Algorithms.Rule_Learning.SRI - package keel.Algorithms.Rule_Learning.SRI
keel.Algorithms.Rule_Learning.Swap1 - package keel.Algorithms.Rule_Learning.Swap1
keel.Algorithms.Rule_Learning.UnoR - package keel.Algorithms.Rule_Learning.UnoR
keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest - package keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest
keel.Algorithms.Semi_Supervised_Learning.APSSC - package keel.Algorithms.Semi_Supervised_Learning.APSSC
keel.Algorithms.Semi_Supervised_Learning.Basic - package keel.Algorithms.Semi_Supervised_Learning.Basic
keel.Algorithms.Semi_Supervised_Learning.Basic.C45 - package keel.Algorithms.Semi_Supervised_Learning.Basic.C45
keel.Algorithms.Semi_Supervised_Learning.C45SSL - package keel.Algorithms.Semi_Supervised_Learning.C45SSL
keel.Algorithms.Semi_Supervised_Learning.CLCC - package keel.Algorithms.Semi_Supervised_Learning.CLCC
keel.Algorithms.Semi_Supervised_Learning.CoBC - package keel.Algorithms.Semi_Supervised_Learning.CoBC
keel.Algorithms.Semi_Supervised_Learning.CoForest - package keel.Algorithms.Semi_Supervised_Learning.CoForest
keel.Algorithms.Semi_Supervised_Learning.CoTraining - package keel.Algorithms.Semi_Supervised_Learning.CoTraining
keel.Algorithms.Semi_Supervised_Learning.DE_TriTraining - package keel.Algorithms.Semi_Supervised_Learning.DE_TriTraining
keel.Algorithms.Semi_Supervised_Learning.Democratic - package keel.Algorithms.Semi_Supervised_Learning.Democratic
keel.Algorithms.Semi_Supervised_Learning.NBSSL - package keel.Algorithms.Semi_Supervised_Learning.NBSSL
keel.Algorithms.Semi_Supervised_Learning.NNSSL - package keel.Algorithms.Semi_Supervised_Learning.NNSSL
keel.Algorithms.Semi_Supervised_Learning.RASCO - package keel.Algorithms.Semi_Supervised_Learning.RASCO
keel.Algorithms.Semi_Supervised_Learning.Rel_RASCO - package keel.Algorithms.Semi_Supervised_Learning.Rel_RASCO
keel.Algorithms.Semi_Supervised_Learning.SelfTraining - package keel.Algorithms.Semi_Supervised_Learning.SelfTraining
keel.Algorithms.Semi_Supervised_Learning.SETRED - package keel.Algorithms.Semi_Supervised_Learning.SETRED
keel.Algorithms.Semi_Supervised_Learning.SMOSSL - package keel.Algorithms.Semi_Supervised_Learning.SMOSSL
keel.Algorithms.Semi_Supervised_Learning.SNNRCE - package keel.Algorithms.Semi_Supervised_Learning.SNNRCE
keel.Algorithms.Semi_Supervised_Learning.TriTraining - package keel.Algorithms.Semi_Supervised_Learning.TriTraining
keel.Algorithms.Semi_Supervised_Learning.utilities - package keel.Algorithms.Semi_Supervised_Learning.utilities
keel.Algorithms.Semi_Supervised_Learning.utilities.KNN - package keel.Algorithms.Semi_Supervised_Learning.utilities.KNN
keel.Algorithms.Shared.ClassicalOptim - package keel.Algorithms.Shared.ClassicalOptim
keel.Algorithms.Shared.Exceptions - package keel.Algorithms.Shared.Exceptions
keel.Algorithms.Shared.Parsing - package keel.Algorithms.Shared.Parsing
keel.Algorithms.Statistical_Classifiers.ClassifierADLinear - package keel.Algorithms.Statistical_Classifiers.ClassifierADLinear
keel.Algorithms.Statistical_Classifiers.ClassifierADQuadratic - package keel.Algorithms.Statistical_Classifiers.ClassifierADQuadratic
keel.Algorithms.Statistical_Classifiers.ClassifierKernel - package keel.Algorithms.Statistical_Classifiers.ClassifierKernel
keel.Algorithms.Statistical_Classifiers.ClassifierLinearLMS - package keel.Algorithms.Statistical_Classifiers.ClassifierLinearLMS
keel.Algorithms.Statistical_Classifiers.ClassifierPolQuadraticLMS - package keel.Algorithms.Statistical_Classifiers.ClassifierPolQuadraticLMS
keel.Algorithms.Statistical_Classifiers.Logistic - package keel.Algorithms.Statistical_Classifiers.Logistic
keel.Algorithms.Statistical_Classifiers.Logistic.core - package keel.Algorithms.Statistical_Classifiers.Logistic.core
keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix - package keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix
keel.Algorithms.Statistical_Classifiers.Naive_Bayes - package keel.Algorithms.Statistical_Classifiers.Naive_Bayes
keel.Algorithms.Statistical_Classifiers.Shared.DiscrAnalysis - package keel.Algorithms.Statistical_Classifiers.Shared.DiscrAnalysis
keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs - package keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs
keel.Algorithms.Statistical_Models.ModelLinear - package keel.Algorithms.Statistical_Models.ModelLinear
keel.Algorithms.Statistical_Models.ModelQuad - package keel.Algorithms.Statistical_Models.ModelQuad
keel.Algorithms.Statistical_Tests.Classification.Clasif_General - package keel.Algorithms.Statistical_Tests.Classification.Clasif_General
keel.Algorithms.Statistical_Tests.Classification.Clasif_Summary - package keel.Algorithms.Statistical_Tests.Classification.Clasif_Summary
keel.Algorithms.Statistical_Tests.Classification.Clasif_Tabular - package keel.Algorithms.Statistical_Tests.Classification.Clasif_Tabular
keel.Algorithms.Statistical_Tests.Classification.ClasifTest_5x2cv - package keel.Algorithms.Statistical_Tests.Classification.ClasifTest_5x2cv
keel.Algorithms.Statistical_Tests.Classification.ClasifTest_f - package keel.Algorithms.Statistical_Tests.Classification.ClasifTest_f
keel.Algorithms.Statistical_Tests.Classification.ClasifTest_rs - package keel.Algorithms.Statistical_Tests.Classification.ClasifTest_rs
keel.Algorithms.Statistical_Tests.Classification.ClasifTest_sw - package keel.Algorithms.Statistical_Tests.Classification.ClasifTest_sw
keel.Algorithms.Statistical_Tests.Classification.ClasifTest_t - package keel.Algorithms.Statistical_Tests.Classification.ClasifTest_t
keel.Algorithms.Statistical_Tests.Classification.ClasifTest_u - package keel.Algorithms.Statistical_Tests.Classification.ClasifTest_u
keel.Algorithms.Statistical_Tests.Classification.Contrast - package keel.Algorithms.Statistical_Tests.Classification.Contrast
keel.Algorithms.Statistical_Tests.Classification.Friedman - package keel.Algorithms.Statistical_Tests.Classification.Friedman
keel.Algorithms.Statistical_Tests.Classification.FriedmanAlligned - package keel.Algorithms.Statistical_Tests.Classification.FriedmanAlligned
keel.Algorithms.Statistical_Tests.Classification.Imbalanced_General - package keel.Algorithms.Statistical_Tests.Classification.Imbalanced_General
keel.Algorithms.Statistical_Tests.Classification.Imbalanced_Summary - package keel.Algorithms.Statistical_Tests.Classification.Imbalanced_Summary
keel.Algorithms.Statistical_Tests.Classification.Imbalanced_Tabular - package keel.Algorithms.Statistical_Tests.Classification.Imbalanced_Tabular
keel.Algorithms.Statistical_Tests.Classification.ImbFriedman - package keel.Algorithms.Statistical_Tests.Classification.ImbFriedman
keel.Algorithms.Statistical_Tests.Classification.ImbWilcoxon - package keel.Algorithms.Statistical_Tests.Classification.ImbWilcoxon
keel.Algorithms.Statistical_Tests.Classification.Multiple - package keel.Algorithms.Statistical_Tests.Classification.Multiple
keel.Algorithms.Statistical_Tests.Classification.Quade - package keel.Algorithms.Statistical_Tests.Classification.Quade
keel.Algorithms.Statistical_Tests.Classification.Wilcoxon - package keel.Algorithms.Statistical_Tests.Classification.Wilcoxon
keel.Algorithms.Statistical_Tests.Regression.Contrast - package keel.Algorithms.Statistical_Tests.Regression.Contrast
keel.Algorithms.Statistical_Tests.Regression.Friedman - package keel.Algorithms.Statistical_Tests.Regression.Friedman
keel.Algorithms.Statistical_Tests.Regression.FriedmanAlligned - package keel.Algorithms.Statistical_Tests.Regression.FriedmanAlligned
keel.Algorithms.Statistical_Tests.Regression.Model_General - package keel.Algorithms.Statistical_Tests.Regression.Model_General
keel.Algorithms.Statistical_Tests.Regression.Model_Summary - package keel.Algorithms.Statistical_Tests.Regression.Model_Summary
keel.Algorithms.Statistical_Tests.Regression.Model_Tabular - package keel.Algorithms.Statistical_Tests.Regression.Model_Tabular
keel.Algorithms.Statistical_Tests.Regression.ModelTest_5x2cv - package keel.Algorithms.Statistical_Tests.Regression.ModelTest_5x2cv
keel.Algorithms.Statistical_Tests.Regression.ModelTest_f - package keel.Algorithms.Statistical_Tests.Regression.ModelTest_f
keel.Algorithms.Statistical_Tests.Regression.ModelTest_rs - package keel.Algorithms.Statistical_Tests.Regression.ModelTest_rs
keel.Algorithms.Statistical_Tests.Regression.ModelTest_sw - package keel.Algorithms.Statistical_Tests.Regression.ModelTest_sw
keel.Algorithms.Statistical_Tests.Regression.ModelTest_t - package keel.Algorithms.Statistical_Tests.Regression.ModelTest_t
keel.Algorithms.Statistical_Tests.Regression.ModelTest_u - package keel.Algorithms.Statistical_Tests.Regression.ModelTest_u
keel.Algorithms.Statistical_Tests.Regression.Multiple - package keel.Algorithms.Statistical_Tests.Regression.Multiple
keel.Algorithms.Statistical_Tests.Regression.Quade - package keel.Algorithms.Statistical_Tests.Regression.Quade
keel.Algorithms.Statistical_Tests.Regression.Wilcoxon - package keel.Algorithms.Statistical_Tests.Regression.Wilcoxon
keel.Algorithms.Statistical_Tests.Shared - package keel.Algorithms.Statistical_Tests.Shared
keel.Algorithms.Statistical_Tests.Shared.nonParametric - package keel.Algorithms.Statistical_Tests.Shared.nonParametric
keel.Algorithms.Subgroup_Discovery.aprioriSD - package keel.Algorithms.Subgroup_Discovery.aprioriSD
keel.Algorithms.Subgroup_Discovery.CN2SD - package keel.Algorithms.Subgroup_Discovery.CN2SD
keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate - package keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate
keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF - package keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF
keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate - package keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate
keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD - package keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD
keel.Algorithms.Subgroup_Discovery.SDAlgorithm - package keel.Algorithms.Subgroup_Discovery.SDAlgorithm
keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate - package keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate
keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA - package keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA
keel.Algorithms.Subgroup_Discovery.SDMap.FPTree - package keel.Algorithms.Subgroup_Discovery.SDMap.FPTree
keel.Algorithms.Subgroup_Discovery.SDMap.SDMap - package keel.Algorithms.Subgroup_Discovery.SDMap.SDMap
keel.Algorithms.SVM.C_SVM - package keel.Algorithms.SVM.C_SVM
keel.Algorithms.SVM.EPSILON_SVR - package keel.Algorithms.SVM.EPSILON_SVR
keel.Algorithms.SVM.NU_SVM - package keel.Algorithms.SVM.NU_SVM
keel.Algorithms.SVM.NU_SVR - package keel.Algorithms.SVM.NU_SVR
keel.Algorithms.SVM.SMO - package keel.Algorithms.SVM.SMO
keel.Algorithms.SVM.SMO.core - package keel.Algorithms.SVM.SMO.core
keel.Algorithms.SVM.SMO.supportVector - package keel.Algorithms.SVM.SMO.supportVector
keel.Algorithms.Symbolic_Regression.crispSymRegGAP - package keel.Algorithms.Symbolic_Regression.crispSymRegGAP
keel.Algorithms.Symbolic_Regression.crispSymRegSAP - package keel.Algorithms.Symbolic_Regression.crispSymRegSAP
keel.Algorithms.Symbolic_Regression.fuzzySymRegGAP - package keel.Algorithms.Symbolic_Regression.fuzzySymRegGAP
keel.Algorithms.Symbolic_Regression.fuzzySymRegSAP - package keel.Algorithms.Symbolic_Regression.fuzzySymRegSAP
keel.Algorithms.Symbolic_Regression.Shared - package keel.Algorithms.Symbolic_Regression.Shared
keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal - package keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal
keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams - package keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams
keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori - package keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori
keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori - package keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori
keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC - package keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC
keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal - package keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal
keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori - package keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori
keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA - package keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA
keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA - package keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA
keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat - package keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat
keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth - package keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth
keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD - package keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD
keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR - package keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR
keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR - package keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR
keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR - package keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR
keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh - package keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh
keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR - package keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR
keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII - package keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII
keel.Dataset - package keel.Dataset
keel.GraphInterKeel.datacf - package keel.GraphInterKeel.datacf
keel.GraphInterKeel.datacf.editData - package keel.GraphInterKeel.datacf.editData
keel.GraphInterKeel.datacf.exportData - package keel.GraphInterKeel.datacf.exportData
keel.GraphInterKeel.datacf.help - package keel.GraphInterKeel.datacf.help
keel.GraphInterKeel.datacf.importData - package keel.GraphInterKeel.datacf.importData
keel.GraphInterKeel.datacf.partitionData - package keel.GraphInterKeel.datacf.partitionData
keel.GraphInterKeel.datacf.partitionData.PartitioningSchemes - package keel.GraphInterKeel.datacf.partitionData.PartitioningSchemes
keel.GraphInterKeel.datacf.util - package keel.GraphInterKeel.datacf.util
keel.GraphInterKeel.datacf.visualizeData - package keel.GraphInterKeel.datacf.visualizeData
keel.GraphInterKeel.experiments - package keel.GraphInterKeel.experiments
keel.GraphInterKeel.help - package keel.GraphInterKeel.help
keel.GraphInterKeel.menu - package keel.GraphInterKeel.menu
keel.GraphInterKeel.statistical - package keel.GraphInterKeel.statistical
keel.GraphInterKeel.statistical.help - package keel.GraphInterKeel.statistical.help
keel.GraphInterKeel.statistical.tests - package keel.GraphInterKeel.statistical.tests
keel.GraphInterKeel.util - package keel.GraphInterKeel.util
keel.RunKeelGraph - package keel.RunKeelGraph
keel.RunKeelTxt - package keel.RunKeelTxt
keel.RunKeelTxtDocente - package keel.RunKeelTxtDocente
KeelDataSet - Class in keel.Algorithms.Neural_Networks.NNEP_Common.data: KeelDataSet implementation (keel dataset)
KeelDataSet(String, String...) - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.data.KeelDataSet: Constructor with the filename and the specification file
KeelDataSet() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.data.KeelDataSet: Constructor without arguments
KeelFile - Class in keel.Algorithms.Instance_Generation.utilities: Keel File function
KeelFile() - Constructor for class keel.Algorithms.Instance_Generation.utilities.KeelFile
KeelFile - Class in keel.Algorithms.Semi_Supervised_Learning.utilities: Keel File function
KeelFile() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.utilities.KeelFile
KeelFileFilter - Class in keel.GraphInterKeel.datacf.util
KeelFileFilter() - Constructor for class keel.GraphInterKeel.datacf.util.KeelFileFilter
KeelFileFilter - Class in keel.GraphInterKeel.experiments
KeelFileFilter() - Constructor for class keel.GraphInterKeel.experiments.KeelFileFilter
KEELIRPropPlusWrapperClas - Class in keel.Algorithms.Neural_Networks.IRPropPlus_Clas: Wrapper of iRProp+ algorithm for KEEL
KEELIRPropPlusWrapperClas() - Constructor for class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.KEELIRPropPlusWrapperClas
KEELIRPropPlusWrapperRegr - Class in keel.Algorithms.Neural_Networks.IRPropPlus_Regr: Wrapper of iRProp+ algorithm for KEEL
KEELIRPropPlusWrapperRegr() - Constructor for class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.KEELIRPropPlusWrapperRegr
KeelToCsv - Class in keel.Algorithms.Preprocess.Converter: KeelToCsv This class extends from the Exporter class.
KeelToCsv(String, String) - Constructor for class keel.Algorithms.Preprocess.Converter.KeelToCsv: KeelToCsv class Constructor.
KeelToDb - Class in keel.Algorithms.Preprocess.Converter: KeelToDb This class extends from the Exporter class.
KeelToDb(String, String, String, String, String) - Constructor for class keel.Algorithms.Preprocess.Converter.KeelToDb: KeelToDb class Constructor.
KeelToDif - Class in keel.Algorithms.Preprocess.Converter: KeelToDif This class extends from the Exporter class.
KeelToDif() - Constructor for class keel.Algorithms.Preprocess.Converter.KeelToDif: KeelToDif class Constructor.
KeelToExcel - Class in keel.Algorithms.Preprocess.Converter: KeelToExcel This class extends from the Exporter class.
KeelToExcel(String) - Constructor for class keel.Algorithms.Preprocess.Converter.KeelToExcel: KeelToExcel class Constructor.
KeelToHtml - Class in keel.Algorithms.Preprocess.Converter: KeelToHtml This class extends from the Exporter class.
KeelToHtml() - Constructor for class keel.Algorithms.Preprocess.Converter.KeelToHtml: KeelToHtml class Constructor.
KeelToPrn - Class in keel.Algorithms.Preprocess.Converter: KeelToPrn This class extends from the Exporter class.
KeelToPrn(String) - Constructor for class keel.Algorithms.Preprocess.Converter.KeelToPrn: KeelToPrn class Constructor.
KeelToPropertyList - Class in keel.Algorithms.Preprocess.Converter: KeelToPropertyList This class extends from the Exporter class.
KeelToPropertyList() - Constructor for class keel.Algorithms.Preprocess.Converter.KeelToPropertyList: KeelToPropertyList class Constructor.
KeelToTxt - Class in keel.Algorithms.Preprocess.Converter: KeelToTxt This class extends from the Exporter class.
KeelToTxt(String) - Constructor for class keel.Algorithms.Preprocess.Converter.KeelToTxt: KeelToTxt class Constructor.
KeelToUci - Class in keel.Algorithms.Preprocess.Converter: KeelToUci This class extends from the Exporter class.
KeelToUci(String, String) - Constructor for class keel.Algorithms.Preprocess.Converter.KeelToUci: KeelToUci class Constructor.
KeelToWeka - Class in keel.Algorithms.Preprocess.Converter: KeelToWeka This class extends from the Exporter class.
KeelToWeka() - Constructor for class keel.Algorithms.Preprocess.Converter.KeelToWeka: KeelToWeka class Constructor.
KeelToXml - Class in keel.Algorithms.Preprocess.Converter: KeelToXml This class extends from the Exporter class.
KeelToXml() - Constructor for class keel.Algorithms.Preprocess.Converter.KeelToXml: KeelToXml class Constructor.
KeelTreeCellRenderer - Class in keel.GraphInterKeel.experiments
KeelTreeCellRenderer() - Constructor for class keel.GraphInterKeel.experiments.KeelTreeCellRenderer: Default builder
KEELWrapperClas - Class in keel.Algorithms.Neural_Networks.NNEP_Clas
KEELWrapperClas() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Clas.KEELWrapperClas
KEELWrapperRegr - Class in keel.Algorithms.Neural_Networks.NNEP_Regr: Wrapper of Neural Net Evolutionary Programming for KEEL
KEELWrapperRegr() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Regr.KEELWrapperRegr
keepRelevantLinks(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Keep relevant links, that is, those links whose weight is higher than certain number
keepRelevantLinks(double) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuralNet: Keep relevant links, that is, those links whose weight is higher than certain number
keepRelevantLinks(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Keep relevant links, that is, those links whose weight is higher than certain number
keepRelevantLinks(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron: Keep relevant links, that is, those links whose weight is higher than certain number
Kernel - Class in keel.Algorithms.Statistical_Classifiers.ClassifierKernel: In this class, a kernel is defined
Kernel(double[][], int[], double, int) - Constructor for class keel.Algorithms.Statistical_Classifiers.ClassifierKernel.Kernel: This is the constructor of the class
Kernel - Class in keel.Algorithms.SVM.SMO.supportVector: Abstract kernel.
Kernel() - Constructor for class keel.Algorithms.SVM.SMO.supportVector.Kernel
kernel(int, char[], int, char[], int) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: the kernel function (Kn).
kernel_type - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
kernel_type - Variable in class org.libsvm.svm_parameter
KernelEvaluation - Class in keel.Algorithms.SVM.SMO.supportVector: Class for evaluating Kernels.
KernelEvaluation() - Constructor for class keel.Algorithms.SVM.SMO.supportVector.KernelEvaluation: default constructor
kernelHelper(int, char[], int, char[], int) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: The kernel helper function, called K' in [1] and [2].
kernelHelper2(int, char[], int, char[], int) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: helper function for the evaluation of the kernel K'' see section 'Efficient Computation of SSK' in [1]
kernelHelper2LP(int, char[], int, char[], int, int) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: helper function for the evaluation of the kernel (K''n) using lambda pruning
kernelHelperLP(int, char[], int, char[], int, int) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: helper function for the evaluation of the kernel (K'n) using lambda pruning
kernelLP(int, char[], int, char[], int, int) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: the kernel function K explained in [1] using lambda pruning, explained in [2].
kernelTipText() - Method in class keel.Algorithms.SVM.SMO.SMO: Returns the tip text for this property
kernelTipText() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Returns the tip text for this property
kernelTipText() - Method in class keel.Algorithms.SVM.SMO.SVMreg: Returns the tip text for this property
key - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Pair: Pair's key.
key - Variable in class keel.Algorithms.Rule_Learning.PART.Pair: key of the pair.
key - Variable in class keel.Algorithms.Rule_Learning.Ripper.Pair: key of the pair.
key - Variable in class keel.Algorithms.Rule_Learning.Slipper.Pair: Pair's key.
keyPressed(KeyEvent) - Method in class keel.GraphInterKeel.experiments.GraphPanel: EDUCATIONAL KEEL **********************
keyPressedAux(KeyEvent) - Method in class keel.GraphInterKeel.experiments.GraphPanel: EDUCATIONAL KEEL **********************
keyReleased(KeyEvent) - Method in class keel.GraphInterKeel.experiments.GraphPanel
keyTyped(KeyEvent) - Method in class keel.GraphInterKeel.experiments.GraphPanel
kfold - Variable in class keel.GraphInterKeel.datacf.importData.ImportPanel: This is used for deciding the default option of the type of partition
kfoldOptions - Variable in class keel.GraphInterKeel.datacf.partitionData.PartitionPanel: An option dialog for obtaining the options of the k-fold partition proccess
KFoldOptionsJDialog - Class in keel.GraphInterKeel.datacf.partitionData
KFoldOptionsJDialog(Frame, boolean) - Constructor for class keel.GraphInterKeel.datacf.partitionData.KFoldOptionsJDialog: Constructor that initializes the dialog
Khiops - Class in keel.Algorithms.Discretizers.Khiops: Khiops Discretizer Implemented by Julian Luengo, March 2010 julianlm@decsai.ugr.es Based on the work of Marc Boullé M.
Khiops() - Constructor for class keel.Algorithms.Discretizers.Khiops.Khiops: Default constructor.
killProcess() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: This method is used for to kill the experiment.
kind - Variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Token: An integer that describes the kind of this token.
kind - Variable in class keel.Algorithms.Rule_Learning.Swap1.Token: An integer that describes the kind of this token.
kind - Variable in class keel.Dataset.Token: An integer that describes the kind of this token.
KLEENE_DIENES - Static variable in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
KMeans(int, double[][], int) - Method in class keel.Algorithms.Discretizers.UCPD.UCPD: It calculates the cutpoints using the K-Means algorithm
kMeans(double[][], int) - Method in class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.GA_MSE_CC_FSM: Execute the kMeans algorithm with the training data given and the number of clusters
kMeans(double[][], int) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.GA_MSE_CC_FSM: Execute the kMeans algorithm with the training data given and the number of clusters
kmeansImpute - Class in keel.Algorithms.Preprocess.Missing_Values.kmeansImpute: This class imputes the missing values by means of the K-means clustering algorithm.
kmeansImpute(String) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.kmeansImpute: Creates a new instance of kmeansImpute
KNN - Class in keel.Algorithms.Decision_Trees.C45_Binarization: File: KNN.java An auxiliary implementation of the KNN classifier for using in Instance Selection algorithms
KNN() - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.KNN
KNN(int, double, double[][], int) - Method in class keel.Algorithms.Discretizers.UCPD.UCPD: It computes the cutpoint using KNN algorithm
KNN - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD: The KNN algorithm tries to find the K nearest instances in the training data, selecting the most present class.
KNN(InstanceSet, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.KNN: Parameter constructor.
KNN - Class in keel.Algorithms.ImbalancedClassification.Ensembles.Basic
KNN() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.KNN
KNN - Class in keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic: File: KNN.java An auxiliary implementation of the KNN classifier for using in Instance Selection algorithms
KNN() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.KNN
KNN - Class in keel.Algorithms.Instance_Generation.utilities.KNN: Implements the KNN algorithm.
KNN() - Constructor for class keel.Algorithms.Instance_Generation.utilities.KNN.KNN
knn(Prototype, PrototypeSet, int) - Static method in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN: Implements the KNN algorithm
knn(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN: Implements the KNN algorithm.
KNN - Class in keel.Algorithms.Lazy_Learning.KNN: File: KNN.java The KNN algorithm tries to find the K nearest instances in the training data, selecting the most present class.
KNN(String) - Constructor for class keel.Algorithms.Lazy_Learning.KNN.KNN: The main method of the class
KNN - Class in keel.Algorithms.MIL.Nearest_Neighbour.KNN
KNN() - Constructor for class keel.Algorithms.MIL.Nearest_Neighbour.KNN.KNN
KNN - Class in keel.Algorithms.Preprocess.Basic: File: KNN.java An auxiliary implementation of the KNN classifier for using in Instance Selection algorithms
KNN() - Constructor for class keel.Algorithms.Preprocess.Basic.KNN
KNN - Class in keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter: The KNN algorithm tries to find the K nearest instances in the training data, selecting the most present class.
KNN(Instance[], Instance[]) - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: The main method of the class
KNN - Class in keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter: The KNN algorithm tries to find the K nearest instances in the training data, selecting the most present class.
KNN(Instance[], Instance[]) - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: The main method of the class
KNN - Class in keel.Algorithms.Semi_Supervised_Learning.utilities.KNN: Implements the KNN algorithm.
KNN() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN
knn(Prototype, PrototypeSet, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Implements the KNN algorithm
knn(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN: Implements the KNN algorithm.
KNNAdaptive - Class in keel.Algorithms.Lazy_Learning.KNNAdaptive: File: KNNAdaptive.java The KNN Adaptive Algorithm.
KNNAdaptive(String) - Constructor for class keel.Algorithms.Lazy_Learning.KNNAdaptive.KNNAdaptive: The main method of the class
KNNClassifier - Class in keel.Algorithms.RST_Learning: File: KNNClassifier.java A KNN classifier with the capabilities of selecting instances and features.
KNNClassifier() - Constructor for class keel.Algorithms.RST_Learning.KNNClassifier
knnImpute - Class in keel.Algorithms.Preprocess.Missing_Values.knnImpute: This class computes the mean (numerical) or mode (nominal) value of the attributes with missing values for the selected neighbours for a given instance with missing values
knnImpute(String) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.knnImpute.knnImpute: Creates a new instance of MostCommonValue
KSNN - Class in keel.Algorithms.Lazy_Learning.KSNN: File: KSNN.java The K Symetrical NN Algorithm.
KSNN(String) - Constructor for class keel.Algorithms.Lazy_Learning.KSNN.KSNN: The main method of the class
KStar - Class in keel.Algorithms.Lazy_Learning.KStar: File: KStar.java The KStar Algorithm.
KStar(String) - Constructor for class keel.Algorithms.Lazy_Learning.KStar.KStar: The main method of the class
kthSmallestValue(AttributeWeka, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns the kth-smallest attribute value of a numeric attribute.
kthSmallestValue(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns the kth-smallest attribute value of a numeric attribute.
kthSmallestValue(int[], int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Returns the kth-smallest value in the array.
kthSmallestValue(double[], int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Returns the kth-smallest value in the array
kthSmallestValue(Attribute, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns the kth-smallest attribute value of a numeric attribute.
kthSmallestValue(int, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns the kth-smallest attribute value of a numeric attribute.
kthSmallestValue(int[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Returns the kth-smallest value in the array.
kthSmallestValue(double[], int) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Returns the kth-smallest value in the array
kthSmallestValue(int[], int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Returns the kth-smallest value in the array.
kthSmallestValue(double[], int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Returns the kth-smallest value in the array
kthSmallestValue(int[], int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Returns the kth-smallest value in the array.
kthSmallestValue(double[], int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Returns the kth-smallest value in the array
kthSmallestValue(int[], int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Returns the kth-smallest value in the array.
kthSmallestValue(double[], int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Returns the kth-smallest value in the array
kthSmallestValue(int[], int) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Returns the kth-smallest value in the array.
kthSmallestValue(double[], int) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Returns the kth-smallest value in the array
kthSmallestValue(Attribute, int) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns the kth-smallest attribute value of a numeric attribute.
kthSmallestValue(int, int) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns the kth-smallest attribute value of a numeric attribute.
kthSmallestValue(int[], int) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Returns the kth-smallest value in the array.
kthSmallestValue(double[], int) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Returns the kth-smallest value in the array
KValueTipText() - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Returns the tip text for this property
KValueTipText() - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Returns the tip text for this property
KValueTipText() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Returns the tip text for this property

L

l - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_problem
l - Variable in class org.libsvm.svm_problem
l_0 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Represents the interval of random numbers that will be generated to be added in the specify operator.
L_0 - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
label(int, Dataset) - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Function to print label for subset index of itemsets.
label(int, Dataset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to print label for subset index of itemsets.
label(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.DataBase: It translates the antecedent id to a valid fuzzy label
label(int, MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Function to print label for subset index of itemsets.
label(int, Dataset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Function to print label for subset index of itemsets.
label(int, Dataset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to print label for subset index of itemsets.
label() - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Returns the label of the prototype (aka first output).
label() - Method in class keel.Algorithms.Instance_Generation.GMCA.Cluster: Gets the label of all prototypes of the cluster.
label - Variable in class keel.Algorithms.Instance_Generation.VQ.Cluster: Class of all the prototypes of the Cluster.
label(int, MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Function to print label for subset index of itemsets.
label(int, MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Function to print label for subset index of itemsets.
label(int, MyDataset) - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Function to print label for subset index of itemsets.
label(int, Dataset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Function to print label for subset index of itemsets.
label() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Returns the label of the prototype (aka first output).
label2(int, Dataset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to print label for subset index of itemsets.
label2(int, Dataset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to print label for subset index of itemsets.
labelCount - Static variable in class keel.GraphInterKeel.experiments.Credits
labels() - Method in class keel.Algorithms.Instance_Generation.MCA.DistanceMatrixByClass
Lambda - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.Parameters
Lambda - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.Parameters
lambda - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
lambda - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters: Problem coefficients
lambda - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters: Problem coefficients
lambda - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters: Problem coefficients
lambda - Variable in class keel.Algorithms.Neural_Networks.net.Parameters: Problem coefficients
lambdaTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Returns the tip text for this property
lanzar() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.GA
lanzar() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.SIA: We execute here the main SIA algorithm and then we creat the output files
Lanzar - Class in keel.Algorithms.RE_SL_Methods.LEL_TSK: Main function class.
Lanzar() - Constructor for class keel.Algorithms.RE_SL_Methods.LEL_TSK.Lanzar
Lanzar - Class in keel.Algorithms.RE_SL_Methods.MamWM: Main function class.
Lanzar() - Constructor for class keel.Algorithms.RE_SL_Methods.MamWM.Lanzar
Lanzar - Class in keel.Algorithms.RE_SL_Methods.mogulHC: Main function class.
Lanzar() - Constructor for class keel.Algorithms.RE_SL_Methods.mogulHC.Lanzar
Lanzar - Class in keel.Algorithms.RE_SL_Methods.mogulIRL: Main function class.
Lanzar() - Constructor for class keel.Algorithms.RE_SL_Methods.mogulIRL.Lanzar
Lanzar - Class in keel.Algorithms.RE_SL_Methods.mogulSC: Main function class.
Lanzar() - Constructor for class keel.Algorithms.RE_SL_Methods.mogulSC.Lanzar
Lanzar - Class in keel.Algorithms.RE_SL_Methods.TSK_IRL: Main function class.
Lanzar() - Constructor for class keel.Algorithms.RE_SL_Methods.TSK_IRL.Lanzar
Lanzar - Class in keel.Algorithms.RE_SL_Postprocess.Mam2TSK: Main function class.
Lanzar() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.Lanzar
Lanzar - Class in keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB: Main function class.
Lanzar() - Constructor for class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.Lanzar
Lanzar - Class in keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules: Main function class.
Lanzar() - Constructor for class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.Lanzar
Lanzar - Class in keel.Algorithms.RE_SL_Postprocess.MamSelect: Main function class.
Lanzar() - Constructor for class keel.Algorithms.RE_SL_Postprocess.MamSelect.Lanzar
Lanzar - Class in keel.Algorithms.RE_SL_Postprocess.MamWSelect: Main function class.
Lanzar() - Constructor for class keel.Algorithms.RE_SL_Postprocess.MamWSelect.Lanzar
Lanzar - Class in keel.Algorithms.RE_SL_Postprocess.MamWTuning: Main function class.
Lanzar() - Constructor for class keel.Algorithms.RE_SL_Postprocess.MamWTuning.Lanzar
Lanzar - Class in keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules: Main function class.
Lanzar() - Constructor for class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.Lanzar
Lanzar - Class in keel.Algorithms.RE_SL_Postprocess.TSKSelect: Main function class.
Lanzar() - Constructor for class keel.Algorithms.RE_SL_Postprocess.TSKSelect.Lanzar
lap() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.StopWatch: Saves a lap time if the watch is running.
lap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.StopWatch: Saves a lap time if the watch is running.
lap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.StopWatch: Saves a lap time if the watch is running.
lap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.StopWatch: Saves a lap time if the watch is running.
lap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.StopWatch: Saves a lap time if the watch is running.
lap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.StopWatch: Saves a lap time if the watch is running.
lap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.StopWatch: Saves a lap time if the watch is running.
lap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.StopWatch: Saves a lap time if the watch is running.
lap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.StopWatch: Saves a lap time if the watch is running.
lap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.StopWatch: Saves a lap time if the watch is running.
lap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.StopWatch: Saves a lap time if the watch is running.
lap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.StopWatch: Saves a lap time if the watch is running.
lap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.StopWatch: Saves a lap time if the watch is running.
lap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.StopWatch: Saves a lap time if the watch is running.
lap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.StopWatch: Saves a lap time if the watch is running.
lap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.StopWatch: Saves a lap time if the watch is running.
Laplace - Static variable in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: Type of Positive Definite Functions supported (Laplace)
laplace(double) - Method in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: Computes the result of the Laplace PDRF
largestDiameter() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Size of the greatest diameter in the set.
largestDiameter() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Size of the greatest diameter in the set.
last10IterationsAccuracyAverage - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Statistics
last10IterationsAccuracyAverage - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Statistics
lastElement() - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector: Returns the last element of the vector.
lastElement() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector: Returns the last element of the vector.
lastElement() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector: Returns the last element of the vector.
lastElement() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector: Returns the last element of the vector.
lastElement() - Method in class keel.Algorithms.SVM.SMO.core.FastVector: Returns the last element of the vector.
lastError - Variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.MSEOptimizablePUNeuralNetClassifier: Wrapped algorithm
lastError - Variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.MSEOptimizableSigmNeuralNetClassifier: Wrapped algorithm
lastError - Variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.MSEOptimizablePUNeuralNetRegressor: Last Error
lastError - Variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.MSEOptimizableSigmNeuralNetRegressor: Wrapped algorithm
lastEvaluation - Variable in class keel.Algorithms.PSO_Learning.CPSO.Particle
lastEvaluation - Variable in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
lastEvaluation - Variable in class keel.Algorithms.PSO_Learning.PSOLDA.Particle
lastEvaluation - Variable in class keel.Algorithms.PSO_Learning.REPSO.Particle
lastInstance() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Returns the last instance in the set.
lastInstance() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns the last instance in the set.
lastInstance() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns the last instance in the set.
lastInstance() - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns the last instance in the set.
lastItemset() - Method in class keel.Algorithms.Decision_Trees.C45.Dataset: Returns the last itemset.
lastItemset() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Returns the last itemset.
lastItemset() - Method in class keel.Algorithms.Decision_Trees.ID3.Dataset: Returns the last itemset.
lastItemset() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Returns the last itemset.
lastItemset() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns the last itemset.
lastItemset() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Returns the last itemset.
lastItemset() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Returns the last itemset.
lastItemset() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Returns the last itemset.
lastItemset() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Returns the last itemset.
lastItemset() - Method in class keel.Algorithms.Rule_Learning.ART.Dataset: Returns the last itemset.
lastItemset() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Returns the last itemset.
lastItemset() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Returns the last itemset.
lastItemset() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Returns the last itemset.
lastItemset() - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Returns the last itemset.
lastItemset() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Returns the last itemset.
lastPath - Static variable in class keel.GraphInterKeel.statistical.StatisticalF
lastSelectedPanel - Variable in class keel.GraphInterKeel.datacf.exportData.ExportPanel: String for having an history of the last selected panel
lastSelectedPanel - Variable in class keel.GraphInterKeel.datacf.importData.ImportPanel: String for having an history of the last selected panel
lastSelectedType - Variable in class keel.GraphInterKeel.datacf.exportData.ExportPanel: String for having an history of the last type of conversion
lastValue - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.CategoricalAttribute: The last value assigned
lateralDisplace(double) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Fuzzy: Modifies the current fuzzy value according to a provided lateral displacement
Layer - Class in keel.GraphInterKeel.experiments
Layer() - Constructor for class keel.GraphInterKeel.experiments.Layer
layerActivo - Static variable in class keel.GraphInterKeel.experiments.Layer
LazyAlgorithm - Class in keel.Algorithms.Lazy_Learning: File: LazyAlgorithm.java A general framework for Lazy Learning Algorithms.
LazyAlgorithm() - Constructor for class keel.Algorithms.Lazy_Learning.LazyAlgorithm
LazyDT - Class in keel.Algorithms.Lazy_Learning.LazyDT: File: LazyDT.java The LazyDT algorithm doesn't build a decision tree model in a training phase and uses the model when we start classifying.
LazyDT(String) - Constructor for class keel.Algorithms.Lazy_Learning.LazyDT.LazyDT: Creates a LazyDT instance by reading the script file that contains all the information needed for running the algorithm
LBR - Class in keel.Algorithms.Lazy_Learning.LBR: File: LBR.java The LBR Algorithm.
LBR(String) - Constructor for class keel.Algorithms.Lazy_Learning.LBR.LBR: The main method of the class
LDA - Class in keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter: Linear Discriminant Analysis class.
LDA() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.LDA: Default constructor.
lde(int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion: Right limit
LDWPSO - Class in keel.Algorithms.PSO_Learning.LDWPSO: Title: Algorithm LDWPSO Description: It contains the implementation of the algorithm Company: KEEL
LDWPSO() - Constructor for class keel.Algorithms.PSO_Learning.LDWPSO.LDWPSO: Default constructor
LDWPSO(parseParameters) - Constructor for class keel.Algorithms.PSO_Learning.LDWPSO.LDWPSO: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
leafNode() - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode: Sets the node to a leaf
leafNode() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode: Sets the node to a leaf
leafNum(M5Instance) - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode: Detects which leaf a instance falls into
leafNum(Itemset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode: Detects which leaf a itemset falls into
leafsSize() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: Returns the number of samples covered by each leaves.
LEARNED_CATEGORY_INDEX - Static variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
learnWeights(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseR: This function adjust the certainty degree for the rules
LeastSquaresDeviation - Class in keel.Algorithms.Decision_Trees.CART.impurities: Implementation of Least Square Deviation impurity Function
LeastSquaresDeviation() - Constructor for class keel.Algorithms.Decision_Trees.CART.impurities.LeastSquaresDeviation
leeConjunto(String, boolean) - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Reads the file of examples(Train&Test)
leeConjunto(String, boolean) - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Reads the file of examples(Train&Test)
leeConjunto(String, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: It reads the examples file (training or test)
leeConjunto(String, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset: It reads the examples file (training or test)
leeFichero(String) - Static method in class keel.Algorithms.RE_SL_Methods.LEL_TSK.Fichero: Function for reading a data file in a String Object
leeFichero(String) - Static method in class keel.Algorithms.RE_SL_Methods.MamWM.Fichero: Function for reading a data file in a String Object
leeFichero(String) - Static method in class keel.Algorithms.RE_SL_Methods.mogulIRL.Fichero: Function for reading a data file in a String Object
leeFichero(String) - Static method in class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.Fichero: Function for reading a data file in a String Object
leeFichero(String) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.Fichero: Function for reading a data file in a String Object
leeFichero(String) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.Fichero: Function for reading a data file in a String Object
leeFichero(String) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamSelect.Fichero: Function for reading a data file in a String Object
leeFichero(String) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamWSelect.Fichero: Function for reading a data file in a String Object
leeFichero(String) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamWTuning.Fichero: Function for reading a data file in a String Object
leeFichero(String) - Static method in class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.Fichero: Function for reading a data file in a String Object
leeFichero(String) - Static method in class keel.Algorithms.RE_SL_Postprocess.TSKSelect.Fichero: Function for reading a data file in a String Object
leeFichero(String) - Static method in class org.core.Fichero
leeFicheroLinea(String) - Static method in class keel.GraphInterKeel.experiments.Files: Read from a file
leer(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.LeerWm: It reads the file
leer(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.LeerWm: It reads the file
leer(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.LeerWm: It reads the file
leer(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.LeerWm: It reads the file
leer(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.LeerWm: It reads the file
leer(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.LeerWm: It reads the file
leer(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.LeerWm: It reads the file
leer(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.LeerWm: It reads the file
leer_BR(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Sel: Reads the previous learned RB from a input file
leer_BR(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Tun: Reads the previous simplified RB from a input file
leerConfiguracion(String) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.ENN: Reads configuration script, and extracts its contents.
leerConfiguracion(String) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Reads the parameters of the algorithm.
leerConfiguracion(String) - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.EHS_CHC
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.SMOTE.MSMOTE
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.SMOTE.SMOTE
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.SPIDER.SPIDER
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.SMOTE: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.ADASYN.ADASYN: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.ADOMS.ADOMS: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.AHCClustering.AHCClustering: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.Borderline_SMOTE.Borderline_SMOTE: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.CNN.CNN: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.CNN_TomekLinks.CNN_TomekLinks: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.CPM.CPM: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.NCL.NCL: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.OSS.OSS: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.RandomOverSampling.RandomOverSampling: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.RandomUnderSampling.RandomUnderSampling: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.Safe_Level_SMOTE.Safe_Level_SMOTE: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SBC.SBC: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE.SMOTE: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_ENN.SMOTE_ENN: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.SMOTE_RSB
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_TomekLinks.SMOTE_TomekLinks: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SPIDER.SPIDER: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SPIDER2.SPIDER2: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.TomekLinks.TomekLinks: Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Generation.Depur.Depur
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Generation.DROP3LVQ3.DROP3LVQ3
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Generation.DROP3PSO.DROP3PSO
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Generation.DROP3SFLSDE.DROP3SFLSDE
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Generation.ICFLVQ3.ICFLVQ3
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Generation.ICFPSO.ICFPSO
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Generation.ICFSFLSDE.ICFSFLSDE
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.SSMALVQ3
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.SSMAPSO
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.AllKNN.AllKNN
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.BSE.BSE
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.CCIS.CCIS
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.CHC.CHC: Reads configuration script, and extracts its contents.
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.CNN.CNN: Reads configuration script, and extracts its contents.
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.CoCoIS.CoCoIS: Process the configuration file
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.CPruner.CPruner
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.DROP1.DROP1
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.DROP2.DROP2
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.DROP3.DROP3
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.ENN.ENN: Reads configuration script, and extracts its contents.
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.ENNRS.ENNRS
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.ENNTh.ENNTh
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.ENRBF.ENRBF
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.Explore.Explore
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.FCNN.FCNN
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.GA_MSE_CC_FSM
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.GCNN.GCNN
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.GG.GG
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.GGA.GGA: Reads configuration script, and extracts its contents.
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.HMNEI.HMNEI
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.IB2.IB2
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.IB3.IB3
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.ICF.ICF
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.IGA.IGA
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.IKNN.IKNN
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.MCNN.MCNN
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.MCS.MCS
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.MENN.MENN
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.MNV.MNV
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.ModelCS.ModelCS
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.MSS.MSS
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.Multiedit.Multiedit
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.NCNEdit.NCNEdit
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.NRMCS.NRMCS
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.PBIL.PBIL: Reads configuration script, and extracts its contents.
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.POP.POP
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.PSC.PSC
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.PSRCG.PSRCG
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.Reconsistent.Reconsistent
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.RENN.RENN
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.RMHC.RMHC
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.RNG.RNG
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.RNN.RNN
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.SGA.SGA: Reads configuration script, and extracts its contents.
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.Shrink.Shrink
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.SNN.SNN
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.SSMA.SSMA: Reads configuration script, and extracts its contents.
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.SVBPS.SVBPS
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.TCNN.TCNN
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.TRKNN.TRKNN
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.VSM.VSM
leerConfiguracion(String) - Method in class keel.Algorithms.Instance_Selection.ZhangTS.ZhangTS
leerConfiguracion(String) - Method in class keel.Algorithms.Neural_Networks.LVQ.LVQ
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Basic.Metodo: Reads the parameters of the algorithm.
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.AllKNN.AllKNN
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.BSE.BSE
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.CCIS.CCIS
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.CHC.CHC: Reads configuration script, and extracts its contents.
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.CNN.CNN: Reads configuration script, and extracts its contents.
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.CoCoIS: Process the configuration file
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.CPruner.CPruner
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.DROP1.DROP1
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.DROP2.DROP2
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.DROP3.DROP3
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.ENN.ENN: Reads configuration script, and extracts its contents.
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.ENNRS.ENNRS
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.ENNTh.ENNTh
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.ENRBF.ENRBF
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.Explore.Explore
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.FCNN.FCNN
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.GA_MSE_CC_FSM
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GCNN.GCNN
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GG.GG
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GGA.GGA: Reads configuration script, and extracts its contents.
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.HMNEI.HMNEI
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.IB2.IB2
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.IB3.IB3
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.ICF.ICF
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.IGA.IGA
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.IKNN.IKNN
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.MCNN.MCNN
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.MCS.MCS
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.MENN.MENN
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.MNV.MNV
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.ModelCS.ModelCS
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.MSS.MSS
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.Multiedit.Multiedit
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.NCNEdit.NCNEdit
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.NRMCS.NRMCS
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.PBIL.PBIL: Reads configuration script, and extracts its contents.
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.POP.POP
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.PSC.PSC
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.PSRCG.PSRCG
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.Reconsistent.Reconsistent
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.RENN.RENN
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.RMHC.RMHC
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.RNG.RNG
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.RNN.RNN
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SGA.SGA: Reads configuration script, and extracts its contents.
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.Shrink.Shrink
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SNN.SNN
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.SSMA: Reads configuration script, and extracts its contents.
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SVBPS.SVBPS
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.TCNN.TCNN
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.TRKNN.TRKNN
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.VSM.VSM
leerConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.ZhangTS
LeerWm - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs: The class that reads the file of the rule base
LeerWm(String) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.LeerWm: It stores the name of the file
LeerWm - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs: The class that reads the file of the rule base
LeerWm(String) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.LeerWm: It stores the name of the file
LeerWm - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs: The class that reads the file of the rule base
LeerWm(String) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.LeerWm: It stores the name of the file
LeerWm - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs: The class that reads the file of the rule base
LeerWm(String) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.LeerWm: It stores the name of the file
LeerWm - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs: The class that reads the file of the rule base
LeerWm(String) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.LeerWm: It stores the name of the file
LeerWm - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs: The class that reads the file of the rule base
LeerWm(String) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.LeerWm: It stores the name of the file
LeerWm - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs: The class that reads the file of the rule base
LeerWm(String) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.LeerWm: It stores the name of the file
LeerWm - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs: The class that reads the file of the rule base
LeerWm(String) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.LeerWm: It stores the name of the file
leftChi2Row - Variable in class keel.Algorithms.Discretizers.Khiops.DeltaValue: the chi2 row of the left interval in our boundary
leftInterval - Variable in class keel.Algorithms.Discretizers.Khiops.DeltaValue: the left interval in our boundary
leftInterval - Variable in class keel.Algorithms.Discretizers.MODL.DeltaValue: the left interval in our boundary
leftSide(Dataset) - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Function to print left side of condition.
leftSide(Dataset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to print left side of condition.
leftSide(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Function to print left side of condition.
leftSide(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Function to print left side of condition.
leftSide(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to print left side of condition.
leftSide(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Function to print left side of condition.
leftSide(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Function to print left side of condition.
leftSide(MyDataset) - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Function to print left side of condition.
leftSide(Dataset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Function to print left side of condition.
leftSide2(Dataset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to print left side of condition.
leftSide2(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to print left side of condition.
leftSideOVO(Dataset) - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Function to print left side of condition.
length() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
length() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
length() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Chromosome
length() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Chromosome
lengthRule() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule: It returns the length of the rule
LESS_EQUAL - Static variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Selector: Number to represent lesser.
LESS_EQUAL - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Selector: Number to represent lesser.
LETTER - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for a letter.
LETTER - Static variable in interface keel.Dataset.DataParserConstants
lexCompositionRank(int[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Combinatoric: Rank of composition a[] (d balls in r=a.length boxes) in lex order is mapped to nondecreasing function r^d.
LexicalError(boolean, int, int, int, String, char) - Static method in error keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.TokenMgrError: Returns a detailed message for the Error when it is thrown by the token manager to indicate a lexical error.
LexicalError(boolean, int, int, int, String, char) - Static method in error keel.Algorithms.Rule_Learning.Swap1.TokenMgrError: Returns a detailed message for the Error when it is thrown by the token manager to indicate a lexical error.
LexicalError(boolean, int, int, int, String, char) - Static method in error keel.Dataset.TokenMgrError: Returns a detailed message for the Error when it is thrown by the token manager to indicate a lexical error.
lexIncFuncRank(int[], int) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Combinatoric: Rank of increasing function a[] (r^d) in lex order.
lexNondecFuncRank(int[], int) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Combinatoric: Rank of nondecreasing function a[] (r^d) in lex order.
lexStateNames - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserTokenManager
lexStateNames - Static variable in class keel.Algorithms.Rule_Learning.Swap1.DataParserTokenManager
lexStateNames - Static variable in class keel.Dataset.DataParserTokenManager
Li - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Apply Li flag.
lift - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
lift - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
lift - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
lift - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
LimitRoulette - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh
LimitRoulette() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.LimitRoulette
limpia() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Poblacion
limpiaCNominales() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Removes all nominal conditions from the particle.
limpiaCNominales() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Removes all nominal conditions.
limpiaUtiles() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Organizacion
lin(int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion: Lower limit
line - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
line - Static variable in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
line - Static variable in class keel.Dataset.SimpleCharStream
LINEAR - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ProbabilityManagement
LINEAR - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.ProbabilityManagement
LINEAR - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.ProbabilityManagement
LINEAR - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ProbabilityManagement
LINEAR - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
LINEAR - Static variable in class org.libsvm.svm_parameter
linearComb(FuzzyAlphaCut, FuzzyAlphaCut, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut: Returns the linear combination of two alpha-cuts a and b with alpha "alphap".
LinearLayer - Class in keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet: Represents a neural net layer with all the nodes of LinearNeuron type
LinearLayer() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinearLayer: Empty constructor
LinearNeuron - Class in keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet: Represents a Linear neuron of a neural net
LinearNeuron() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinearNeuron: Default constructor.
LinearNeuronParametricMutator - Class in keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric: Parametric Mutator of Linear Neurons.
LinearNeuronParametricMutator() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.LinearNeuronParametricMutator: Empty constructor
LinearNeuronStructuralMutator - Class in keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural: Structural Mutator of Linear Neurons.
LinearNeuronStructuralMutator() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.LinearNeuronStructuralMutator: Empty constructor
LinearRegression - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat: Statistics class used for linear regression stats.
LinearRegression() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression
LinearRegression - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix: Class for performing (ridged) linear regression.
LinearRegression(Matrix, Matrix, double) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.LinearRegression: Performs a (ridged) linear regression.
LinearRegression(Matrix, Matrix, double[], double) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.LinearRegression: Performs a weighted (ridged) linear regression.
LinearSearchBrent - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting
LinearSearchBrent(Fun, double[], double[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.LinearSearchBrent
LinearSearchBrent - Class in keel.Algorithms.Preprocess.NoiseFilters.ANR: Brent's method is a complicated but popular root-finding algorithm combining the bisection method, the secant method and inverse quadratic interpolation.
LinearSearchBrent(FUN, double[][][], double[][][]) - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.ANR.LinearSearchBrent: Constructor for linear search based on Brent's method.
LinearSearchBrent - Class in keel.Algorithms.Shared.ClassicalOptim: Brent's method is a complicated but popular root-finding algorithm combining the bisection method, the secant method and inverse quadratic interpolation.
LinearSearchBrent(FUN, double[][][], double[][][]) - Constructor for class keel.Algorithms.Shared.ClassicalOptim.LinearSearchBrent: Constructor for linear search based on Brent's method.
Link - Class in keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet: Link of a neuron
Link() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.Link: Empty constructor
LinkedLayer - Class in keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet: Base implementation of a hidden or output layer
LinkedLayer() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Empty constructor
LinkedNeuron - Class in keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet: Base implementation of a neuron of a hidden or output layer
LinkedNeuron() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron: Empty constructor
links - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron: Link array
linksEmpty() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Checks if this neural net is empty of links
linksEmpty() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuralNet: Checks if this neural net is empty of links
linksEmpty() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Checks if this layer is empty of links
linksFull() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Checks if this neural net is full of links
linksFull() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuralNet: Checks if this neural net is full of links
linksFull(ILayer<? extends INeuron>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Checks if this layer is full of links
list - Variable in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.valueAssociations: Frequency list of classes for a given value
list - Variable in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.valueAssociations: Frequency list of classes for a given value
list - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.valueAssociations: Frequency list of classes for a given value
list - Variable in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.valueAssociations: Frequency list of classes for a given value
list - Variable in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.valueAssociations: Frequency list of classes for a given value
list - Variable in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.valueAssociations: Frequency list of classes for a given value
list - Variable in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.valueAssociations: Frequency list of classes for a given value
list - Variable in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.valueAssociations: Frequency list of classes for a given value
lista_valueChanged(ListSelectionEvent) - Method in class keel.GraphInterKeel.help.HelpOptions: Manages changes in list
ListaAtributos - Class in keel.Algorithms.Decision_Trees.SLIQ: This class manages an ordered list of each attribute.
ListaAtributos(double, int) - Constructor for class keel.Algorithms.Decision_Trees.SLIQ.ListaAtributos: Parameter Constructor.
ListaClases - Class in keel.Algorithms.Decision_Trees.SLIQ: This class manages that list of classes of a dataset.
ListaClases(int, Node) - Constructor for class keel.Algorithms.Decision_Trees.SLIQ.ListaClases: Parameter Constructor.
listaCondiciones() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Returns the list of conditions of the rule.
listaCondiciones() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Returns the list of conditions of the rule.
listaCondiciones() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Returns the list of conditions of the rule.
listaCondiciones() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Returns the list of conditions of the rule.
listaCondicionesContinuos() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Returns the list of continuous conditions of the rule.
listaCondicionesNominales() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Returns the list of nominal conditions of the rule.
listaProbabilidadesAtributoClase(Atributo, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Returns the probabilities vector related to examples with the pair attribute-value and the list of existing classes given.
listaProbabilidadesAtributoClase(Atributo, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Returns the probabilities vector related to examples with the pair attribute-value and the list of existing classes given.
listaProbabilidadesAtributoClase(Atributo, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ConjuntoDatos: Returns the probabilities vector related to examples with the pair attribute-value and the list of existing classes given.
listaProbabilidadesAtributoClase(Atributo, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Returns the probabilities vector related to examples with the pair attribute-value and the list of existing classes given.
listaProbabilidadesAtributoClase(Atributo, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Returns the probabilities vector related to examples with the pair attribute-value and the list of existing classes given.
ListChildrenText(Element, int) - Method in class keel.Algorithms.Preprocess.Converter.HtmlToKeel: Recursive method that returns the text containing all the descendants of a xml tag.
ListChildrenText(Element, int) - Method in class keel.Algorithms.Preprocess.Converter.PropertyListToKeel: Recursive method that returns the text containing all the descendants of a xml tag.
ListChildrenText(Element, int) - Method in class keel.Algorithms.Preprocess.Converter.XmlToKeel: Recursive method that returns the text containing all the descendants of a xml tag.
listData - Variable in class keel.GraphInterKeel.experiments.Experiments
listDataC - Variable in class keel.GraphInterKeel.experiments.Experiments
listDataC_LQD - Variable in class keel.GraphInterKeel.experiments.Experiments
listDataLQD_C - Variable in class keel.GraphInterKeel.experiments.Experiments
listDir(String, Vector) - Static method in class keel.GraphInterKeel.experiments.FileUtils: Gets a directory listing and stores it
listener - Variable in class keel.GraphInterKeel.experiments.EducationalPartitionsRun
listener - Variable in class keel.GraphInterKeel.experiments.EducationalRun
listeners - Static variable in class keel.Algorithms.Neural_Networks.NNEP_Clas.KEELWrapperClas: Listener list
listeners - Static variable in class keel.Algorithms.Neural_Networks.NNEP_Regr.KEELWrapperRegr: Listener list
listKO - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.matchProfileAgent
listOK - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.matchProfileAgent
listOptions() - Method in class keel.Algorithms.Decision_Trees.M5.M5
listOptions() - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Returns an enumeration describing the available options
listOptions() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Classifier: Returns an enumeration describing the available options.
listOptions() - Method in interface keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.OptionHandler: Returns an enumeration of all the available options..
listOptions() - Method in interface keel.Algorithms.Statistical_Classifiers.Logistic.core.OptionHandler: Returns an enumeration of all the available options..
listOptions() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Returns an enumeration describing the available options
listOptions() - Method in class keel.Algorithms.SVM.SMO.core.Check: Returns an enumeration describing the available options.
listOptions() - Method in interface keel.Algorithms.SVM.SMO.core.OptionHandler: Returns an enumeration of all the available options..
listOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: Returns an enumeration describing the available options.
listOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.Kernel: Returns an enumeration describing the available options.
listOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: Returns an enumeration describing the available options.
listOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.PolyKernel: Returns an enumeration describing the available options.
listOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.Puk: Returns an enumeration describing the available options.
listOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.RBFKernel: Returns an enumeration describing the available options.
listOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: Gets an enumeration describing the available options.
listOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMO: Returns an enumeration describing the available options
listOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: Returns an enumeration describing the available options
listOptions() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Returns an enumeration describing the available options.
listOptions() - Method in class keel.Algorithms.SVM.SMO.SVMreg: Returns an enumeration describing the available options.
listPathFiles - Variable in class keel.GraphInterKeel.experiments.EducationalReport
listPathFilesExtra - Variable in class keel.GraphInterKeel.experiments.EducationalReport
Lists - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR: Title: Class List Description: In this class implements the structure and methods of a list Company: KEEL
Lists() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Lists: Default constructor.
Lists - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh: Title: Class List Description: In this class implements the structure and methods of a list Company: KEEL
Lists() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Lists: Default constructor.
Lists - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII: Title: Class List Description: In this class implements the structure and methods of a list Company: KEEL
Lists() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Lists: Default constructor.
Literal - Class in keel.Algorithms.Associative_Classification.ClassifierCPAR: Class to store a Literal.
Literal() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCPAR.Literal: Default Constructor
Literal(int, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCPAR.Literal: Parameters Constructor
Literals - Class in keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter: This class transforms the data set into a set of literals given the positive class
Literals(Instance[], int) - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Literals: Constructor of the class
liz(int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion: Left limit
LLETRA - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
LLSImpute - Class in keel.Algorithms.Preprocess.Missing_Values.LLSImpute: This class implements the Local Least Squares Imputation
LLSImpute() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.LLSImpute: Default constructor.
LLSImpute(String) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.LLSImpute: Constructor which extract the parameters from a KEEL pattern file and initializes the InstanceSet structures
LM - Class in keel.Algorithms.Neural_Networks.gmdh: Class representing the Levenberg Marquard method
LM() - Constructor for class keel.Algorithms.Neural_Networks.gmdh.LM: Empty constructor
LM_convergence - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters
LM_convergence - Variable in class keel.Algorithms.Neural_Networks.gmdh.SetupParameters
LMSTrain(double[][], double[][], int, int, double) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Uses LMS to train the net.
lnfbeta(double, double) - Static method in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Computes natural logarithm of the beta function.
lnfgamma(double) - Static method in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Computes natural logarithm of the gamma function.
lnGamma(double) - Static method in class keel.Algorithms.Lazy_Learning.Statistics: Returns natural logarithm of gamma function.
lnGamma(double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics: Returns natural logarithm of gamma function.
lnsrch(double[], double[], double[], double, boolean[], double[][], Optimization.DynamicIntArray) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization: Find a new point x in the direction p from a point xold at which the value of the function has decreased sufficiently, the positive definiteness of B matrix (approximation of the inverse of the Hessian) is preserved and no bound constraints are violated.
load(InputStream) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.ProtectedProperties: Overrides a method to prevent the properties from being modified.
load(InputStream) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ProtectedProperties: Overrides a method to prevent the properties from being modified. inStream returns without throwing an exception.
Load(InstanceSet, TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableDat: Creates and fill TableDat with the examples of the dataset
Load(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Fill TableVar with the characteristics of the variables and creates characteristics and intervals for the fuzzy sets
Load(InstanceSet, TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableDat: Creates and fill TableDat with the examples of the dataset
Load(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Fill TableVar with the characteristics of the variables and creates characteristics and intervals for the fuzzy sets
Load(InstanceSet, TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableDat: Creates and fill TableDat with the examples of the dataset
Load(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Fill TableVar with the characteristics of the variables and creates characteristics and intervals for the fuzzy sets
load_data(ExternalObjectDescription, Vector, Vector) - Method in class keel.GraphInterKeel.experiments.Experiments: Load dat sets
loadCSVData(String) - Method in class keel.GraphInterKeel.statistical.statTableModel: Load the contents of the table in CSV format
LoadEnsemble(String) - Method in class keel.Algorithms.Neural_Networks.ensemble.Ensemble: Load ensemble from file_name
loadFile(File, boolean) - Method in class keel.GraphInterKeel.datacf.editData.EditPanel: Loads a new file in the tab
loadInfoFromFS() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams
loadInfoFromFS() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Loads all the information needed for the Fingrams object, setting all its variables, from a .fs file located on fileLocation.
LoadNetwork(String) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network: Load network weights from a file
LoadNetwork(String) - Method in class keel.Algorithms.Neural_Networks.gann.Network: Load network weights from a file
LoadNetwork(String) - Method in class keel.Algorithms.Neural_Networks.gmdh.Network: Load network weights from file
LoadNetwork(String) - Method in class keel.Algorithms.Neural_Networks.net.Network: Load network weights from a file
loadObjValues() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Method to load the values of the objectives from the computed
LoadParameters(String) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters: Load parameters from file_name to global
LoadParameters(String) - Method in class keel.Algorithms.Neural_Networks.ensemble.EnsembleParameters: Load parameters from file_name to global
LoadParameters(String) - Method in class keel.Algorithms.Neural_Networks.gann.Parameters: Load parameters from file_name to global
LoadParameters(String) - Method in class keel.Algorithms.Neural_Networks.gann.SetupParameters: Method that takes the global parameters from a file
LoadParameters(String) - Method in class keel.Algorithms.Neural_Networks.gmdh.Parameters: Load parameters from file_name to global
LoadParameters(String) - Method in class keel.Algorithms.Neural_Networks.gmdh.SetupParameters: Method that takes the global parameters from a file
LoadParameters(String) - Method in class keel.Algorithms.Neural_Networks.net.Parameters: Load parameters from file_name to global
loadRule(double[], int) - Method in class keel.Algorithms.Hyperrectangles.BNGE.Rule: Reinitialices a rule, loading it with the contents of a single instance
loadRule(double[], int) - Method in class keel.Algorithms.Hyperrectangles.RISE.Rule: Reinitialices a rule, loading it with the contents of a single instance
loadSVDMmatrix(double[][], int[]) - Static method in class keel.Algorithms.Hyperrectangles.RISE.Rule: Computes the SVDM distance matrix
loadTestDataset() - Method in class keel.Algorithms.MIL.AbstractMIAlgorithm
loadTrainDataset() - Method in class keel.Algorithms.MIL.AbstractMIAlgorithm
localClusterCenter(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCGenerator: Returns the local cluster centers for a given prototype set.
LocalImprovementCAN(TableVar, TableDat) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Locally improves the rule generated.
LocalImprovementDNF(TableVar, TableDat) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Locally improves the rule generated.
localOptimization(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGAP.FuzzyGAPClassifier: This method performs the local optimization: as this method does not have any local optimization defined an exception is thrown.
localOptimization(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGP.FuzzyGPClassifier: This method performs the local optimization: as this method does not have any local optimization defined an exception is thrown.
localOptimization(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyPittsBurgh.PittsburghClassifier: This method performs the local optimization: as this method does not have any local optimization defined an exception is thrown.
localOptimization(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySAP.FuzzySAPClassifier: This method performs the local optimization: as this method does not have any local optimization defined an exception is thrown.
localOptimization(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividual: This abstract method calculate a local optimization
localOptimization(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGAPModelIndividual: This method calculates a local optimization
localOptimization(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPModelIndividual: This method calculate a local optimization
localOptimization(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.PittsburghModel: This method calculate a local optimization
localOptimization(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.RegSymFuzzyGP: This method calculates a local optimization
localSearch(Classifier[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.GA
locate() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: sets up the structure
locateIndex(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5SparseInstance: Locates the greatest index that is not greater than the given index.
locateIndex(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance: Locates the greatest index that is not greater than the given index.
log - Static variable in class keel.Algorithms.Decision_Trees.C45.Algorithm: The log file.
log - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm: The log file.
log - Static variable in class keel.Algorithms.Decision_Trees.ID3.Algorithm: The log file.
log - Static variable in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm: Log buffer.
log() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut: Returns the logarithm of the present FuzzyInterval.
log - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm: The log file.
log - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm: The log file.
log - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm: The log file.
log - Static variable in class keel.Algorithms.Rule_Learning.ART.Algorithm: The log file.
log - Static variable in class keel.Algorithms.Rule_Learning.C45Rules.Algorithm: The log file.
log - Static variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Algorithm: The log file.
log - Static variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm: The log file.
log - Static variable in class keel.Algorithms.Rule_Learning.PART.Algorithm: The log file.
log10(double) - Static method in class keel.Algorithms.Neural_Networks.gmdh.math: Calculates base 10 logarithm of argument x
log2 - Static variable in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: The natural logarithm of 2.
log2(double) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Returns the logarithm of a for base 2.
log2(double) - Method in class keel.Algorithms.Discretizers.Cluster_Analysis.Cluster_Analysis: Calculate the log base 2 of a number
log2(double) - Method in class keel.Algorithms.Discretizers.Fayyad_Discretizer.FayyadDiscretizer: Calculate the log base 2 of a number
log2 - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: The natural logarithm of 2.
log2(double) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Returns the logarithm of a for base 2.
log2 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: The natural logarithm of 2.
log2(double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Returns the logarithm of a for base 2.
log2(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Fayyad_Discretizer.FayyadDiscretizer
log2 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Utilities: The log of 2.
log2 - Static variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: The natural logarithm of 2.
log2(double) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Returns the logarithm of a for base 2.
log2(double) - Static method in class keel.Algorithms.Rule_Learning.C45Rules.Utilities: Returns the logarithm of a for base 2.
log2(double) - Static method in class keel.Algorithms.Rule_Learning.C45RulesSA.Utilities: Returns the logarithm of a for base 2.
log2 - Static variable in class keel.Algorithms.Rule_Learning.PART.Utilities: The log of 2.
log2 - Static variable in class keel.Algorithms.Rule_Learning.Ripper.Utilities: The natural logarithm of 2.
log2(double) - Static method in class keel.Algorithms.Rule_Learning.Ripper.Utilities: Returns the logarithm of a for base 2.
log2 - Static variable in class keel.Algorithms.Rule_Learning.Slipper.Utilities: The natural logarithm of 2.
log2(double) - Static method in class keel.Algorithms.Rule_Learning.Slipper.Utilities: Returns the logarithm of a for base 2.
log2 - Static variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: The natural logarithm of 2.
log2(double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Returns the logarithm of a for base 2.
log2 - Static variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: The natural logarithm of 2.
log2(double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Returns the logarithm of a for base 2.
log2 - Static variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: The natural logarithm of 2.
log2(double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Returns the logarithm of a for base 2.
log2 - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: The natural logarithm of 2.
log2(double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Returns the logarithm of a for base 2.
log2 - Static variable in class keel.Algorithms.SVM.SMO.core.Utils: The natural logarithm of 2.
log2(double) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Returns the logarithm of a for base 2.
LOG_OUT - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters: Printer.
logFunc(double) - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Returns the log2
logFunc(double) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Returns the log2
logFunc(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Returns the log2
logFunc(double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.PART.Utilities: Help method for computing entropy.
logFunc(double) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Returns the log2
logFunc(double) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Returns the log2
logFunc(double) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Returns the log2
logFunc(double) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Returns the log2
logFunc(double) - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Returns the log2
logFunc(double) - Static method in class keel.Algorithms.Rule_Learning.PART.Utilities: Help method for computing entropy.
logFunc(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Returns the log2
Logistic - Class in keel.Algorithms.Statistical_Classifiers.Logistic: Class for building and using a multinomial logistic regression model with a ridge estimator.
Logistic(String) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Creates a new instance of Logistic with a file parameter of KEEL format
Logistic() - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Default constructor
LogisticErrorFunction - Class in keel.Algorithms.Neural_Networks.NNEP_Clas.problem.errorfunctions: Logistic Error Function.
LogisticErrorFunction() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.errorfunctions.LogisticErrorFunction: Empty constructor
LogManager - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
LogManager() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.LogManager
LogManager - Class in keel.Algorithms.Genetic_Rule_Learning.Globals: LogManager.java.
LogManager() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Globals.LogManager
LogManager - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals
LogManager() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.LogManager
logotipo_mouseEntered(MouseEvent) - Method in class keel.GraphInterKeel.experiments.Credits: Entering logo
logotipo_mouseEntered(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Entering in KEEL logo
logotipo_mouseEntered(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Entering in logo
logotipo_mouseExited(MouseEvent) - Method in class keel.GraphInterKeel.experiments.Credits: Exiting logo
logotipo_mouseExited(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Exiting from KEEL logo
logotipo_mouseExited(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Exiting from logo
logotipo_mousePressed(MouseEvent) - Method in class keel.GraphInterKeel.experiments.Credits: Pressing logo
logotipo_mousePressed(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Clicking in KEEL logo
logotipo_mousePressed(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Clicking in KEEL logo
logotipoSoft_mousePressed(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Clicking in software logo
logotipoSoft_mousePressed(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Clicking in software logo
logOutputFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
logOutputFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
logOutputFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
logOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters: Log information output file.
logOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Parameters: Log information output file.
logOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters: Log information output file.
logOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters: Log information output file.
logOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Parameters: Log information output file.
logOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters: Log information output file.
logOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Parameters: Log information output file.
logOutputFile - Static variable in class keel.Algorithms.Rule_Learning.Swap1.Parameters
LOGPI - Static variable in class keel.Algorithms.Lazy_Learning.Statistics: Logarithm of PI
LOGPI - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics
logPSI - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: The constant - log( sqrt(2 pi) )
logs2probs(double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Converts an array containing the natural logarithms of probabilities stored in a vector back into probabilities.
logs2probs(double[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Converts an array containing the natural logarithms of probabilities stored in a vector back into probabilities.
logs2probs(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Converts an array containing the natural logarithms of probabilities stored in a vector back into probabilities.
logs2probs(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Converts an array containing the natural logarithms of probabilities stored in a vector back into probabilities.
logs2probs(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Converts an array containing the natural logarithms of probabilities stored in a vector back into probabilities.
logs2probs(double[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Converts an array containing the natural logarithms of probabilities stored in a vector back into probabilities.
logs2probs(double[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Converts an array containing the natural logarithms of probabilities stored in a vector back into probabilities.
logTransformation - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Logarithm transformation
long_poblacion - Variable in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Param: Number of rules
long_poblacion - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Param: Number of rules
long_poblacion - Variable in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Param: Number of rules
long_tabla - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyDataset: Total number of variables.
long_tabla - Variable in class keel.Algorithms.RE_SL_Methods.LEL_TSK.MiDataset
long_tabla - Variable in class keel.Algorithms.RE_SL_Methods.MamWM.MiDataset
long_tabla - Variable in class keel.Algorithms.RE_SL_Methods.mogulHC.MiDataset
long_tabla - Variable in class keel.Algorithms.RE_SL_Methods.mogulIRL.MiDataset
long_tabla - Variable in class keel.Algorithms.RE_SL_Methods.mogulSC.MiDataset
long_tabla - Variable in class keel.Algorithms.RE_SL_Methods.TSK_IRL.MiDataset
long_tabla - Variable in class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.MiDataset
long_tabla - Variable in class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.MiDataset
long_tabla - Variable in class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.MiDataset
long_tabla - Variable in class keel.Algorithms.RE_SL_Postprocess.MamSelect.MiDataset
long_tabla - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWSelect.MiDataset
long_tabla - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWTuning.MiDataset
long_tabla - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.MiDataset
long_tabla - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKSelect.MiDataset
LOWER - Static variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule: number to indentify the operator <=.
LOWER - Static variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleRule: number to indentify the operator <=.
LOWER - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule: number to indentify operator <=.
LOWER - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.SimpleRule: number to indentify the operator <=.
LOWER - Static variable in class keel.Algorithms.Rule_Learning.C45Rules.Rule: Flag for lower operator
LOWER - Static variable in class keel.Algorithms.Rule_Learning.C45Rules.SimpleRule: Flag for lower operator
LOWER - Static variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule: Flag for lower operator
LOWER - Static variable in class keel.Algorithms.Rule_Learning.C45RulesSA.SimpleRule: Flag for lower operator
LOWER - Static variable in class keel.Algorithms.Rule_Learning.PART.Rule: Flag for lower operator
LOWER - Static variable in class keel.Algorithms.Rule_Learning.PART.SimpleRule: Flag for lower operator
LOWER - Static variable in class keel.Algorithms.Rule_Learning.Ripper.Rule: Flag for lower operator
LOWER - Static variable in class keel.Algorithms.Rule_Learning.Ripper.SimpleRule: Flag for lower operator
LOWER - Static variable in class keel.Algorithms.Rule_Learning.Slipper.Rule: Flag for lower operator
LOWER - Static variable in class keel.Algorithms.Rule_Learning.Slipper.SimpleRule: Flag for lower operator
lower_approximation_set(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
lower_approximation_set(int, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
lower_aproximation() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff: Compute the lower aproximations of the dataset
lower_aproximation - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal
lower_aproximation() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal: Compute the lower aproximations of the dataset
lower_aproximation_Set(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal: Compute the lower aproximation of a set
lowerbound - Variable in class keel.Algorithms.Discretizers.MVD.Interval: Interval lower bound.
lowerNumericBoundIsOpen() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Returns whether the lower numeric bound of the attribute is open.
lowerrealBoundIsOpen() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Returns whether the lower real bound of the attribute is open.
LOWEST_FIRST - Static variable in class keel.Algorithms.Discretizers.HellingerBD.Quicksort: Configuration tag (Lowest first).
LOWEST_FIRST - Static variable in class keel.Algorithms.Discretizers.UCPD.Quicksort: Configuration tags.
LOWEST_FIRST - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Quicksort: LOWEST_FIRST tag.
LOWEST_FIRST - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Quicksort: Configuration tag (LOWEST_FIRST).
LOWEST_FIRST - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Quicksort: Configuration tag (LOWEST_FIRST).
LOWEST_FIRST - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Quicksort: Configuration tag (LOWEST_FIRST).
LQD - Static variable in class keel.GraphInterKeel.experiments.Experiments
lqd_crisp_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.DinamicDataset: LQD to Crisp button
lqd_crisp_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectData: LQD to Crisp button
lqd_mouseEntered(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Enter in LQD button
lqd_mouseExited(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Exit from lqd button
lqd_mouseReleased(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Entering in LQD module
lsff(double, double, PrototypeSet[], int, int) - Method in class keel.Algorithms.Instance_Generation.DROP3SFLSDE.DROP3SFLSDE: Local Search Fitness Function
lsff(double, double, PrototypeSet[], int, int) - Method in class keel.Algorithms.Instance_Generation.ICFSFLSDE.ICFSFLSDE: Local Search Fitness Function
lsff(double, double, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.IPLDE.IPLDEGenerator: Local Search Fitness Function
lsff(double, double, PrototypeSet[], int, int) - Method in class keel.Algorithms.Instance_Generation.SFLSDE.SFLSDEGenerator: Local Search Fitness Function
lsff(double, double, PrototypeSet[], int, int) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE: Local Search Fitness Function
lsu(int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion: Upper limit
lu() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: LU Decomposition
lu_decompose(double[][], int, double[], double[][], int[]) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Matrix: Lu Descomposition of a matrix
lu_decompose(double[][], int, double[], double[][], int[]) - Static method in class keel.Algorithms.Neural_Networks.net.Matrix: Lu Descomposition of a matrix
lu_solve(double[], double[], int, double[][], int[]) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Matrix: Lu solution of a matrix
lu_solve(double[], double[], int, double[][], int[]) - Static method in class keel.Algorithms.Neural_Networks.net.Matrix: Lu solution of a matrix
lubksb(int, int[], double[]) - Method in class keel.Algorithms.Decision_Trees.M5.M5Matrix: LU backward substitution
lubksb(double[][], int[], double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
lubksb(int, int[], double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5Matrix: LU backward substitution
lubksb(double[][], int[], double[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
ludcmp(int, int[]) - Method in class keel.Algorithms.Decision_Trees.M5.M5Matrix: LU decomposition
ludcmp(double[][], int[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
ludcmp(int, int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5Matrix: LU decomposition
ludcmp(double[][], int[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
LUDecomposition - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix: LU Decomposition.
LUDecomposition() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Performs a LUDecomposition on the matrix.
LUDecomposition(Matrix) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.LUDecomposition: LU Decomposition
LUKASIEWICZ - Static variable in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
LVFIncon - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVF: Las Vegas filter for feature selection This class implements LVF algorithm.
LVFIncon(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVF.LVFIncon: Creates a new instance of LVFIncon
LVFIncon - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVF_IEP: Las Vegas filter for feature selection This class implements LVF algorithm.
LVFIncon(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVF_IEP.LVFIncon: Creates a new instance of LVFIncon
LVO(boolean[]) - Method in class keel.Algorithms.Preprocess.Feature_Selection.Datos: Calculates the precision (errors/total_instances) in the prediction of the instance class.
LVOTest(boolean[]) - Method in class keel.Algorithms.Preprocess.Feature_Selection.Datos: calculates the precision (errors/total_instances) in the classification of all instances in the TEST DATASET using the given features and THE SAME TEST DATASET TO PREDICT.
LVQ - Class in keel.Algorithms.Neural_Networks.LVQ: File: LVQ.java The LVQ Neural Networks algorithm.
LVQ(String) - Constructor for class keel.Algorithms.Neural_Networks.LVQ.LVQ: Constructor.
LVQ1 - Class in keel.Algorithms.Instance_Generation.LVQ: LVQ1 Implements LVQ1 algorithm.
LVQ1(PrototypeSet, int, int, double) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ1: Constructs a new LVQ1 algorithm.
LVQ1(PrototypeSet, PrototypeSet, int, int, double) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ1: WITH INITIAL CODE-BOOKS Constructs a new LVQ1 algorithm.
LVQ1(PrototypeSet, int, double, double) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ1: Constructs a new LVQ1 algorithm.
LVQ1(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ1: Constructs a new LVQ1 algorithm.
LVQ1Algorithm - Class in keel.Algorithms.Instance_Generation.LVQ: LVQ1 algorithm calling.
LVQ1Algorithm() - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ1Algorithm
LVQ2 - Class in keel.Algorithms.Instance_Generation.LVQ: LVQ2 algorithm
LVQ2(PrototypeSet, int, int, double, double) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ2: Constructs a new LVQ2 algorithm.
LVQ2(PrototypeSet, PrototypeSet, int, int, double, double) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ2: WITH INITIAL CODE-BOOKS Constructs a new LVQ2 algorithm.
LVQ2(PrototypeSet, int, double, double, double) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ2: Constructs a new LVQ2 algorithm.
LVQ2(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ2: Constructs a new LVQ2 algorithm.
LVQ2_1 - Class in keel.Algorithms.Instance_Generation.LVQ: LVQ2.1 algorithm
LVQ2_1(PrototypeSet, int, int, double, double) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ2_1: Construct an LVQ2.1 algorithm.
LVQ2_1(PrototypeSet, int, double, double, double) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ2_1: Construct an LVQ2.1 algorithm.
LVQ2_1(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ2_1: Construct an LVQ2.1 algorithm.
LVQ2_1Algorithm - Class in keel.Algorithms.Instance_Generation.LVQ: LVQ2.1 algorithm calling.
LVQ2_1Algorithm() - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ2_1Algorithm
LVQ2Algorithm - Class in keel.Algorithms.Instance_Generation.LVQ: LVQ2 algorithm calling.
LVQ2Algorithm() - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ2Algorithm
LVQ3 - Class in keel.Algorithms.Instance_Generation.LVQ: LVQ3 algorithm for reduction prototype sets.
LVQ3(PrototypeSet, int, int, double, double, double) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ3: Construct a new LVQ3 algorithm.
LVQ3(PrototypeSet, PrototypeSet, int, int, double, double, double) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ3: WITH INITIAL CODE-BOOKS Construct a new LVQ3 algorithm.
LVQ3(PrototypeSet, int, double, double, double, double) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ3: Construct a new LVQ3 algorithm.
LVQ3(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ3: Construct a new LVQ3 algorithm.
LVQ3Algorithm - Class in keel.Algorithms.Instance_Generation.LVQ: LVQ3 algorithm calling.
LVQ3Algorithm() - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQ3Algorithm
LVQGenerator - Class in keel.Algorithms.Instance_Generation.LVQ: LVQGenerator Abstract class parent of every LVQGenerator-type algorithm.
LVQGenerator(PrototypeSet, int, int) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQGenerator: Constructs a new LVQGenerator algorithm.
LVQGenerator(PrototypeSet, PrototypeSet, int, int) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQGenerator: WITH INITIAL CODE-BOOKS Constructs a new LVQGenerator algorithm.
LVQGenerator(PrototypeSet, int, double) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQGenerator: Constructs a new LVQGenerator algorithm.
LVQGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQGenerator: Constructs a new LVQGenerator algorithm.
LVQPRU - Class in keel.Algorithms.Instance_Generation.LVQ: Implements LVQPRU algorithm
LVQPRU(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQPRU: Constructs a new LVQPRU algorithm.
LVQPRU(PrototypeSet, int, double, double, int, double, double, int) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQPRU: Builds a LVQPRU algorithm.
LVQPRUAlgorithm - Class in keel.Algorithms.Instance_Generation.LVQ: LVQPRU algorithm calling.
LVQPRUAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQPRUAlgorithm
LVQTC - Class in keel.Algorithms.Instance_Generation.LVQ: Implements LVQTC algorithm
LVQTC(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQTC: Constructor based on the training dataset and the parameters
LVQTC(PrototypeSet, int, double, double, double, int, int) - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQTC: Constructor based on the training dataset and the parameters
LVQTCAlgorithm - Class in keel.Algorithms.Instance_Generation.LVQ: LVQTC algorithm calling.
LVQTCAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.LVQ.LVQTCAlgorithm
LVWLVO - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVW: Las Vegas wrapper for feature selection This class implements LVW algorithm.
LVWLVO(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVW.LVWLVO: Creates a new instance of LVWLVO

M

m - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForModels
m - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForSymbRegr
m - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Row and column dimensions.
M5 - Class in keel.Algorithms.Decision_Trees.M5: M5 algorithm main class.
M5() - Constructor for class keel.Algorithms.Decision_Trees.M5.M5
M5 - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules
M5(parseParameters) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: Constructor by parameters file.
M5(MyDataset, double, boolean, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5
M5Attribute - Class in keel.Algorithms.Decision_Trees.M5: Class for handling an attribute.
M5Attribute(String) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Attribute: Constructor for a numeric attribute.
M5Attribute(String, M5Vector) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Attribute: Constructor for nominal attributes and string attributes.
M5AttrStats - Class in keel.Algorithms.Decision_Trees.M5: A Utility class that contains summary information on an the values that appear in a dataset for a particular attribute.
M5AttrStats() - Constructor for class keel.Algorithms.Decision_Trees.M5.M5AttrStats
M5Instance - Class in keel.Algorithms.Decision_Trees.M5: Class for handling an instance.
M5Instance(M5Instance) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Instance: Constructor that copies the attribute values and the weight from the given instance.
M5Instance(double, double[]) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Instance: Constructor that inititalizes instance variable with given values.
M5Instance(int) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Instance: Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.
M5Instance() - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Instance: Private constructor for subclasses.
M5Instances - Class in keel.Algorithms.Decision_Trees.M5: Class for handling an ordered set of weighted instances.
M5Instances(Reader) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Instances: Reads an data file from a reader, and assigns a weight of one to each instance.
M5Instances(Reader, int) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Instances: Reads the header of an file from a reader and reserves space for the given number of instances.
M5Instances(M5Instances) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Instances: Constructor copying all instances and references to the header information from the given set of instances.
M5Instances(M5Instances, int) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Instances: Constructor creating an empty set of instances.
M5Instances(M5Instances, int, int) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Instances: Creates a new set of instances by copying a subset of another set.
M5Instances(String, M5Vector, int) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Instances: Creates an empty set of instances.
M5Kernel - Class in keel.Algorithms.Decision_Trees.M5: Simple kernel density estimator.
M5Kernel(double) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Kernel: Constructor that takes a precision argument.
M5Matrix - Class in keel.Algorithms.Decision_Trees.M5: Class for handling a matrix
M5Matrix(int, int) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Matrix: Constructs a matrix
M5Matrix - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules: Class for handling a matrix
M5Matrix(int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5Matrix: Constructs a matrix
M5SparseInstance - Class in keel.Algorithms.Decision_Trees.M5: Class for storing an instance as a sparse vector.
M5SparseInstance() - Constructor for class keel.Algorithms.Decision_Trees.M5.M5SparseInstance: Default Constructor.
M5SparseInstance(M5Instance) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5SparseInstance: Constructor that generates a sparse instance from the given instance.
M5SparseInstance(M5SparseInstance) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5SparseInstance: Constructor that copies the info from the given instance.
M5SparseInstance(double, double[]) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5SparseInstance: Constructor that generates a sparse instance from the given parameters.
M5SparseInstance(double, double[], int[], int) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5SparseInstance: Constructor that inititalizes instance variable with given values.
M5SparseInstance(int) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5SparseInstance: Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.
M5StaticUtils - Class in keel.Algorithms.Decision_Trees.M5: Class implementing some simple utility methods.
M5StaticUtils() - Constructor for class keel.Algorithms.Decision_Trees.M5.M5StaticUtils
M5StaticUtils - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules: Class implementing some simple utility methods.
M5StaticUtils() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils
M5TreeNode - Class in keel.Algorithms.Decision_Trees.M5: Class for handing a node in the tree or the subtree under this node
M5TreeNode(M5Instances, M5TreeNode) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5TreeNode: Constructs a new node
M5TreeNode(M5Instances, M5TreeNode, InformationHandler) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5TreeNode: Constructs the root of a tree
M5TreeNode - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules: Class for handing a node in the tree or the subtree under this node
M5TreeNode(MyDataset, M5TreeNode) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode: Constructs a new node
M5TreeNode(MyDataset, M5TreeNode, int, double, double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode: Constructs the root of a tree
M5Vector - Class in keel.Algorithms.Decision_Trees.M5: Implements a fast vector class without synchronized methods.
M5Vector() - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Vector: Constructs an empty vector with initial capacity zero.
M5Vector(int) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Vector: Constructs a vector with the given capacity.
M5Vector(int, int, double) - Constructor for class keel.Algorithms.Decision_Trees.M5.M5Vector: Constructs a vector with the given capacity, capacity increment and capacity mulitplier.
M5Vector.FastVectorEnumeration - Class in keel.Algorithms.Decision_Trees.M5: Class for enumerating the vector's elements.
m_0 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: It indicates the range of the uniform distribution to use in the mutation of a real allele.
m_0 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It indicates the range of the uniform distribution to use in the mutation of a real allele.
M_0 - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
m_Additional - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation: additional technical informations
m_Additional - Variable in class keel.Algorithms.SVM.SMO.core.TechnicalInformation: additional technical informations
m_ALF - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization: Alpha value.
m_Alin - Variable in class keel.Algorithms.SVM.SMO.SMOreg: The parameters of the linear transforamtion realized by the filter on the class attribute
m_AllowedIndices - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: the attribute indices that may be inspected
m_AllowUnclassifiedInstances - Variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Whether unclassified instances are allowed
m_AllowUnclassifiedInstances - Variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Whether unclassified instances are allowed
m_AllowUnclassifiedInstances - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Whether unclassified instances are allowed
m_alpha - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: The Lagrange multipliers.
m_alpha - Variable in class keel.Algorithms.SVM.SMO.SMOreg: The Lagrange multipliers
m_alpha - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: alpha and alpha* arrays containing weights for solving dual problem
m_alpha1 - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegSMO: alpha value for first candidate
m_alpha1Star - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegSMO: alpha* value for first candidate
m_alpha2 - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegSMO: alpha value for second candidate
m_alpha2Star - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegSMO: alpha* value for second candidate
m_alpha_ - Variable in class keel.Algorithms.SVM.SMO.SMOreg: The Lagrange multipliers
m_alphaStar - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: alpha and alpha* arrays containing weights for solving dual problem
m_Antds - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule: The vector of antecedents of this rule
m_Arrays - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets
m_Attribute - Variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: The attribute to split on.
m_Attribute - Variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: The attribute to split on.
m_Attribute - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: The attribute to split on.
m_Attributes - Variable in class keel.Algorithms.Decision_Trees.M5.M5Instances: The attribute information.
m_Attributes - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: contains the attribute locations, either true or false Boolean objects
m_Attributes - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: The attribute information.
m_Attributes - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: The attribute information.
m_Attributes - Variable in class keel.Algorithms.SVM.SMO.core.Instances: The attribute information.
m_AttValues - Variable in class keel.Algorithms.Decision_Trees.M5.M5Instance: The instance's attribute values.
m_AttValues - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: The instance's attribute values.
m_AttValues - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: The instance's attribute values.
m_AttValues - Variable in class keel.Algorithms.SVM.SMO.core.Instance: The instance's attribute values.
m_b - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.FuzzyRuleSet: output values of the rules
m_b - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: The thresholds.
m_b - Variable in class keel.Algorithms.SVM.SMO.SMOreg: The thresholds.
m_b - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: offset
m_b0 - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.FuzzyRuleSet: Default output value of this rule set
m_bContinuous - Variable in class keel.GraphInterKeel.experiments.DatasetXML
m_BETA - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization: Beta value.
m_bImprecise - Variable in class keel.GraphInterKeel.experiments.DatasetXML
m_bInputContinuous - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
m_bInputContinuous - Variable in class keel.GraphInterKeel.experiments.Node
m_bInputImprecise - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
m_bInputImprecise - Variable in class keel.GraphInterKeel.experiments.Node
m_bInputInteger - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
m_bInputInteger - Variable in class keel.GraphInterKeel.experiments.Node
m_bInputMIL - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
m_bInputMIL - Variable in class keel.GraphInterKeel.experiments.Node
m_bInputMissing - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
m_bInputMissing - Variable in class keel.GraphInterKeel.experiments.Node
m_bInputMultiClass - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
m_bInputMultiClass - Variable in class keel.GraphInterKeel.experiments.Node
m_bInputMultiOutput - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
m_bInputMultiOutput - Variable in class keel.GraphInterKeel.experiments.Node
m_bInputNominal - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
m_bInputNominal - Variable in class keel.GraphInterKeel.experiments.Node
m_bInteger - Variable in class keel.GraphInterKeel.experiments.DatasetXML
m_Blin - Variable in class keel.Algorithms.SVM.SMO.SMOreg: The parameters of the linear transforamtion realized by the filter on the class attribute
m_bLow - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: The thresholds.
m_bLow - Variable in class keel.Algorithms.SVM.SMO.SMOreg: The thresholds.
m_bLow - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: b.up and b.low boundaries used to determine stopping criterion
m_bMIL - Variable in class keel.GraphInterKeel.experiments.DatasetXML
m_bMissing - Variable in class keel.GraphInterKeel.experiments.DatasetXML
m_bModelBuilt - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: flag to indicate whether the model is built yet
m_bMultiClass - Variable in class keel.GraphInterKeel.experiments.DatasetXML
m_bMultiOutput - Variable in class keel.GraphInterKeel.experiments.DatasetXML
m_bNominal - Variable in class keel.GraphInterKeel.experiments.DatasetXML
m_bOutputContinuous - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
m_bOutputContinuous - Variable in class keel.GraphInterKeel.experiments.Node
m_bOutputImprecise - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
m_bOutputImprecise - Variable in class keel.GraphInterKeel.experiments.Node
m_bOutputInteger - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
m_bOutputInteger - Variable in class keel.GraphInterKeel.experiments.Node
m_bOutputMIL - Variable in class keel.GraphInterKeel.experiments.Node
m_bOutputMissing - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
m_bOutputMissing - Variable in class keel.GraphInterKeel.experiments.Node
m_bOutputMultiClass - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
m_bOutputMultiClass - Variable in class keel.GraphInterKeel.experiments.Node
m_bOutputMultiOutput - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
m_bOutputMultiOutput - Variable in class keel.GraphInterKeel.experiments.Node
m_bOutputNominal - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
m_bOutputNominal - Variable in class keel.GraphInterKeel.experiments.Node
m_bUp - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: The thresholds.
m_bUp - Variable in class keel.Algorithms.SVM.SMO.SMOreg: The thresholds.
m_bUp - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: b.up and b.low boundaries used to determine stopping criterion
m_C - Variable in class keel.Algorithms.SVM.SMO.SMO: The complexity parameter.
m_C - Variable in class keel.Algorithms.SVM.SMO.SMOreg: The complexity parameter
m_C - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: capacity parameter, copied from SVMreg
m_C - Variable in class keel.Algorithms.SVM.SMO.SVMreg: capacity parameter
m_Cache - Static variable in class keel.Algorithms.SVM.SMO.core.ClassDiscovery: for caching queries (classname+packagename <-> Vector with classnames)
m_cacheHits - Variable in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: Counts the number of kernel cache hits.
m_cacheSize - Variable in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: The size of the cache (a prime number)
m_cacheSlots - Variable in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: number of cache slots in an entry
m_checksTurnedOff - Variable in class keel.Algorithms.SVM.SMO.SMO: Turn off all checks and conversions?
m_checksTurnedOff - Variable in class keel.Algorithms.SVM.SMO.SMOreg: Turn off all checks and conversions?
m_ChecksTurnedOff - Variable in class keel.Algorithms.SVM.SMO.supportVector.Kernel: Turns off all checks
m_Class - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: The class attribute of the data
m_class - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: The transformed class values.
m_classAttribute - Variable in class keel.Algorithms.SVM.SMO.SMO: The class attribute
m_ClassDistribution - Variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Class probabilities from the training data.
m_ClassDistribution - Variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Class probabilities from the training data.
m_ClassDistribution - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Class probabilities from the training data.
m_classifiers - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.PDFC: The binary classifier(s)
m_classifiers - Variable in class keel.Algorithms.SVM.SMO.SMO: The binary classifier(s)
m_ClassIndex - Variable in class keel.Algorithms.Decision_Trees.M5.M5Instances: The class attribute's index
m_ClassIndex - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: The class attribute's index
m_ClassIndex - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: The class attribute's index
m_ClassIndex - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: The index of the class attribute
m_ClassIndex - Variable in class keel.Algorithms.SVM.SMO.core.Instances: The class attribute's index
m_classIndex - Variable in class keel.Algorithms.SVM.SMO.SMO: The class index from the training data
m_classIndex - Variable in class keel.Algorithms.SVM.SMO.SMOreg: The class index from the training data
m_classIndex - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: index of class variable in data set
m_Coefficients - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.LinearRegression: the coefficients
m_Comment - Variable in enum keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation.Field: the comment about this type
m_Comment - Variable in enum keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation.Type: the comment about this type
m_Comment - Variable in enum keel.Algorithms.SVM.SMO.core.TechnicalInformation.Field: the comment about this type
m_Comment - Variable in enum keel.Algorithms.SVM.SMO.core.TechnicalInformation.Type: the comment about this type
m_confidence - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Antd: Confidence / weight of this antecedent.
m_Consequent - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule: The internal representation of the class label to be predicted
m_Contents - Variable in class keel.Algorithms.Decision_Trees.M5.Queue.QueueNode: The nodes contents
m_Contents - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Queue.QueueNode: The nodes contents
m_Contents - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue.QueueNode: The nodes contents
m_Contents - Variable in class keel.Algorithms.SVM.SMO.core.Queue.QueueNode: The nodes contents
m_d - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.PDFC: The 'd' value for the Positive-Definite Reference Functions
m_d - Variable in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: The d value for the PDRF kernel.
m_Data - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: the referenced data
m_Data - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal
m_data - Variable in class keel.Algorithms.MIL.ExceptionDatasets
m_Data - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: The data saved as a matrix
m_data - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: The training data.
m_data - Variable in class keel.Algorithms.SVM.SMO.SMOreg: The training data.
m_data - Variable in class keel.Algorithms.SVM.SMO.supportVector.Kernel: The dataset
m_data - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: points to data set
m_Dataset - Variable in class keel.Algorithms.Decision_Trees.M5.M5Instance: The dataset the instance has access to.
m_Dataset - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: The dataset the instance has access to.
m_Dataset - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: The dataset the instance has access to.
m_Dataset - Variable in class keel.Algorithms.SVM.SMO.core.Instance: The dataset the instance has access to.
m_Debug - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Classifier: Whether the classifier is run in debug mode.
m_Debug - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule: Whether in a debug mode
m_Debug - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Whether in a debug mode
m_Debug - Variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Debug flag.
m_Debug - Variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Debug flag.
m_Debug - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Debug flag.
m_Debug - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization: Debug flag.
m_Debug - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Debugging output
m_Debug - Variable in class keel.Algorithms.SVM.SMO.core.Check: Debugging mode, gives extra output if true
m_Debug - Variable in class keel.Algorithms.SVM.SMO.supportVector.Kernel: enables debugging output
m_Del - Static variable in class keel.Algorithms.SVM.SMO.SMO: Precision constant for updating sets
m_Del - Static variable in class keel.Algorithms.SVM.SMO.SMOreg: Precision constant for updating sets
m_Del - Static variable in class keel.Algorithms.SVM.SMO.supportVector.RegSMO: Precision constant for updating sets
m_Display - Variable in enum keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation.Field: the string used in toString()
m_Display - Variable in enum keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation.Type: the string used in toString()
m_Display - Variable in enum keel.Algorithms.SVM.SMO.core.TechnicalInformation.Field: the string used in toString()
m_Display - Variable in enum keel.Algorithms.SVM.SMO.core.TechnicalInformation.Type: the string used in toString()
m_Distributions - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: The predicted class distribution
m_eps - Variable in class keel.Algorithms.SVM.SMO.SMO: Epsilon for rounding.
m_eps - Variable in class keel.Algorithms.SVM.SMO.SMOreg: The parameter eps
m_eps - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegSMO: tolerance parameter, smaller changes on alpha in inner loop will be ignored
m_Epsilon - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization: Compute machine precision
m_epsilon - Variable in class keel.Algorithms.SVM.SMO.SMOreg: The parameter epsilon
m_epsilon - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: epsilon of epsilon-insensitive cost function
m_errors - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: The current set of errors for all non-bound examples.
m_Evaluations - Variable in class keel.Algorithms.SVM.SMO.supportVector.KernelEvaluation: the kernel evaluation results
m_exponent - Variable in class keel.Algorithms.SVM.SMO.supportVector.PolyKernel: The exponent for the polynomial kernel.
m_f - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization: function value
m_factor - Variable in class keel.Algorithms.SVM.SMO.supportVector.Puk: Cached factor for the Puk kernel.
m_FailReason - Variable in class keel.Algorithms.MIL.ExceptionDatasets
m_fcache - Variable in class keel.Algorithms.SVM.SMO.SMOreg: The current set of errors for all non-bound examples.
m_filterType - Variable in class keel.Algorithms.SVM.SMO.SMO: Whether to normalize/standardize/neither
m_filterType - Variable in class keel.Algorithms.SVM.SMO.SMOreg: Whether to normalize/standardize/neither
m_filterType - Variable in class keel.Algorithms.SVM.SMO.SVMreg: Whether to normalize/standardize/neither
m_fitLogisticModels - Variable in class keel.Algorithms.SVM.SMO.SMO: Whether logistic models are to be fit
m_gamma - Variable in class keel.Algorithms.SVM.SMO.supportVector.RBFKernel: Gamma for the RBF kernel.
m_Head - Variable in class keel.Algorithms.Decision_Trees.M5.Queue: Store a reference to the head of the queue
m_Head - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Queue: Store a reference to the head of the queue
m_Head - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue: Store a reference to the head of the queue
m_Head - Variable in class keel.Algorithms.SVM.SMO.core.Queue: Store a reference to the head of the queue
m_I0 - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: {i: 0 < m_alpha[i] < C}
m_I0 - Variable in class keel.Algorithms.SVM.SMO.SMOreg: {i: 0 < m_alpha[i] < C || 0 < m_alpha_[i] < C}
m_I0 - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: The different sets used by the algorithm.
m_I1 - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: {i: m_class[i] = 1, m_alpha[i] = 0}
m_I1 - Variable in class keel.Algorithms.SVM.SMO.SMOreg: {i: m_class[i] = 0, m_alpha_[i] = 0}
m_I2 - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: {i: m_class[i] = -1, m_alpha[i] =C}
m_I2 - Variable in class keel.Algorithms.SVM.SMO.SMOreg: {i: m_class[i] = 0, m_alpha_[i] = C}
m_I3 - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: {i: m_class[i] = 1, m_alpha[i] = C}
m_I3 - Variable in class keel.Algorithms.SVM.SMO.SMOreg: {i: m_class[i] = C, m_alpha_[i] = 0}
m_I4 - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: {i: m_class[i] = -1, m_alpha[i] = 0}
m_ID - Variable in class keel.Algorithms.Decision_Trees.M5.Association: The ID
m_ID - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation: the unique identifier of this information, will be generated automatically if left empty
m_ID - Variable in class keel.Algorithms.SVM.SMO.core.Tag: The ID
m_ID - Variable in class keel.Algorithms.SVM.SMO.core.TechnicalInformation: the unique identifier of this information, will be generated automatically if left empty
m_IDStr - Variable in class keel.Algorithms.SVM.SMO.core.Tag: The unique string for this tag, doesn't have to be numeric
m_iLow - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: The indices for m_bLow and m_bUp
m_iLow - Variable in class keel.Algorithms.SVM.SMO.SMOreg: The indices for m_bLow and m_bUp
m_iLow - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: index of the instance that gave us b.up and b.low
m_IndexString - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.SingleIndex: Record the string representation of the number
m_Indices - Variable in class keel.Algorithms.Decision_Trees.M5.M5SparseInstance: The index of the attribute associated with each stored value.
m_Indices - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: the indices
m_Indices - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance: The index of the attribute associated with each stored value.
m_IndicesBuffer - Variable in class keel.Algorithms.Decision_Trees.M5.M5Instances: Buffer of indices for sparse instance
m_IndicesBuffer - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Buffer of indices for sparse itemsets
m_IndicesBuffer - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Buffer of indices for sparse instance
m_Info - Variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: The header information.
m_Info - Variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: The header information.
m_Info - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: The header information.
m_Instances - Variable in class keel.Algorithms.Decision_Trees.M5.M5Instances: The instances.
m_Instances - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: The instances.
m_Instances - Variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
m_Instances - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: The instances.
m_Instances - Variable in class keel.Algorithms.SVM.SMO.core.Instances: The instances.
m_iSet - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: Index set {i: 0 < m_alpha[i] < C || 0 < m_alphaStar[i] < C}}
m_iUp - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: The indices for m_bLow and m_bUp
m_iUp - Variable in class keel.Algorithms.SVM.SMO.SMOreg: The indices for m_bLow and m_bUp
m_iUp - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: index of the instance that gave us b.up and b.low
m_kernel - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.FuzzyRuleSet: The Kernel associated to this Rule set
m_kernel - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: Kernel to use
m_kernel - Variable in class keel.Algorithms.SVM.SMO.SMO: the kernel to use
m_kernel - Variable in class keel.Algorithms.SVM.SMO.SMOreg: Kernel to use
m_kernel - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: the kernel
m_kernel - Variable in class keel.Algorithms.SVM.SMO.SVMreg: the configured kernel
m_kernelEvals - Variable in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: Counts the number of kernel evaluations.
m_KernelIsLinear - Variable in class keel.Algorithms.SVM.SMO.SMO: whether the kernel is a linear one
m_KernelIsLinear - Variable in class keel.Algorithms.SVM.SMO.SMOreg: whether the kernel is a linear one
m_kernelMatrix - Variable in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: The kernel matrix if full cache is used (i.e. size is set to 0)
m_kernelPrecalc - Variable in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: The precalculated dotproducts of <inst_i,inst_i>
m_kernelPrecalc - Variable in class keel.Algorithms.SVM.SMO.supportVector.Puk: The precalculated dotproducts of <inst_i,inst_i>
m_kernelPrecalc - Variable in class keel.Algorithms.SVM.SMO.supportVector.RBFKernel: The precalculated dotproducts of <inst_i,inst_i>
m_keys - Variable in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: Kernel cache (keys)
m_KValue - Variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ADE_CoForestGenerator: Final number of features that were considered in last build.
m_KValue - Variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: The number of attributes considered for a split.
m_KValue - Variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCGenerator: Final number of features that were considered in last build.
m_KValue - Variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: The number of attributes considered for a split.
m_KValue - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.CoForestGenerator: Final number of features that were considered in last build.
m_KValue - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: The number of attributes considered for a split.
m_lambda - Variable in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: the decay factor that penalizes non-continuous substring matches.
m_Lines - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: The lines read so far in case of incremental loading.
m_Lines - Variable in class keel.Algorithms.SVM.SMO.core.Instances: The lines read so far in case of incremental loading.
m_LL - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Log-likelihood of the searched model
m_LocatorIndices - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: the indices of locator objects
m_Locators - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: contains the locator locations, either null or a AttributeLocator reference
m_logistic - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: Stores logistic regression model for probability estimate
m_lowerOrder - Variable in class keel.Algorithms.SVM.SMO.supportVector.PolyKernel: Use lower-order terms?
m_Matrix - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

The actual matrix
m_MaxDepth - Variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: The maximum depth of the tree (0 = unlimited)
m_MaxDepth - Variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: The maximum depth of the tree (0 = unlimited)
m_MaxDepth - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: The maximum depth of the tree (0 = unlimited)
m_MAXITS - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization: Maximun iterations.
m_MinNum - Variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Minimum number of instances for leaf.
m_MinNum - Variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Minimum number of instances for leaf.
m_MinNum - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Minimum number of instances for leaf.
m_NameClassIndex - Variable in class keel.Algorithms.Decision_Trees.M5.M5Instances: name of the class with output
m_nCacheHits - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: number of kernel cache hits, used for printing statistics only
m_nEvals - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: number of kernel evaluations, used for printing statistics only
m_NewBatch - Variable in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Record whether the filter is at the start of a batch
m_NewBatch - Variable in class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter: Record whether the filter is at the start of a batch
m_Next - Variable in class keel.Algorithms.Decision_Trees.M5.Queue.QueueNode: The next node in the queue
m_Next - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Queue.QueueNode: The next node in the queue
m_Next - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue.QueueNode: The next node in the queue
m_Next - Variable in class keel.Algorithms.SVM.SMO.core.Queue.QueueNode: The next node in the queue
m_nInstances - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: number of instances in data set
m_nominalToBinary - Variable in class keel.Algorithms.SVM.SMO.SMO: Whether to convert nominal attributes into binary values
m_nSeed - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: seed for initializing random number generator
m_NumAttributes - Variable in class keel.Algorithms.Decision_Trees.M5.M5SparseInstance: The maximum number of values that can be stored.
m_NumAttributes - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance: The maximum number of values that can be stored.
m_NumCacheHits - Variable in class keel.Algorithms.SVM.SMO.supportVector.KernelEvaluation: the number of cache hits
m_NumClasses - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: The number of the class labels
m_NumClasses - Variable in class keel.Algorithms.SVM.SMO.SMO: The number of the class labels
m_NumEvals - Variable in class keel.Algorithms.SVM.SMO.supportVector.KernelEvaluation: the number of performed evaluations
m_numFeatures - Variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ADE_CoForestGenerator: Number of features to consider in random feature selection.
m_numFeatures - Variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCGenerator: Number of features to consider in random feature selection.
m_numFeatures - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.CoForestGenerator: Number of features to consider in random feature selection.
m_NumFolds - Variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Determines how much data is used for backfitting
m_NumFolds - Variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Determines how much data is used for backfitting
m_NumFolds - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Determines how much data is used for backfitting
m_numFolds - Variable in class keel.Algorithms.SVM.SMO.SMO: The number of folds for the internal cross-validation
m_numInsts - Variable in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: The number of instance in the dataset
m_NumPredictors - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: The number of attributes in the model
m_omega - Variable in class keel.Algorithms.SVM.SMO.supportVector.Puk: Omega for the Puk kernel.
m_onlyNumeric - Variable in class keel.Algorithms.SVM.SMO.SVMreg: Only numeric attributes in the dataset?
m_optimizer - Variable in class keel.Algorithms.SVM.SMO.SVMreg: contains the algorithm used for learning
m_Options - Variable in class keel.Algorithms.SVM.SMO.supportVector.KernelEvaluation: user-supplied options
m_Par - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: The coefficients (optimized parameters) of the model
m_powersOflambda - Variable in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: the precalculated powers of lambda
m_Prop - Variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: The proportions of training instances going down each branch.
m_Prop - Variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: The proportions of training instances going down each branch.
m_Prop - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: The proportions of training instances going down each branch.
m_PruningMethod - Variable in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: the pruning method
m_Random - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Random object used in this class
m_random - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: random number generator
m_randomSeed - Variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: The random seed to use.
m_randomSeed - Variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: The random seed to use.
m_randomSeed - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: The random seed to use.
m_randomSeed - Variable in class keel.Algorithms.SVM.SMO.SMO: The random number seed
m_Readable - Variable in class keel.Algorithms.Decision_Trees.M5.Association: The descriptive text
m_Readable - Variable in class keel.Algorithms.SVM.SMO.core.Tag: The descriptive text
m_RelationName - Variable in class keel.Algorithms.Decision_Trees.M5.M5Instances: The dataset's name.
m_RelationName - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: The dataset's name.
m_RelationName - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: The dataset's name.
m_RelationName - Variable in class keel.Algorithms.SVM.SMO.core.Instances: The dataset's name.
m_Result - Variable in class keel.Algorithms.SVM.SMO.supportVector.KernelEvaluation: the result string
m_Ridge - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: The ridge parameter.
m_rules - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.FuzzyRuleSet: The actual number of rules
m_Ruleset - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: The ruleset
m_ruleSet - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.PDFC: The fuzzy rule sets
m_RulesetStats - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: The RuleStats for the ruleset of each class value
m_sDatasetHasContinuous - Variable in class keel.GraphInterKeel.experiments.Node
m_sDatasetHasImprecise - Variable in class keel.GraphInterKeel.experiments.Node
m_sDatasetHasInteger - Variable in class keel.GraphInterKeel.experiments.Node
m_sDatasetHasMIL - Variable in class keel.GraphInterKeel.experiments.Node
m_sDatasetHasMissing - Variable in class keel.GraphInterKeel.experiments.Node
m_sDatasetHasMultiClass - Variable in class keel.GraphInterKeel.experiments.Node
m_sDatasetHasMultiOutput - Variable in class keel.GraphInterKeel.experiments.Node
m_sDatasetHasNominal - Variable in class keel.GraphInterKeel.experiments.Node
m_Seed - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: The seed to perform randomization
m_seed - Variable in class keel.Algorithms.SVM.SMO.SMO: The seed used for of the class labels
m_Selected - Variable in class keel.Algorithms.Decision_Trees.M5.SelectedAssociation: The index of the selected tag
m_Selected - Variable in class keel.Algorithms.SVM.SMO.core.SelectedTag: The index of the selected tag
m_SelectedIndex - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.SingleIndex: The selected index
m_sig - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal
m_sigma - Variable in class keel.Algorithms.SVM.SMO.supportVector.Puk: Sigma for the Puk kernel.
m_Silent - Variable in class keel.Algorithms.SVM.SMO.core.Check: Silent mode, for no output at all to stdout
m_Size - Variable in class keel.Algorithms.Decision_Trees.M5.Queue: Store the current number of elements in the queue
m_Size - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Queue: Store the c m_Tail.m_Nexturrent number of elements in the queue
m_Size - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue: Store the current number of elements in the queue
m_Size - Variable in class keel.Algorithms.SVM.SMO.core.Queue: Store the c m_Tail.m_Nexturrent number of elements in the queue
m_sparseIndices - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: Variables to hold indices vector in sparse form.
m_sparseIndices - Variable in class keel.Algorithms.SVM.SMO.SMOreg: Variables to hold indices vector in sparse form.
m_sparseIndices - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: Variables to hold indices vector in sparse form.
m_sparseWeights - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: Variables to hold weight vector in sparse form.
m_sparseWeights - Variable in class keel.Algorithms.SVM.SMO.SMOreg: Variables to hold weight vector in sparse form.
m_sparseWeights - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: Variables to hold weight vector in sparse form.
m_SplitPoint - Variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: The split point.
m_SplitPoint - Variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: The split point.
m_SplitPoint - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: The split point.
m_storage - Variable in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: Kernel cache
m_STPMX - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization: STPMX value.
m_Successors - Variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: The subtrees appended to this tree.
m_Successors - Variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: The subtrees appended to this tree.
m_Successors - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: The subtrees appended to this tree.
m_sumOfWeights - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: Stores the weight of the training instances
m_supportVectors - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: The set of support vectors
m_supportVectors - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: set of support vectors, that is, vectors with alpha(*)!
m_SVM - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: parent SVMreg class
m_Tags - Variable in class keel.Algorithms.Decision_Trees.M5.SelectedAssociation: The set of tags to choose from
m_Tags - Variable in class keel.Algorithms.SVM.SMO.core.SelectedTag: The set of tags to choose from
m_Tail - Variable in class keel.Algorithms.Decision_Trees.M5.Queue: Store a reference to the tail of the queue
m_Tail - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Queue: Store a reference to the tail of the queue
m_Tail - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue: Store a reference to the tail of the queue
m_Tail - Variable in class keel.Algorithms.SVM.SMO.core.Queue: Store a reference to the tail of the queue
m_target - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: class values/desired output vector
m_tol - Variable in class keel.Algorithms.SVM.SMO.SMO: Tolerance for accuracy of result.
m_tol - Variable in class keel.Algorithms.SVM.SMO.SMOreg: The parameter tol
m_TOLX - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization: ToleranceX value.
m_Total - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: # of all the possible conditions in a rule
m_Type - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator: the type of the attribute
m_Type - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation: the type of this technical information
m_Type - Variable in class keel.Algorithms.SVM.SMO.core.TechnicalInformation: the type of this technical information
m_type - Variable in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: The type of function used by this kernel
m_Upper - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.SingleIndex: Store the maximum value permitted. -1 indicates that no upper value has been set
m_ValueBuffer - Variable in class keel.Algorithms.Decision_Trees.M5.M5Instances: Buffer of values for sparse instance
m_ValueBuffer - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Buffer of values for sparse itemsets
m_ValueBuffer - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Buffer of values for sparse instance
m_Values - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation: stores all the values associated with the fields (FIELD - String)
m_Values - Variable in class keel.Algorithms.SVM.SMO.core.TechnicalInformation: stores all the values associated with the fields (FIELD - String)
m_Weight - Variable in class keel.Algorithms.Decision_Trees.M5.M5Instance: The instance's weight.
m_Weight - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: The instance's weight.
m_Weight - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: The instance's weight.
m_Weight - Variable in class keel.Algorithms.SVM.SMO.core.Instance: The instance's weight.
m_weights - Variable in class keel.Algorithms.SVM.SMO.SMO.BinarySMO: Weight vector for linear machine.
m_weights - Variable in class keel.Algorithms.SVM.SMO.SMOreg: Weight vector for linear machine.
m_weights - Variable in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: weights for linear kernel
m_x0 - Variable in class keel.Algorithms.SVM.SMO.SVMreg: coefficients used by normalization filter for doing its linear transformation so that result = svmoutput * m_x1 + m_x0
m_x1 - Variable in class keel.Algorithms.SVM.SMO.SVMreg: coefficients used by normalization filter for doing its linear transformation so that result = svmoutput * m_x1 + m_x0
m_z - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.FuzzyRuleSet: location parameters of the different membership functions
m_Zero - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization: Compute machine precision
MACHEP - Static variable in class keel.Algorithms.Lazy_Learning.Statistics: MACHEP constant
MACHEP - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics: Some constants
MachineAccuracy - Class in keel.Algorithms.Preprocess.Missing_Values.BPCA: determines machine accuracy
MachineAccuracy() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.BPCA.MachineAccuracy
MachineAccuracy - Class in keel.Algorithms.Preprocess.Missing_Values.EM.util: determines machine accuracy
MachineAccuracy() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EM.util.MachineAccuracy
Main - Class in keel.Algorithms.Associative_Classification.ClassifierCBA: It reads the configuration file (data-set files and parameters) and launch the algorithm.
Main() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Main: Main Program
Main - Class in keel.Algorithms.Associative_Classification.ClassifierCBA2: It reads the configuration file (data-set files and parameters) and launch the algorithm
Main() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Main: Main Program
Main - Class in keel.Algorithms.Associative_Classification.ClassifierCMAR: It reads the configuration file (data-set files and parameters) and launch the algorithm
Main() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCMAR.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.Main: Main Program
Main - Class in keel.Algorithms.Associative_Classification.ClassifierCPAR: It reads the configuration file (data-set files and parameters) and launch the algorithm
Main() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCPAR.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.Main: Main Program
Main - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR: It reads the configuration file (data-set files and parameters) and launch the algorithm
Main() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Main: Main Program
Main - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Main: Main Program
Main - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA: It reads the configuration file (data-set files and parameters) and launch the algorithm
Main() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Main: Main Program
main(String[]) - Static method in class keel.Algorithms.Clustering_Algorithms.ClusterKMeans.ClusterKMeans: This public static method runs the algorithm that this class concerns with.
Main - Class in keel.Algorithms.Coevolution.CIW_NN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Coevolution.CIW_NN.Main
main(String[]) - Static method in class keel.Algorithms.Coevolution.CIW_NN.Main: The main method of the class
Main - Class in keel.Algorithms.Coevolution.IFS_COCO: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Coevolution.IFS_COCO.Main
main(String[]) - Static method in class keel.Algorithms.Coevolution.IFS_COCO.Main: The main method of the class
main(String[]) - Static method in class keel.Algorithms.Complexity_Metrics.ComplexityMetrics: It runs the algorithm
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.C45.C45: Main function.
Main - Class in keel.Algorithms.Decision_Trees.C45_Binarization: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.Main: Main Program
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.CART.classification.RunClassificationCART: It runs the CART method for classification
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.CART.regression.RunRegressionCART: It runs the CART method for regression
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.DT_GA.C45.C45: Main function.
Main - Class in keel.Algorithms.Decision_Trees.DT_GA: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.DT_GA.Main: Main Program
Main - Class in keel.Algorithms.Decision_Trees.DT_oblicuo: It reads the configuration file (data-set files and parameters) and launch the algorithm
Main() - Constructor for class keel.Algorithms.Decision_Trees.DT_oblicuo.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.DT_oblicuo.Main: Main Program
Main - Class in keel.Algorithms.Decision_Trees.FunctionalTrees: This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Decision_Trees.FunctionalTrees.Main
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.FunctionalTrees.Main: The main method of the class
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.ID3.ID3: Main function.
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.Distributions: Main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: A test method for this class.
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5: Main method for M5' algorithm
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5Attribute: Simple main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5Kernel: Main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5SparseInstance: Main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.Queue: Main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.SerializedObject: Test routine, reads an data file from stdin and measures memory usage (the data file should have long string attribute values)
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.SimpleStatistics: Tests the paired stats object from the command line.
Main - Class in keel.Algorithms.Decision_Trees.PUBLIC: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Decision_Trees.PUBLIC.Main
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.PUBLIC.Main: The main method of the class
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.SLIQ.SLIQ: Main function.
Main - Class in keel.Algorithms.Decision_Trees.Target: It reads the configuration file (data-set files and parameters) and launch the algorithm
Main() - Constructor for class keel.Algorithms.Decision_Trees.Target.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Decision_Trees.Target.Main: Main Program
Main - Class in keel.Algorithms.Discretizers.Ameva_Discretizer: This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Discretizers.Ameva_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.Ameva_Discretizer.Main: It runs the method
Main - Class in keel.Algorithms.Discretizers.Bayesian_Discretizer: This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Discretizers.Bayesian_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.Bayesian_Discretizer.Main: It runs the algorithm
Main - Class in keel.Algorithms.Discretizers.CACC: Main class of CACC algorithm (discretization algorithm based on Class-Attribute Contingency Coefficient)
Main() - Constructor for class keel.Algorithms.Discretizers.CACC.Main: Creates a new instance of Main.
main(String[]) - Static method in class keel.Algorithms.Discretizers.CACC.Main: Main method
Main - Class in keel.Algorithms.Discretizers.CADD_Discretizer: Main class CADD discretizer.
Main() - Constructor for class keel.Algorithms.Discretizers.CADD_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.CADD_Discretizer.Main: It runs the algorithm
Main - Class in keel.Algorithms.Discretizers.CAIM_Discretizer: Main class CADD discretizer.
Main() - Constructor for class keel.Algorithms.Discretizers.CAIM_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.CAIM_Discretizer.Main: It runs the algorithm
Main - Class in keel.Algorithms.Discretizers.Chi2_Discretizer: Main class Chi2 discretizer.
Main() - Constructor for class keel.Algorithms.Discretizers.Chi2_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.Chi2_Discretizer.Main
Main - Class in keel.Algorithms.Discretizers.ChiMerge_Discretizer: Main class Chi-Merge discretizer.
Main() - Constructor for class keel.Algorithms.Discretizers.ChiMerge_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.ChiMerge_Discretizer.Main: It runs the algorithm
Main - Class in keel.Algorithms.Discretizers.Cluster_Analysis: Main class Cluster Analysis Discretizer.
Main() - Constructor for class keel.Algorithms.Discretizers.Cluster_Analysis.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.Cluster_Analysis.Main: It runs the algorithm
Main - Class in keel.Algorithms.Discretizers.DIBD: Main class of DIBD (Distribution-Index-Based Discretizer)
Main() - Constructor for class keel.Algorithms.Discretizers.DIBD.Main: It creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.DIBD.Main: Main method
Main - Class in keel.Algorithms.Discretizers.ExtendedChi2_Discretizer
Main() - Constructor for class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Main
Main - Class in keel.Algorithms.Discretizers.Fayyad_Discretizer: Main class Fayyad discretizer.
Main() - Constructor for class keel.Algorithms.Discretizers.Fayyad_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.Fayyad_Discretizer.Main: Main method
Main - Class in keel.Algorithms.Discretizers.FixedFrequency_Discretizer: Main class Uniform Frequency discretizer.
Main() - Constructor for class keel.Algorithms.Discretizers.FixedFrequency_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.FixedFrequency_Discretizer.Main: Main method
Main - Class in keel.Algorithms.Discretizers.FUSINTER: Main class of the FUSINTER discretizer.
Main() - Constructor for class keel.Algorithms.Discretizers.FUSINTER.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.FUSINTER.Main: Main method
Main - Class in keel.Algorithms.Discretizers.HDD: Main class HDD discretizer.
Main() - Constructor for class keel.Algorithms.Discretizers.HDD.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.HDD.Main: Main method
Main - Class in keel.Algorithms.Discretizers.HellingerBD: Main class of HellingerBD (Hellinger-Based Discretizer)
Main() - Constructor for class keel.Algorithms.Discretizers.HellingerBD.Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.HellingerBD.Main: Main method
Main - Class in keel.Algorithms.Discretizers.HeterDisc: Main class of Heter-Disc algorithm (discretization algorithm based on Heterogeneity Criterion)
Main() - Constructor for class keel.Algorithms.Discretizers.HeterDisc.Main: It creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.HeterDisc.Main: Main method
Main - Class in keel.Algorithms.Discretizers.Id3_Discretizer: Main class IID3 discretizer.
Main() - Constructor for class keel.Algorithms.Discretizers.Id3_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.Id3_Discretizer.Main: It runs the algorithm
Main - Class in keel.Algorithms.Discretizers.IDD: Main class of IDD (Interval distance based-Method for Discretization)
Main() - Constructor for class keel.Algorithms.Discretizers.IDD.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.IDD.Main: Main method
Main - Class in keel.Algorithms.Discretizers.Khiops: Main class of Khiops
Main() - Constructor for class keel.Algorithms.Discretizers.Khiops.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.Khiops.Main: It runs the algorithm
Main - Class in keel.Algorithms.Discretizers.MantarasDist_Discretizer: Main class Mantaras Distance-Based Discretizer.
Main() - Constructor for class keel.Algorithms.Discretizers.MantarasDist_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.MantarasDist_Discretizer.Main: It runs the algorithm
Main - Class in keel.Algorithms.Discretizers.ModifiedChi2_Discretizer: Main class Chi2 discretizer.
Main() - Constructor for class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Main
Main - Class in keel.Algorithms.Discretizers.MODL: Main class
Main() - Constructor for class keel.Algorithms.Discretizers.MODL.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.MODL.Main
Main - Class in keel.Algorithms.Discretizers.MVD: Main class of MVD (Multivariate Discretization) algorithm
Main() - Constructor for class keel.Algorithms.Discretizers.MVD.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.MVD.Main: Main method
Main - Class in keel.Algorithms.Discretizers.OneR: Main class
Main() - Constructor for class keel.Algorithms.Discretizers.OneR.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.OneR.Main: It runs the algorithm
Main - Class in keel.Algorithms.Discretizers.Proportional_Discretizer: Main class Proportional discretizer.
Main() - Constructor for class keel.Algorithms.Discretizers.Proportional_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.Proportional_Discretizer.Main: Main method
Main - Class in keel.Algorithms.Discretizers.Random_Discretizer: Main class of the Random Discretizer
Main() - Constructor for class keel.Algorithms.Discretizers.Random_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.Random_Discretizer.Main: Main method
Main - Class in keel.Algorithms.Discretizers.UCPD: Main class of UCPD (Unsupervised Correlation Preserving Discretization) algorithm
Main() - Constructor for class keel.Algorithms.Discretizers.UCPD.Main: It creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.UCPD.Main: Main method
Main - Class in keel.Algorithms.Discretizers.UniformFrequency_Discretizer: Main class Uniform Frequency discretizer.
Main() - Constructor for class keel.Algorithms.Discretizers.UniformFrequency_Discretizer.Main: It creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.UniformFrequency_Discretizer.Main: Main method
Main - Class in keel.Algorithms.Discretizers.UniformWidth_Discretizer: Main class Uniform Width discretizer.
Main() - Constructor for class keel.Algorithms.Discretizers.UniformWidth_Discretizer.Main: It creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.UniformWidth_Discretizer.Main: Main method
Main - Class in keel.Algorithms.Discretizers.USD_Discretizer: Main class USD discretizer.
Main() - Constructor for class keel.Algorithms.Discretizers.USD_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.USD_Discretizer.Main
Main - Class in keel.Algorithms.Discretizers.Zeta_Discretizer: This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Discretizers.Zeta_Discretizer.Main: It creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Discretizers.Zeta_Discretizer.Main: Main method
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.CFKNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.CFKNN.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.CFKNN.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.D_SKNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.D_SKNN.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.D_SKNN.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.FCMKNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.FCMKNN.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FCMKNN.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.FENN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.FENN.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FENN.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.FRKNNA: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRKNNA.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRKNNA.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_FRS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_FRS.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_FRS.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_VQRS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_VQRS.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_VQRS.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyKNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyKNN.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyKNN.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyNPC: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyNPC.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyNPC.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.GAFuzzyKNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.GAFuzzyKNN.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.GAFuzzyKNN.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.IF_KNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.IF_KNN.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IF_KNN.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.IFSKNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFSKNN.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFSKNN.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.IFV_NP: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFV_NP.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFV_NP.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.IT2FKNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.IT2FKNN.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IT2FKNN.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.PFKNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.PFKNN.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.PFKNN.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.PosIBL: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.PosIBL.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.PosIBL.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.VWFuzzyKNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.VWFuzzyKNN.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.VWFuzzyKNN.Main: The main method of the class
Main - Class in keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW: It reads the configuration file (data-set files and parameters) and launch the algorithm
Main() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Main: Main Program
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.ClassifierFuzzyWangMendel.ClassifierFuzzyWangMendel: This public static method runs the algorithm that this class concerns with.
Main - Class in keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted: It reads the configuration file (data-set files and parameters) and launch the algorithm
Main() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Main: Main Program
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyAdaBoost.ClassifierFuzzyAdaBoost: This public static method runs the algorithm that this class concerns with.
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGAP.ClassifierFuzzyGAP: This public static method runs the algorithm that this class concerns with.
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGP.ClassifierFuzzyGP: This public static method runs the algorithm that this class concerns with.
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyLogitBoost.ClassifierFuzzyLogitBoost: This public static method runs the algorithm that this class concerns with.
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyMaxLogitBoost.ClassifierFuzzyMaxLogitBoost: This public static method runs the algorithm that this class concerns with.
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyPittsBurgh.ClassifierFuzzyPittsBurgh: This public static method runs the algorithm that this class concerns with.
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySAP.ClassifierFuzzySAP: This public static method runs the algorithm that this class concerns with.
Main - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD: It reads the configuration file (data-set files and parameters) and launch the algorithm
Main() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Main: Main Program
Main - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99: Main Class of the Program It reads the configuration file (data-set files and parameters) and launch the algorithm
Main() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.Main: Main Program
Main - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL: Main Class of the Program.
Main() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.Main: Main Program
Main - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV
Main() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.Main: Main Program
Main - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen
Main() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.Main: Main Program
Main - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE
Main() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.Main: Main Program
Main - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2
Main() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.Main: Main Program
Main - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0
Main() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.Main: Main Program
Main - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Main: Main Program
Main - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Main: Main Program
Main - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Main: Main Program
Main - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the GP-COACH algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Main: Main Program
Main - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Main: Main Program
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzyGAP.ModelFuzzyGAP: This public static method runs the algorithm that this class concerns with.
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzyGP.ModelFuzzyGP: This public static method runs the algorithm that this class concerns with.
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzyPittsBurgh.ModelFuzzyPittsBurgh: This public static method runs the algorithm that this class concerns with.
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzySAP.ModelFuzzySAP: This public static method runs the algorithm that this class concerns with.
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FB
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
Main - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Main: Main Program
Main - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.Main: Main Program
Main - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS: Main Class
Main() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.Main
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.Main: It runs the algorirthm
main(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Random_Sets.FSS98.FSS98: This public static method runs the algorithm that this class concerns with.
Main - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Main: Default constructor.
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Main: The main method of the class
Main - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Main: Default constructor.
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Main: The main method of the class
Main - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Main: Default constructor.
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Main: The main method of the class
Main - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Main: Default constructor.
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Main: The main method of the class
Main - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL: Main class of BioHEL algorithm
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Main: It creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Main: Main method
Main - Class in keel.Algorithms.Genetic_Rule_Learning.Bojarczuk_GP: Bojarczuk classification algorithm
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Bojarczuk_GP.Main
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Bojarczuk_GP.Main: Main method
Main - Class in keel.Algorithms.Genetic_Rule_Learning.COGIN: Main class
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.COGIN.Main
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Main: It runs the algorithm
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran: Main function of the program
The scheme of the steady-state Pitts-GIRLA method is the following one: Selection Crossover Mutation Replacement Evaluation Until X generations have been carried out
Main - Class in keel.Algorithms.Genetic_Rule_Learning.CORE
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.CORE.Main
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Main
Main - Class in keel.Algorithms.Genetic_Rule_Learning.DMEL: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.DMEL.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.Main: Main Program
Main - Class in keel.Algorithms.Genetic_Rule_Learning.Falco_GP: Falco classification algorithm
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Falco_GP.Main
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Falco_GP.Main: Main method
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Control
Main - Class in keel.Algorithms.Genetic_Rule_Learning.GIL: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GIL.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Main: Main Program
Main - Class in keel.Algorithms.Genetic_Rule_Learning.Hider
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Main
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Main: Main function for the algorithm Hider.
Main - Class in keel.Algorithms.Genetic_Rule_Learning.ILGA: Main class for algorithm ILGA.
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.ILGA.Main
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Main: Main function for algorithm ILGA.
Main - Class in keel.Algorithms.Genetic_Rule_Learning.LogenPro
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Main: Main Program
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue: Main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleStatistics: Tests the paired stats object from the command line.
Main - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.ChiMerge_Discretizer: Main class for algorithm MPLCS.
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.ChiMerge_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.ChiMerge_Discretizer.Main
Main - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Fayyad_Discretizer: Main class for the algorithm MPLCS Fayyad_Dicretizer.
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Fayyad_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Fayyad_Discretizer.Main
Main - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Id3_Discretizer: Main class for algorithm MPLCS ID3_Discretizer.
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Id3_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Id3_Discretizer.Main
Main - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.UniformFrequency_Discretizer: Main class for algorithm MPLCS UniformFrequency_Discretizer.
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.UniformFrequency_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.UniformFrequency_Discretizer.Main
Main - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.UniformWidth_Discretizer: Main class for algorithm MPLCS UniformWidth_Discretizer.
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.UniformWidth_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.UniformWidth_Discretizer.Main
Main - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.USD_Discretizer: Main class for algorithm MPLCS USD_Discretizer.
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.USD_Discretizer.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.USD_Discretizer.Main
Main - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS: Main class.
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Main: Creates a new instance of Control
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Main
Main - Class in keel.Algorithms.Genetic_Rule_Learning.OCEC: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OCEC.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Main: Main Program
Main - Class in keel.Algorithms.Genetic_Rule_Learning.OIGA: Main class
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OIGA.Main
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Main: It runs the algorithm
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
Main - Class in keel.Algorithms.Genetic_Rule_Learning.PSO_ACO: File: Main.java This is the main class of the PSO-ACO algorithm.
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Main: Default constructor.
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Main: The main method of the class
Main - Class in keel.Algorithms.Genetic_Rule_Learning.RMini: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.RMini.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.RMini.Main: Main Program
Main - Class in keel.Algorithms.Genetic_Rule_Learning.SIA: Title: SIA Main Program Description: This is the main class, which is executed when we launch the program
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.SIA.Main: Default builder
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Main: Main program
Main - Class in keel.Algorithms.Genetic_Rule_Learning.Tan_GP: Falco classification algorithm
Main() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Tan_GP.Main
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Tan_GP.Main: Main method
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.UCS.UCSControl: It is the main procedure.
main(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.XCSControl: It is the main procedure.
Main - Class in keel.Algorithms.Hyperrectangles.BNGE: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Hyperrectangles.BNGE.Main
main(String[]) - Static method in class keel.Algorithms.Hyperrectangles.BNGE.Main: The main method of the class
Main - Class in keel.Algorithms.Hyperrectangles.EACH: Main class of the Each algorithm.
Main() - Constructor for class keel.Algorithms.Hyperrectangles.EACH.Main: Default constructor.
main(String[]) - Static method in class keel.Algorithms.Hyperrectangles.EACH.Main: Main program
Main - Class in keel.Algorithms.Hyperrectangles.EHS_CHC: Main class.
Main() - Constructor for class keel.Algorithms.Hyperrectangles.EHS_CHC.Main
main(String[]) - Static method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Main: Main function.
Main - Class in keel.Algorithms.Hyperrectangles.INNER: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Hyperrectangles.INNER.Main
main(String[]) - Static method in class keel.Algorithms.Hyperrectangles.INNER.Main: The main method of the class
Main - Class in keel.Algorithms.Hyperrectangles.RISE: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Hyperrectangles.RISE.Main
main(String[]) - Static method in class keel.Algorithms.Hyperrectangles.RISE.Main: The main method of the class
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.C45CS: Main function.
Main - Class in keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost: File: Main.java A Main class to process the parameters of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.Main: Main method
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.MLPerceptronBackpropCS: Main function
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.C45: Main function.
Main - Class in keel.Algorithms.ImbalancedClassification.Ensembles: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Main: Main Program
Main - Class in keel.Algorithms.ImbalancedClassification.Ensembles.SMOTE: Main.java Main class for the algorithm SMOTE.
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.SMOTE.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.SMOTE.Main: Main function.
Main - Class in keel.Algorithms.ImbalancedClassification.Ensembles.SPIDER: Main.java Main class for the algorithm SPIDER.
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.SPIDER.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.SPIDER.Main: Main function.
Main - Class in keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the GP-COACH-H algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Main: Main Program
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.ADASYN: File: Main.java A Main class to process the paramethers of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.ADASYN.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.ADASYN.Main: Main method
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.ADOMS: File: Main.java A Main class to process the paramethers of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.ADOMS.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.ADOMS.Main: Main method
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.AHCClustering: File: Main.java A Main class to process the paramethers of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.AHCClustering.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.AHCClustering.Main: Main method
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.Borderline_SMOTE: File: Main.java A Main class to process the paramethers of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.Borderline_SMOTE.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.Borderline_SMOTE.Main: Main method
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.CNN: File: Main.java A Main class to process the paramethers of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.CNN.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.CNN.Main: Main method
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.CNN_TomekLinks: File: Main.java A Main class to process the paramethers of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.CNN_TomekLinks.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.CNN_TomekLinks.Main: Main method
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.CPM: File: Main.java A Main class to process the parameters of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.CPM.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.CPM.Main: Main method
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.NCL: File: Main.java A Main class to process the paramethers of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.NCL.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.NCL.Main: Main method
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.OSS: File: Main.java A Main class to process the paramethers of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.OSS.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.OSS.Main: Main method
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.RandomOverSampling: File: Main.java A Main class to process the paramethers of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.RandomOverSampling.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.RandomOverSampling.Main: Main method
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.RandomUnderSampling: File: Main.java A Main class to process the paramethers of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.RandomUnderSampling.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.RandomUnderSampling.Main: Main method
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.Safe_Level_SMOTE: File: Main.java A Main class to process the paramethers of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.Safe_Level_SMOTE.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.Safe_Level_SMOTE.Main: Main method
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.SBC: File: Main.java A Main class to process the paramethers of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SBC.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SBC.Main: Main method
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE: File: Main.java A Main class to process the parameters of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE.Main: Main method
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_ENN: File: Main.java A Main class to process the paramethers of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_ENN.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_ENN.Main: Main method
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB: File: Main.java A Main class to process the paramethers of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Main: Main method
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Simple main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SInstances
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance: Main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Stats: Tests the paired stats object from the command line.
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Main method for testing this class.
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_TomekLinks: Main.java
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_TomekLinks.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_TomekLinks.Main: Main function
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.SPIDER: File: Main.java A Main class to process the paramethers of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SPIDER.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SPIDER.Main: Main method
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.SPIDER2: File: Main.java A Main class to process the paramethers of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SPIDER2.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SPIDER2.Main: Main method
Main - Class in keel.Algorithms.ImbalancedClassification.Resampling.TomekLinks: File: Main.java A Main class to process the paramethers of the method and launch the algorithm
Main() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.TomekLinks.Main
main(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.TomekLinks.Main: Main method
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.AMPSO.AMPSOAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.AMPSO.AMPSOGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.Basic.AccuracyMeter: Main.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich will contain the test data set 3: k Number of neighbors used in the KNN function
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.BasicMethods.ARS: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich will contain the test data set
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.BasicMethods.ARSAlgorithm: Main function.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.BasicMethods.AVG: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich will contain the test data set
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.BasicMethods.AVGAlgorithm: Main function.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.BasicMethods.CNN: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich will contain the test data set 3: k Number of neighbors used in the KNN function
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.BasicMethods.RandomSelector: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich will contain the test data set
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.BasicMethods.SAVG: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich will contain the test data set
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.BasicMethods.SAVGAlgorithm: Main method
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.BTS3.BTS3Algorithm: The main method of the class
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.BTS3.BTS3Generator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich will contain the test data set 2: Seed of the random generator. 3: Number of prototypes to be generated. 4: Nearest-Neighbors used in the internal KNN use. 5: Random Trials (number of bootstrappings performed).
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.Chen.ChenAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.Chen.ChenGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich contains the test data set. 3: Seed of the random number generator. 4: Number of prototypes to be generated.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.DE.DEAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.DE.DEGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.DEGL.DEGLAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.DEGL.DEGLGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
Main - Class in keel.Algorithms.Instance_Generation.Depur: Main class
Main() - Constructor for class keel.Algorithms.Instance_Generation.Depur.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.Depur.Main: Main Function.
Main - Class in keel.Algorithms.Instance_Generation.DROP3LVQ3: Main class
Main() - Constructor for class keel.Algorithms.Instance_Generation.DROP3LVQ3.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.DROP3LVQ3.Main: Main Function.
Main - Class in keel.Algorithms.Instance_Generation.DROP3PSO: Main class
Main() - Constructor for class keel.Algorithms.Instance_Generation.DROP3PSO.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.DROP3PSO.Main: Main Function.
Main - Class in keel.Algorithms.Instance_Generation.DROP3SFLSDE: Main class
Main() - Constructor for class keel.Algorithms.Instance_Generation.DROP3SFLSDE.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.DROP3SFLSDE.Main: Main Function.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.DSM.DSMAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.DSM.DSMGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich contains the test data set. 3: Number of iterations. 4: Number of prototypes to be generated. 5: Alpha constant parameter.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.ENPC.ENPCAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.ENPC.ENPCGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.GENN.GENNAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.GENN.GENNGenerator: General main for GENNGenerator prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich contains the test data set. 2: Seed used in the random generator. 3: k (size of neighborhood in KNN).
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.GMCA.GMCAAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.GMCA.GMCAGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich will contain the test data set 3: k Number of neighbors used in the KNN function
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.HYB.HYBAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.HYB.HYBGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich contains the test data set. 3: Seed of the random generator. 4: Iterations performed in the search of optimal LVQ3-parameters. 5: Iterations performed in optimal LVQ3 process. 6: % of prototypes generated in optimal LVQ3 process. 7: Alpha0 LVQ3-parameter. 8: Initial value of window width. 9: Final value of window width. 10: Increment in each step of the value of window width. 11: Initial value of epsilon. 12: Final value of epsilon. 13: Increment in each step of the value of epsilon. 14: % de initial partition in the training set. 15: Type of initial reduction performed. 16 and so on: Parameters of the initial reduction process
Main - Class in keel.Algorithms.Instance_Generation.ICFLVQ3: Main class
Main() - Constructor for class keel.Algorithms.Instance_Generation.ICFLVQ3.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.ICFLVQ3.Main: Main Function.
Main - Class in keel.Algorithms.Instance_Generation.ICFPSO: Main class
Main() - Constructor for class keel.Algorithms.Instance_Generation.ICFPSO.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.ICFPSO.Main: Main Function.
Main - Class in keel.Algorithms.Instance_Generation.ICFSFLSDE: Main class
Main() - Constructor for class keel.Algorithms.Instance_Generation.ICFSFLSDE.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.ICFSFLSDE.Main: Main Function.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.ICPL.ICPLAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.ICPL.ICPLGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.IPLDE.IPLDEAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.IPLDE.IPLDEGenerator: General main for all the prototoype generators
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.JADE.JADEAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.JADE.JADEGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.LVQ.LVQ1: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich will contain the test data set 3: k Number of neighbors used in the KNN function
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.LVQ.LVQ1Algorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.LVQ.LVQ2: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich will contain the test data set 3: k Number of neighbors used in the KNN function
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.LVQ.LVQ2_1: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich will contain the test data set 3: k Number of neighbors used in the KNN function
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.LVQ.LVQ2_1Algorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.LVQ.LVQ2Algorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.LVQ.LVQ3: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich will contain the test data set 3: k Number of neighbors used in the KNN function
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.LVQ.LVQ3Algorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.LVQ.LVQPRU: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich will contain the test data set 3: k Number of neighbors used in the KNN function
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.LVQ.LVQPRUAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.LVQ.LVQTC: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich will contain the test data set 3: k Number of neighbors used in the KNN function
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.LVQ.LVQTCAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.MCA.MCAAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.MCA.MCAGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich will contain the test data set 3: k Number of neighbors used in the KNN function
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.MixtGauss.MixtGaussAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.MixtGauss.MixtGaussGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.MSE.MSEAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.MSE.MSEGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.OBDE.OBDEAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.OBDE.OBDEGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.PNN.PNNAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.PNN.PNNGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich contains the test data set. 3: Seed of the Random Number Generator. 4: number of prototypes to be generated (OPTIONAL)
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.POC.POCAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.POC.POCGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.PSCSA.PSCSAAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.PSCSA.PSCSAGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.PSO.PSOAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.PSO.PSOGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.RSP.RSPAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.RSP.RSPGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.SADE.SADEAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.SADE.SADEGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.SFLSDE.SFLSDEAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.SFLSDE.SFLSDEGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.SGP.SGPAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.SGP.SGPGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
Main - Class in keel.Algorithms.Instance_Generation.SSMALVQ3: Main class
Main() - Constructor for class keel.Algorithms.Instance_Generation.SSMALVQ3.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.SSMALVQ3.Main: Main Function.
Main - Class in keel.Algorithms.Instance_Generation.SSMAPSO: Main class
Main() - Constructor for class keel.Algorithms.Instance_Generation.SSMAPSO.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.SSMAPSO.Main: Main Function.
Main - Class in keel.Algorithms.Instance_Generation.SSMASFLSDE: Main class
Main() - Constructor for class keel.Algorithms.Instance_Generation.SSMASFLSDE.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.Main: Main Function.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.Trivial.TrivialAlgorithm: Main Function.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.VQ.AVQAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.VQ.AVQGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich contains the test data set 2: Seed of the random generator. 3: Number of prototypes to be generated.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.VQ.VQAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Instance_Generation.VQ.VQGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich contains the test data set 2: Number of iterations of the algorithm. 3: Number of prototypes of the generated set. 4: Alpha0 constant of the process. 5: k Number of neighbors used in the KNN function
Main - Class in keel.Algorithms.Instance_Selection.AllKNN: Main class
Main() - Constructor for class keel.Algorithms.Instance_Selection.AllKNN.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.AllKNN.Main: Main Function.
Main - Class in keel.Algorithms.Instance_Selection.BSE: Main class
Main() - Constructor for class keel.Algorithms.Instance_Selection.BSE.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.BSE.Main: Main Function.
Main - Class in keel.Algorithms.Instance_Selection.CCIS: Main class
Main() - Constructor for class keel.Algorithms.Instance_Selection.CCIS.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.CCIS.Main: Main Function.
Main - Class in keel.Algorithms.Instance_Selection.CHC: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.CHC.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.CHC.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.CNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.CNN.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.CNN.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.CoCoIS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.CoCoIS.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.CoCoIS.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.CPruner: Main class
Main() - Constructor for class keel.Algorithms.Instance_Selection.CPruner.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.CPruner.Main: Main Function.
Main - Class in keel.Algorithms.Instance_Selection.DROP1: Main class
Main() - Constructor for class keel.Algorithms.Instance_Selection.DROP1.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.DROP1.Main: Main Function.
Main - Class in keel.Algorithms.Instance_Selection.DROP2: Main class
Main() - Constructor for class keel.Algorithms.Instance_Selection.DROP2.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.DROP2.Main: Main Function.
Main - Class in keel.Algorithms.Instance_Selection.DROP3: Main class
Main() - Constructor for class keel.Algorithms.Instance_Selection.DROP3.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.DROP3.Main: Main Function.
Main - Class in keel.Algorithms.Instance_Selection.ENN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.ENN.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.ENN.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.ENNRS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.ENNRS.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.ENNRS.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.ENNTh: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.ENNTh.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.ENNTh.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.ENRBF: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.ENRBF.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.ENRBF.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.Explore: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.Explore.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.Explore.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.FCNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.FCNN.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.FCNN.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.GCNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.GCNN.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.GCNN.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.GG: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.GG.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.GG.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.GGA: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.GGA.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.GGA.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.HMNEI: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.HMNEI.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.HMNEI.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.IB2: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.IB2.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.IB2.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.IB3: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.IB3.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.IB3.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.ICF: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.ICF.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.ICF.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.IGA: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.IGA.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.IGA.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.IKNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.IKNN.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.IKNN.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.MCNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.MCNN.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.MCNN.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.MCS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.MCS.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.MCS.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.MENN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.MENN.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.MENN.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.MNV: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.MNV.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.MNV.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.ModelCS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.ModelCS.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.ModelCS.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.MSS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.MSS.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.MSS.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.Multiedit: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.Multiedit.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.Multiedit.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.NCNEdit: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.NCNEdit.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.NCNEdit.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.NRMCS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.NRMCS.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.NRMCS.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.PBIL: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.PBIL.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.PBIL.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.POP: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.POP.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.POP.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.PSC: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.PSC.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.PSC.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.PSRCG: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.PSRCG.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.PSRCG.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.Reconsistent: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.Reconsistent.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.Reconsistent.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.RENN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.RENN.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.RENN.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.RMHC: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.RMHC.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.RMHC.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.RNG: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.RNG.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.RNG.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.RNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.RNN.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.RNN.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.SGA: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.SGA.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.SGA.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.Shrink: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.Shrink.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.Shrink.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.SNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.SNN.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.SNN.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.SSMA: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.SSMA.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.SSMA.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.SVBPS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.SVBPS.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.SVBPS.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.TCNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.TCNN.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.TCNN.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.TRKNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.TRKNN.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.TRKNN.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.VSM: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.VSM.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.VSM.Main: The main method of the class
Main - Class in keel.Algorithms.Instance_Selection.ZhangTS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Instance_Selection.ZhangTS.Main
main(String[]) - Static method in class keel.Algorithms.Instance_Selection.ZhangTS.Main: The main method of the class
Main - Class in keel.Algorithms.Lazy_Learning.CamNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Lazy_Learning.CamNN.Main
main(String[]) - Static method in class keel.Algorithms.Lazy_Learning.CamNN.Main: The main method of the class
Main - Class in keel.Algorithms.Lazy_Learning.CenterNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Lazy_Learning.CenterNN.Main
main(String[]) - Static method in class keel.Algorithms.Lazy_Learning.CenterNN.Main: The main method of the class
Main - Class in keel.Algorithms.Lazy_Learning.CPW: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Lazy_Learning.CPW.Main
main(String[]) - Static method in class keel.Algorithms.Lazy_Learning.CPW.Main: The main method of the class
Main - Class in keel.Algorithms.Lazy_Learning.CW: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Lazy_Learning.CW.Main
main(String[]) - Static method in class keel.Algorithms.Lazy_Learning.CW.Main: The main method of the class
Main - Class in keel.Algorithms.Lazy_Learning.Deeps: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Lazy_Learning.Deeps.Main
main(String[]) - Static method in class keel.Algorithms.Lazy_Learning.Deeps.Main: The main method of the class
Main - Class in keel.Algorithms.Lazy_Learning.DeepsNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Lazy_Learning.DeepsNN.Main
main(String[]) - Static method in class keel.Algorithms.Lazy_Learning.DeepsNN.Main: The main method of the class
Main - Class in keel.Algorithms.Lazy_Learning.IDIBL: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Lazy_Learning.IDIBL.Main
main(String[]) - Static method in class keel.Algorithms.Lazy_Learning.IDIBL.Main: The main method of the class
Main - Class in keel.Algorithms.Lazy_Learning.KNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Lazy_Learning.KNN.Main
main(String[]) - Static method in class keel.Algorithms.Lazy_Learning.KNN.Main: The main method of the class
Main - Class in keel.Algorithms.Lazy_Learning.KNNAdaptive: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Lazy_Learning.KNNAdaptive.Main
main(String[]) - Static method in class keel.Algorithms.Lazy_Learning.KNNAdaptive.Main: The main method of the class
Main - Class in keel.Algorithms.Lazy_Learning.KSNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Lazy_Learning.KSNN.Main
main(String[]) - Static method in class keel.Algorithms.Lazy_Learning.KSNN.Main: The main method of the class
Main - Class in keel.Algorithms.Lazy_Learning.KStar: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Lazy_Learning.KStar.Main
main(String[]) - Static method in class keel.Algorithms.Lazy_Learning.KStar.Main: The main method of the class
Main - Class in keel.Algorithms.Lazy_Learning.LazyDT: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Lazy_Learning.LazyDT.Main
main(String[]) - Static method in class keel.Algorithms.Lazy_Learning.LazyDT.Main: The main method of the class
Main - Class in keel.Algorithms.Lazy_Learning.LBR: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Lazy_Learning.LBR.Main
main(String[]) - Static method in class keel.Algorithms.Lazy_Learning.LBR.Main: The main method of the class
Main - Class in keel.Algorithms.Lazy_Learning.NM: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Lazy_Learning.NM.Main
main(String[]) - Static method in class keel.Algorithms.Lazy_Learning.NM.Main: The main method of the class
Main - Class in keel.Algorithms.Lazy_Learning.NSC: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Lazy_Learning.NSC.Main
main(String[]) - Static method in class keel.Algorithms.Lazy_Learning.NSC.Main: The main method of the class
Main - Class in keel.Algorithms.Lazy_Learning.PW: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Lazy_Learning.PW.Main
main(String[]) - Static method in class keel.Algorithms.Lazy_Learning.PW.Main: The main method of the class
main(String[]) - Static method in class keel.Algorithms.Lazy_Learning.Statistics: Main method for testing this class.
Main - Class in keel.Algorithms.LQD.methods.FGFS_costInstances: File: Main.java Read the parameters given by the user.
Main() - Constructor for class keel.Algorithms.LQD.methods.FGFS_costInstances.Main
main(String[]) - Static method in class keel.Algorithms.LQD.methods.FGFS_costInstances.Main: The main method of the class
Main - Class in keel.Algorithms.LQD.methods.FGFS_Minimum_Risk: File: Main.java Read the parameters given by the user.
Main() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.Main
main(String[]) - Static method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.Main: The main method of the class
Main - Class in keel.Algorithms.LQD.methods.FGFS_Original: File: Main.java Read the parameters given by the user.
Main() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Original.Main
main(String[]) - Static method in class keel.Algorithms.LQD.methods.FGFS_Original.Main: The main method of the class
Main - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight: File: Main.java Read the parameters given by the user.
Main() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.Main
main(String[]) - Static method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.Main: The main method of the class
Main - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty: File: Main.java Read the parameters given by the user.
Main() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.Main
main(String[]) - Static method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.Main: The main method of the class
Main - Class in keel.Algorithms.LQD.preprocess.Expert: File: Main.java Apply a prelabelling method, to get that the semi-labelled or unlabelled instances have only one class.
Main() - Constructor for class keel.Algorithms.LQD.preprocess.Expert.Main
main(String[]) - Static method in class keel.Algorithms.LQD.preprocess.Expert.Main: The main method of the class
Main - Class in keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE: File: Main.java Read the parameters given by the user.
Main() - Constructor for class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.Main
main(String[]) - Static method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.Main: The main method of the class
Main - Class in keel.Algorithms.LQD.preprocess.Prelabelling: File: Main.java Apply a prelabelling method, to get that the semi-labelled or unlabelled instances have only one class.
Main() - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling.Main
main(String[]) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling.Main: The main method of the class
Main - Class in keel.Algorithms.LQD.preprocess.Prelabelling_Expert: File: Main.java Apply a prelabelling method, to get that the semi-labelled or unlabelled instances have only one class.
Main() - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.Main
main(String[]) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.Main
Main - Class in keel.Algorithms.LQD.tests.IntermediateBoost: File: Main.java Read the original dataset and obtain 100 bootstrap from this dataset
Main() - Constructor for class keel.Algorithms.LQD.tests.IntermediateBoost.Main
main(String[]) - Static method in class keel.Algorithms.LQD.tests.IntermediateBoost.Main
Main - Class in keel.Algorithms.LQD.tests.Results: File: Main.java From 100 bootstrap we obtain the minimum and maximum mean (for both train and test).
Main() - Constructor for class keel.Algorithms.LQD.tests.Results.Main
main(String[]) - Static method in class keel.Algorithms.LQD.tests.Results.Main
Main - Class in keel.Algorithms.MIL.APR.GFS_AllPositive_APR: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.MIL.APR.GFS_AllPositive_APR.Main
main(String[]) - Static method in class keel.Algorithms.MIL.APR.GFS_AllPositive_APR.Main: The main method of the class
Main - Class in keel.Algorithms.MIL.APR.GFS_ElimCount_APR: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.MIL.APR.GFS_ElimCount_APR.Main
main(String[]) - Static method in class keel.Algorithms.MIL.APR.GFS_ElimCount_APR.Main: The main method of the class
Main - Class in keel.Algorithms.MIL.APR.GFS_Kde_APR: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.MIL.APR.GFS_Kde_APR.Main
main(String[]) - Static method in class keel.Algorithms.MIL.APR.GFS_Kde_APR.Main: The main method of the class
Main - Class in keel.Algorithms.MIL.APR.IteratedDiscrimination: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.MIL.APR.IteratedDiscrimination.Main
main(String[]) - Static method in class keel.Algorithms.MIL.APR.IteratedDiscrimination.Main: The main method of the class
Main - Class in keel.Algorithms.MIL.Diverse_Density.DD: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.MIL.Diverse_Density.DD.Main
main(String[]) - Static method in class keel.Algorithms.MIL.Diverse_Density.DD.Main: The main method of the class
Main - Class in keel.Algorithms.MIL.Diverse_Density.EMDD: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.MIL.Diverse_Density.EMDD.Main
main(String[]) - Static method in class keel.Algorithms.MIL.Diverse_Density.EMDD.Main: The main method of the class
Main - Class in keel.Algorithms.MIL.G3PMI: Title: MIDD Main Program.
Main() - Constructor for class keel.Algorithms.MIL.G3PMI.Main
main(String[]) - Static method in class keel.Algorithms.MIL.G3PMI.Main: Main method
Main - Class in keel.Algorithms.MIL.Nearest_Neighbour.CKNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.MIL.Nearest_Neighbour.CKNN.Main
main(String[]) - Static method in class keel.Algorithms.MIL.Nearest_Neighbour.CKNN.Main: The main method of the class
Main - Class in keel.Algorithms.MIL.Nearest_Neighbour.KNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.MIL.Nearest_Neighbour.KNN.Main
main(String[]) - Static method in class keel.Algorithms.MIL.Nearest_Neighbour.KNN.Main: The main method of the class
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.ClassifierMLPerceptron.ClassifierMLPerceptron: Method that calls the private wrapper method "neuralClassificationLS" that creates and runs a neural network for solving a classification problem using the Conjugated Gradient algorithm.
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.ensemble.Genesis: Main function
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.doEvRBF_Cl: Main Function: reads the parameters, creates the population, evolves it, gets the best individual, writes results and finishes.
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.gann.Gann: Main method
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.gann.Genesis: Main function
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.gmdh.Genesis: Main function
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.gmdh.gmdh: Main function
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.KEELIRPropPlusWrapperClas: Main method
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.KEELIRPropPlusWrapperRegr: Main method
Main - Class in keel.Algorithms.Neural_Networks.LVQ: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Neural_Networks.LVQ.Main
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.LVQ.Main: The main method of the class
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.ModelMLPerceptron.ModelMLPerceptron: Method that calls the private wrapper method "neuralModelling" that creates and runs a neural network for solving a modelling problem using the Conjugated Gradient algorithm.
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.net.Genesis: Main function
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.NNEP_Clas.KEELWrapperClas: Main method
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.NNEP_Regr.KEELWrapperRegr: Main method
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN.doRbfn: Main Function
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.doRbfnCl: Main Function
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.doRbfnDec: Main Function
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.doRbfnDecCl: Main Function
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.doRbfnInc: Main Function
main(String[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.doRbfnIncCl: Main Function
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CHC.filter: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CHC.filter.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CHC.filter.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CHC.wrapper: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CHC.wrapper.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CHC.wrapper.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_BinCod.filter: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_BinCod.filter.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_BinCod.filter.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_BinCod.wrapper: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_BinCod.wrapper.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_BinCod.wrapper.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_IntCod.filter: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_IntCod.filter.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_IntCod.filter.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_IntCod.wrapper: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_IntCod.wrapper.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_Gen_IntCod.wrapper.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_BinCod.filter: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_BinCod.filter.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_BinCod.filter.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_BinCod.wrapper: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_BinCod.wrapper.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_BinCod.wrapper.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_IntCod.filter: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_IntCod.filter.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_IntCod.filter.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_IntCod.wrapper: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_IntCod.wrapper.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_IntCod.wrapper.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GGA: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GGA.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GGA.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_IEP: This class calls the main method
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_IEP.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_IEP.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_LIU: This class calls the main method
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_LIU.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_LIU.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_MI: This class calls the main method
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_MI.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.ABB_MI.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.BACKWARD.filter: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.BACKWARD.filter.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.BACKWARD.filter.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.BACKWARD.wrapper: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.BACKWARD.wrapper.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.BACKWARD.wrapper.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FOCUS: This class realizes the call to the main method
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FOCUS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FOCUS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FORWARD.filter: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FORWARD.filter.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FORWARD.filter.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FORWARD.wrapper: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FORWARD.wrapper.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FORWARD.wrapper.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_IEP: This class calls the main method
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_IEP.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_IEP.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_LIU: This class calls the main method
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_LIU.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_LIU.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_MI: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_MI.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.FULL_MI.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.filter.im: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.filter.im.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.filter.im.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.filter.inconsistency: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.filter.inconsistency.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.filter.inconsistency.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.wrapper: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.wrapper.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.GRASP.wrapper.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVF: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVF.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVF.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVF_IEP: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVF_IEP.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVF_IEP.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVW: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVW.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.LVW.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.RELIEF: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.RELIEF.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.RELIEF.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.RELIEF_F: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.RELIEF_F.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.RELIEF_F.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_IEP_FS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_IEP_FS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_IEP_FS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_LIU_FS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_LIU_FS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_LIU_FS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_MI_FS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_MI_FS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_MI_FS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_IEP_FS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_IEP_FS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_IEP_FS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_LIU_FS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_LIU_FS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_LIU_FS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_MI_FS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_MI_FS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_MI_FS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_IEP_FS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_IEP_FS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_IEP_FS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_LIU_FS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_LIU_FS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_LIU_FS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_MI_FS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_MI_FS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_MI_FS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.AllKNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.AllKNN.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.AllKNN.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.BSE: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.BSE.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.BSE.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.CCIS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.CCIS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.CCIS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.CHC: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.CHC.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.CHC.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.CNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.CNN.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.CNN.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.CoCoIS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.CPruner: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.CPruner.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.CPruner.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.DROP1: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.DROP1.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.DROP1.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.DROP2: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.DROP2.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.DROP2.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.DROP3: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.DROP3.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.DROP3.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.ENN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.ENN.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.ENN.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.ENNRS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.ENNRS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.ENNRS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.ENNTh: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.ENNTh.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.ENNTh.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.ENRBF: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.ENRBF.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.ENRBF.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.Explore: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.Explore.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.Explore.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.FCNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.FCNN.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.FCNN.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.GCNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.GCNN.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.GCNN.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.GG: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.GG.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.GG.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.GGA: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.GGA.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.GGA.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.HMNEI: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.HMNEI.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.HMNEI.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.IB2: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.IB2.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.IB2.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.IB3: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.IB3.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.IB3.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.ICF: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.ICF.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.ICF.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.IGA: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.IGA.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.IGA.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.IKNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.IKNN.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.IKNN.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.MCNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.MCNN.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.MCNN.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.MCS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.MCS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.MCS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.MENN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.MENN.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.MENN.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.MNV: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.MNV.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.MNV.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.ModelCS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.ModelCS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.ModelCS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.MSS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.MSS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.MSS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.Multiedit: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.Multiedit.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.Multiedit.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.NCNEdit: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.NCNEdit.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.NCNEdit.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.NRMCS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.NRMCS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.NRMCS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.PBIL: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.POP: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.POP.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.POP.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.PSC: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.PSC.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.PSC.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.PSRCG: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.PSRCG.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.PSRCG.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.Reconsistent: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.Reconsistent.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.Reconsistent.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.RENN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.RENN.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.RENN.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.RMHC: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.RMHC.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.RMHC.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.RNG: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.RNG.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.RNG.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.RNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.RNN.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.RNN.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.SGA: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.SGA.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.SGA.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.Shrink: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.Shrink.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.Shrink.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.SNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.SNN.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.SNN.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.SSMA: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.SVBPS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.SVBPS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.SVBPS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.TCNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.TCNN.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.TCNN.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.TRKNN: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.TRKNN.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.TRKNN.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.VSM: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.VSM.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.VSM.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Instance_Selection.ZhangTS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.Main: The main method of the class
Main - Class in keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.Main
Main - Class in keel.Algorithms.Preprocess.Missing_Values.BPCA
Main() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.BPCA.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.BPCA.Main
Main - Class in keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues
Main() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.Main
Main - Class in keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue
Main() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.Main
Main - Class in keel.Algorithms.Preprocess.Missing_Values.EM: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EM.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.Main
Main - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering
Main() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.MersenneTwister: Tests the code.
main(String[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.StatFunc: Main function.
Main - Class in keel.Algorithms.Preprocess.Missing_Values.fkmeans
Main() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.fkmeans.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.Main
Main - Class in keel.Algorithms.Preprocess.Missing_Values.ignore_missing
Main() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.ignore_missing.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.ignore_missing.Main
Main - Class in keel.Algorithms.Preprocess.Missing_Values.kmeansImpute
Main() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.Main
Main - Class in keel.Algorithms.Preprocess.Missing_Values.knnImpute
Main() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.knnImpute.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.Main
Main - Class in keel.Algorithms.Preprocess.Missing_Values.LLSImpute
Main() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.Main
Main - Class in keel.Algorithms.Preprocess.Missing_Values.MostCommonValue
Main() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.Main
Main - Class in keel.Algorithms.Preprocess.Missing_Values.SVDimpute
Main() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.SVDimpute.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.SVDimpute.Main
Main - Class in keel.Algorithms.Preprocess.Missing_Values.svmImpute
Main() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.svmImpute.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.Main
Main - Class in keel.Algorithms.Preprocess.Missing_Values.wknnImpute
Main() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ClassifierMLPerceptron: Method that calls the private wrapper method "neuralClassificationLS" that creates and runs a neural network for solving a classification problem using the Conjugated Gradient algorithm.
Main - Class in keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter: Main class of the algorithm
Main() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Main: It creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Main: Main method
Main - Class in keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter: Main class of the algorithm
Main() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Main: It creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Main: Main method
Main - Class in keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter: Main class of the algorithm
Main() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Main: It creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Main: Main method
Main - Class in keel.Algorithms.Preprocess.NoiseFilters.INFFC: Main class of the algorithm
Main() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Main: It creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Main: Main method
Main - Class in keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter: Main class of the algorithm
Main() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Main: It creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Main: Main method
Main - Class in keel.Algorithms.Preprocess.NoiseFilters.PANDA: Main class of the algorithm
Main() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Main: It creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Main: Main method
Main - Class in keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter: Main class of the algorithm
Main() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Main: It creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Main: Main method
Main - Class in keel.Algorithms.Preprocess.Transformations.CleanAttributes
Main() - Constructor for class keel.Algorithms.Preprocess.Transformations.CleanAttributes.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Transformations.CleanAttributes.Main
Main - Class in keel.Algorithms.Preprocess.Transformations.decimal_scaling
Main() - Constructor for class keel.Algorithms.Preprocess.Transformations.decimal_scaling.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Transformations.decimal_scaling.Main
Main - Class in keel.Algorithms.Preprocess.Transformations.min_max
Main() - Constructor for class keel.Algorithms.Preprocess.Transformations.min_max.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Transformations.min_max.Main
Main - Class in keel.Algorithms.Preprocess.Transformations.Nominal2Binary
Main() - Constructor for class keel.Algorithms.Preprocess.Transformations.Nominal2Binary.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Transformations.Nominal2Binary.Main
Main - Class in keel.Algorithms.Preprocess.Transformations.z_score
Main() - Constructor for class keel.Algorithms.Preprocess.Transformations.z_score.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.Preprocess.Transformations.z_score.Main
Main - Class in keel.Algorithms.PSO_Learning.CPSO: Title: Main Class of the Program CPSO Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.PSO_Learning.CPSO.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.PSO_Learning.CPSO.Main: Main Program
Main - Class in keel.Algorithms.PSO_Learning.LDWPSO: Title: Main Class of the Program LDWPSO Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.PSO_Learning.LDWPSO.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.PSO_Learning.LDWPSO.Main: Main Program
Main - Class in keel.Algorithms.PSO_Learning.PSOLDA: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.PSO_Learning.PSOLDA.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.PSO_Learning.PSOLDA.Main: Main Program
Main - Class in keel.Algorithms.PSO_Learning.REPSO: Title: Main Class of the Program CPSO Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.PSO_Learning.REPSO.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.PSO_Learning.REPSO.Main: Main Program
main(String[]) - Static method in class keel.Algorithms.RE_SL_Methods.LEL_TSK.Lanzar: The main method of the class
main(String[]) - Static method in class keel.Algorithms.RE_SL_Methods.MamWM.Lanzar: The main method of the class
main(String[]) - Static method in class keel.Algorithms.RE_SL_Methods.mogulHC.Lanzar: The main method of the class
main(String[]) - Static method in class keel.Algorithms.RE_SL_Methods.mogulIRL.Lanzar: The main method of the class
main(String[]) - Static method in class keel.Algorithms.RE_SL_Methods.mogulSC.Lanzar: The main method of the class
Main - Class in keel.Algorithms.RE_SL_Methods.P_FCS1
Main() - Constructor for class keel.Algorithms.RE_SL_Methods.P_FCS1.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.RE_SL_Methods.P_FCS1.Main: Main Program
Main - Class in keel.Algorithms.RE_SL_Methods.SEFC
Main() - Constructor for class keel.Algorithms.RE_SL_Methods.SEFC.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.RE_SL_Methods.SEFC.Main: Main Program
main(String[]) - Static method in class keel.Algorithms.RE_SL_Methods.TSK_IRL.Lanzar: The main method of the class
Main - Class in keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Main: Main Program
main(String[]) - Static method in class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.Lanzar: The main method of the class
main(String[]) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.Lanzar: The main method of the class
main(String[]) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.Lanzar: The main method of the class
main(String[]) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamSelect.Lanzar: The main method of the class
main(String[]) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamWSelect.Lanzar: The main method of the class
main(String[]) - Static method in class keel.Algorithms.RE_SL_Postprocess.MamWTuning.Lanzar: The main method of the class
Main - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Main: Main Program
Main - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Main: Main Program
Main - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Main: Main Program
Main - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Main: Main Program
Main - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Main: Main Program
main(int, double[][], double[], int, double[][], double[], int, int, int, double, double[], int, int, long, String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Chc: Function main
Main - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Main: Main Program
Main - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Main: Main Program
Main - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Main: Main Program
main(String[]) - Static method in class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.Lanzar: The main method of the class
main(String[]) - Static method in class keel.Algorithms.RE_SL_Postprocess.TSKSelect.Lanzar: The main method of the class
Main - Class in keel.Algorithms.RST_Learning.EFS_RPS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.RST_Learning.EFS_RPS.Main
main(String[]) - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.Main: The main method of the class
Main - Class in keel.Algorithms.RST_Learning.EIS_RFS: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.RST_Learning.EIS_RFS.Main
main(String[]) - Static method in class keel.Algorithms.RST_Learning.EIS_RFS.Main: The main method of the class
Main - Class in keel.Algorithms.Rule_Learning.AQ: Title: AQ Main Program Description: This is the main class, which is executed when we launch the program
Main() - Constructor for class keel.Algorithms.Rule_Learning.AQ.Main: Default builder
main(String[]) - Static method in class keel.Algorithms.Rule_Learning.AQ.Main: Main program
main(String[]) - Static method in class keel.Algorithms.Rule_Learning.ART.ART: Main function.
Main - Class in keel.Algorithms.Rule_Learning.CN2: Title: CN2 Main Program Description: This is the main class, which is executed when we launch the program
Main() - Constructor for class keel.Algorithms.Rule_Learning.CN2.Main: Default builder
main(String[]) - Static method in class keel.Algorithms.Rule_Learning.CN2.Main: Main program
main(String[]) - Static method in class keel.Algorithms.Rule_Learning.DataSqueezer.DataSqueezer: Main function.
Main - Class in keel.Algorithms.Rule_Learning.LEM1: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Rule_Learning.LEM1.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Rule_Learning.LEM1.Main: Main Program
Main - Class in keel.Algorithms.Rule_Learning.LEM2: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Rule_Learning.LEM2.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Rule_Learning.LEM2.Main: Main Program
Main - Class in keel.Algorithms.Rule_Learning.Prism: Reads the parameters y starts the PRISM CD algorithm
Main() - Constructor for class keel.Algorithms.Rule_Learning.Prism.Main: Constructor
main(String[]) - Static method in class keel.Algorithms.Rule_Learning.Prism.Main: Main program
Main - Class in keel.Algorithms.Rule_Learning.Riona: Main class
Main() - Constructor for class keel.Algorithms.Rule_Learning.Riona.Main: Default constructor.
main(String[]) - Static method in class keel.Algorithms.Rule_Learning.Riona.Main: Main program
Main - Class in keel.Algorithms.Rule_Learning.Ritio: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Rule_Learning.Ritio.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Rule_Learning.Ritio.Main: Main Program
Main - Class in keel.Algorithms.Rule_Learning.Rules6: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Rule_Learning.Rules6.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Rule_Learning.Rules6.Main: Main Program
Main - Class in keel.Algorithms.Rule_Learning.SRI: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Rule_Learning.SRI.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Rule_Learning.SRI.Main: Main Program
Main - Class in keel.Algorithms.Rule_Learning.Swap1: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Rule_Learning.Swap1.Main
main(String[]) - Static method in class keel.Algorithms.Rule_Learning.Swap1.Main: Main Program
Main - Class in keel.Algorithms.Rule_Learning.UnoR: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Rule_Learning.UnoR.Main: Default constructor.
main(String[]) - Static method in class keel.Algorithms.Rule_Learning.UnoR.Main: Main Program
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ADE_CoForestAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ADE_CoForestGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ContingencyTables: Main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Main method for this class.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.APSSC.APSSCAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.APSSC.APSSCGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.AccuracyMeter: Main.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename wich will contain the test data set 3: k Number of neighbors used in the KNN function
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.C45SSL.C45SSLAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.C45SSL.C45SSLGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.ContingencyTables: Main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Main method for this class.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoBC.CoBCAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoBC.CoBCGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.CoForestAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.CoForestGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.ContingencyTables: Main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Main method for this class.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoTraining.CoTrainingAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoTraining.CoTrainingGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.DE_TriTraining.DE_TriTrainingAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.DE_TriTraining.DE_TriTrainingGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Democratic.DemocraticAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Democratic.DemocraticGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.NBSSL.NBSSLAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.NBSSL.NBSSLGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.NNSSL.NNSSLAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.NNSSL.NNSSLGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.RASCO.RASCOAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.RASCO.RASCOGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Rel_RASCO.Rel_RASCOAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Rel_RASCO.Rel_RASCOGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.SelfTraining.SelfTrainingAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.SelfTraining.SelfTrainingGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.SETRED.SETREDAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.SETRED.SETREDGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.SMOSSL.SMOSSLAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.SMOSSL.SMOSSLGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.SNNRCE.SNNRCEAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.SNNRCE.SNNRCEGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.TriTraining.TriTrainingAlgorithm: Main method.
main(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.TriTraining.TriTrainingGenerator: General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.
main(String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.ClassifierADLinear.ClassifierADLinear: This method runs ClassifierADLinear
main(String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.ClassifierADQuadratic.ClassifierADQuadratic: This method runs ClassifierADQuadratic
main(String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.ClassifierKernel.ClassifierKernel: This method runs ClassifierKernel
main(String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.ClassifierLinearLMS.ClassifierLinearLMS: This method runs ClassifierLinearLMS
main(String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.ClassifierPolQuadraticLMS.ClassifierPolQuadraticLMS: This method runs ClassifierPolQuadraticLMS
main(String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Main function.
main(String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector: Tests the IntVector class
main(String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics: Main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation: Prints some examples of technical informations if there are no commandline options given.
main(String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Main method for testing this class.
Main - Class in keel.Algorithms.Statistical_Classifiers.Logistic: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.Main
main(String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.Main: The main method of the class
Main - Class in keel.Algorithms.Statistical_Classifiers.Naive_Bayes: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.Main: Main Program
main(String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
main(String[]) - Static method in class keel.Algorithms.Statistical_Models.ModelLinear.ModelLinearLMS: This method calls ModelLinearLMS
main(String[]) - Static method in class keel.Algorithms.Statistical_Models.ModelQuad.ModelPolQuadraticLMS: This public static method runs ModelPolQuadraticLMS
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.Clasif_General.Clasif_General: This method reads a configuration file and calls statisticalTest with appropriate values to run Clasif_General output module for classification problems, defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.Clasif_Summary.Clasif_Summary: This method reads a configuration file and calls statisticalTest with appropriate values to run Model_Summary output module for classification problems, defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.Clasif_Tabular.Clasif_Tabular: This method reads a configuration file and calls statisticalTest with appropriate values to run Model_Tabular output module for classification problems, defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.ClasifTest_5x2cv.ClasifTest_5x2cv: This method reads a configuration file and calls statisticalTest with appropriate values to run Dietterich 5x2cv test for classification problems, defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.ClasifTest_f.ClasifTest_f: This method reads a configuration file and calls statisticalTest with appropriate values to run the f test for classification problems, defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.ClasifTest_rs.ClasifTest_rs: This method reads a configuration file and calls statisticalTest for classification problems, with appropriate values to run the Wilcoxon signed rank test defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.ClasifTest_sw.ClasifTest_sw: This method reads a configuration file and calls statisticalTest with appropriate values to run the Shapiro Wilk test for classification problems, defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.ClasifTest_t.ClasifTest_t: This method reads a configuration file and calls statisticalTest with appropriate values to run the t test for classification problems, defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.ClasifTest_u.ClasifTest_u: This method reads a configuration file and calls statisticalTest with appropriate values to run the Mann Whitney U test for classification problems, defined in StatTest class
Main - Class in keel.Algorithms.Statistical_Tests.Classification.Contrast: File: Main.java A Main class to process the paramethers of the method and launch the test
Main() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.Contrast.Main
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.Contrast.Main: Main method
Main - Class in keel.Algorithms.Statistical_Tests.Classification.Friedman: File: Main.java A Main class to process the paramethers of the method and launch the test
Main() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.Friedman.Main
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.Friedman.Main: Main method
Main - Class in keel.Algorithms.Statistical_Tests.Classification.FriedmanAlligned: File: Main.java A Main class to process the paramethers of the method and launch the test
Main() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.FriedmanAlligned.Main
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.FriedmanAlligned.Main: Main method
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.Imbalanced_General.Imbalanced_General: This method reads a configuration file and calls statisticalTest with appropriate values to run Clasif_General output module for classification problems, defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.Imbalanced_Summary.Imbalanced_Summary: This method reads a configuration file and calls statisticalTest with appropriate values to run Model_Summary output module for classification problems, defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.Imbalanced_Tabular.Imbalanced_Tabular: This method reads a configuration file and calls statisticalTest with appropriate values to run Model_Tabular output module for classification problems, defined in StatTest class
Main - Class in keel.Algorithms.Statistical_Tests.Classification.ImbFriedman: File: Main.java A Main class to process the paramethers of the method and launch the test
Main() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.ImbFriedman.Main
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.ImbFriedman.Main: Main method
Main - Class in keel.Algorithms.Statistical_Tests.Classification.ImbWilcoxon: This class has only a main method that calls Wilcoxon test ('global' version) for classification problems, defined in StatTest
Main() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.ImbWilcoxon.Main
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.ImbWilcoxon.Main: This method reads a configuration file and calls statisticalTest with appropriate values to run the Wilcoxon test ('global' version) for classification problems, defined in StatTest class
Main - Class in keel.Algorithms.Statistical_Tests.Classification.Multiple: File: Main.java A Main class to process the paramethers of the method and launch the test
Main() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.Multiple.Main
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.Multiple.Main: Main method
Main - Class in keel.Algorithms.Statistical_Tests.Classification.Quade: File: Main.java A Main class to process the paramethers of the method and launch the test
Main() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.Quade.Main
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.Quade.Main: Main method
Main - Class in keel.Algorithms.Statistical_Tests.Classification.Wilcoxon: This class has only a main method that calls Wilcoxon test ('global' version) for classification problems, defined in StatTest
Main() - Constructor for class keel.Algorithms.Statistical_Tests.Classification.Wilcoxon.Main
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Classification.Wilcoxon.Main: This method reads a configuration file and calls statisticalTest with appropriate values to run the Wilcoxon test ('global' version) for classification problems, defined in StatTest class
Main - Class in keel.Algorithms.Statistical_Tests.Regression.Contrast: File: Main.java A Main class to process the paramethers of the method and launch the test
Main() - Constructor for class keel.Algorithms.Statistical_Tests.Regression.Contrast.Main
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Regression.Contrast.Main: Main method
Main - Class in keel.Algorithms.Statistical_Tests.Regression.Friedman: File: Main.java A Main class to process the paramethers of the method and launch the test
Main() - Constructor for class keel.Algorithms.Statistical_Tests.Regression.Friedman.Main
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Regression.Friedman.Main: Main method
Main - Class in keel.Algorithms.Statistical_Tests.Regression.FriedmanAlligned: File: Main.java A Main class to process the paramethers of the method and launch the test
Main() - Constructor for class keel.Algorithms.Statistical_Tests.Regression.FriedmanAlligned.Main
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Regression.FriedmanAlligned.Main: Main method
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Regression.Model_General.Model_General: This method reads a configuration file and calls statisticalTest with appropriate values to run Model_general output module for regression problems, defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Regression.Model_Summary.Model_Summary: This method reads a configuration file and calls statisticalTest with appropriate values to run Model_Summary output module for regression problems, defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Regression.Model_Tabular.Model_Tabular: This method reads a configuration file and calls statisticalTest with appropriate values to run Model_Tabular output module for regression problems, defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Regression.ModelTest_5x2cv.ModelTest_5x2cv: This method reads a configuration file and calls statisticalTest with appropriate values to run Dietterich 5x2cv test for regression problems, defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Regression.ModelTest_f.ModelTest_f: This method reads a configuration file and calls statisticalTest with appropriate values to run the f test for regression problems, defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Regression.ModelTest_rs.ModelTest_rs: This method reads a configuration file and calls statisticalTest for regression problems, with appropriate values to run the Wilcoxon signed rank test defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Regression.ModelTest_sw.ModelTest_sw: This method reads a configuration file and calls statisticalTest with appropriate values to run the Shapiro Wilk test for regression problems, defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Regression.ModelTest_t.ModelTest_t: This method reads a configuration file and calls statisticalTest with appropriate values to run the t test for regression problems, defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Regression.ModelTest_u.ModelTest_u: This method reads a configuration file and calls statisticalTest with appropriate values to run the Mann Whitney U test for regression problems, defined in StatTest class
Main - Class in keel.Algorithms.Statistical_Tests.Regression.Multiple: File: Main.java A Main class to process the paramethers of the method and launch the test
Main() - Constructor for class keel.Algorithms.Statistical_Tests.Regression.Multiple.Main
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Regression.Multiple.Main: Main method
Main - Class in keel.Algorithms.Statistical_Tests.Regression.Quade: File: Main.java A Main class to process the paramethers of the method and launch the test
Main() - Constructor for class keel.Algorithms.Statistical_Tests.Regression.Quade.Main
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Regression.Quade.Main: Main method
Main - Class in keel.Algorithms.Statistical_Tests.Regression.Wilcoxon: This class has only a main method that calls Wilcoxon test ('global' version) for regression problems, defined in StatTest
Main() - Constructor for class keel.Algorithms.Statistical_Tests.Regression.Wilcoxon.Main
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Regression.Wilcoxon.Main: This method reads a configuration file and calls statisticalTest with appropriate values to run the Wilcoxon test ('global' version) for regression problems, defined in StatTest class
main(String[]) - Static method in class keel.Algorithms.Statistical_Tests.Shared.genLatex: This is the main method of the class, it calls all the other ones.
Main - Class in keel.Algorithms.Subgroup_Discovery.aprioriSD: /** Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm
Main() - Constructor for class keel.Algorithms.Subgroup_Discovery.aprioriSD.Main: Default constructor.
main(String[]) - Static method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Main: Main Program
Main - Class in keel.Algorithms.Subgroup_Discovery.CN2SD: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.Subgroup_Discovery.CN2SD.Main: Default constructor.
main(String[]) - Static method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Main: Main Program
main(String[]) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.MESDIF: Main method of the algorithm
main(String[]) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.NMEEFSD: Main method of the algorithm
Main - Class in keel.Algorithms.Subgroup_Discovery.SDAlgorithm: Title: Main class of the SD algorithm
Main() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Main: Constructor
main(String[]) - Static method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Main: Main method
main(String[]) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.SDIGA: Main method of the algorithm
Main - Class in keel.Algorithms.Subgroup_Discovery.SDMap.SDMap: It reads the configuration file (data-set files and parameters) and launch the algorithm
Main() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.Main: Main Program algorith = <algorithm name>
inputData = "<training file>" "<test file>"
outputData = "<training file>" "<test file>" "<rule file>" "<measure file>"

<MinimumSupport> = <value1>
<MinimumConfidence> = <value2>
<RulesReturn> = <value3>
Main - Class in keel.Algorithms.SVM.C_SVM
Main() - Constructor for class keel.Algorithms.SVM.C_SVM.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.SVM.C_SVM.Main
Main - Class in keel.Algorithms.SVM.EPSILON_SVR
Main() - Constructor for class keel.Algorithms.SVM.EPSILON_SVR.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.SVM.EPSILON_SVR.Main
Main - Class in keel.Algorithms.SVM.NU_SVM
Main() - Constructor for class keel.Algorithms.SVM.NU_SVM.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.SVM.NU_SVM.Main
Main - Class in keel.Algorithms.SVM.NU_SVR
Main() - Constructor for class keel.Algorithms.SVM.NU_SVR.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Algorithms.SVM.NU_SVR.Main
main(String[]) - Static method in class keel.Algorithms.SVM.SMO.core.ClassDiscovery: Possible calls: weka.core.ClassDiscovery <packages>
Prints all the packages in the current classpath weka.core.ClassDiscovery <classname> <packagename(s)>
Prints the classes it found.
main(String[]) - Static method in class keel.Algorithms.SVM.SMO.core.Queue: Main method for testing this class.
main(String[]) - Static method in class keel.Algorithms.SVM.SMO.core.SerializationHelper: Outputs information about a class on the commandline, takes class name as arguments
main(String[]) - Static method in class keel.Algorithms.SVM.SMO.core.TechnicalInformation: Prints some examples of technical informations if there are no commandline options given.
main(String[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Main method for testing this class.
Main - Class in keel.Algorithms.SVM.SMO: File: Main.java This is the main class of the algorithm.
Main() - Constructor for class keel.Algorithms.SVM.SMO.Main
main(String[]) - Static method in class keel.Algorithms.SVM.SMO.Main: The main method of the class
main(String[]) - Static method in class keel.Algorithms.Symbolic_Regression.crispSymRegGAP.crispSymRegGAP: Loads the configuration for SA algorithm (with crisp sets) from file args[0] using class ProcessConfig.
main(String[]) - Static method in class keel.Algorithms.Symbolic_Regression.crispSymRegSAP.crispSymRegSAP: Loads the configuration for SA algorithm (with crisp values) from file args[0] using class ProcessConfig.
main(String[]) - Static method in class keel.Algorithms.Symbolic_Regression.fuzzySymRegGAP.fuzzySymRegGAP: Loads the configuration for GAP algorithm (with fuzzy sets) from file args[0] using class ProcessConfig.
main(String[]) - Static method in class keel.Algorithms.Symbolic_Regression.fuzzySymRegSAP.fuzzySymRegSAP: Loads the configuration for SA algorithm (with fuzzy sets) from file args[0] using class ProcessConfig.
Main - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal
Main() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Main: Main Program
Main - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams
Main() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Main: Main Program
Main - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori
Main() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Main: Main Program
Main - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori
Main() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Main: Main Program
Main - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC
Main() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Main: Main Program
Main - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal
Main() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Main: Main Program
Main - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.Main: Main Program
Main - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Main: Main Program
Main - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Main: Main Program
Main - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.Main: Main Program
Main - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth
Main() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.Main: Main Program
Main - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Main: Main Program
Main - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Main: Main Program
Main - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR: Title: Main Class of the Program Description: It reads the configuration file (data-set files and parameters) and launch the algorithm Company: KEEL
Main() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Main: Main Program
Main - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh
Main() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Main: Main Program
Main - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR: It reads the configuration file (data-set files and parameters) and launch the algorithm
Main() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Main: Main Program
Main - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII
Main() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Main: Default Constructor
main(String[]) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Main: Main Program
Main - Class in keel.Dataset
Main(String, String) - Constructor for class keel.Dataset.Main: Creates a new instance of Main
main(String[]) - Static method in class keel.Dataset.Main
main(String[]) - Static method in class keel.GraphInterKeel.datacf.DataCFApp: Main method launching the application.
main(String[]) - Static method in class keel.GraphInterKeel.datacf.DataCFFrame: Main Program
main(String[]) - Static method in class keel.GraphInterKeel.datacf.partitionData.HoldOutOptionsJDialog: Main method
main(String[]) - Static method in class keel.GraphInterKeel.datacf.partitionData.KFoldOptionsJDialog: Main method
main(String[]) - Static method in class keel.GraphInterKeel.datacf.Tuneados: Main method launching the application.
main(String[]) - Static method in class keel.GraphInterKeel.datacf.util.OptionsDialog: Main method
main(String[]) - Static method in class keel.GraphInterKeel.experiments.Container
main(String[]) - Static method in class keel.GraphInterKeel.experiments.Container_Selected
main(String[]) - Static method in class keel.GraphInterKeel.experiments.Description_algorithm
main(String[]) - Static method in class keel.GraphInterKeel.experiments.EducationalRun
main(String[]) - Static method in class keel.GraphInterKeel.experiments.Experiments
main(String[]) - Static method in class keel.GraphInterKeel.menu.BrowserControl: Simple example.
main(String[]) - Static method in class keel.GraphInterKeel.menu.GraphInterKeel: Main method
main(String[]) - Static method in class keel.GraphInterKeel.statistical.StatisticalF
main(String[]) - Static method in class keel.RunKeelGraph.Application1: Main method
main(String[]) - Static method in class keel.RunKeelTxt.runkeeltxt: Main method.
main2(String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.SerializedObject: Test routine, reads text from stdin and measures memory usage
main_c - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV: Main Class of the Program It reads the configuration file (data-set files and parameters) and launch the algorithm
main_c() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.main_c: Dafault constructor.
main_c - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2: Main Class of the Program It reads the configuration file (data-set files and parameters) and launch the algorithm
main_c() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.main_c: Default constructor.
main_c - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0: Main Class of the Program It reads the configuration file (data-set files and parameters) and launch the algorithm
main_c() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.main_c: Default constructor.
mainSplitPane1 - Variable in class keel.GraphInterKeel.experiments.Experiments
MAJOR - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
MAJOR - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_DefaultC
MAJOR - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_DefaultC
MAJOR - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene
majorityClass - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
majorityOfSameClass(Prototype, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.GENN.GENNGenerator: Informs if there are a majority of prototypes whose class is the same as other prototype.
makeAction(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.DECEnvironment: This function returns the reward given when applying the action in the environment.
makeAction(int) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Environment: This function returns the reward given when applying the action in the environment.
makeAction(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MPEnvironment: This function returns the reward given when applying the action in the environment.
makeAction(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MSEnvironment: This function returns the reward given when applying the action to the environment.
makeAction(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.PAREnvironment: This function returns the reward given when applying the action to the environment.
makeAction(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.POSEnvironment: This function returns the reward given when applying the action to the environment.
makeAction(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RMPEnvironment: This function returns the reward given when applying the action to the environment.
makeAction(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.SSFileEnvironment: This function returns the reward given at applying the action in the environment.
makeAveragePrototype(Prototype, Prototype) - Method in class keel.Algorithms.Instance_Generation.MCA.MCAGenerator: Builds a new averaged prototype.
makeCopies(Classifier, int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Classifier: Creates a given number of deep copies of the given classifier using serialization.
makeCopies(Kernel, int) - Static method in class keel.Algorithms.SVM.SMO.supportVector.Kernel: Creates a given number of deep copies of the given kernel using serialization.
makeCopy(Classifier) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Classifier: Creates a deep copy of the given classifier using serialization.
makeCopy(Kernel) - Static method in class keel.Algorithms.SVM.SMO.supportVector.Kernel: Creates a deep copy of the given kernel using serialization.
makeCover(Instance) - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Chromosome: Forces this individual to cover the provided instance
makeCrossover(Classifier, Classifier, Classifier, Classifier) - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Crossover: Makes the crossover.
makeCrossover(Classifier, Classifier, Classifier, Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TwoPointCrossover: Applies crossover.
makeCrossover(Classifier, Classifier, Classifier, Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.UniformCrossover: Applies crossover according to the uniform crossover operator.
makeCrossover(Classifier, Classifier, Classifier, Classifier) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Crossover: Makes the crossover.
makeCrossover(Classifier, Classifier, Classifier, Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TwoPointCrossover: Applies crossover.
makeCrossover(Classifier, Classifier, Classifier, Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.UniformCrossover: Applies crossover according to the uniform crossover operator.
makeIncStatistics(Population, int, int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Statistic: It makes the incremental statistics.
makeIncStatistics(Population, Population, int, int[], double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Statistic: It makes the incremental statistics.
makeNull() - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Convert the prototype in the null prototype
makeNull() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Convert the prototype in the null prototype
makeOptionString(Kernel) - Static method in class keel.Algorithms.SVM.SMO.supportVector.KernelEvaluation: Generates an option string to output on the commandline.
makePartition(double) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Makes a partition of the set.
makePartition(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Makes a partition of the set.
makePartitionPerClass(double) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Makes a partition of the set.
makePartitionPerClass(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Makes a partition of the set.
makeReduction(Population, Environment) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.DixonReduction: Compacts the ruleSet of the population using the Dixon method described in the article as the "alternative reduction".
makeReduction(Population, Environment) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Reduction: Makes a reduction of the population applying the chosen method by the user (with the configuration file).
makeReduction(Population, Environment) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.WilsonReduction: Compacts the ruleSet of the population.
makeReductionOf(PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.BasicMethods.CNN: Make a selection by the CNN method.
makeReductionOf(PrototypeSet, int) - Static method in class keel.Algorithms.Instance_Generation.BasicMethods.CNN: Make a selection by the CNN method.
makeSelection(Population) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RouletteSelection: Performs the roulette wheel selection
makeSelection(Population) - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Selection: Does make the random selection of a classifier.
makeSelection(Population) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TournamentSelection: Applies the tournament selection.
makeSelection(Population) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RouletteSelection: Performs the roulette wheel selection
makeSelection(Population) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Selection: Does make the random selection of a classifier.
makeSelection(Population) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TournamentSelection: Applies the tournament selection.
makeSpecify(double) - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Attribute: It applies the specify operator.
makeSpecify(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep: If the random number generated is less than the specify probability (parameter in the Config), the interval is specified with an uniform distribution [0..l_0]
makeSpecify(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep: Changes the allele if it is a don't care symbol and the random number generated is less than Pspecify.
makeSpecify(double) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute: It applies the specify operator.
makeSpecify(double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Changes all the don't care symbols by the state in the environment, with Pspecify probability
makeSpecify(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: If the random number that is generated is less than the specify probability (parameter in the Config), the interval is specified with an uniform distribution [0..l_0]
makeSpecify(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: If the random number generated is less than the specify probability (parameter in the Config), the interval is specified with an uniform distribution [0..l_0]
makeSpecify(double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: Changes all the don't care symbols by the state in the environment, with Pspecify probability
makeSpecify(Population, Population, double[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Specify: It applies the specify operator to the population.
makeSpecify(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep: Changes the allele if it is a don't care symbol and the random number generated is less than Pspecify.
makeTestStatistics(Population, int, int[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Statistic: It makes the test statistics.
makeTestStatistics(Population, Population, int, int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Statistic: It makes the test statistics.
makeTimeStatistics(TimeControl) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Statistic: It makes the time statistics.
makeTimeStatistics(TimeControl) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Statistic: It makes the time statistics.
makeTrainOrTestStatistics(PrintWriter, Population, int, int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Statistic: It makes the train statistics.
makeTrainOrTestStatistics(PrintWriter, Population, Population, int, int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Statistic: It makes the train statistics.
makeTrainStatistics(Population, int, int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Statistic: It makes the train statistics.
makeTrainStatistics(Population, Population, int, int[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Statistic: It makes the train statistics.
manhattanDistance(double[], double[]) - Method in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Calculates the Manhattan distance between two instances
manhattanDistance(double[], double[]) - Method in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Calculates the Manhattan distance between two instances
manhattanDistance(double[], double[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: Calculates the Manhattan distance between two instances
manhattanDistance(double[], double[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: Calculates the Manhattan distance between two instances
MannWhitneyC - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Classification Mann-Whitney Stat-test identifier.
MannWhitneyR - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Regression Mann-Whitney Stat-test identifier.
MantarasDistDiscretizer - Class in keel.Algorithms.Discretizers.MantarasDist_Discretizer: This is the class with the operations of the Mantaras Distance-Based discretization.
MantarasDistDiscretizer() - Constructor for class keel.Algorithms.Discretizers.MantarasDist_Discretizer.MantarasDistDiscretizer
map(String, String) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Applies a method to the vector
mapKO - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.matchProfileAgent
mapOK - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.matchProfileAgent
mapType - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
Mark(int[], int, double[], double[], double[], double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
Mark(int[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set: Marks the examples in "v" as covered and set their coverage degree to 1
Mark(int[], int, double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set: Marks the examples in "v" as covered and set their coverage degree to the one in "grado"
MarkClase(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set
MarkClase(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set
masComun(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: Returns the most frequent value of the ith attribute.
masComun(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: Returns the most frequent value of the ith attribute.
masComun(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: Returns the most frequent value of the ith attribute.
masComun(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: Returns the most frequent value of the ith attribute.
masComun(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: It return the most common value for the i-th atribute
masComun(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: Returns the most frequent value of the ith attribute.
masComun(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: It return the most common value for the i-th atribute
masComun(int) - Method in class keel.Algorithms.Rule_Learning.AQ.Dataset: It return the most common value for the i-th atribute
masComun(int) - Method in class keel.Algorithms.Rule_Learning.CN2.Dataset: It return the most common value for the i-th atribute
masComun(int) - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Return the value most common of the attribute 'i'
masComun(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Return the value most common of the attribute 'i'
masComun(int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: It return the most common value for the i-th atribute
masComun(int) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset: It return the most common value for the i-th atribute
masComun(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset: It return the most common value for the i-th atribute
Mask - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules: Representation of a mask over a MyDataset.
Mask() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask: Constructor (Warning: only for arrays definition)
Mask(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask: Constructs a Mask of a given length.
Mask(int, boolean) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask: Constructs a Mask of a given length The cursor is set atBegin (that's a non valid position and it will be necessary a next() to reach the first active position).
Mask - Class in keel.Algorithms.Genetic_Rule_Learning.PART: Representation of a mask over a MyDataset.
Mask() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Constructor (Warning: only for arrays definition)
Mask(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Constructs a Mask of a given length.
Mask(int, boolean) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Constructs a Mask of a given length The cursor is set atBegin (that's a non valid position and it will be necessary a next() to reach the first active position).
Mask - Class in keel.Algorithms.Rule_Learning.C45Rules: Representation of a mask over a MyDataset.
Mask() - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.Mask: Constructor (Warning: only for arrays definition)
Mask(int) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.Mask: Constructs a Mask of a given length.
Mask(int, boolean) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.Mask: Constructs a Mask of a given length The cursor is set atBegin (that's a non valid position and it will be necessary a next() to reach the first active position).
Mask - Class in keel.Algorithms.Rule_Learning.C45RulesSA: Representation of a mask over a MyDataset.
Mask() - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Constructor (Warning: only for arrays definition)
Mask(int) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Constructs a Mask of a given length.
Mask(int, boolean) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Constructs a Mask of a given length The cursor is set atBegin (that's a non valid position and it will be necessary a next() to reach the first active position).
Mask - Class in keel.Algorithms.Rule_Learning.PART: Representation of a mask over a MyDataset.
Mask() - Constructor for class keel.Algorithms.Rule_Learning.PART.Mask: Constructor (Warning: only for arrays definition)
Mask(int) - Constructor for class keel.Algorithms.Rule_Learning.PART.Mask: Constructs a Mask of a given length.
Mask(int, boolean) - Constructor for class keel.Algorithms.Rule_Learning.PART.Mask: Constructs a Mask of a given length The cursor is set atBegin (that's a non valid position and it will be necessary a next() to reach the first active position).
Mask - Class in keel.Algorithms.Rule_Learning.Ripper: Representation of a mask over a MyDataset.
Mask(int) - Constructor for class keel.Algorithms.Rule_Learning.Ripper.Mask: Constructs a Mask of a given length.
Mask(int, boolean) - Constructor for class keel.Algorithms.Rule_Learning.Ripper.Mask: Constructs a Mask of a given length The cursor is set atBegin (that's a non valid position and it will be necessary a next() to reach the first active position).
Mask - Class in keel.Algorithms.Rule_Learning.Slipper: Representation of a mask over a MyDataset.
Mask(int) - Constructor for class keel.Algorithms.Rule_Learning.Slipper.Mask: Constructs a Mask of a given length.
Mask(int, boolean) - Constructor for class keel.Algorithms.Rule_Learning.Slipper.Mask: Constructs a Mask of a given length The cursor is set atBegin (that's a non valid position and it will be necessary a next() to reach the first active position).
massCentre() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.Fuzzy: Returns the centroid of the present fuzzy number.
massCentre() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut: Returns the centroid of the present alpha-cut.
massCentre() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyInterval: Returns the centroid of the present interval fuzzy set.
massCentre() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyNumberTRIANG: Returns the centroid of the present fuzzy number.
massCentre() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyNumberTRLEFT: Returns the centroid of the present fuzzy number.
massCentre() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzySingleton: Returns the centroid of the present fuzzy number.
match(boolean[], boolean[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule
match(double) - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Attribute: It checks if the position of the classifier matches with the value in the environment
match(double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Indicates if the classifier matches with the environmental state
match(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep: Returns true if the allele matches with the environment
match(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep: Returns true if the allele matches with the environment.
match(double) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute: It checks if the position of the classifier matches with the value in the environment
match(double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Indicates if the classifier matches with the environmental state
match(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: Returns true if the allele matches with the environment
match(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: Returns true if the allele matches with the environment
match(double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: Returns true if the allele matches with the environment
match(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep: Returns true if the allele matches with the environment.
match(float[], Vector<fuzzyPartition>) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
match(float[], Vector<fuzzyPartition>) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
match(float[], Vector<partition>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
match(float[], Vector<partition>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
match_alfa(fuzzy[], Vector<fuzzyPartition>, int) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
match_alfa(fuzzy[], Vector<fuzzyPartition>, int) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
match_alpha(Interval[], Vector<partition>, int) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.rule
match_alpha(fuzzy[], Vector<partition>, int) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
match_alpha(fuzzy[], Vector<partition>, int) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
match_alpha(Vector<fuzzy>, Vector<fuzzypartition>, int) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzyrule
match_alpha(Vector<fuzzy>, Vector<fuzzypartition>, int) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzyrule
match_salida(Vector<Float>, int, int, Vector<Float>, Vector<Vector<fuzzy>>, String) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
match_salida(Vector<Float>, int, int, Vector<Float>, Vector<Vector<fuzzy>>, String) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
match_salida(Vector<Float>, int, int, Vector<Float>, Vector<Vector<Float>>) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.rule
match_salida(Vector<Float>, int, int, Vector<Float>, Vector<Vector<Float>>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
match_salida(Vector<Float>, int, int, Vector<Float>, Vector<Vector<Float>>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
matching(int, int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.DataBase: It checks if the value of a specific label in a specific attribute matchs with a given value
matching(int[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: Function to check if a given example matchs with the rule (the rule correctly classifies it).
matching(int, int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.DataBase: It checks if the value of a specific label in a specific attribute matchs with a given value
matching(int[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: Function to check if a given example matchs with the rule (the rule correctly classifies it)
matching(int, int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.DataBase: It checks if the value of a specific label in a specific attribute matchs with a given value
matching(int, int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.DataBase: It checks if the value of a specific label in a specific attribute matchs with a given value
matching(int[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.Rule: Function to check if a given example matchs with the rule (the rule correctly classifies it)
matching(int, int, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.DataBase: It checks if the value of a specific label in a specific attribute matchs with a given value
matching(double[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule: Function to check if a given example matchs with the rule (the rule correctly classifies it)
matching(int, int, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.DataBase: Checks if the value of a specific label in a specific attribute matchs with a given value
matching(double[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Rule: Function to check if a given example matchs with the rule (the rule correctly classifies it)
matching(int, int, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.DataBase: It checks if the value of a specific label in a specific attribute matchs with a given value
matching(double[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Rule: Function to check if a given example matchs with the rule (the rule correctly classifies it)
matching(int, int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.DataBase
matching(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule
matching(double, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Condition: Determines if the value is covered by this condition
matching(double[], boolean[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual: Determines if an example is covered by the individual
matching(int, int, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.DataBase: Returns the fuzzied value for given variable, label and value.
matchingDegree(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.FuzzyAntecedent: Computes the matching degree of a specified value with a set of fuzzy labels, using a specified t_conorm as OR operator
matchingDegree(double, int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Computes the matching degree of a specified value with a set of fuzzy labels, using a specified t_conorm as OR operator
matchProfileAgent - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS
matchProfileAgent(int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.matchProfileAgent
matchValue(double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Regla
matdif(double[][], double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
math - Class in keel.Algorithms.Neural_Networks.gmdh: Class with mathematical operations
math() - Constructor for class keel.Algorithms.Neural_Networks.gmdh.math: Empty constructor
Maths - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix: Utility class.
Maths() - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths
matmul(double[][], double) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
matmul(double[][], double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
matmul(double[][], double) - Static method in class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
matmul(double[][], double[][]) - Static method in class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
matprn(double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
matprn(double[][]) - Static method in class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
Matrix - Class in keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS: Class with matrix operations
Matrix() - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Matrix
matrix - Variable in class keel.Algorithms.Instance_Generation.PNN.MatrixOfDistances: Warehouse of distance between prototypes in A and prototypes in B
Matrix - Class in keel.Algorithms.Neural_Networks.net: Class with matrix operations
Matrix() - Constructor for class keel.Algorithms.Neural_Networks.net.Matrix
Matrix - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core: Deprecated.
Use keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix instead - only for backwards compatibility.
Matrix(int, int) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Constructs a matrix and initializes it with default values.
Matrix(double[][]) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Constructs a matrix using a given array.
Matrix(Reader) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Reads a matrix from a reader.
Matrix - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix: Jama = Java Matrix class.
Matrix(int, int) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Construct an m-by-n matrix of zeros.
Matrix(int, int, double) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Construct an m-by-n constant matrix.
Matrix(double[][]) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Construct a matrix from a 2-D array.
Matrix(double[][], int, int) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Construct a matrix quickly without checking arguments.
Matrix(double[], int) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Construct a matrix from a one-dimensional packed array
Matrix(Reader) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Reads a matrix from a reader.
MatrixCalcs - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting
MatrixCalcs() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
MatrixCalcs - Class in keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs
MatrixCalcs() - Constructor for class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
MatrixCalcs.ErrorDimension - Exception in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting
MatrixCalcs.ErrorSingular - Exception in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting
matrixConfussion - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Confussion matrix (YES-NO)
matrixConfussion - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Confussion matrix flag.
matrixFiringDegrees - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams: Matrix that stores the firingDegrees up to which the examples fire the rules.
matrixFiringDegreesRulesXExamples - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Matrix that stores the firing degrees examples by rules.
MatrixOfDistances - Class in keel.Algorithms.Instance_Generation.PNN: Matrix of distances between two sets of prototypes.
MatrixOfDistances(PrototypeSet, PrototypeSet) - Constructor for class keel.Algorithms.Instance_Generation.PNN.MatrixOfDistances: Construct a matrix of the distances between elements of the set A and B.
matrixRulesXExamples - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Matrix that stores the examples that fire the rules.
MatrizR - Class in keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM
MatrizR(BaseR) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.MatrizR
MatrizR(int, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.MatrizR
MatrizR(MatrizR, MatrizR, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.MatrizR
matsum(double[][], double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
matsum(double[][], double[][]) - Static method in class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
max - Variable in class keel.Algorithms.Decision_Trees.M5.SimpleStatistics: The maximum value seen, or Double.NaN if no values seen
max - Variable in class keel.Algorithms.Discretizers.MVD.MVD: Maximum value.
Max() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.vectordouble: Returns the maximum value of the vector
max - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
max - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleStatistics: The maximum value seen, or Double.NaN if no values seen
Max(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Arrays
max - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Stats: The maximum value seen, or Double.NaN if no values seen
max(int, int) - Method in class keel.Algorithms.MIL.APR.AbstractAPR
max(ArrayList<IInstance>, int) - Method in class keel.Algorithms.MIL.APR.IteratedDiscrimination.IteratedDiscrimination
max(DenseVector) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Finds the maximum element of a vector
max(double, double, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.Function: The maximum of this function over an interval [a,A] evaluated at N+1 evenly distributed values
max - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.ConfidenceInterval: Maximum value of the interval.
max() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Gen: Returns the maximum variable.
max() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.TipoIntervalo: Returns the maximum variable.
max() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Gen: Returns the maximum variable.
max() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.TipoIntervalo: Returns the maximum variable.
max() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Gen: Returns the maximum variable.
max() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.TipoIntervalo: Returns the maximum variable.
max() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Gen: Returns the maximum variable.
max() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.TipoIntervalo: Returns the maximum variable.
max() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Gen: Returns the maximum variable.
max() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.TipoIntervalo: Returns the maximum variable.
max() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Gen: Returns the maximum variable.
max() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.TipoIntervalo: Returns the maximum variable.
max() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Gen: Returns the maximum variable.
max() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.TipoIntervalo: Returns the maximum variable.
max() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Gen: Returns the maximum variable.
max() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.TipoIntervalo: Returns the maximum variable.
max() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Returns the maximum value of all elements
max_generations - Variable in class keel.Algorithms.Neural_Networks.gann.SetupParameters
MAX_LATERAL_TUNING - Static variable in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC_Chromosome: Maximum lateral tuning.
max_nodes - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters
max_nodes - Variable in class keel.Algorithms.Neural_Networks.gmdh.SetupParameters
MAX_NUM_FREQUENT_SETS - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: The maximum number of frequent sets that may be generated.
MAX_POWER_OF_LAMBDA - Static variable in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: powers of lambda are prepared prior to kernel evaluations.
max_reglas - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Sel: Maximum number of rules.
max_reglas - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Tun: Maximum number of rules.
MAXALG - Static variable in class keel.GraphInterKeel.statistical.StatisticalF
maxBDAttributeValue - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
maxBDAttributeValue - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
maxClass() - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Returns class with highest frequency over all values.
maxClass(int) - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Returns class with highest frequency for given value.
maxClass() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Returns class with highest frequency over all values.
maxClass(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Returns class with highest frequency for given value.
maxClass() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Returns class with highest frequency over all values.
maxClass(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Returns class with highest frequency for given value.
maxClass() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Returns class with highest frequency over all values.
maxClass(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Returns class with highest frequency for given value.
maxClass() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Returns class with highest frequency over all values.
maxClass(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Returns class with highest frequency for given value.
maxClass() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Returns class with highest frequency over all values.
maxClass(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Returns class with highest frequency for given value.
maxClass() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Returns class with highest frequency over all values.
maxClass(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Returns class with highest frequency for given value.
maxClass() - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Returns class with highest frequency over all values.
maxClass(int) - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Returns class with highest frequency for given value.
maxClass() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Returns class with highest frequency over all values.
maxClass(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Returns class with highest frequency for given value.
maxD - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_UBR
MAXDATA - Static variable in class keel.GraphInterKeel.statistical.StatisticalF
maxDepthTipText() - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Returns the tip text for this property
maxDepthTipText() - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Returns the tip text for this property
maxDepthTipText() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Returns the tip text for this property
maxDistance(double[][]) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils: Computes the maximun distance between vectors in a double[][]
maxDistance(double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils: Computes the maximun distance between vectors in a double[][]
maxDistance(double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils: Computes the maximun distance between vectors in a double[][]
maxDistance(double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils: Computes the maximun distance between vectors in a double[][]
maxDistance(double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils: Computes the maximun distance between vectors in a double[][]
maxDistance(double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils: Computes the maximun distance between vectors in a double[][]
maxDistance(double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils: Computes the maximun distance between vectors in a double[][]
maxDistanceTo(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Returns the greatest distance between center.
maxDistanceTo(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Returns the greatest distance between center.
maxFun - Variable in class keel.Algorithms.Preprocess.Missing_Values.EM.util.UnivariateMinimum: maximum number of function evaluations (default 0 indicates no limit on calls)
MAXGAM - Static variable in class keel.Algorithms.Lazy_Learning.Statistics: Maximun gamma value
MAXGAM - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics
MAXIMIZE - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
MAXIMIZE - Static variable in class keel.GraphInterKeel.statistical.StatisticalF
maximo_cubrimiento - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Ttabla: cover level
maximo_cubrimiento - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Ttabla: cover level
maximo_cubrimiento - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Ttabla: cover level
maximo_cubrimiento - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Ttabla: cover level
maximo_cubrimiento - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Ttabla: cover level
maximo_cubrimiento - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Ttabla: cover level
maximo_cubrimiento - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Ttabla: cover level
maximo_cubrimiento - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Ttabla: cover level
MAXIMUM - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.GP_COACH: Configuration flag (MAXIMUM).
maximum(double[], double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method implements the maximum operation between two vectors elements
maximum(double[][], double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method implements the maximum operation between two matrix elements
maximum(double[][][], double[][][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method implements the maximum operation between two cubic matrix elements
MAXIMUM - Static variable in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.GP_COACH_H: Configuration flag (MAXIMUM)
maximum(double[], double[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV: Returns a vector with the maximum of each component of a and b
maximum(double[][], double[][]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV: Returns a matrix with the maximum of each component of a and b
maximum(double[][][], double[][][]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV: Returns a matrix with the maximum of each component of a and b
maximum(double[], double[]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV: Returns a vector with the maximum of each component of a and b
maximum(double[][], double[][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV: Returns a matrix with the maximum of each component of a and b
maximum(double[][][], double[][][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV: Returns a matrix with the maximum of each component of a and b
Maximum(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Utils: Returns the maximum of two float values
Maximum(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Utils: Returns the maximum of two float values
Maximum(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Utils: Returns the maximum of two float values
Maximum(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Utils: Returns the maximum of two float values
Maximum(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Utils: Returns the maximum of two float values
Maximum(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Utils: Returns the maximum of two float values
Maximum(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Utils: Returns the maximum of two float values
maximumDelta - Variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus: Maximum Value for Delta in RpropPlus Algorithm
maximumDistance - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Maximum distance between data
maximumReduction() - Method in class keel.Algorithms.Instance_Generation.PNN.PNNGenerator: Performs the maximum reduction of the training data set by the PNNGenerator (aka Chang) method.
maximumSupportDifference(int[][]) - Method in class keel.Algorithms.Discretizers.MVD.MVD: Gets the maximum support difference
maxIndex(double[]) - Static method in class keel.Algorithms.Decision_Trees.C45.C45: Returns index of maximum element in a given array of doubles.
maxIndex(double[]) - Static method in class keel.Algorithms.Decision_Trees.DT_GA.C45.C45: Returns index of maximum element in a given array of doubles.
maxIndex(double[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Returns index of maximum element in a given array of doubles.
maxIndex(int[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Returns index of maximum element in a given array of integers.
maxIndex(double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Returns index of maximum element in a given array of doubles.
maxIndex(int[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Returns index of maximum element in a given array of integers.
maxIndex(double[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Returns index of maximum element in a given array of doubles.
maxIndex(int[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Returns index of maximum element in a given array of integers.
maxIndex(double[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.PART.C45: Returns index of maximum element in a given array of doubles.
maxIndex(double[]) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.C45CS: Returns index of maximum element in a given array of doubles.
maxIndex(double[]) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.C45: Returns index of maximum element in a given array of doubles.
maxIndex(double[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Returns index of maximum element in a given array of doubles.
maxIndex(int[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Returns index of maximum element in a given array of integers.
maxIndex(double[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.C45: Returns index of maximum element in a given array of doubles.
maxIndex(double[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.C45: Returns index of maximum element in a given array of doubles.
maxIndex(double[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.C45: Returns index of maximum element in a given array of doubles.
maxIndex(double[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.C45: Returns index of maximum element in a given array of doubles.
maxIndex(double[]) - Static method in class keel.Algorithms.Rule_Learning.C45Rules.C45: Returns index of maximum element in a given array of doubles.
maxIndex(double[]) - Static method in class keel.Algorithms.Rule_Learning.C45RulesSA.C45: Returns index of maximum element in a given array of doubles.
maxIndex(double[]) - Static method in class keel.Algorithms.Rule_Learning.PART.C45: Returns index of maximum element in a given array of doubles.
maxIndex(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Returns index of maximum element in a given array of doubles.
maxIndex(int[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Returns index of maximum element in a given array of integers.
maxIndex(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45: Returns index of maximum element in a given array of doubles.
maxIndex(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Returns index of maximum element in a given array of doubles.
maxIndex(int[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Returns index of maximum element in a given array of integers.
maxIndex(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Returns index of maximum element in a given array of doubles.
maxIndex(int[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Returns index of maximum element in a given array of integers.
maxIndex(double[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Returns index of maximum element in a given array of doubles.
maxIndex(int[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Returns index of maximum element in a given array of integers.
maxIndex(double[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Returns index of maximum element in a given array of doubles.
maxIndex(int[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Returns index of maximum element in a given array of integers.
maxInfoGain - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Antd: The maximum infoGain achieved by this antecedent test in the growing data
maxIntervals - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
maxIntervals - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
maxIntervals - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters: Maximum number of intervals.
maxInVector(double[]) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils: Returns the index of the higher value
maxInVector(double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils: Returns the index of the higher value
maxInVector(double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils: Returns the index of the higher value
maxInVector(double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils: Returns the index of the higher value
maxInVector(double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils: Returns the index of the higher value
maxInVector(double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils: Returns the index of the higher value
maxInVector(double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils: Returns the index of the higher value
maxIterations - Variable in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
maxItsTipText() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Returns the tip text for this property
maxLinksAdd - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Maximum number of links to add in the mutations
maxLinksDel - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Maximum number of links to remove in the mutations
MAXLOG - Static variable in class keel.Algorithms.Lazy_Learning.Statistics: Maximum logarithm value
MAXLOG - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics
maxMin - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.timerEvolutionStats
maxmin(int, int) - Method in class keel.Algorithms.MIL.APR.IteratedDiscrimination.IteratedDiscrimination
maxNeuronsAdd - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Maximum number of neurons to add in the mutations
maxNeuronsDel - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Maximum number of neurons to remove in the mutations
maxNextCharInd - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
maxNextCharInd - Static variable in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
maxNextCharInd - Static variable in class keel.Dataset.SimpleCharStream
maxNodos - Static variable in class keel.Algorithms.Decision_Trees.DT_oblicuo.Tree: Maximum number of nodes allowed.
maxNodos - Static variable in class keel.Algorithms.Decision_Trees.Target.Tree: Maximum number of nodes.
maxNofneurons - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Maximum number of neurons of each LinkedLayer of the neural nets
maxnofneurons - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputLayer: Maximum number of neurons
maxnofneurons - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Maximum number of neurons for the layer
maxOfGenerations - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Maximum of generations (stop criterium)
maxPositiveTermsSizeTipText() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
maxRadiusLength() - Method in class keel.Algorithms.Instance_Generation.GMCA.ClusterSet: Returns the maximum radius length of set of clusters.
maxRadiusLengthOfClass(double) - Method in class keel.Algorithms.Instance_Generation.GMCA.ClusterSet: Returns the maximum radius length of set of clusters of one class.
maxSubsequenceLengthTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Returns the tip text for this property
maxValue() - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Returns index of value containing maximum number of itemsets.
maxValue() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Returns index of value containing maximum number of itemsets.
maxValue() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Returns index of value containing maximum number of itemsets.
maxValue() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Returns index of value containing maximum number of itemsets.
maxValue() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Returns index of value containing maximum number of itemsets.
maxValue() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Returns index of value containing maximum number of itemsets.
maxValue() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Returns index of value containing maximum number of itemsets.
maxValue() - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Returns index of value containing maximum number of itemsets.
maxValue() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Returns index of value containing maximum number of itemsets.
MAYOR - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.Selector: Identifier for the greater condition operator.
MAYOR - Static variable in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Condition: Tag (GREATER).
mayor_zero() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.Interval
mayor_zero() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.Interval
mayor_zero() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.Interval
mayor_zero() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.Interval
mayor_zero() - Method in class keel.Algorithms.LQD.preprocess.Expert.interval
mayor_zero() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.interval
mayor_zero() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.interval
MAYORIGUAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Condition: Tag (GREATER EQUAL).
MCAAlgorithm - Class in keel.Algorithms.Instance_Generation.MCA: MCA algorithm calling.
MCAAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.MCA.MCAAlgorithm
MCAGenerator - Class in keel.Algorithms.Instance_Generation.MCA: Modified Chang Algorithm.
MCAGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.MCA.MCAGenerator: Build a new algorithm MCAGenerator that will reduce a prototype set.
MCAGenerator(PrototypeSet) - Constructor for class keel.Algorithms.Instance_Generation.MCA.MCAGenerator: Build a new algorithm PNNGenerator that will reduce a prototype set.
MCF - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Fuzzy_Chi: Configuration flags.
MCF - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Fuzzy_Ish: Configuration flags.
MCF - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Fuzzy_Ish: Configuration flag (MCF).
MCF - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Fuzzy_Ish
MCNN - Class in keel.Algorithms.Instance_Selection.MCNN: File: MCNN.java The MCNN Instance Selection algorithm.
MCNN(String) - Constructor for class keel.Algorithms.Instance_Selection.MCNN.MCNN: Default constructor.
MCNN - Class in keel.Algorithms.Preprocess.Instance_Selection.MCNN: File: MCNN.java The MCNN Instance Selection algorithm.
MCNN(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.MCNN.MCNN: Default constructor.
MCS - Class in keel.Algorithms.Instance_Selection.MCS: File: MCS.java The MCS Instance Selection algorithm.
MCS(String) - Constructor for class keel.Algorithms.Instance_Selection.MCS.MCS: Default constructor.
MCS - Class in keel.Algorithms.Preprocess.Instance_Selection.MCS: File: MCS.java The MCS Instance Selection algorithm.
MCS(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.MCS.MCS: Default constructor.
mdlFitness(classifier, agentPerformanceTraining) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.timerMDL
mdlFitness(Classifier) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_MDL
mdlFitness(Classifier) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_MDL
mdlWeightRelaxFactor - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
mean(double[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Computes the mean for an array of doubles.
mean - Variable in class keel.Algorithms.Decision_Trees.M5.SimpleStatistics: The mean of values at the last calculateDerived() call
mean(double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Computes the mean for an array of doubles.
mean(double[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Computes the mean for an array of doubles.
mean - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleStatistics: The mean of values at the last calculateDerived() call
mean - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Stats: The mean of values at the last calculateDerived() call
mean(double[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Computes the mean for an array of doubles.
mean(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Computes the mean for an array of doubles.
mean(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Computes the mean for an array of doubles.
mean(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Computes the mean for an array of doubles.
mean(double[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Computes the mean for an array of doubles.
mean(double[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Computes the mean for an array of doubles.
mean - Variable in class keel.Algorithms.SVM.SMO.SMO: Variable with the mean of each attribute.
meanAbsoluteError() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Returns the mean absolute error.
MeanFitness() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
MeanFitness() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
MeanFitness() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
MeanFitness_Stationary() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
MeanFitness_Stationary(Double_t, Double_t, Double_t, Double_t, Double_t, Double_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
meanOrMode(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanOrMode(M5Attribute) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanOrMode(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanOrMode(AttributeWeka) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanOrMode(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanOrMode(MyAttribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanOrMode(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanOrMode(Attribute) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanOrMode(int) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanOrMode(Attribute) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanPriorAbsoluteError() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Returns the mean absolute error of the prior.
measure_file - Variable in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Param: Auxiliary output file for quality measures of the rules
measure_file - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Param: Auxiliary output file for quality measures of the rules
measure_file - Variable in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Param: Auxiliary output file for quality measures of the rules
measureCacheHits() - Method in class keel.Algorithms.SVM.SMO.SVMreg: number of kernel cache hits used during learing
measureError(PrototypeSet, int) - Method in class keel.Algorithms.Semi_Supervised_Learning.DE_TriTraining.DE_TriTrainingGenerator: Measure combined error excluded the classifier 'id' on the given data set
measureError(PrototypeSet, int) - Method in class keel.Algorithms.Semi_Supervised_Learning.TriTraining.TriTrainingGenerator: Measure combined error excluded the classifier 'id' on the given data set
measureIEP(boolean[]) - Method in class keel.Algorithms.Preprocess.Feature_Selection.Datos: Calculates the inconsistent example pairs ratio (IEP)
measureKernelEvaluations() - Method in class keel.Algorithms.SVM.SMO.SVMreg: number of kernel evaluations used in learing
measureNumLeaves() - Method in class keel.Algorithms.Decision_Trees.M5.M5: return the number of leaves in the tree
measureNumLeaves() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: return the number of leaves in the tree
measureNumLinearModels() - Method in class keel.Algorithms.Decision_Trees.M5.M5: return the number of linear models
measureNumLinearModels() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: return the number of linear models
measureNumRules() - Method in class keel.Algorithms.Decision_Trees.M5.M5: return the number of rules
measureNumRules() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: return the number of rules
measures(M5Instances, boolean) - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode: Computes performance measures of a tree
Measures - Class in keel.Algorithms.Decision_Trees.M5: Class for performance measures
Measures() - Constructor for class keel.Algorithms.Decision_Trees.M5.Measures: Constructs a Measures object which could containing the performance measures
measures(MyDataset, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode: Computes performance measures of a tree
Measures - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules: Class for performance measures
Measures() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Measures: Constructs a Measures object which could containing the performance measures
measuresToString(Measures[], M5Instances, int, int, String) - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode: Converts the performance measures into a string
measuresToString(Measures[], MyDataset, int, int, String) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode: Converts the performance measures into a string
media() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
media() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.Interval
media() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
media() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.Interval
media() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.Interval
media() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.Interval
media() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.Interval
media() - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
media() - Method in class keel.Algorithms.LQD.preprocess.Expert.interval
media() - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
media() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
media() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.interval
media() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
media() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.interval
media() - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
media() - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.interval
medidaInconsistencia(boolean[]) - Method in class keel.Algorithms.Preprocess.Feature_Selection.Datos: Calculates the inconcistency ratio.
medidas - Variable in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Measurements of the individual.
medidas - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Measurements of the individual.
medidas - Variable in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual: Measurements of the individual.
medVect(double[][]) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils: Computes the average of the rows of matrix v
medVect(double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils: Computes the average of the rows of matrix v
medVect(double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils: Computes the average of the rows of matrix v
medVect(double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils: Computes the average of the rows of matrix v
medVect(double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils: Computes the average of the rows of matrix v
medVect(double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils: Computes the average of the rows of matrix v
medVect(double[][]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils: Computes the average of the rows of matrix v
mejorBR() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Poblacion
mejoresParticulas(PrototypeSet[], double[]) - Method in class keel.Algorithms.Instance_Generation.JADE.JADEGenerator
mejoresParticulas(PrototypeSet[]) - Method in class keel.Algorithms.Instance_Generation.OBDE.OBDEGenerator
mejorSolucion() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.Poblacion: Returns the best solution obtained by the GA.
mejorSolucion() - Method in class keel.Algorithms.Decision_Trees.Target.Poblacion: Returns the best solution founded.
membership(int, int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseD: It computes the membership degree for a input value
membership(int, int, int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.DataBase: It computes the membership degree for a input value
membership(int, int, int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.DataBase: It computes the membership degree for a input value
membership(int, float) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyPartition
membership(int, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyPartition
membership(int, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.partition
membership(int, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.partition
membership(int, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.partition
membership(int, float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzypartition
membership(int, float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzypartition
membershipFunction(int, int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.DataBase: It computes the membership degree for a input value
membershipFunction(int, int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.DataBase: It computes the membership degree for a input value
membershipFunction(int, int, double) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.DataBase: It computes the membership degree for a input value
MembershipFunction - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori
MembershipFunction() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.MembershipFunction: Default constructor
MembershipFunction - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC
MembershipFunction() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.MembershipFunction: Default constructor
MemFun - Class in keel.Algorithms.RE_SL_Methods.SEFC
MemFun() - Constructor for class keel.Algorithms.RE_SL_Methods.SEFC.MemFun: Default constructor
MENN - Class in keel.Algorithms.Instance_Selection.MENN: File: MENN.java The MENN Instance Selection algorithm.
MENN(String) - Constructor for class keel.Algorithms.Instance_Selection.MENN.MENN: Default constructor.
MENN - Class in keel.Algorithms.Preprocess.Instance_Selection.MENN: File: MENN.java The MENN Instance Selection algorithm.
MENN(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.MENN.MENN: Default constructor.
MENOR - Static variable in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Condition: Tag (LESSER).
MENOR_IGUAL - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.Selector: Identifier for the lesser-equal condition operator.
MENORIGUAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Condition: Tag (LESSER EQUAL).
merge(Rule) - Method in class keel.Algorithms.Hyperrectangles.BNGE.Rule: Merge two rules
merge(Cluster, Cluster) - Method in class keel.Algorithms.Instance_Generation.GMCA.ClusterSet: Merge two clusters.
Merge(PrototypeSet, boolean[], Prototype, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.ICPL.ICPLGenerator: Merge
mergeCostVariation(ArrayList<Double>, int, double, ArrayList<Double>, int, double, int[]) - Method in class keel.Algorithms.Discretizers.Khiops.Khiops: Computes the cost derived form merging two adjacent intervals na and nb
mergeCostVariation(ArrayList<Double>, int, ArrayList<Double>, int, int, int[]) - Method in class keel.Algorithms.Discretizers.MODL.MODL: Computes the cost derived form merging two adjacent intervals na and nb
mergedRowChi2Value(ArrayList<Double>, int, int[]) - Method in class keel.Algorithms.Discretizers.Khiops.Khiops: This method calculates the contribution to the global chi square value of a new interval (produced by merging two adjacent ones).
mergedThreshold - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
mergeInstance(M5Instance) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Merges this instance with the given instance and returns the result.
mergeInstance(M5Instance) - Method in class keel.Algorithms.Decision_Trees.M5.M5SparseInstance: Merges this instance with the given instance and returns the result.
mergeInstance(Instance) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Merges this instance with the given instance and returns the result.
mergeInstance(Itemset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Merges this instance with the given instance and returns the result.
mergeInstance(Instance) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Merges this instance with the given instance and returns the result.
mergeInstance(Instance) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance: Merges this instance with the given instance and returns the result.
mergeInstance(Instance) - Method in class keel.Algorithms.SVM.SMO.core.Instance: Merges this instance with the given instance and returns the result.
mergeInstances(M5Instances, M5Instances) - Static method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Merges two sets of M5Instances together.
mergeInstances(Instances, Instances) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Merges two sets of Instances together.
mergeInstances(Instances, Instances) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Merges two sets of Instances together.
mergeInstances(Instances, Instances) - Static method in class keel.Algorithms.SVM.SMO.core.Instances: Merges two sets of Instances together.
mergeIntervals(Interval) - Method in class keel.Algorithms.Discretizers.MVD.Interval: Merges the interval with the provided one
mergeSort(long[], int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.PART.Utilities: Mergesort algorithm for an array of long integers.
mergeSort(Pair[], int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.PART.Utilities: Mergesort algorithm for an array of Pairs.
mergeSort(long[], int) - Static method in class keel.Algorithms.Rule_Learning.C45Rules.Utilities: Mergesort algorithm for an array of long integers.
mergeSort(double[], int) - Static method in class keel.Algorithms.Rule_Learning.C45Rules.Utilities: Mergesort algorithm for an array of long integers.
mergeSort(long[], int) - Static method in class keel.Algorithms.Rule_Learning.C45RulesSA.Utilities: Mergesort algorithm for an array of long integers.
mergeSort(double[], int) - Static method in class keel.Algorithms.Rule_Learning.C45RulesSA.Utilities: Mergesort algorithm for an array of long integers.
mergeSort(long[], int) - Static method in class keel.Algorithms.Rule_Learning.PART.Utilities: Mergesort algorithm for an array of long integers.
mergeSort(Pair[], int) - Static method in class keel.Algorithms.Rule_Learning.PART.Utilities: Mergesort algorithm for an array of Pairs.
mergeSort(long[], int) - Static method in class keel.Algorithms.Rule_Learning.Ripper.Utilities: Mergesort algorithm for an array of long integers.
mergeSort(double[], int) - Static method in class keel.Algorithms.Rule_Learning.Ripper.Utilities: Mergesort algorithm for an array of long integers.
mergeSort(Pair[], int) - Static method in class keel.Algorithms.Rule_Learning.Ripper.Utilities: Mergesort algorithm for an array of Pairs.
mergeSort(Trio[], int) - Static method in class keel.Algorithms.Rule_Learning.Ripper.Utilities: Mergesort algorithm for a vector of Trio.
mergeSort(long[], int) - Static method in class keel.Algorithms.Rule_Learning.Slipper.Utilities: Mergesort algorithm for an array of long integers.
mergeSort(Pair[], int) - Static method in class keel.Algorithms.Rule_Learning.Slipper.Utilities: Mergesort algorithm for an array of Pairs.
mergeSort(Trio[], int) - Static method in class keel.Algorithms.Rule_Learning.Slipper.Utilities: Mergesort algorithm for a vector of Trio.
MERGING - Static variable in class keel.Algorithms.Genetic_Rule_Learning.OCEC.OCEC: Number to indentify the different types of scheme (MERGING).
MersenneTwister - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat: Mersenne Twister and MersenneTwisterFast: MersenneTwister is a drop-in subclass replacement for java.util.Random.
MersenneTwister() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.MersenneTwister: Constructor using the default seed.
MersenneTwister(long) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.MersenneTwister: Constructor using a given seed.
MESDIF - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF: MESDIF Algorithm for the discovery of rules describing subgroups
MESDIF() - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.MESDIF
metadata - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset: Dataset specification
Metadata - Class in keel.Algorithms.Neural_Networks.NNEP_Common.data: Implementation of IMetadata interface.
Metadata() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.data.Metadata: Empty constructor
method_output - Variable in class keel.Algorithms.SVM.SMO.SMO: Model output filename.
methodsSelectionTree - Variable in class keel.GraphInterKeel.experiments.Experiments
Metodo - Class in keel.Algorithms.Decision_Trees.C45_Binarization: File: Metodo.java An auxiliary class to initialize Instance Selection algorithms
Metodo() - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Default builder
Metodo(String) - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Builder.
Metodo - Class in keel.Algorithms.ImbalancedClassification.Ensembles.Basic
Metodo() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
Metodo(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
Metodo - Class in keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic: File: Metodo.java An auxiliary class to initialize Instance Selection algorithms
Metodo() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo: Default builder
Metodo(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo: Builder.
Metodo - Class in keel.Algorithms.Preprocess.Basic: File: Metodo.java An auxiliary class to initialize Instance Selection algorithms
Metodo() - Constructor for class keel.Algorithms.Preprocess.Basic.Metodo: Default builder
Metodo(String) - Constructor for class keel.Algorithms.Preprocess.Basic.Metodo: Builder.
Metodo(String, InstanceSet) - Constructor for class keel.Algorithms.Preprocess.Basic.Metodo: Parameter constructor.
mezclar() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Poblacion: Combines each organization with another one if the useful attributes of the organization is contained in the other one to be combined with.
michigan() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.RuleBase: Genetic Cooperative-Competetive procedure
MiDataset - Class in keel.Algorithms.RE_SL_Methods.LEL_TSK
MiDataset(String, boolean) - Constructor for class keel.Algorithms.RE_SL_Methods.LEL_TSK.MiDataset: Stores in memory the contents of the data file "f"
MiDataset - Class in keel.Algorithms.RE_SL_Methods.MamWM
MiDataset(String, boolean) - Constructor for class keel.Algorithms.RE_SL_Methods.MamWM.MiDataset
MiDataset - Class in keel.Algorithms.RE_SL_Methods.mogulHC
MiDataset(String, boolean) - Constructor for class keel.Algorithms.RE_SL_Methods.mogulHC.MiDataset: Stores in memory the contents of the data file "f"
MiDataset - Class in keel.Algorithms.RE_SL_Methods.mogulIRL
MiDataset(String, boolean) - Constructor for class keel.Algorithms.RE_SL_Methods.mogulIRL.MiDataset: Stores in memory the contents of the data file "f"
MiDataset - Class in keel.Algorithms.RE_SL_Methods.mogulSC
MiDataset(String, boolean) - Constructor for class keel.Algorithms.RE_SL_Methods.mogulSC.MiDataset: Stores in memory the contents of the data file "f"
MiDataset - Class in keel.Algorithms.RE_SL_Methods.TSK_IRL
MiDataset(String, boolean) - Constructor for class keel.Algorithms.RE_SL_Methods.TSK_IRL.MiDataset: Stores in memory the contents of the data file "f"
MiDataset - Class in keel.Algorithms.RE_SL_Postprocess.Mam2TSK
MiDataset(String, boolean) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.MiDataset: Stores in memory the contents of the data file "f"
MiDataset - Class in keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB
MiDataset(String, boolean) - Constructor for class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.MiDataset: Stores in memory the contents of the data file "f"
MiDataset - Class in keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules
MiDataset(String, boolean) - Constructor for class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.MiDataset: Stores in memory the contents of the data file "f"
MiDataset - Class in keel.Algorithms.RE_SL_Postprocess.MamSelect
MiDataset(String, boolean) - Constructor for class keel.Algorithms.RE_SL_Postprocess.MamSelect.MiDataset: Stores in memory the contents of the data file "f"
MiDataset - Class in keel.Algorithms.RE_SL_Postprocess.MamWSelect
MiDataset(String, boolean) - Constructor for class keel.Algorithms.RE_SL_Postprocess.MamWSelect.MiDataset: Stores in memory the contents of the data file "f"
MiDataset - Class in keel.Algorithms.RE_SL_Postprocess.MamWTuning
MiDataset(String, boolean) - Constructor for class keel.Algorithms.RE_SL_Postprocess.MamWTuning.MiDataset: Stores in memory the contents of the data file "f"
MiDataset - Class in keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules
MiDataset(String, boolean) - Constructor for class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.MiDataset: Stores in memory the contents of the data file "f"
MiDataset - Class in keel.Algorithms.RE_SL_Postprocess.TSKSelect
MiDataset(String, boolean) - Constructor for class keel.Algorithms.RE_SL_Postprocess.TSKSelect.MiDataset: Stores in memory the contents of the data file "f"
MIGRATING - Static variable in class keel.Algorithms.Genetic_Rule_Learning.OCEC.OCEC: Number to indentify the different types of scheme (MIGRATING).
mil_mouseEntered(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Enter in MIL button
mil_mouseExited(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Exit from MIL button
mil_mouseReleased(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Entering in MIL module
min - Variable in class keel.Algorithms.Decision_Trees.M5.SimpleStatistics: The minimum value seen, or Double.NaN if no values seen
min - Variable in class keel.Algorithms.Discretizers.MVD.MVD: Minimum value.
Min(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Sel: Minimum T-norm
Min(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Tun: Minimum T-norm
Min() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.vectordouble: Returns the minimum value of the vector
Min(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseR
min - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
min - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleStatistics: The minimum value seen, or Double.NaN if no values seen
min(double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ruleOrderAgent
Min(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Arrays
min - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Stats: The minimum value seen, or Double.NaN if no values seen
min(int, int) - Method in class keel.Algorithms.MIL.APR.AbstractAPR
min(ArrayList<IInstance>, int) - Method in class keel.Algorithms.MIL.APR.IteratedDiscrimination.IteratedDiscrimination
min(DenseVector) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Finds the minimum element of the vector
min(DenseVector, ArrayList<Integer>) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Finds the minimum element of the vector, and returns it
min(double, double, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.Function: The minimum of this function over an interval [a,A] evaluated at N+1 evenly distributed values
min - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.ConfidenceInterval: Minimum value of the interval.
min() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Gen: Returns the minimum variable.
min() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.TipoIntervalo: Returns the minimum variable.
min() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Gen: Returns the minimum variable.
min() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.TipoIntervalo: Returns the minimum variable.
min() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Gen: Returns the minimum variable.
min() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.TipoIntervalo: Returns the minimum variable.
min() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Gen: Returns the minimum variable.
min() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.TipoIntervalo: Returns the minimum variable.
min() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Gen: Returns the minimum variable.
min() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.TipoIntervalo: Returns the minimum variable.
min() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Gen: Returns the minimum variable.
min() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.TipoIntervalo: Returns the minimum variable.
min() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Gen: Returns the minimum variable.
min() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.TipoIntervalo: Returns the minimum variable.
min() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Gen: Returns the minimum variable.
min() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.TipoIntervalo: Returns the minimum variable.
MIN - Static variable in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
MIN_CONS - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
MIN_LATERAL_TUNING - Static variable in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC_Chromosome: Minimum lateral tuning.
min_max - Class in keel.Algorithms.Preprocess.Transformations.min_max: This class performs the min-max transformation.
min_max(String) - Constructor for class keel.Algorithms.Preprocess.Transformations.min_max.min_max: Creates a new instance of min_max
MINALG - Static variable in class keel.GraphInterKeel.statistical.StatisticalF
MINALGF - Static variable in class keel.GraphInterKeel.statistical.StatisticalF
minBDAttributeValue - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: The following parameters are to save the BD examples maximum and minimum values.
minBDAttributeValue - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: The following parameters are to save the BD examples maximum and minimum values.
minD - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_UBR
MINDATA - Static variable in class keel.GraphInterKeel.statistical.StatisticalF
minDataDLIfDeleted(int, double, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Compute the minimal data description length of the ruleset if the rule in the given position is deleted.
minDataDLIfExists(int, double, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Compute the minimal data description length of the ruleset if the rule in the given position is NOT deleted.
minDist(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Returns the smallest distance between uno and all prototypes of the particle.
minDist(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: return the smallest distance between uno and all prototypes of the particle.
minDistanceTo(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Returns the smallest distance between center and other prototype and that prototype.
minDistanceTo(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Returns the smallest distance between center and other prototype and that prototype.
minFunction - Variable in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
miniFont - Variable in class keel.GraphInterKeel.experiments.Credits
minimalCov(int, Vector<pnPair>) - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.SaturationFilter: It runs the algorithm
MINIMIZE - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
MINIMIZE - Static variable in class keel.GraphInterKeel.statistical.StatisticalF
MINIMO - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Fuzzy_Ish
MINIMUM - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Fuzzy_Chi: Configuration flags.
MINIMUM - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Fuzzy_Ish: Configuration flags.
MINIMUM - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Fuzzy_Ish: Configuration flag (MINIMUM).
MINIMUM - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.GP_COACH: Configuration flag (MINIMUM).
minimum(double[], double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method implements the minimum operation between two vectors elements
minimum(double[][], double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method implements the minimum operation between two matrix elements
minimum(double[][][], double[][][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method implements the minimum operation between two cubic matrix elements
MINIMUM - Static variable in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.GP_COACH_H: Configuration flag (MINIMUM)
minimum(double[], double[][]) - Method in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
minimum(double[], double[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV: Returns a vector with the minimum of each component of a and b
minimum(double[][], double[][]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV: Returns a matrix with the minimum of each component of a and b
minimum(double[][][], double[][][]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV: Returns a matrix with the minimum of each component of a and b
minimum(double[], double[]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV: Returns a vector with the minimum of each component of a and b
minimum(double[][], double[][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV: Returns a matrix with the minimum of each component of a and b
minimum(double[][][], double[][][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV: Returns a matrix with the minimum of each component of a and b
Minimum(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Utils: Returns the minimum of two float values
Minimum(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Utils: Returns the minimum of two float values
Minimum(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Utils: Returns the minimum of two float values
Minimum(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Utils: Returns the minimum of two float values
Minimum(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Utils: Returns the minimum of two float values
Minimum(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Utils: Returns the minimum of two float values
Minimum(float, float) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Utils: Returns the minimum of two float values
MINIMUM_CLASS_SET_SIZE - Static variable in class keel.Algorithms.Instance_Generation.LVQ.LVQPRU: Minimum class part set.
minimumDelta - Variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus: Minimum Value for Delta in RpropPlus Algorithm
minimumLengthAndNearestTo(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Return the nearest prototype to another in the set.
minimumLengthAndNearestTo(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Return the nearest prototype to another in the set.
minimumLengthAndNearestWithSameClassAs(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Return the nearest prototype to another in the set.
minimumLengthAndNearestWithSameClassAs(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Return the nearest prototype to another in the set.
minimumSearch(Randomize) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.LinearSearchBrent
minimumSearch(Randomize) - Method in class keel.Algorithms.Shared.ClassicalOptim.LinearSearchBrent: Minimize function g().
minimumValuesOfSameClassPerInterval - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
minIndex(int[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Returns index of minimum element in a given array of integers.
minIndex(double[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Returns index of minimum element in a given array of doubles.
minIndex(int[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Returns index of minimum element in a given array of integers.
minIndex(double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Returns index of minimum element in a given array of doubles.
minIndex(int[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Returns index of minimum element in a given array of integers.
minIndex(double[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Returns index of minimum element in a given array of doubles.
minIndex(double[]) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.C45CS: Returns index of minimum element in a given array of doubles.
minIndex(int[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Returns index of minimum element in a given array of integers.
minIndex(double[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Returns index of minimum element in a given array of doubles.
minIndex(int[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Returns index of minimum element in a given array of integers.
minIndex(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Returns index of minimum element in a given array of doubles.
minIndex(int[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Returns index of minimum element in a given array of integers.
minIndex(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Returns index of minimum element in a given array of doubles.
minIndex(int[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Returns index of minimum element in a given array of integers.
minIndex(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Returns index of minimum element in a given array of doubles.
minIndex(int[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Returns index of minimum element in a given array of integers.
minIndex(double[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Returns index of minimum element in a given array of doubles.
minIndex(int[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Returns index of minimum element in a given array of integers.
minIndex(double[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Returns index of minimum element in a given array of doubles.
minIntervals - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
minIntervals - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters: Minimum number of intervals.
minLinksAdd - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Minimum number of links to add in the mutations
minLinksDel - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Minimum number of links to remove in the mutations
MINLOG - Static variable in class keel.Algorithms.Lazy_Learning.Statistics: Minimum logarithm value
MINLOG - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics
minmax(int, int) - Method in class keel.Algorithms.MIL.APR.IteratedDiscrimination.IteratedDiscrimination
minNeuronsAdd - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Minimum number of neurons to add in the mutations
minNeuronsDel - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Minimum number of neurons to remove in the mutations
minNofneurons - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Minimum number of neurons of each LinkedLayer of the neural nets
minnofneurons - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Minimum number of neurons for the net
minNoTipText() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Returns the tip text for this property
minNumTipText() - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Returns the tip text for this property
minNumTipText() - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Returns the tip text for this property
minNumTipText() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Returns the tip text for this property
MINOR - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
MINOR - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_DefaultC
MINOR - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_DefaultC
MINOR - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene
minSupport - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Minimum support value in terms of number of rows.
minSupport - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
minSupport - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Minimum support value in terms of number of rows.
minSupport - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Minimum support value in terms of number of rows.
minus(double[], double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method implements the subtract of vectors
minus(double[][], double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method implements the subtract of matrix
minus(double[][][], double[][][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method implements the subtract of cubic matrix
minus(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Returns a copy of the set without an element.
minus(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Returns a copy of the set without an element.
minus(double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Subtracts a value
minus(DoubleVector) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Subtracts another DoubleVector element by element
minus(Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: C = A - B
minusEquals(double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Subtracts a value in place
minusEquals(DoubleVector) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Subtracts another DoubleVector element by element in place
minusEquals(Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: A = A - B
MINVALPRO - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
MINVALPRO - Static variable in class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
minx - Variable in class keel.Algorithms.Preprocess.Missing_Values.EM.util.UnivariateMinimum: last minimum
missedValue - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArffDataSet: Symbol which represents missed values
missedValue - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.KeelDataSet: Symbol which represents missed values
MISSING - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set: Encodes a set of examples (including information about if the example is covered by a rule, the coverage degree, ...)
MISSING - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.fuzzy_t: Defines a trapezoidal fuzzy set type
MISSING - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.vectordouble: Encodes a vector of double values
missing - Class in keel.Algorithms.LQD.methods.FGFS_costInstances: File: missing.java Properties and functions when the variables have missing values.
missing() - Constructor for class keel.Algorithms.LQD.methods.FGFS_costInstances.missing
Missing - Class in keel.Algorithms.LQD.methods.FGFS_Minimum_Risk: File: missing.java Properties and functions when the variables have missing values.
Missing() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.Missing
missing - Class in keel.Algorithms.LQD.methods.FGFS_Original: File: missing.java Properties and functions when the variables have missing values.
missing() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Original.missing
missing - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight: File: missing.java Properties and functions when the variables have missing values.
missing() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.missing
missing - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty: File: missing.java Properties and functions when the variables have missing values.
missing() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.missing
missing - Class in keel.Algorithms.LQD.preprocess.Expert: File: missing.java Properties and functions when the variables have missing values.
missing() - Constructor for class keel.Algorithms.LQD.preprocess.Expert.missing
missing - Class in keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE: File: missing.java Properties and functions when the variables have missing values.
missing() - Constructor for class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.missing
missing - Class in keel.Algorithms.LQD.preprocess.Prelabelling: File: missing.java Properties and functions when the variables have missing values.
missing() - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling.missing
missing - Class in keel.Algorithms.LQD.preprocess.Prelabelling_Expert: File: missing.java Properties and functions when the variables have missing values.
missing() - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.missing
missing - Variable in class keel.GraphInterKeel.experiments.DataSet: stores the missing partitions
missing - Variable in class keel.GraphInterKeel.experiments.DatasetXML
MISSING_ID - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation: will be returned if no ID can be generated
MISSING_ID - Static variable in class keel.Algorithms.SVM.SMO.core.TechnicalInformation: will be returned if no ID can be generated
MISSING_VALUE - Static variable in class keel.Algorithms.Decision_Trees.C45.Itemset: Constant that represents the missing value.
MISSING_VALUE - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset: Constant that represents the missing value.
MISSING_VALUE - Static variable in class keel.Algorithms.Decision_Trees.ID3.Itemset: Constant that represents the missing value.
MISSING_VALUE - Static variable in class keel.Algorithms.Decision_Trees.M5.M5Instance: Constant representing a missing value.
MISSING_VALUE - Static variable in class keel.Algorithms.Decision_Trees.SLIQ.Itemset: Constant that represents the missing value.
MISSING_VALUE - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Constant representing a missing value.
MISSING_VALUE - Static variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Constant that represents the missing value.
MISSING_VALUE - Static variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset: Constant that represents the missing value.
MISSING_VALUE - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset: Constant that represents the missing value.
MISSING_VALUE - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset: Constant that represents the missing value.
MISSING_VALUE - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset: Constant that represents the missing value.
MISSING_VALUE - Static variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Constant representing a missing value.
MISSING_VALUE - Static variable in class keel.Algorithms.Rule_Learning.ART.Itemset: Constant that represents the missing value.
MISSING_VALUE - Static variable in class keel.Algorithms.Rule_Learning.C45Rules.Itemset: Constant that represents the missing value.
MISSING_VALUE - Static variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset: Constant that represents the missing value.
MISSING_VALUE - Static variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset: Constant that represents the missing value.
MISSING_VALUE - Static variable in class keel.Algorithms.Rule_Learning.PART.Itemset: Constant that represents the missing value.
MISSING_VALUE - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset: Constant that represents the missing value.
MISSING_VALUE - Static variable in class keel.Algorithms.SVM.SMO.core.Instance: Constant representing a missing value.
missingCount - Variable in class keel.Algorithms.Decision_Trees.M5.M5AttrStats: The number of missing values
missingCount - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.AttributeStats: The number of missing values
missingValue() - Static method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Returns the double that codes "missing".
missingValue() - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Returns the double that codes "missing".
missingValue() - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Returns the double that codes "missing".
missingValue() - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Returns the value used to code a missing value.
missingValue() - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Returns the value used to code a missing value.
missingValue() - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Returns the value used to code a missing value.
missingValue() - Static method in class keel.Algorithms.SVM.SMO.core.Instance: Returns the double that codes "missing".
mix(Cluster) - Method in class keel.Algorithms.Instance_Generation.GMCA.Cluster: Merges two cluster but not including the representatives of the cluster arguments.
Mixed_Crossover(int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
Mixed_Crossover(int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
mixLabels(FuzzyAntecedent) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.FuzzyAntecedent: Mix the labels associated to this FuzzyAntecedent with the labels associated to another FuzzyAntecedent
mixLabels(FuzzyAntecedent) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: Mix the labels associated to this FuzzyAntecedent with the labels associated to another FuzzyAntecedent
MixtGaussAlgorithm - Class in keel.Algorithms.Instance_Generation.MixtGauss: PSO algorithm calling.
MixtGaussAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.MixtGauss.MixtGaussAlgorithm
MixtGaussGenerator - Class in keel.Algorithms.Instance_Generation.MixtGauss
MixtGaussGenerator(PrototypeSet, int, String) - Constructor for class keel.Algorithms.Instance_Generation.MixtGauss.MixtGaussGenerator: Build a new PSOGenerator Algorithm
MixtGaussGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.MixtGauss.MixtGaussGenerator: Build a new MixtGaussGenerator Algorithm
mkdir(String) - Static method in class keel.GraphInterKeel.experiments.FileUtils: Creates a new directory if needed (no exists previously)
mkdirs(String) - Static method in class keel.GraphInterKeel.experiments.FileUtils: Creates a new directory tree if needed (no exists previously)
ml() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Difuso
ml() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Difuso
ml() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Difuso
ml() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Difuso
ml() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Difuso
ml() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Difuso
ml() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Difuso
ml() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Difuso
MLPerceptronBackpropCS - Class in keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS: Class for generating the individuals
MLPerceptronBackpropCS() - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.MLPerceptronBackpropCS: Empty constructor
MNV - Class in keel.Algorithms.Instance_Selection.MNV: File: MNV.java The MNV Instance Selection algorithm.
MNV(String) - Constructor for class keel.Algorithms.Instance_Selection.MNV.MNV: Default constructor.
MNV - Class in keel.Algorithms.Preprocess.Instance_Selection.MNV: File: MNV.java The MNV Instance Selection algorithm.
MNV(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.MNV.MNV: Default constructor.
model - Variable in class keel.Algorithms.Decision_Trees.C45.Tree: The selected model.
model - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Tree: The selected model.
Model - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model: Class for define abstract methods
Model() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.Model
model - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree: The selected model.
model - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Tree: The selected model.
model - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree: The selected model.
model - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Tree: The selected model.
model - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree: The selected model.
model - Variable in class keel.Algorithms.Rule_Learning.PART.Tree: The selected model.
model - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: The selected model.
Model_General - Class in keel.Algorithms.Statistical_Tests.Regression.Model_General: This class has only a main method that calls Model_General output method for regression problems, defined in StatTest
Model_General() - Constructor for class keel.Algorithms.Statistical_Tests.Regression.Model_General.Model_General
MODEL_LINEAR_REGRESSION - Static variable in class keel.Algorithms.Decision_Trees.M5.M5: Number to represent type of model used (Lineal regression).
MODEL_LINEAR_REGRESSION - Static variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5
MODEL_MODEL_TREE - Static variable in class keel.Algorithms.Decision_Trees.M5.M5: Number to represent type of model used (tree model).
MODEL_MODEL_TREE - Static variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5
model_output - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters: File names
model_output - Variable in class keel.Algorithms.Neural_Networks.net.Parameters: File names
MODEL_REGRESSION_TREE - Static variable in class keel.Algorithms.Decision_Trees.M5.M5: Number to represent type of model used (tree regression).
MODEL_REGRESSION_TREE - Static variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5
Model_Summary - Class in keel.Algorithms.Statistical_Tests.Regression.Model_Summary: This class has only a main method that calls Model_Summary output method for regression problems, defined in StatTest
Model_Summary() - Constructor for class keel.Algorithms.Statistical_Tests.Regression.Model_Summary.Model_Summary
Model_Tabular - Class in keel.Algorithms.Statistical_Tests.Regression.Model_Tabular: This class has only a main method that calls Model_Tabular output method for regression problems, defined in StatTest
Model_Tabular() - Constructor for class keel.Algorithms.Statistical_Tests.Regression.Model_Tabular.Model_Tabular
modelBuilt() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: flag to indicate whether the model was built yet
ModelCS - Class in keel.Algorithms.Instance_Selection.ModelCS: File: ModelCS.java The ModelCS Instance Selection algorithm.
ModelCS(String) - Constructor for class keel.Algorithms.Instance_Selection.ModelCS.ModelCS: Default constructor.
ModelCS - Class in keel.Algorithms.Preprocess.Instance_Selection.ModelCS: File: ModelCS.java The ModelCS Instance Selection algorithm.
ModelCS(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.ModelCS.ModelCS: Default constructor.
modelDataset - Variable in class keel.Algorithms.Decision_Trees.C45.Algorithm: The model dataset.
modelDataset - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm: The model dataset.
modelDataset - Variable in class keel.Algorithms.Decision_Trees.ID3.Algorithm: The model dataset.
modelDataset - Variable in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm: Model dataset.
modelDataset - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm: The model dataset.
modelDataset - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm: The model dataset.
modelDataset - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm: The model dataset.
modelDataset - Variable in class keel.Algorithms.Rule_Learning.ART.Algorithm: The model dataset.
modelDataset - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Algorithm: The model dataset.
modelDataset - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Algorithm: The model dataset.
modelDataset - Variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm: The model dataset.
modelDataset - Variable in class keel.Algorithms.Rule_Learning.PART.Algorithm: The model dataset.
modelDataset - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45: The model dataset.
modelFileName - Static variable in class keel.Algorithms.Decision_Trees.C45.Algorithm: The name of the file that contains the information to build the model.
modelFileName - Static variable in class keel.Algorithms.Decision_Trees.CART.RunCART: The name of the file that contains the information to build the model.
modelFileName - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm: The name of the file that contains the information to build the model.
modelFileName - Static variable in class keel.Algorithms.Decision_Trees.ID3.Algorithm: The name of the file that contains the information to build the model.
modelFileName - Static variable in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm: File's name which contains all the information needed to build the model.
modelFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm: The name of the file that contains the information to build the model.
modelFileName - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm: The name of the file that contains the information to build the model.
modelFileName - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm: The name of the file that contains the information to build the model.
modelFileName - Static variable in class keel.Algorithms.Rule_Learning.ART.Algorithm: The name of the file that contains the information to build the model.
modelFileName - Static variable in class keel.Algorithms.Rule_Learning.C45Rules.Algorithm: The name of the file that contains the information to build the model.
modelFileName - Static variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Algorithm: The name of the file that contains the information to build the model.
modelFileName - Static variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm: The name of the file that contains the information to build the model.
modelFileName - Static variable in class keel.Algorithms.Rule_Learning.PART.Algorithm: The name of the file that contains the information to build the model.
ModelFuzzyGAP - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzyGAP: ModelFuzzyGAP is intended to generate a Fuzzy Rule Based System (FRBS) model using an Genetic Algorithm and Programming (GAP).
ModelFuzzyGAP() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzyGAP.ModelFuzzyGAP
ModelFuzzyGP - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzyGP: ModelFuzzyGP is intended to generate a Fuzzy Rule Based System (FRBS) model using an Genetic Programming (GP).
ModelFuzzyGP() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzyGP.ModelFuzzyGP
ModelFuzzyPittsBurgh - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzyPittsBurgh: ModelFuzzyPittsBurgh is intended to generate a Fuzzy Rule Based System (FRBS) classifier using the Pittsburgh genetic algorihm Approach.
ModelFuzzyPittsBurgh() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzyPittsBurgh.ModelFuzzyPittsBurgh
ModelFuzzySAP - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzySAP: ModelFuzzySAP is intended to generate a Fuzzy Rule Based System (FRBS) model using an Simulate Annealing Algorithm and Programming (SAP).
ModelFuzzySAP() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ModelFuzzySAP.ModelFuzzySAP
ModelLinearLMS - Class in keel.Algorithms.Statistical_Models.ModelLinear: In this class, Least Squares Linear Regression is implemented
ModelLinearLMS() - Constructor for class keel.Algorithms.Statistical_Models.ModelLinear.ModelLinearLMS
modellingTest(double[][], int, double[]) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Evaluates the net for modeling problem
ModelMLPerceptron - Class in keel.Algorithms.Neural_Networks.ModelMLPerceptron: Regression model by means of a multi-layered perceptron.
ModelMLPerceptron() - Constructor for class keel.Algorithms.Neural_Networks.ModelMLPerceptron.ModelMLPerceptron
ModelPolQuadraticLMS - Class in keel.Algorithms.Statistical_Models.ModelQuad: In this class, Quadratic Least Squares Regression is implemented
ModelPolQuadraticLMS() - Constructor for class keel.Algorithms.Statistical_Models.ModelQuad.ModelPolQuadraticLMS
ModelTest_5x2cv - Class in keel.Algorithms.Statistical_Tests.Regression.ModelTest_5x2cv: This class has only a main method that calls Dietterich 5x2cv test for regression problems, defined in StatTest
ModelTest_5x2cv() - Constructor for class keel.Algorithms.Statistical_Tests.Regression.ModelTest_5x2cv.ModelTest_5x2cv
ModelTest_f - Class in keel.Algorithms.Statistical_Tests.Regression.ModelTest_f: This class has only a main method that calls f test for regression problems, defined in StatTest
ModelTest_f() - Constructor for class keel.Algorithms.Statistical_Tests.Regression.ModelTest_f.ModelTest_f
ModelTest_rs - Class in keel.Algorithms.Statistical_Tests.Regression.ModelTest_rs: This class has only a main method that calls Wilcoxon signed rank test for regression problems, defined in StatTest
ModelTest_rs() - Constructor for class keel.Algorithms.Statistical_Tests.Regression.ModelTest_rs.ModelTest_rs
ModelTest_sw - Class in keel.Algorithms.Statistical_Tests.Regression.ModelTest_sw: This class has only a main method that calls Shapiro Wilk test for regression problems, defined in StatTest
ModelTest_sw() - Constructor for class keel.Algorithms.Statistical_Tests.Regression.ModelTest_sw.ModelTest_sw
ModelTest_t - Class in keel.Algorithms.Statistical_Tests.Regression.ModelTest_t: This class has only a main method that calls t for regression problems, test defined in StatTest
ModelTest_t() - Constructor for class keel.Algorithms.Statistical_Tests.Regression.ModelTest_t.ModelTest_t
ModelTest_u - Class in keel.Algorithms.Statistical_Tests.Regression.ModelTest_u: This class has only a main method that calls Mann Whitney U test for regression problems, defined in StatTest
ModelTest_u() - Constructor for class keel.Algorithms.Statistical_Tests.Regression.ModelTest_u.ModelTest_u
modelTime - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Generation model time.
modelTime - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Generation model time.
modelTime - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Generation model time.
MODENAR - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR: Title: Algorithm Description: It contains the implementation of the MODENAR algorithm Company: KEEL
MODENAR() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENAR: Default constructor
MODENAR(parseParameters) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENAR: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
MODENARProcess - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR: It provides the implementation of the MODENAR algorithm to be run in a process
MODENARProcess() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess: Default constructor.
MODENARProcess(myDataset, int, int, double, int, double[], double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess: It creates a new process for the algorithm by setting up its parameters
modifica(double[]) - Method in class keel.Algorithms.Decision_Trees.DT_GA.Selector: Modifies slightly the selector to cover the given example.
modificador - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Sel: Modifiers representations.
modificador - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Tun: Modifiers representations.
Modified(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
Modified(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary
Modified(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger
Modified(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal
Modified(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
Modified(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
Modified(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationinteger
Modified(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal
Modified(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
Modified(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
Modified(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationinteger
Modified(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationreal
modified - Variable in class keel.GraphInterKeel.experiments.DataSet
ModifiedChi2Discretizer - Class in keel.Algorithms.Discretizers.ModifiedChi2_Discretizer: This class implements the Chi2 discretizer.
ModifiedChi2Discretizer() - Constructor for class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.ModifiedChi2Discretizer: Default constructor.
modifyLocation(Prototype, PrototypeSet, Prototype[], int) - Method in class keel.Algorithms.Instance_Generation.MSE.MSEGenerator: Correct the position of the prototype function.
modifyRbf(Rbf, String) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Changes a neuron in the net
modifyRbf(Rbf, String) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbfn: Changes a neuron in the net
modifyRbf(Rbf, String) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn: Changes a neuron in the net
modifyRbf(Rbf, String) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn: Changes a neuron in the net
modifyRbf(Rbf, String) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn: Changes a neuron in the net
modifyRbf(Rbf, String) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn: Changes a neuron in the net
modifyRbf(Rbf, String) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn: Changes a neuron in the net
modifyValue(int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Modifies the instances value for an attribute (floating point representation).
MODL - Class in keel.Algorithms.Discretizers.MODL: MODL Discretizer, based on the work of Marc Boullé M.
MODL(String) - Constructor for class keel.Algorithms.Discretizers.MODL.MODL: Parameter constructor.
modl(Vector, int[]) - Method in class keel.Algorithms.Discretizers.MODL.MODL: Computes the MODL value for a current discretization scheme
modl(ArrayList<ArrayList<Double>>, int[]) - Method in class keel.Algorithms.Discretizers.MODL.MODL: Computes the MODL value for a current discretization scheme
module() - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Computes the module of a Prototype.
module() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype
modules_mouseEntered(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Enter in modules button
modules_mouseExited(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Exit from modules button
modules_mouseReleased(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame: Entering in additional modules
MOEA_Ghosh - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh: It gathers all the parameters, launches the MOEA algorithm, and prints out the results
MOEA_Ghosh() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_Ghosh: Default constructor
MOEA_Ghosh(parseParameters) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_Ghosh: It reads the data from the input files and parse all the parameters from the parameters array
MOEA_GhoshProcess - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh: It provides the implementation of the MOEA algorithm to be run in a process
MOEA_GhoshProcess(myDataset, int, int, int, int, double, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_GhoshProcess: It creates a new process for the algorithm by setting up its parameters
MOEA_Gosh - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh: It gathers all the parameters, launches the MOEA algorithm, and prints out the results
MOEA_Gosh() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_Gosh: Default constructor
MOEA_Gosh(parseParameters) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_Gosh: It reads the data from the input files and parse all the parameters from the parameters array
MOEA_GoshProcess - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh: It provides the implementation of the MOEA algorithm to be run in a process
MOEA_GoshProcess(myDataset, int, int, int, int, double, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_GoshProcess: It creates a new process for the algorithm by setting up its parameters
mogbest - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Maximum number of generations allowed without improving the best fitness
mogmean - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Maximum number of generations allowed without improving the fitness mean of best individuals
momentum - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network: Momentum term
momentum - Variable in class keel.Algorithms.Neural_Networks.gann.Network: Momentum term
momentum - Variable in class keel.Algorithms.Neural_Networks.gmdh.Network: Momentum term
momentum - Variable in class keel.Algorithms.Neural_Networks.net.Network: Momentum term
MOPNAR - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR
MOPNAR() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.MOPNAR: Default constructor
MOPNAR(parseParameters) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.MOPNAR: It reads the data from the input files and parse all the parameters from the parameters array
MOPNARProcess - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR
MOPNARProcess(myDataset, int, int, int, int, double, int, double, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.MOPNARProcess: It creates a new process for the algorithm by setting up its parameters
moreFreq(ValueFreq) - Method in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.ValueFreq: Compares the frequencies of two pair, and test if this object's frequency is higher than the provided one
moreFreq(ValueFreq) - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.ValueFreq: Compares the frequencies of two pair, and test if this object's frequency is higher than the provided one
moreFreq(ValueFreq) - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.ValueFreq: Compares the frequencies of two pair, and test if this object's frequency is higher than the provided one
moreFreq(ValueFreq) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValueFreq: Compares the frequencies of two pair, and test if this object's frequency is higher than the provided one
moreFreq(ValuesFreq) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValuesFreq: Compares the frequencies of two pairs, and test if this object's frequency is higher than the provided one
moreFreq(ValueFreq) - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.ValueFreq: Compares the frequencies of two pair, and test if this object's frequency is higher than the provided one
moreFreq(ValueFreq) - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.ValueFreq: Compares the frequencies of two pair, and test if this object's frequency is higher than the provided one
moreFreq(ValueFreq) - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.ValueFreq: Compares the frequencies of two pair, and test if this object's frequency is higher than the provided one
moreFreq(ValueFreq) - Method in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.ValueFreq: Compares the frequencies of two pair, and test if this object's frequency is higher than the provided one
moreFreq(ValueFreq) - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.ValueFreq: Compares the frequencies of two pair, and test if this object's frequency is higher than the provided one
moreFreq(ValueFreq) - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.ValueFreq: Compares the frequencies of two pair, and test if this object's frequency is higher than the provided one
moreInputData(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
moreOutputData(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
mostCommon(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Return the value most common of the attribute 'i'
mostCommon() - Method in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.FreqList: Extract the most commmon element, i.e. the element with highest frequency
mostCommon() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.FreqList: Extract the most commmon element, i.e. the element with highest frequency
mostCommon() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.FreqList: Extracts the most common element, i.e. the element with highest frequency
mostCommon() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqList: Extracts the most common element, i.e. the element with highest frequency
mostCommon() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqListPair: Returns the most frequent pair
mostCommon() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.FreqList: Extracts the most common element, i.e. the element with highest frequency
mostCommon() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.FreqList: Extracts the most common element, i.e. the element with highest frequency
mostCommon() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.FreqList: Extracts the most common element, i.e. the element with highest frequency
mostCommon() - Method in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.FreqList: Extract the most commmon element, i.e. the element with highest frequency
mostCommon() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.FreqList: Extracts the most common element, i.e. the element with highest frequency
mostCommon() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.FreqList: Extracts the most common element, i.e. the element with highest frequency
mostCommon(int) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Returns the value most comon of the 'i' attribute
MostCommonValue - Class in keel.Algorithms.Preprocess.Missing_Values.MostCommonValue: This class computes the mean (numerical) or mode (nominal) value of the attributes with missing values for all classes
MostCommonValue(String) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.MostCommonValue: Creates a new instance of MostCommonValue
mostFrequentClass() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It returns the most frequent class in the dataset
mostFrequentClass() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Returns the most frequent class
mostFrequentClass(long) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Calculates the class most frecuent in the set of values
mostFrequentClass() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Returns the most frequent class
mostrarRegla() - Method in class keel.Algorithms.Rule_Learning.Rules6.Regla: Prints on the standard output the rule information.
mostrarRegla() - Method in class keel.Algorithms.Rule_Learning.SRI.Regla: Prints on the standard output the rule information.
mostrarReglas() - Method in class keel.Algorithms.Rule_Learning.LEM1.BaseReglas
mostrarReglas() - Method in class keel.Algorithms.Rule_Learning.LEM2.BaseReglas
mostrarReglas() - Method in class keel.Algorithms.Rule_Learning.Ritio.BaseReglas
mostrarReglas() - Method in class keel.Algorithms.Rule_Learning.Rules6.BaseReglas: Prints on the standard output the stored rules.
mostrarReglas() - Method in class keel.Algorithms.Rule_Learning.SRI.BaseReglas: Prints on the standard output the stored rules.
mostSpecificGeneralization(double[]) - Method in class keel.Algorithms.Hyperrectangles.RISE.Rule: Performs the most specific possible generalization over the rule to cover a new instance
mouseClicked(MouseEvent) - Method in class keel.GraphInterKeel.experiments.GraphPanel
mouseDragged(MouseEvent) - Method in class keel.GraphInterKeel.experiments.GraphPanel: Dragging mouse
mouseEntered(MouseEvent) - Method in class keel.GraphInterKeel.experiments.GraphPanel
mouseExited(MouseEvent) - Method in class keel.GraphInterKeel.experiments.GraphPanel
mouseMoved(MouseEvent) - Method in class keel.GraphInterKeel.experiments.GraphPanel
mousePressed(MouseEvent) - Method in class keel.GraphInterKeel.experiments.GraphPanel: Management of mouse events
mouseReleased(MouseEvent) - Method in class keel.GraphInterKeel.experiments.GraphPanel: Releasing mouse
move(PrototypeSet, PrototypeSet[][]) - Method in class keel.Algorithms.Instance_Generation.ENPC.ENPCGenerator: Moves all to the centroid.
move(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset: Move cursor to index position
move(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArrayDataset: Move cursor to index position
move(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.FileDataset: Move cursor to index position
move(int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IDataset: Move cursor to index position
moverParticula(int, float[][]) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Moves the particle to the next position, with the actual position and its velocity.
moveTo(Prototype, Cluster) - Method in class keel.Algorithms.Instance_Generation.GMCA.ClusterSet: Move one prototype to a cluster.
MPEnvironment - Class in keel.Algorithms.Genetic_Rule_Learning.UCS: This is the typical example for a single step problem, the multiplexer.
MPEnvironment() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.MPEnvironment: It is the constructor of the class.
MPEnvironment - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: This is the typical example for a single step problem, the multiplexer.
MPEnvironment() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.MPEnvironment: It is the constructor of the class.
MRIDGE - Static variable in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: MRIDGE flag.
mridge(DenseMatrix, DenseMatrix, DenseMatrix, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: MATLAB - Multiple ridge regression with generalized cross-validation.
mrqmin(double[][], double[], double[], int, double[], int[], int, double[][], double[][], double[], double[], int, double[], double[], double[], double[], double[][], SetupParameters) - Static method in class keel.Algorithms.Neural_Networks.gmdh.LM: Levenberg - Marquardt method
MSE() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForSymbRegr: This method calculate the mean square error
MSEAlgorithm - Class in keel.Algorithms.Instance_Generation.MSE: PSO algorithm calling.
MSEAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.MSE.MSEAlgorithm
MSEErrorFunction - Class in keel.Algorithms.Neural_Networks.NNEP_Regr.problem.errorfunctions: MSE Error Function.
MSEErrorFunction() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Regr.problem.errorfunctions.MSEErrorFunction: Empty constructor
MSEGenerator - Class in keel.Algorithms.Instance_Generation.MSE: MSEGenerator
MSEGenerator(PrototypeSet, int, int, double, double) - Constructor for class keel.Algorithms.Instance_Generation.MSE.MSEGenerator: Build a new MSEGenerator Algorithm
MSEGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.MSE.MSEGenerator: Build a new RSPGenerator Algorithm
MSEnvironment - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: It is the base class for all the multiple step problems.
MSEnvironment() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.MSEnvironment: It is the constructor of the class.
MSEOptimizablePUNeuralNetClassifier - Class in keel.Algorithms.Neural_Networks.IRPropPlus_Clas
MSEOptimizablePUNeuralNetClassifier() - Constructor for class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.MSEOptimizablePUNeuralNetClassifier
MSEOptimizablePUNeuralNetRegressor - Class in keel.Algorithms.Neural_Networks.IRPropPlus_Regr
MSEOptimizablePUNeuralNetRegressor() - Constructor for class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.MSEOptimizablePUNeuralNetRegressor
MSEOptimizableSigmNeuralNetClassifier - Class in keel.Algorithms.Neural_Networks.IRPropPlus_Clas: Sigmoid Neural Net with only a hidden layer and multiple outputs (classifier) Prepared for optimizing MSE.
MSEOptimizableSigmNeuralNetClassifier() - Constructor for class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.MSEOptimizableSigmNeuralNetClassifier
MSEOptimizableSigmNeuralNetRegressor - Class in keel.Algorithms.Neural_Networks.IRPropPlus_Regr
MSEOptimizableSigmNeuralNetRegressor() - Constructor for class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.MSEOptimizableSigmNeuralNetRegressor
MSMOTE - Class in keel.Algorithms.ImbalancedClassification.Ensembles.SMOTE
MSMOTE(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.SMOTE.MSMOTE
MSMOTE(InstanceSet, long, int, int, int, boolean, double, String) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.SMOTE.MSMOTE
MSS - Class in keel.Algorithms.Instance_Selection.MSS: File: MSS.java The MSS Instance Selection algorithm.
MSS(String) - Constructor for class keel.Algorithms.Instance_Selection.MSS.MSS: Default constructor.
MSS - Class in keel.Algorithms.Preprocess.Instance_Selection.MSS: File: MSS.java The MSS Instance Selection algorithm.
MSS(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.MSS.MSS: Default constructor.
MTwister - Class in keel.Algorithms.Genetic_Rule_Learning.Globals
MTwister() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Globals.MTwister
MTwister(long) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Globals.MTwister
MTwister(long[]) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Globals.MTwister
MTwister - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals
MTwister() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.MTwister
MTwister(long) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.MTwister
MTwister(long[]) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.MTwister
MTwister - Class in org.core
MTwister() - Constructor for class org.core.MTwister
MTwister(long) - Constructor for class org.core.MTwister
MTwister(long[]) - Constructor for class org.core.MTwister
Muestra - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner: Title: Muestra (Sample) Description: Sample class: Stores an example of a dataset.
Muestra() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Muestra: Default constructor.
Muestra(Vector, Atributo, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Muestra: Paramater Constructor.
Muestra - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus: Title: Muestra (Sample) Description: Sample class: Stores an example of a dataset.
Muestra() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Muestra: Default constructor.
Muestra(Vector, Atributo, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Muestra: Paramater Constructor.
Muestra - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner: Title: Muestra (Sample) Description: Sample class: Stores an example of a dataset.
Muestra() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Muestra: Default constructor.
Muestra(Vector, Atributo, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Muestra: Paramater Constructor.
Muestra - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus: Title: Muestra (Sample) Description: Sample class: Stores an example of a dataset.
Muestra() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Muestra: Default constructor.
Muestra(Vector, Atributo, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Muestra: Paramater Constructor.
Muestra - Class in keel.Algorithms.Genetic_Rule_Learning.PSO_ACO: Title: Muestra (Sample) Description: Sample class: Stores an example of a dataset.
Muestra() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Muestra: Default constructor.
Muestra(Vector, Atributo, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Muestra: Paramater Constructor.
Muestra - Class in keel.Algorithms.Rule_Learning.Prism: Stores one data with the form: attribute attribute class
Muestra(double[], int, int) - Constructor for class keel.Algorithms.Rule_Learning.Prism.Muestra: Constructor
Muestra(int) - Constructor for class keel.Algorithms.Rule_Learning.Prism.Muestra: Other constructor, simpler
Muestra - Class in keel.Algorithms.Rule_Learning.UnoR: Title: Muestra (Sample) Description: Sample class: Stores an example of a dataset.
Muestra(double[], int, int) - Constructor for class keel.Algorithms.Rule_Learning.UnoR.Muestra: Constructor
Muestra(int) - Constructor for class keel.Algorithms.Rule_Learning.UnoR.Muestra: Other constructor, more simple
Muestra - Class in keel.Algorithms.Subgroup_Discovery.aprioriSD: Stores one data with the form: attribute attribute class (02-08-2004)
Muestra(double[], int, int) - Constructor for class keel.Algorithms.Subgroup_Discovery.aprioriSD.Muestra: Constructor
Muestra(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.aprioriSD.Muestra: Other constructor, simpler
Muestra - Class in keel.Algorithms.Subgroup_Discovery.CN2SD: Stores one data with the form: attribute attribute class
Muestra(double[], int, int) - Constructor for class keel.Algorithms.Subgroup_Discovery.CN2SD.Muestra: Constructor
Muestra(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.CN2SD.Muestra: Other constructor, more easy
MuestraBase() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
muestraLista() - Method in class keel.Algorithms.Rule_Learning.LEM2.Atributo_valor
muestraResultados() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ACO: Prints on the standard output the algorithm results.
muestraResultados() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ACO: Prints on the standard output the algorithm results.
muestraResultados() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ACO: Prints on the standard output the algorithm results.
muestraResultados() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ACO: Prints on the standard output the algorithm results.
muestraURL(URL) - Method in class keel.GraphInterKeel.datacf.help.HelpContent: Set the URL to be shown
muestraURL(URL) - Method in class keel.GraphInterKeel.help.HelpContent: Shows a URL
muestraURL(URL) - Method in class keel.GraphInterKeel.statistical.help.HelpContent: Set the URL to be shown
mul(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Multiply component by component like a scalar product.
mul(double) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Performs product operation between one prototype and a double.
mul(Function, Function) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.Function: the product of two functions
mul(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Multiply component by component like a scalar product.
mul(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Performs product operation between one prototype and a double.
mulEscalar(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Multiplies component by component like a scalar product and sums the products.
mulEscalar(double) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Multiply the set by a number given.
mulEscalar(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Multiply component by component and sum them like a scalar product.
mulEscalar(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Multiplicar un conjunto por un Escalar.
MultCuad(double[], double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method genetares a matrix multiplication from two vectors
multi(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
multi(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
multi(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
multi(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
multi(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
multi(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
multi(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
multi_C45 - Class in keel.Algorithms.ImbalancedClassification.Ensembles: Title: multi_C45 Description: Main class to compute the algorithm procedure Company: KEEL
multi_C45() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.multi_C45: Default constructor
multi_C45(parseParameters) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.multi_C45: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Multiclassifier - Class in keel.Algorithms.Decision_Trees.C45_Binarization: Title: Multiclassifier Description: This class implements the Main execution class for the Binarization methodology (OVO and OVO ) Company: KEEL
Multiclassifier() - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.Multiclassifier: Default constructor
Multiclassifier(parseParameters) - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.Multiclassifier: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Multiclassifier(boolean, Multiclassifier) - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.Multiclassifier: It constructs a new set of OVO classifiers for NESTING aggregation
multiCuad(double[], double[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV: returns square matrix with multiplication of each component of a by b and vice versa.
multiCuad(double[], double[]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV: returns square matrix with multiplication of each component of a by b and vice versa.
Multiedit - Class in keel.Algorithms.Instance_Selection.Multiedit: File: Multiedit.java The Multiedit Instance Selection algorithm.
Multiedit(String) - Constructor for class keel.Algorithms.Instance_Selection.Multiedit.Multiedit: Default constructor.
Multiedit - Class in keel.Algorithms.Preprocess.Instance_Selection.Multiedit: File: Multiedit.java The Multiedit Instance Selection algorithm.
Multiedit(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.Multiedit.Multiedit: Default constructor.
MULTIINSTANCE - Static variable in class keel.GraphInterKeel.experiments.Experiments
multiInstanceData - Variable in class keel.Algorithms.MIL.Diverse_Density.EMDD.EMDD
multinumber(float, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
multinumero(float, fuzzy) - Static method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
multinumero(float, fuzzy) - Static method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
multinumero(float, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
multinumero(float, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
multinumero(float, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
multinumero(float, fuzzy) - Static method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
Multiple - Class in keel.Algorithms.Statistical_Tests.Shared.nonParametric: File: Multiple.java This class performs several statistical comparisons between NxN methods
Multiple() - Constructor for class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Multiple: Builder
Multiple - Class in keel.GraphInterKeel.statistical.tests: File: Multiple.java This class performs several statistical comparisons between NxN methods
Multiple() - Constructor for class keel.GraphInterKeel.statistical.tests.Multiple: Builder
MultipleC - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Classification Multiple Stat-test identifier.
MultipleClassifierSystem - Class in keel.Algorithms.Preprocess.NoiseFilters.INFFC
MultipleClassifierSystem() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.INFFC.MultipleClassifierSystem
MultiplePair - Class in keel.Algorithms.Statistical_Tests.Shared.nonParametric: File: MultiplePair.java This class defines a comparable pair of two double values.
MultiplePair() - Constructor for class keel.Algorithms.Statistical_Tests.Shared.nonParametric.MultiplePair: Default builder
MultiplePair(double, double) - Constructor for class keel.Algorithms.Statistical_Tests.Shared.nonParametric.MultiplePair: Builder
MultiplePair - Class in keel.GraphInterKeel.statistical.tests: File: MultiplePair.java This class defines a comparable pair of two double values.
MultiplePair() - Constructor for class keel.GraphInterKeel.statistical.tests.MultiplePair: Default builder
MultiplePair(double, double) - Constructor for class keel.GraphInterKeel.statistical.tests.MultiplePair: Builder
MultipleR - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Regression Multiple Stat-test identifier.
multipleSelection - Variable in class keel.GraphInterKeel.experiments.GraphPanel
Multiplexor - Class in keel.GraphInterKeel.experiments
Multiplexor(Point, GraphPanel) - Constructor for class keel.GraphInterKeel.experiments.Multiplexor: Builder
Multiplexor(int, Point, GraphPanel, int) - Constructor for class keel.GraphInterKeel.experiments.Multiplexor: Builder
multiplica(double, double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Discretizacion: Multiplies two number.
multiplicar(float) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.Interval
multiplicar(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.Interval
multiplicar(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.Interval
multiplicar(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.Interval
multiplicar(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.Interval
multiplicar(float) - Method in class keel.Algorithms.LQD.preprocess.Expert.interval
multiplicar(float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.interval
multiplicar(float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.interval
multiplicar(float) - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.interval
multiply(M5Matrix, int, int, int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Matrix: Reurns the multiplication of two matrices
multiply(double, double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method multiplies each element of a vector by a double value
multiply(double[], double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method multiplies two matrix and add the result of the multiplications
multiply(double[][], double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method multiply each row of a matrix by a double value
multiply(double, double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method multiplies each double element of a matrix by a double value
multiply(double[][], double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method multiplies two matrix and add the result of the multiplications
multiply(double, double[][][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method multiplies each double element of a cubic matrix by a double value
multiply(double[][][], double[][][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV: This static method multiplies two cubic matrix and add the result of the multiplications
multiply(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut: Returns the multiplication of the present FuzzyInterval and the parameter x.
multiply(FuzzyAlphaCut) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut: Returns the multiplication of the present FuzzyInterval and the parameter x.
multiply(M5Matrix, int, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5Matrix: Reurns the multiplication of two matrices
multiply(double, double[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV: returns the multiplication of scalar k by vector a.
multiply(double[], double[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV: returns the multiplication of vector a and b.
multiply(double[][], double[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV: returns a vector with multiplication of each row of matrix A by vector x.
multiply(double, double[][]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV: returns the multiplication of scalar k by matrix a.
multiply(double[][], double[][]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV: returns sum of the respective row vectors multiplication.
multiply(double, double[][][]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV: returns the multiplication of scalar k by matrix a.
multiply(double[][][], double[][][]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV: returns the sum of the respective 2-dimensions matrix multiplication (a[i] by b[i]).
multiply(double, double[]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV: returns the multiplication of scalar k by vector a.
multiply(double[], double[]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV: returns the multiplication of vector a and b.
multiply(double[][], double[]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV: returns a vector with multiplication of each row of matrix A by vector x.
multiply(double, double[][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV: returns the multiplication of scalar k by matrix a.
multiply(double[][], double[][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV: returns sum of the respective row vectors multiplication.
multiply(double, double[][][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV: returns the multiplication of scalar k by matrix a.
multiply(double[][][], double[][][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV: returns the sum of the respective 2-dimensions matrix multiplication (a[i] by b[i]).
multiply(Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Returns the multiplication of two matrices
MultipointCrossover(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Multipoint cross operator for the genetic algorithm Better chromosomes of Inter are at the first positions as the cross individual, stored in Des
MultipointCrossoverCAN(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Cross operator for the genetic algorithm, where only cross the two better individuals
MultipointCrossoverDNF(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Cross operator for the genetic algorithm, where only cross the two better individuals
multipopulation - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV
multiPopulation - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE: It creates a multipopulation.
multipopulation - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2
multipopulation - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0
MultivariateFunction - Interface in keel.Algorithms.Preprocess.Missing_Values.EM.util: interface for a function of several variables
mutacion() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoosting
mutacion() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoostingMaxMin
mutacion(double, double) - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Cromosoma: Mutation operator
mutacion(double, double) - Method in class keel.Algorithms.Instance_Selection.CHC.Cromosoma: Mutation operator
mutacion(int, int[], int, int, double[][], double[][], int[][], boolean[][], int[], int, boolean) - Method in class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Function that does the mutation
mutacion(double, double) - Method in class keel.Algorithms.Instance_Selection.GGA.Cromosoma: Mutation operator
mutacion(double, double) - Method in class keel.Algorithms.Instance_Selection.IGA.Cromosoma: Mutation operator
mutacion(double, double) - Method in class keel.Algorithms.Instance_Selection.PBIL.Cromosoma: Mutation operator
mutacion(double, double) - Method in class keel.Algorithms.Instance_Selection.SGA.Cromosoma: Mutation operator
mutacion(double, double) - Method in class keel.Algorithms.Preprocess.Instance_Selection.CHC.Cromosoma: Mutation operator
mutacion(int, int[], int, int, double[][], double[][], int[][], boolean[][], int[], int, boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Function that does the mutation
mutacion(double, double) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GGA.Cromosoma: Mutation operator
mutacion(double, double) - Method in class keel.Algorithms.Preprocess.Instance_Selection.IGA.Cromosoma: Mutation operator
mutacion(double, double) - Method in class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Cromosoma: Mutation operator
mutacion(double, double) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SGA.Cromosoma: Mutation operator
mutant(PrototypeSet[], int, int) - Method in class keel.Algorithms.Instance_Generation.DE.DEGenerator
mutant(PrototypeSet[], int, int, int) - Method in class keel.Algorithms.Instance_Generation.DEGL.DEGLGenerator: Mutation operator.
mutant(PrototypeSet[], int, int, double) - Method in class keel.Algorithms.Instance_Generation.DROP3SFLSDE.DROP3SFLSDE
mutant(PrototypeSet[], int, int, double) - Method in class keel.Algorithms.Instance_Generation.ICFSFLSDE.ICFSFLSDE
mutant(PrototypeSet, double) - Method in class keel.Algorithms.Instance_Generation.IPLDE.IPLDEGenerator
mutant(PrototypeSet[], double[], int, PrototypeSet[], int) - Method in class keel.Algorithms.Instance_Generation.JADE.JADEGenerator
mutant(PrototypeSet[], int, int) - Method in class keel.Algorithms.Instance_Generation.OBDE.OBDEGenerator
mutant(PrototypeSet[], int, int) - Method in class keel.Algorithms.Instance_Generation.SADE.SADEGenerator: I modified the order of the list of strategies, i need it because i want to do the Same POOL like the paper.
mutant(PrototypeSet[], int, int, double) - Method in class keel.Algorithms.Instance_Generation.SFLSDE.SFLSDEGenerator
mutant(PrototypeSet[], int, int, double) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE
mutar(double) - Method in class keel.Algorithms.Decision_Trees.DT_GA.Individuo: Mutates the individual with the given probability.
mutar() - Method in class keel.Algorithms.Decision_Trees.DT_GA.Selector: Mutates the selector by chaging the condition value.
mutar(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseR
mutar(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Individuo
mutar(myDataset, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Regla
mutar(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Individuo
mutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.Cromosoma: mutation operator
mutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaBinario: binary mutation operator in one point
mutar() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaEntero: integer mutation operator in one point
mutar(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Individuo
mutar(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.MatrizR
mutate(myDataset, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Rule: Mutatation Operator.
mutate() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.RuleBase: Mutation operator
mutate() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome: Applies a mutation on this chromosome, adding a gene (0.5 prob) or deleting one gene (0.5 prob or if this chromosome has genes for all the attributes)
mutate() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Core: Applies mutation in the new poblation
mutate() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Gene: Mutates this gene.
mutate(int, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Population: Mutates a chromosome in a specified subpopulation
mutate(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Subpopulation: Mutates a specific rule and gene
mutate() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Ilga: Applies mutation in the new poblation
mutate(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.RuleSet: Mutate a variable of the rule set (i.e. the activation, limits or class of a rule from the rule set)
mutate() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.SEM: Applies mutation in the new poblation
mutate() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Oiga: Applies mutation in the new poblation
mutate(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.RuleSet: Mutate a variable of the rule set (i.e. the activation, limits or class of a rule from the rule set)
mutate() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.SEM: Applies mutation in the new poblation
mutate(double) - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Attribute: The mutation is done from the environmental state.
mutate(double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Mutates the classifier.
mutate(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep: Mutates the 2 reals contained in the representation.
mutate(char, char) - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryMutation: Mutates the character.
mutate(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep: Mutates the character.
mutate(char, char) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TFreeMutation: Mutates the character.
mutate(char, char) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TNichedMutation: Mutates the character.
mutate(double) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute: The mutation is done from the environmental state.
mutate(double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Mutates the classifier.
mutate(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: Muates the 2 reals contained in the representation.
mutate(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: Mutates the 2 reals contained in the representation.
mutate(double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: Mutates the classifier.
mutate(char, char) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryMutation: Mutates the character.
mutate(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep: Mutates the character.
mutate(char, char) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TFreeMutation: Mutates the character.
mutate(char, char) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TNichedMutation: Mutates the character.
mutate(String[], String[], double) - Method in class keel.Algorithms.Instance_Generation.PSCSA.PSCSAGenerator: Production of mutated clones
mutate_copopulation() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Core: Applies the mutation in the current copopulation.
mutateAction() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: Mutates the action of the classifier.
mutateGene(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome: Applies a random mutation of the specified gene
mutateLower(double, double, double) - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.RealMutation: Mutates the lower real value.
mutateLower(double, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RFreeMutation: Mutates the lower real value.
mutateLower(double, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RNichedMutation: Mutates the lower real value.
mutateLower(int, int, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IFreeMutation: Mutates the lower real value.
mutateLower(int, int, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.INichedMutation: Mutates the lower real value.
mutateLower(int, int, int, int) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerMutation: Mutates the lower real value of the interval.
mutateLower(double, double, double) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.RealMutation: Mutates the lower real value.
mutateLower(double, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RFreeMutation: Mutates the lower real value.
mutateLower(double, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RNichedMutation: Mutates the lower real value.
mutateNext() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Mutates the next individual
mutateNext() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Mutates the next individual
mutateUpper(double, double, double) - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.RealMutation: Mutates the upper real value.
mutateUpper(double, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RFreeMutation: Mutates the upper real value.
mutateUpper(double, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RNichedMutation: Mutates the upper real value.
mutateUpper(int, int, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IFreeMutation: Mutates the upper real value.
mutateUpper(int, int, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.INichedMutation: Mutates the upper real value.
mutateUpper(int, int, int, int) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerMutation: Mutates the upper real value.
mutateUpper(double, double, double) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.RealMutation: Mutates the upper real value.
mutateUpper(double, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RFreeMutation: Mutates the upper real value.
mutateUpper(double, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RNichedMutation: Mutates the upper real value.
mutation(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Individual: It applies the mutation operator
mutation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGAP.FuzzyGAPClassifier: This method performs the mutation genetic operation of the current FuzzyGAPClassifier.
mutation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGP.FuzzyGPClassifier: This method performs the mutation genetic operation of the current FuzzyGPClassifier.
mutation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyPittsBurgh.PittsburghClassifier: This method performs the mutation genetic operation of the current FuzzyGAPClassifier.
mutation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySAP.FuzzySAPClassifier: This method performs the mutation genetic operation of the current FuzzySAPClassifier.
mutation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.Genotype: abstract method for carrying out the mutation genetic operations.
mutation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGAP: Method for carrying out the mutation genetic operations.
mutation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGP: Method for carrying out the mutation genetic operations.
mutation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGPRegSym: Method for carrying out the mutation genetic operations.
mutation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypePitts: Method for carrying out the mutation genetic operations.
mutation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividual: This abstract method implement the mutation operation
mutation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGAPModelIndividual: This method implement the mutation operation
mutation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPModelIndividual: This method implement the mutation operation
mutation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.PittsburghModel: This method implement the mutation operation
mutation(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.RegSymFuzzyGP: This method implement the mutation operation
mutation() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
mutation() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list
mutation() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
mutation() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.geneticAlgorithm
mutation(RuleSet, Vector<Rule>, double, int[][], int[], int[], Vector<Rule>, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.RuleSet
mutation(int[], int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveAttribute
mutation(int[], int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.AdaptiveRule
mutation(int[], int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveAttribute
mutation(int[], int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.AdaptiveRule
mutation(PrototypeSet, PrototypeSet[][]) - Method in class keel.Algorithms.Instance_Generation.ENPC.ENPCGenerator: Mutation operator.
mutation(int, double, double[][], double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma: Function that does the mutation
mutation(int, double, double[][], double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma: Function that does the mutation
mutation(int, double, double[][], double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma: Function that does the mutation
mutation(int, double, double[][], double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Instance_Selection.SSMA.Cromosoma: Mutation operator
mutation() - Method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.Chromosome: Mutation Operator
mutation(int, double, double[][], double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma: Mutation operator
mutation() - Method in class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome: Mutates the chromosome.
mutation() - Method in class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome: Mutates the chromosome.
Mutation(TableVar, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Mutates an individual
mutation_prob - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
MUTATION_RATE - Static variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
MutationCAN(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Applies the mutation operator.
MutationDNF(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic: Applies the mutation operator.
mutationOffset(float, float, float) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list
mutationOffset(float, float, float) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
mutationProbability - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
mutationRateTipText() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
MUTATIONTYPE - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
mutator1 - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Individual mutator1
mutator2 - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Individual mutator2
MVD - Class in keel.Algorithms.Discretizers.MVD: This class implements the UCPD algorithm
MVD(InstanceSet, int, double) - Constructor for class keel.Algorithms.Discretizers.MVD.MVD: Constructor of the class
myAttribute - Class in keel.Algorithms.Decision_Trees.FunctionalTrees: This class contains the most useful information about an attribute, and provides a set of functions to manage this information easily.
myAttribute() - Constructor for class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute: Creates an attribute with empty values that we can identify
myAttribute(String, int, boolean) - Constructor for class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute: Creates an attribute with the name of the attribute, the data type of the attribute and whether the attribute is input or output; the rest of the values are initialized with empty values that we can identify.
myAttribute(String, int, double, double, boolean) - Constructor for class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute: Creates an attribute with the name of the attribute, the data type of the attribute, the minimum value for the attribute, the maximum value for the attribute, the minimum value for the attribute and whether the attribute is input or output; the rest of the values arte initialized with empty values that we can identify.
myAttribute(myAttribute) - Constructor for class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute: Creates an attribute from another existing attribute
myAttribute - Class in keel.Algorithms.Decision_Trees.PUBLIC: File: myAttribute.java This class contains the most useful information about an attribute, and provides a set of functions to manage this information easily.
myAttribute() - Constructor for class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute: Creates an attribute with empty values that we can identify
myAttribute(String, int, boolean) - Constructor for class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute: Creates an attribute with the name of the attribute, the data type of the attribute and whether the attribute is input or output; the rest of the values are initialized with empty values that we can identify.
myAttribute(String, int, double, double, boolean) - Constructor for class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute: Creates an attribute with the name of the attribute, the data type of the attribute, the minimum value for the attribute, the maximum value for the attribute, the minimum value for the attribute and whether the attribute is input or output; the rest of the values arte initialized with empty values that we can identify.
myAttribute(myAttribute) - Constructor for class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute: Creates an attribute from another existing attribute
MyAttribute - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules: Class to implement an attribute
MyAttribute(String, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Constructor for continuous attributes.
MyAttribute(String, Vector, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Constructor for discret attributes.
MyAttribute(String) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Constructor for continuous attributes.
MyAttribute(String, Vector) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Constructor for discret attributes.
MyAttribute - Class in keel.Algorithms.Genetic_Rule_Learning.PART: Class to implement an attribute
MyAttribute(String, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.MyAttribute: Constructor for continuous attributes.
MyAttribute(String, Vector, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.MyAttribute: Constructor for discret attributes.
MyAttribute - Class in keel.Algorithms.Rule_Learning.C45Rules: Class to implement an attribute
MyAttribute(String, int) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.MyAttribute: Constructor for continuous attributes.
MyAttribute(String, Vector, int) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.MyAttribute: Constructor for discret attributes.
MyAttribute - Class in keel.Algorithms.Rule_Learning.C45RulesSA: Class to implement an attribute
MyAttribute(String, int) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.MyAttribute: Constructor for continuous attributes.
MyAttribute(String, Vector, int) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.MyAttribute: Constructor for discret attributes.
MyAttribute - Class in keel.Algorithms.Rule_Learning.PART: Class to implement an attribute
MyAttribute(String, int) - Constructor for class keel.Algorithms.Rule_Learning.PART.MyAttribute: Constructor for continuous attributes.
MyAttribute(String, Vector, int) - Constructor for class keel.Algorithms.Rule_Learning.PART.MyAttribute: Constructor for discret attributes.
myDataset - Class in keel.Algorithms.Associative_Classification.ClassifierCBA: It contains the methods to read a Classification/Regression Dataset.
myDataset() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Init a new set of instances.
myDataset - Class in keel.Algorithms.Associative_Classification.ClassifierCBA2: It contains the methods to read a Classification/Regression Dataset
myDataset() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Associative_Classification.ClassifierCMAR: It contains the methods to read a Classification/Regression Dataset
myDataset() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Associative_Classification.ClassifierCPAR: It contains the methods to read a Classification/Regression Dataset
myDataset() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR: It contains the methods to read a Classification/Regression Dataset
myDataset() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA: Description: It contains the methods to read a Classification Dataset
myDataset() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Decision_Trees.C45_Binarization: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: Init a new set of instances
myDataset(myDataset, int, int) - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It generates a new binary dataset for the OVO scheme
myDataset(myDataset, int) - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It generates a new binary dataset for the OVA scheme
myDataset(myDataset, int, int, int[]) - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It generates a new binary dataset for the OVO scheme (NESTING)
myDataset - Class in keel.Algorithms.Decision_Trees.DT_GA: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Decision_Trees.DT_oblicuo: It contains the methods to read a Classification/Regression Dataset
myDataset() - Constructor for class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Decision_Trees.FunctionalTrees: This class contains the most useful information about a dataset, and provides a set of functions to manage this information easily.
myDataset(String, int) - Constructor for class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset: Creates a dataset by reading the .dat file that contains the information of it, and gives values to every field of the class
myDataset(myDataset) - Constructor for class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset: Creates a dataset from another existing dataset
myDataset - Class in keel.Algorithms.Decision_Trees.PUBLIC: File: myDataset.java This class contains the most useful information about a dataset, and provides a set of functions to manage this information easily.
myDataset(String, int) - Constructor for class keel.Algorithms.Decision_Trees.PUBLIC.myDataset: Creates a dataset by reading the .dat file that contains the information of it, and gives values to every field of the class
myDataset(myDataset) - Constructor for class keel.Algorithms.Decision_Trees.PUBLIC.myDataset: Creates a dataset from another existing dataset
myDataset - Class in keel.Algorithms.Decision_Trees.Target: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Decision_Trees.Target.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW: It contains the methods to read a Classification/Regression Dataset
myDataset() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted: It contains the methods to read a Classification/Regression Dataset
myDataset() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD: It contains the methods to read a Classification/Regression Dataset
myDataset() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99: It contains the methods to read a Classification/Regression Dataset
myDataset() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: Init a new set of instances
MyDataset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL: It contains the methods to read a Dataset
MyDataset(String, boolean) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyDataset: Stores in memory the contents of the data file "f"
myDataset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV
myDataset() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen: It contains the methods to read a Classification/Regression Dataset
myDataset() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE: It contains the methods to read a Classification/Regression Dataset.
myDataset() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2: It contains the methods to read a Classification/Regression Dataset
myDataset() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0: It contains the methods to read a Classification/Regression Dataset
myDataset() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid: Title: myDataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH: Title: myDataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner: Title: myDataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: Constructor.
myDataset - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus: Title: myDataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: Constructor.
myDataset - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner: Title: myDataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: Constructor.
myDataset - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus: Title: myDataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: Constructor.
myDataset - Class in keel.Algorithms.Genetic_Rule_Learning.Corcoran: Title: myDataset Description: Manage the data-sets
myDataset() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Corcoran.myDataset: Builder.
myDataset - Class in keel.Algorithms.Genetic_Rule_Learning.DMEL: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Genetic_Rule_Learning.GIL: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Genetic_Rule_Learning.LogenPro: It contains the methods to read a Classification/Regression Dataset.
myDataset() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: Init a new set of instances
MyDataset - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules: Class to implement the dataset
MyDataset(String, boolean) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Function to read the .dat file that contains the information of the dataset.
MyDataset(String, Vector, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Creates an empty set of itemsets.
MyDataset(MyDataset) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Constructor that copies another dataset.
MyDataset(MyDataset, int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Creates a new set of itemsets by copying a subset of another set.
MyDataset(MyDataset, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Constructor to copy all the attributes of another dataset but the itemsets.
myDataset - Class in keel.Algorithms.Genetic_Rule_Learning.OCEC: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: Init a new set of instances
MyDataset - Class in keel.Algorithms.Genetic_Rule_Learning.PART: Class to implement the dataset
MyDataset(String, boolean) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Function to read the .dat file that contains the information of the dataset.
MyDataset(MyDataset) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Constructor that copies another dataset.
MyDataset(MyDataset, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Constructor to copy all the attributes of another dataset but the itemsets.
myDataset - Class in keel.Algorithms.Genetic_Rule_Learning.PSO_ACO: Title: myDataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: Constructor.
myDataset - Class in keel.Algorithms.Genetic_Rule_Learning.RMini: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Genetic_Rule_Learning.SIA: Title: Data-set Description: Manage the data-sets
myDataset() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.SIA.myDataset: Builder.
myDataset - Class in keel.Algorithms.ImbalancedClassification.Ensembles: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Init a new set of instances
myDataset(myDataset, double[]) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Generates a new binary dataset by copying it from the dataset given.
myDataset(myDataset) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Generates a new binary dataset by copying it from the dataset given.
myDataset(myDataset, int, int) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It generates a new binary dataset
myDataset(myDataset, String) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Generates a new binary dataset by copying it from the dataset given and preprocessing it.
myDataset(myDataset, int, int, int[]) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It generates a new binary dataset by copying the instances indicated by the value 1 in the array given.
myDataset(myDataset, int, double[][], int, double[][]) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It generates a new binary dataset
myDataset - Class in keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H: Title: myDataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.PSO_Learning.CPSO: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.PSO_Learning.CPSO.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.PSO_Learning.LDWPSO: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.PSO_Learning.PSOLDA: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.PSO_Learning.REPSO: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.PSO_Learning.REPSO.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.RE_SL_Methods.P_FCS1: It contains the methods to read a Classification/Regression Dataset
myDataset() - Constructor for class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.RE_SL_Methods.SEFC: It contains the methods to read a Classification/Regression Dataset
myDataset() - Constructor for class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Rule_Learning.AQ: Title: myDataset Description: Manage the data-sets
myDataset() - Constructor for class keel.Algorithms.Rule_Learning.AQ.myDataset: Builder.
MyDataset - Class in keel.Algorithms.Rule_Learning.C45Rules: Class to implement the dataset
MyDataset(String, boolean) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Function to read the .dat file that contains the information of the dataset.
MyDataset(MyDataset) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Constructor that copies another dataset.
MyDataset(MyDataset, int) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Constructor to copy all the attributes of another dataset but the itemsets.
MyDataset - Class in keel.Algorithms.Rule_Learning.C45RulesSA: Class to implement the dataset
MyDataset(String, boolean) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Function to read the .dat file that contains the information of the dataset.
MyDataset(MyDataset) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Constructor that copies another dataset.
MyDataset(MyDataset, int) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Constructor to copy all the attributes of another dataset but the itemsets.
myDataset - Class in keel.Algorithms.Rule_Learning.CN2: Title: Data-set Description: Manage the data-sets
myDataset() - Constructor for class keel.Algorithms.Rule_Learning.CN2.myDataset: Builder.
myDataset - Class in keel.Algorithms.Rule_Learning.LEM1: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Rule_Learning.LEM1.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Rule_Learning.LEM2: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Rule_Learning.LEM2.myDataset: Init a new set of instances
MyDataset - Class in keel.Algorithms.Rule_Learning.PART: Class to implement the dataset
MyDataset(String, boolean) - Constructor for class keel.Algorithms.Rule_Learning.PART.MyDataset: Function to read the .dat file that contains the information of the dataset.
MyDataset(MyDataset) - Constructor for class keel.Algorithms.Rule_Learning.PART.MyDataset: Constructor that copies another dataset.
MyDataset(MyDataset, int) - Constructor for class keel.Algorithms.Rule_Learning.PART.MyDataset: Constructor to copy all the attributes of another dataset but the itemsets.
MyDataset - Class in keel.Algorithms.Rule_Learning.Ripper: Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
MyDataset() - Constructor for class keel.Algorithms.Rule_Learning.Ripper.MyDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Rule_Learning.Ritio: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Rule_Learning.Ritio.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Rule_Learning.Rules6: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Rule_Learning.Rules6.myDataset: Init a new set of instances
MyDataset - Class in keel.Algorithms.Rule_Learning.Slipper: Contains the methods to read a Classification/Regression Dataset
MyDataset() - Constructor for class keel.Algorithms.Rule_Learning.Slipper.MyDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Rule_Learning.SRI: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Rule_Learning.SRI.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Statistical_Classifiers.Naive_Bayes: Title: Dataset Description: It contains the methods to read a Classification/Regression Dataset Company: KEEL
myDataset() - Constructor for class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.Subgroup_Discovery.SDMap.SDMap: It contains the methods to read a Dataset for the Association Rules Mining problem
myDataset(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: Initialize a new set of instances
myDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal: It contains the methods to read a Dataset for the Association Rules Mining problem
myDataset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: Initialize a new set of instances
myDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams: It contains the methods to read a Dataset for the Association Rules Mining problem
myDataset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: Initialize a new set of instances
myDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori: It contains the methods to read a Dataset for the Association Rules Mining problem
myDataset(int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: Initialize a new set of instances
myDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori: It contains the methods to read a Dataset for the Association Rules Mining problem
myDataset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: Initialize a new set of instances
myDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC: It contains the methods to read a Dataset for the Association Rules Mining problem
myDataset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: Initialize a new set of instances
myDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal: It contains the methods to read a Dataset for the Association Rules Mining problem
myDataset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: Initialize a new set of instances
myDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori: It contains the methods to read a Dataset for the Association Rules Mining problem
myDataset(int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: Initialize a new set of instances
myDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA: Title: Dataset Description: It contains the methods to read a Dataset for creating Association Rules Company: KEEL
myDataset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA: Title: Dataset Description: It contains the methods to read a Dataset for creating Association Rules Company: KEEL
myDataset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat: It contains the methods to read a Dataset for the Association Rules Mining problem
myDataset(int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: Initialize a new set of instances
myDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth: It contains the methods to read a Dataset for the Association Rules Mining problem
myDataset(int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: Initialize a new set of instances
myDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR: Title: Dataset Description: It contains the methods to read a Dataset for creating Association Rules Company: KEEL
myDataset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR: Title: Dataset Description: It contains the methods to read a Dataset for creating Association Rules Company: KEEL
myDataset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR: It contains the methods to read a Classification/Regression Dataset
myDataset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: Init a new set of instances
myDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh: It contains the methods to read a Dataset for the Association Rules Mining problem
myDataset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: Initialize a new set of instances
myDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR: It contains the methods to read a Dataset for the Association Rules Mining problem
myDataset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: Initialize a new set of instances
myDataset - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII: It contains the methods to read a Dataset for the Association Rules Mining problem
myDataset() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: Initialize a new set of instances
MyFile - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL: Functions for dealing with files
MyFile() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyFile
myOutput(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase: Returns output (Wang-Mendel) for input x.
mytypeid - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node

N

n - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Row and column dimensions.
N_Antecedent() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
N_Antecedent() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
N_Antecedent() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
N_Antecedents() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: It returns the number of antecedents in the list of variables
n_etiq - Variable in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Param: Number of labels
n_etiq - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Param: Number of labels
n_etiq - Variable in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Param: Number of labels
n_etiquetas - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Sel: Type of fuzzy rules identifier.
n_etiquetas - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Tun: Type of fuzzy rules identifier.
n_eval - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Number of evalutations.
N_Examples() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
N_Examples() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set: Returns the number of examples in the set
N_Examples() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set
N_Examples() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set
n_generations - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
n_gens - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
n_indiv - Variable in class keel.Algorithms.Neural_Networks.gann.SetupParameters
N_individuals() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
N_individuals() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
N_individuals() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
n_inputs - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyDataset: Total number of variables.
N_labels() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
N_labels() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
N_labels() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Returns the number of labels in the domain
N_labels() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t: Returns the number of labels in the variable's domain.
N_labels() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
N_labels() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
N_labels() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
N_labels() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
n_minoritaria() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It returns the number of minority class examples
N_Partitions() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
N_Partitions() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set: Returns the number of partitions forming the set of examples
N_Partitions() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set
N_Partitions() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set
n_reglas - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Sel: Number of total rules.
n_reglas - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Tun: Number of total rules.
n_reglas_distintas - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Tun: Maximum number of rules.
N_rule() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.ruleset: Returns the number of rules of the ruleset
N_rule() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.ruleset: Returns the number of rules of the ruleset
N_rule() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.ruleset: Returns the number of rules of the ruleset
n_test_patterns - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters: Number of train, validation and test patterns
n_test_patterns - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters: Number of train, validation and test patterns
n_test_patterns - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters: Number of train, validation and test patterns
n_test_patterns - Variable in class keel.Algorithms.Neural_Networks.net.Parameters: Number of train, validation and test patterns
n_train_patterns - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters: Number of train, validation and test patterns
n_train_patterns - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters: Number of train, validation and test patterns
n_train_patterns - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters: Number of train, validation and test patterns
n_train_patterns - Variable in class keel.Algorithms.Neural_Networks.net.Parameters: Number of train, validation and test patterns
N_Val() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
N_Val() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
N_Val() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
n_val_patterns - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters: Number of train, validation and test patterns
n_val_patterns - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters: Number of train, validation and test patterns
n_val_patterns - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters: Number of train, validation and test patterns
n_val_patterns - Variable in class keel.Algorithms.Neural_Networks.net.Parameters: Number of train, validation and test patterns
n_var_control - Variable in class keel.Algorithms.RE_SL_Methods.LEL_TSK.MiDataset
n_var_control - Variable in class keel.Algorithms.RE_SL_Methods.MamWM.MiDataset
n_var_control - Variable in class keel.Algorithms.RE_SL_Methods.mogulHC.MiDataset
n_var_control - Variable in class keel.Algorithms.RE_SL_Methods.mogulIRL.MiDataset
n_var_control - Variable in class keel.Algorithms.RE_SL_Methods.mogulSC.MiDataset
n_var_control - Variable in class keel.Algorithms.RE_SL_Methods.TSK_IRL.MiDataset
n_var_control - Variable in class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.MiDataset
n_var_control - Variable in class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.MiDataset
n_var_control - Variable in class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.MiDataset
n_var_control - Variable in class keel.Algorithms.RE_SL_Postprocess.MamSelect.MiDataset
n_var_control - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWSelect.MiDataset
n_var_control - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWTuning.MiDataset
n_var_control - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.MiDataset
n_var_control - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKSelect.MiDataset
n_var_estado - Variable in class keel.Algorithms.RE_SL_Methods.LEL_TSK.MiDataset
n_var_estado - Variable in class keel.Algorithms.RE_SL_Methods.MamWM.MiDataset
n_var_estado - Variable in class keel.Algorithms.RE_SL_Methods.mogulHC.MiDataset
n_var_estado - Variable in class keel.Algorithms.RE_SL_Methods.mogulIRL.MiDataset
n_var_estado - Variable in class keel.Algorithms.RE_SL_Methods.mogulSC.MiDataset
n_var_estado - Variable in class keel.Algorithms.RE_SL_Methods.TSK_IRL.MiDataset
n_var_estado - Variable in class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.MiDataset
n_var_estado - Variable in class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.MiDataset
n_var_estado - Variable in class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.MiDataset
n_var_estado - Variable in class keel.Algorithms.RE_SL_Postprocess.MamSelect.MiDataset
n_var_estado - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWSelect.MiDataset
n_var_estado - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWTuning.MiDataset
n_var_estado - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.MiDataset
n_var_estado - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKSelect.MiDataset
n_variables - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyDataset: Total number of variables.
N_Variables() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
N_Variables() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set: Returns the number of variables of the problem
N_Variables() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set
N_Variables() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set
n_variables - Variable in class keel.Algorithms.RE_SL_Methods.LEL_TSK.MiDataset
n_variables - Variable in class keel.Algorithms.RE_SL_Methods.MamWM.MiDataset
n_variables - Variable in class keel.Algorithms.RE_SL_Methods.mogulHC.MiDataset
n_variables - Variable in class keel.Algorithms.RE_SL_Methods.mogulIRL.MiDataset
n_variables - Variable in class keel.Algorithms.RE_SL_Methods.mogulSC.MiDataset
n_variables - Variable in class keel.Algorithms.RE_SL_Methods.TSK_IRL.MiDataset
n_variables - Variable in class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.MiDataset
n_variables - Variable in class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.MiDataset
n_variables - Variable in class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.MiDataset
n_variables - Variable in class keel.Algorithms.RE_SL_Postprocess.MamSelect.MiDataset
n_variables - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWSelect.MiDataset
n_variables - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWTuning.MiDataset
n_variables - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.MiDataset
n_variables - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKSelect.MiDataset
name - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Naming.
name() - Method in class keel.Algorithms.Decision_Trees.C45.Attribute: Returns the name of the attribute.
name - Variable in class keel.Algorithms.Decision_Trees.C45.Dataset: The name of the dataset.
name() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Attribute: Returns the name of the attribute.
name - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: The name of the dataset.
name() - Method in class keel.Algorithms.Decision_Trees.ID3.Attribute: Returns the name of the attribute.
name - Variable in class keel.Algorithms.Decision_Trees.ID3.Dataset: The name of the dataset.
name() - Method in class keel.Algorithms.Decision_Trees.M5.Information: Returns the option's name.
name() - Method in class keel.Algorithms.Decision_Trees.M5.M5Attribute: Returns the attribute's name.
name() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Attribute: Returns the name of the attribute.
name - Variable in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: The name of the dataset.
name - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: Algorithm name.
name() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Returns the attribute's name.
name() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Returns the name of the attribute.
name - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: The name of the dataset.
name - Variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: The name of the dataset.
name() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA_Attribute: Returns the name of the attribute.
name() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyAttribute: Returns the name of the attribute.
name - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: The name of the dataset.
name - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Naming.
name() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Attribute: Returns the name of the attribute.
name - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: The name of the dataset.
name() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Attribute: Returns the name of the attribute.
name - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: The name of the dataset.
name() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Returns the attribute's name.
name - Static variable in class keel.Algorithms.Instance_Generation.utilities.Parameters: Configuration file name.
name - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Naming.
name - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractAttribute: Attribute name
name - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset: Dataset name
name - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm: Naming.
name - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm: Naming.
name() - Method in class keel.Algorithms.Rule_Learning.ART.Attribute: Returns the name of the attribute.
name - Variable in class keel.Algorithms.Rule_Learning.ART.Dataset: The name of the dataset.
name() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyAttribute: Returns the name of the attribute.
name - Variable in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: The name of the dataset.
name() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyAttribute: Returns the name of the attribute.
name - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: The name of the dataset.
name() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Attribute: Returns the name of the attribute.
name - Variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: The name of the dataset.
name() - Method in class keel.Algorithms.Rule_Learning.PART.MyAttribute: Returns the name of the attribute.
name - Variable in class keel.Algorithms.Rule_Learning.PART.MyDataset: The name of the dataset.
name - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: The name of the dataset.
name - Static variable in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Contains the name of the parameters file.
name() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Option: Returns the option's name.
name() - Method in class keel.Algorithms.SVM.SMO.core.Option: Returns the option's name.
name - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
name - Variable in class keel.GraphInterKeel.experiments.ExternalObjectDescription
name - Variable in class keel.GraphInterKeel.help.HelpSheet: Name of the sheet.
nameAbr - Variable in class keel.GraphInterKeel.experiments.DatasetXML
nameAttribute(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: Returns the name of the given attribute.
nameAttribute(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: Returns the name of the given attribute.
NameClasses - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV: Classes names wrapper.
NameClasses - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2: Classes names wrapper.
NameClasses - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0: Classes names wrapper.
NameClassIndex() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Returns the class attribute's name of the @output.
nameComplete - Variable in class keel.GraphInterKeel.experiments.DatasetXML
nameFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: file name
nameFile - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: File name.
nameJar - Variable in class keel.GraphInterKeel.experiments.ExternalObjectDescription
NameLabelValid(String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToXml: Transforms an attribute name given of the KEEL file to a valid xml tag.
nameNominal(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: Returns the nominal representation of the given value of the given attribute.
names() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Returns the names of the different attributes of the data-set.
names() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It returns the name of the attributes
names() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It returns the name of every input attributes.
names() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It returns the name of every input attributes.
names() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It returns the name of the attributes
names() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It returns the name of the attributes
names() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It returns the name of every input attributes.
names() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It returns the names for all input variables
names() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It returns the name of the attributes
names - Variable in class keel.Algorithms.Instance_Generation.utilities.Parameters: Names of the parameteres.
names - Variable in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Names of the parameteres.
names() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It returns the name of the attributes
names() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: It returns the names of the variables
names() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: It returns the names of the variables
names() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: It returns the names of the variables
names() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: It returns the names of the variables
names() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: It returns the names of the variables
names() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: It returns the names of the variables
names() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: It returns the names of the variables
names() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: It returns the names of the variables
names() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: It returns the names of the variables
nameVar(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It returns the name of the atribute in position "pos"
NAnd - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node
NAND_NOR_Stationary(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary: Given the individuals "indiv1" and "indiv2", it selects two points and makes the NAND/NOR operation between their central zones.
NAND_NOR_Stationary(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal: Given the individuals "indiv1" and "indiv2", it selects two points and makes the NAND/NOR operation between their central zones.
nAnts - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome
nAnts - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
nAnts - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
nAnts - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
nAnts - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
nAnts - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
nAnts - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
nAttributes - Variable in class keel.GraphInterKeel.experiments.DatasetXML
NBSSLAlgorithm - Class in keel.Algorithms.Semi_Supervised_Learning.NBSSL: NBSSL algorithm calling.
NBSSLAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.NBSSL.NBSSLAlgorithm
NBSSLGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.NBSSL: This class implements the Self-traning wrapper.
NBSSLGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.NBSSL.NBSSLGenerator: Build a new NBSSLGenerator Algorithm
NBSSLGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.NBSSL.NBSSLGenerator: Build a new NBSSLGenerator Algorithm
nChildren() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node: This method return the number of children of the node
NCL - Class in keel.Algorithms.ImbalancedClassification.Resampling.NCL: File: NCL.java The NCL algorithm is an undersampling method used to deal with the imbalanced problem.
NCL(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.NCL.NCL: Constructor of the class.
nClasses - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Number of classes.
nClasses - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: Number of classes.
nClasses - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyDataset: Number of classes.
nClasses - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Number of classes.
nClasses - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Number of classes.
nClasses - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm: Number of classes.
nClasses - Variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: Number of classes.
nClasses - Variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: Number of classes.
nClasses - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm: Number of classes.
nClasses - Variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Number of classes of the problem.
nClasses - Variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Number of classes.
nClasses - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Number of classes.
nClasses - Variable in class keel.GraphInterKeel.experiments.DatasetXML
NCNEdit - Class in keel.Algorithms.Instance_Selection.NCNEdit: File: NCNEdit.java The NCNEdit Instance Selection algorithm.
NCNEdit(String) - Constructor for class keel.Algorithms.Instance_Selection.NCNEdit.NCNEdit: Default constructor.
NCNEdit - Class in keel.Algorithms.Preprocess.Instance_Selection.NCNEdit: File: NCNEdit.java The NCNEdit Instance Selection algorithm.
NCNEdit(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.NCNEdit.NCNEdit: Default constructor.
nCons - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
NConsequent - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node
nearestCenter(Instance) - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.fuzzygCenter: Computes the nearest cluster to the given instance (for nominal values only)
nearestCenter(Instance) - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.gCenter: Computes the nearest cluster to the given instance
nearestClustersWithSameClass() - Method in class keel.Algorithms.Instance_Generation.GMCA.ClusterSet: Returns a list of pairs of clusters in inter-cluster ascending order.
nearestNeighbors - Variable in class keel.Algorithms.Instance_Generation.BTS3.BTS3Generator: Size of the merged data set.
nearestPair() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Returns the nearest pair of prototypes of the set.
nearestPair() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Returns the nearest pair of prototypes of the set.
nearestPrototypesIn(PrototypeSet, PrototypeSet, MatrixOfDistances) - Method in class keel.Algorithms.Instance_Generation.PNN.PNNGenerator: Returns the two nearest prototypes in two different sets.
nearestSample(Complex, int, long, int, int) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Gets the most near example
nearestTo(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Return the nearest prototype to another in the set.
nearestTo(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Return the nearest prototype to all the prototypes of a set.
nearestTo(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Return the nearest prototype to another in the set.
nearestToWithClass(Prototype, double) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Return the nearest prototype to another in the set with a specified class.
nearestToWithClass(Prototype, double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Return the nearest prototype to another in the set with a specified class.
nearestToWithDifferentClass(Prototype, double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Return the nearest prototype to another in the set with a specified class.
nearestValidNeighbor(Instance, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.knnImpute: Finds the nearest neighbor with a valid value in the specified attribute
nearestValidNeighbor(Instance, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.wknnImpute: Finds the nearest neighbor with a valid value in the specified attribute
nearnestPrototypesForEachClass() - Method in class keel.Algorithms.Instance_Generation.MCA.DistanceMatrixByClass: Returns the nearest prototype for each class in the set.
NEARZERO - Static variable in class keel.Algorithms.Neural_Networks.gmdh.node: Absolute minimum value
needExponentialFormat(double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FlexibleDecimalFormat: Checks if the given number needs a ExponentialFormat.
needReEval() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowing
needReEval() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowingGWS
needReEval() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowingILAS
needsUID(String) - Static method in class keel.Algorithms.SVM.SMO.core.SerializationHelper: checks whether a class needs to declare a serialVersionUID, i.e., it implements the java.io.Serializable interface but doesn't declare a serialVersionUID.
needsUID(Class) - Static method in class keel.Algorithms.SVM.SMO.core.SerializationHelper: checks whether a class needs to declare a serialVersionUID, i.e., it implements the java.io.Serializable interface but doesn't declare a serialVersionUID.
neg(fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
negative_cost() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Computes the cost for a instance from the negative class
negativeEta - Variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus: Value of negative Eta
NEGATIVO - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.Fitness
Neighborhood - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
Neighbour - Class in keel.Algorithms.Discretizers.MODL: This class represents an operation of the post-optimization of the MODL discretization algorithm
Neighbour() - Constructor for class keel.Algorithms.Discretizers.MODL.Neighbour
nejemplos - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
nelem - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
Nem - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Nem boolean
Nem - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Apply Nem flag.
nEntradas - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Number of inputs.
nentradas - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
nEntradas - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
nEntradas - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
nEntradas - Variable in class keel.Algorithms.Preprocess.Basic.Metodo: Number of input attributes
NEs - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node
Net - Variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.SquaresErrorNN
Net - Variable in class keel.Algorithms.Shared.ClassicalOptim.SquaresErrorNN: Neural network container.
NetClassifyPattern(double[]) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network: Check if a pattern is correctly classified
NetClassifyPattern(double[]) - Method in class keel.Algorithms.Neural_Networks.gann.Network: Check if a pattern is correctly classified
NetClassifyPattern(double[]) - Method in class keel.Algorithms.Neural_Networks.gmdh.Network: Check if a pattern is correctly classified
NetClassifyPattern(double[]) - Method in class keel.Algorithms.Neural_Networks.net.Network: Check if a pattern is correctly classified
netConf - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
netConf - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
netConf - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
netConf - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
netConf - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
netConf - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
NetGetClassOfPattern(double[], int, double, double) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network: Return the class where a pattern is classified
NetGetClassOfPattern(double[]) - Method in class keel.Algorithms.Neural_Networks.gann.Network: Return the class where a pattern is classified
NetGetClassOfPattern(double[]) - Method in class keel.Algorithms.Neural_Networks.gmdh.Network: Return the class where a pattern is classified
NetGetClassOfPattern(double[]) - Method in class keel.Algorithms.Neural_Networks.net.Network: Return the class where a pattern is classified
Network - Class in keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS: Class representing a neural network
Network() - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network: Empty Constructor.
Network(Parameters) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network: Constructor
Network - Class in keel.Algorithms.Neural_Networks.gann: Class representing a neural network
Network() - Constructor for class keel.Algorithms.Neural_Networks.gann.Network: Empty Constructor.
Network(SetupParameters) - Constructor for class keel.Algorithms.Neural_Networks.gann.Network: Constructor
Network - Class in keel.Algorithms.Neural_Networks.gmdh: Class representing a Nural Network
Network() - Constructor for class keel.Algorithms.Neural_Networks.gmdh.Network: Empty Constructor.
Network(Parameters) - Constructor for class keel.Algorithms.Neural_Networks.gmdh.Network: Constructor that receives parameters
Network - Class in keel.Algorithms.Neural_Networks.net: Class representing a neural network
Network() - Constructor for class keel.Algorithms.Neural_Networks.net.Network: Empty Constructor.
Network(Parameters) - Constructor for class keel.Algorithms.Neural_Networks.net.Network: Constructor
NeuralNetAlgorithm - Class in keel.Algorithms.Neural_Networks.NNEP_Common.algorithm: Base implementation for all neural net algorithms
NeuralNetAlgorithm() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Empty constructor
NeuralNetClassifier - Class in keel.Algorithms.Neural_Networks.NNEP_Clas.neuralnet: Neural net used as a classifier, with the posibility of estimating probability of each class.
NeuralNetClassifier() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Clas.neuralnet.NeuralNetClassifier: Empty constructor
NeuralNetCreator - Class in keel.Algorithms.Neural_Networks.NNEP_Common: Creation of NeuralNetIndividual (and subclasses).
NeuralNetCreator() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetCreator: Empty constructor
NeuralNetIndividual - Class in keel.Algorithms.Neural_Networks.NNEP_Common: Individuals with a INeuralNet as genotype
NeuralNetIndividual() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividual: Empty contructor
NeuralNetIndividual(INeuralNet) - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividual: Constructor that sets individual genotype
NeuralNetIndividual(INeuralNet, IFitness) - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividual: Constructor that sets individual genotype and fitness
NeuralNetIndividualSpecies - Class in keel.Algorithms.Neural_Networks.NNEP_Common: Individuals that use a INeuralNet as genotype
NeuralNetIndividualSpecies() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies: Empty constructor
NeuralNetMutator - Class in keel.Algorithms.Neural_Networks.NNEP_Common.mutators: NeuralNetIndividual mutator.
NeuralNetMutator() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.NeuralNetMutator: Empty constructor
NeuralNetRegressor - Class in keel.Algorithms.Neural_Networks.NNEP_Regr.neuralnet: Neural net used as a regressor
NeuralNetRegressor() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Regr.neuralnet.NeuralNetRegressor: Empty constructor
NeuralNetReporterClas - Class in keel.Algorithms.Neural_Networks.NNEP_Clas.listener: Generation reporter of neural net algorithms
NeuralNetReporterClas() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Clas.listener.NeuralNetReporterClas: Empty constructor
NeuralNetReporterRegr - Class in keel.Algorithms.Neural_Networks.NNEP_Regr.listener: Generation reporter of neural net algorithms
NeuralNetReporterRegr() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Regr.listener.NeuralNetReporterRegr: Empty constructor
neuralNetType - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Type of neuralnets
neuronCreation(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQTC: Creates new neurons.
neuronParametricMutators - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Parametric Mutators of a specific neuron
neuronPruning(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQTC: Prunes the neurons which have their counting variables lesser than the retention threshold.
neurons - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputLayer: Array of neurons of the layer
neurons - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Array of neurons of the layer
neuronsEmpty() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Checks if this neural net is empty of neurons
neuronsEmpty() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuralNet: Checks if this neural net is empty of neurons
neuronsEmpty() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Checks if this layer is empty of neurons
neuronsFull() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Checks if this neural net is full of neurons
neuronsFull() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuralNet: Checks if this neural net is full of neurons
neuronsFull() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Checks if this layer is full of neurons
neuronStructuralMutators - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Structural Mutators of a specific neuron
neuronTypes - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Types of each neuron for hibrid layers
New - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
new_dataset(Point, ExternalObjectDescription, int) - Method in class keel.GraphInterKeel.experiments.GraphPanel: Creation of new data set
NewBasicNode() - Method in class keel.Algorithms.Neural_Networks.gmdh.sonn: Adds a new node
newClassifier() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Factory
newClassifier() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Factory
newEntropy(Classification) - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Function to compute entropy of classification after cutting.
newEntropy(Classification) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to compute entropy of classification after cutting.
newEntropy(Classification) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Function to compute entropy of classification after cutting.
newEntropy(Classification) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Function to compute entropy of classification after cutting.
newEntropy(Classification) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to compute entropy of classification after cutting.
newEntropy(Classification) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Function to compute entropy of classification after cutting.
newEntropy(Classification) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Function to compute entropy of classification after cutting.
newEntropy(Classification) - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Function to compute entropy of classification after cutting.
newEntropy(Classification) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Function to compute entropy of classification after cutting.
newEvent(myDataset, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.RuleSet
newIteration(boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.instanceSet
newIteration(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.timerMDL
newIteration() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowing
newIteration() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowingGWS
newIteration() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowingILAS
newIteration(int, Classifier[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_MDL
newIteration() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Windowing
newIteration(int, Classifier[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_MDL
newIteration() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Windowing
NewRandomNode(SetupParameters, Data) - Method in class keel.Algorithms.Neural_Networks.gmdh.sonn: Creates a new random node
newState() - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Environment: Creates or selects a new example of the problem.
newState() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.MPEnvironment: Creates a new example of the problem.
newState() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.SSFileEnvironment: Does create a new state of the problem.
newState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.DECEnvironment: Creates a new state of the problem.
newState() - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Environment: Creates a new state of the problem.
newState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MPEnvironment: Creates a new state of the problem.
newState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MSEnvironment: Create a new state of the problem.
newState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.PAREnvironment: Creates a new state of the problem.
newState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.POSEnvironment: Creates a new state of the problem.
newState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RMPEnvironment: Creates a new state of the problem.
newState() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.SSFileEnvironment: Does create a new state of the problem.
newTable() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyDataset: It create a new table with the examples from the Data Set
newToken(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Token: Returns a new Token object, by default.
newToken(int) - Static method in class keel.Algorithms.Rule_Learning.Swap1.Token: Returns a new Token object, by default.
newToken(int) - Static method in class keel.Dataset.Token: Returns a new Token object, by default.
newtonInterp(double[], double[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.Interpolation: Interpolates with Horners-s method, computing the polynomial a0+(t-x0)(a1+(t-x1)(a2+(t-x2)(a3+(...
NExp - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node
NExprArit - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node
NExprHold - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node
next(Queue.QueueNode) - Method in class keel.Algorithms.Decision_Trees.M5.Queue.QueueNode: Sets the next node in the queue, and returns it.
next() - Method in class keel.Algorithms.Decision_Trees.M5.Queue.QueueNode: Gets the next node in the queue.
next - Variable in class keel.Algorithms.Discretizers.Khiops.DeltaValue: pointer for the next merge of intervals
next - Variable in class keel.Algorithms.Discretizers.MODL.DeltaValue: pointer for the next bound of intervals
next(Queue.QueueNode) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Queue.QueueNode: Sets the next node in the queue, and returns it.
next() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Queue.QueueNode: Gets the next node in the queue.
next() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask: Advances the cursor to the next active entry.
next(Queue.QueueNode) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue.QueueNode: Sets the next node in the queue, and returns it.
next() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue.QueueNode: Gets the next node in the queue.
next() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Advances the cursor to the next active entry.
next - Variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Token: A reference to the next regular (non-special) token from the input stream.
next() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset: Return the next instance
next() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArffDataSet: Return the next instance
next() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArrayDataset: Return the next instance
next() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.FileDataset: Return the next instance
next() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IDataset: Return the next instance
next() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.KeelDataSet: Return the next instance
next(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.MersenneTwister
next() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask: Advances the cursor to the next active entry.
next() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Advances the cursor to the next active entry.
next() - Method in class keel.Algorithms.Rule_Learning.PART.Mask: Advances the cursor to the next active entry.
next() - Method in class keel.Algorithms.Rule_Learning.Ripper.Mask: Advances the cursor to the next active entry.
next() - Method in class keel.Algorithms.Rule_Learning.Slipper.Mask: Advances the cursor to the next active entry.
next - Variable in class keel.Algorithms.Rule_Learning.Swap1.Token: A reference to the next regular (non-special) token from the input stream.
next(Queue.QueueNode) - Method in class keel.Algorithms.SVM.SMO.core.Queue.QueueNode: Sets the next node in the queue, and returns it.
next() - Method in class keel.Algorithms.SVM.SMO.core.Queue.QueueNode: Gets the next node in the queue.
next - Variable in class keel.Dataset.Token: A reference to the next regular (non-special) token from the input stream.
next_arc(int) - Method in class keel.GraphInterKeel.experiments.Graph: Search for the next arc
nextBoolean() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.MersenneTwister
nextByte() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.MersenneTwister: Returns the next pseudo-random Byte.
nextBytes(byte[]) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.MersenneTwister: A bug fix for all versions of the JDK.
nextChar() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.MersenneTwister: Returns the next pseudo-random char.
nextDouble() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.MersenneTwister
nextElement() - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector.FastVectorEnumeration: Returns the next element.
nextElement() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector.FastVectorEnumeration: Returns the next element.
nextElement() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector.FastVectorEnumeration: Returns the next element.
nextElement() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector.FastVectorEnumeration: Returns the next element.
nextElement() - Method in class keel.Algorithms.SVM.SMO.core.FastVector.FastVectorEnumeration: Returns the next element.
nextFloat() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.MersenneTwister
nextInt(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.MersenneTwister
nextIteration() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_ADI
nextIteration() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_ADI
nextLevelExists - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: The next level indicator flag: set to true if new level generated and by default.
nextNondecFunc(int[], int) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Combinatoric: next nondecreasing function (r^d) in lex order
nextShort() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.MersenneTwister: Returns the next pseudo-random char.
nf - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.ExponentialFormat: Decimal format of this exponential one.
nf - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FloatingPointFormat: Decimal format.
Nhidden - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network: Number of units in each layer
Nhidden - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters: Number of units in each layer
Nhidden - Variable in class keel.Algorithms.Neural_Networks.gann.Network: Number of units in each layer
Nhidden - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters: Number of units in each layer
Nhidden - Variable in class keel.Algorithms.Neural_Networks.gmdh.Network: Number of units in each layer
Nhidden - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters: Number of units in each layer
Nhidden - Variable in class keel.Algorithms.Neural_Networks.net.Network: Number of units in each layer
Nhidden - Variable in class keel.Algorithms.Neural_Networks.net.Parameters: Number of units in each layer
Nhidden_layers - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters: No of hidden layers
Nhidden_layers - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters: Number of hidden layers
Nhidden_layers - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters: Number of hidden layers
Nhidden_layers - Variable in class keel.Algorithms.Neural_Networks.net.Parameters: No of hidden layers
Ninputs - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network: Number of units in each layer
Ninputs - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters: Number of units in each layer
Ninputs - Variable in class keel.Algorithms.Neural_Networks.gann.Network: Number of units in each layer
Ninputs - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters: Number of units in each layer
Ninputs - Variable in class keel.Algorithms.Neural_Networks.gmdh.Network: Number of units in each layer
Ninputs - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters: Number of units in each layer
Ninputs - Variable in class keel.Algorithms.Neural_Networks.net.Network: Number of units in each layer
Ninputs - Variable in class keel.Algorithms.Neural_Networks.net.Parameters: Number of units in each layer
nInputs - Variable in class keel.GraphInterKeel.datacf.util.Dataset: Input number
nInstances - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Number of instances of each classes.
nInstances - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: Number of instances of each classes.
nInstances - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Number of instances of each classes.
nInstances - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Number of instances of each classes.
nInstances - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm: Number of instances of each classes.
nInstances - Variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: Number of instances of each classes.
nInstances - Variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: Number of instances of each classes.
nInstances - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm: Number of instances of each classes.
nInstances - Variable in class keel.GraphInterKeel.experiments.DatasetXML
nivel_cubrimiento - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Ttabla: cover level
nivel_cubrimiento - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Ttabla: cover level
nivel_cubrimiento - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Ttabla: cover level
nivel_cubrimiento - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Ttabla: cover level
nivel_cubrimiento - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Ttabla: cover level
nivel_cubrimiento - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Ttabla: cover level
nivel_cubrimiento - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Ttabla: cover level
nivel_cubrimiento - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Ttabla: cover level
nJobFinished() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: Return true if a job "n" has finished
NLabel - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node
nLabels(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.DataBase: It returns the number of possible labels for a variable.
nlabels - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoosting
Nlayers - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network: Number of layers
Nlayers - Variable in class keel.Algorithms.Neural_Networks.gann.Network: Number of layers
Nlayers - Variable in class keel.Algorithms.Neural_Networks.gmdh.Network: Number of layers
Nlayers - Variable in class keel.Algorithms.Neural_Networks.net.Network: Number of layers
NLog - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node
NM - Class in keel.Algorithms.Lazy_Learning.NM: File: NM.java The Nearest Mean Algorithm.
NM(String) - Constructor for class keel.Algorithms.Lazy_Learning.NM.NM: The main method of the class
NMEEFSD - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD: NMEEFSD Non-dominated Multi-objective Evolutionary algorithm for Extracting Fuzzy rules in Subgroup Discovery Algorithm for the discovery of rules describing subgroups
NMEEFSD() - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.NMEEFSD
NMinus - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node
NN(int, double[][], double[]) - Method in class keel.Algorithms.Neural_Networks.LVQ.LVQ: Computes and returns the Nearest Neighbour (NN) of the given example in the given dataset.
nn(double[], double[][][]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ConjGradNN: Calculates the output of a perceptron with weights W for input x
nn(double[], double[][][]) - Method in class keel.Algorithms.Shared.ClassicalOptim.ConjGradNN: Calculates the output of a perceptron with weights W for input x
nnoutput(double[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.GCNet: Calculated the output of present perceptron with input x and returns it in original scale.
nnoutput(double[]) - Method in class keel.Algorithms.Shared.ClassicalOptim.GCNet: Calculated the output of present perceptron with input x and returns it in original scale.
NNSSLAlgorithm - Class in keel.Algorithms.Semi_Supervised_Learning.NNSSL: NNSSL algorithm calling.
NNSSLAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.NNSSL.NNSSLAlgorithm
NNSSLGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.NNSSL: This class implements the Self-traning wrapper.
NNSSLGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.NNSSL.NNSSLGenerator: Build a new NNSSLGenerator Algorithm
NNSSLGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.NNSSL.NNSSLGenerator: Build a new NNSSLGenerator Algorithm
nntrain(int, int, double[][], double[][], int[], double[], Randomize) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.GCNet: trains a perceptron with Conjugated Gradient algorithm and returns the mean square error of neural network output compared to expected output.
nntrain(int, int, double[][], double[][], int[], double[], Randomize) - Method in class keel.Algorithms.Shared.ClassicalOptim.GCNet: trains a perceptron with Conjugated Gradient algorithm and returns the mean square error of neural network output compared to expected output.
nntrainGD(int, int, double[][], double[][], int[], double[], Randomize) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.GCNet: trains a perceptron with Conjugated Descendent algorithm and returns the mean square error of neural network output compared to expected output.
nntrainGD(int, int, double[][], double[][], int[], double[], Randomize) - Method in class keel.Algorithms.Shared.ClassicalOptim.GCNet: trains a perceptron with Conjugated Descendent algorithm and returns the mean square error of neural network output compared to expected output.
NO - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Fuzzy_Ish: Configuration flags.
no_cubiertos - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyDataset: Number of uncovered instaces.
no_cubiertos - Variable in class keel.Algorithms.RE_SL_Methods.LEL_TSK.MiDataset
no_cubiertos - Variable in class keel.Algorithms.RE_SL_Methods.MamWM.MiDataset
no_cubiertos - Variable in class keel.Algorithms.RE_SL_Methods.mogulHC.MiDataset
no_cubiertos - Variable in class keel.Algorithms.RE_SL_Methods.mogulIRL.MiDataset
no_cubiertos - Variable in class keel.Algorithms.RE_SL_Methods.mogulSC.MiDataset
no_cubiertos - Variable in class keel.Algorithms.RE_SL_Methods.TSK_IRL.MiDataset
no_cubiertos - Variable in class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.MiDataset
no_cubiertos - Variable in class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.MiDataset
no_cubiertos - Variable in class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.MiDataset
no_cubiertos - Variable in class keel.Algorithms.RE_SL_Postprocess.MamSelect.MiDataset
no_cubiertos - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWSelect.MiDataset
no_cubiertos - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWTuning.MiDataset
no_cubiertos - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.MiDataset
no_cubiertos - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKSelect.MiDataset
NO_RW - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Fuzzy_Chi: Configuration flags.
NO_RW - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.GP_COACH: Configuration flag (NO_RW).
NO_RW - Static variable in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.GP_COACH_H: Configuration flag (NO_RW)
noClasificadas() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: Number of instances not removed.
noClasificadas() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: Number of instances not removed.
noClasificadas() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: Number of instances not removed.
NOCUBRE - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.Fitness
Node - Class in keel.Algorithms.Decision_Trees.PUBLIC: File: Node.java Data structure that is used in the construction of the decision tree.
Node() - Constructor for class keel.Algorithms.Decision_Trees.PUBLIC.Node: Creates a node with a base constructor that doesn't initialize the node structures
Node(myDataset, int) - Constructor for class keel.Algorithms.Decision_Trees.PUBLIC.Node: Creates a node from a complete dataset and its corresponding id.
Node(Node) - Constructor for class keel.Algorithms.Decision_Trees.PUBLIC.Node: Creates a node from another existing node
Node - Class in keel.Algorithms.Decision_Trees.SLIQ: This class implements the nodes of SLIQ decision tree.
Node(int) - Constructor for class keel.Algorithms.Decision_Trees.SLIQ.Node: Paramenter constructor.
Node - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: The class defines the characteristics of a node.
Node(int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node: Constructor.
Node(int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node: Constructor.
Node(Node) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node: Constructor.
node - Class in keel.Algorithms.Neural_Networks.gmdh: Class that represents a node of a Neural Network
node() - Constructor for class keel.Algorithms.Neural_Networks.gmdh.node: Constructor
Node - Class in keel.GraphInterKeel.experiments
Node() - Constructor for class keel.GraphInterKeel.experiments.Node: Builder
Node(ExternalObjectDescription, Point, int) - Constructor for class keel.GraphInterKeel.experiments.Node: Builder
node_selected - Variable in class keel.GraphInterKeel.experiments.GraphPanel
NodeAdd - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: The class define a node with two children.
NodeAdd(NodeExprArit, NodeExprArit) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeAdd: Constructor.
NodeAdd(NodeAdd) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeAdd: Constructor: Generate a new NodeAdd from a given one (NodeAdd)
NodeAnd - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: Class for management and nodes.
NodeAnd(NodeAssert, NodeAssert) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeAnd: Constructor.
NodeAnd(NodeAnd) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeAnd: Constructor: Generate a new NodeAnd from a given one (NodeAnd)
NodeAssert - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: Class for constructor and define abstrac methods.
NodeAssert(int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeAssert: Constructor.
NodeConsequent - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: Class for management the consequent of a node.
NodeConsequent(int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeConsequent: Constructor.
NodeConsequent(NodeConsequent) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeConsequent: Constructor.
NodeExp - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: Class for management of nodes that are evaluated to alfa-cuts family
NodeExp(NodeExprArit) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeExp: Constructor.
NodeExp(NodeExp) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeExp: Constructor.
NodeExprArit - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: Class for constructor and define abstract methods.
NodeExprArit(int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeExprArit: Constructor.
NodeExprHold - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: Class for management of nodes that are evaluated to a vector of fuzzy numbers.
NodeExprHold(NodeExprArit[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeExprHold: Constructor.
NodeExprHold(NodeExprHold) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeExprHold: Constructor.
NodeIs - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: Class for management a node witch is evaluated to a real number.
NodeIs(NodeVariable, NodeLabel) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeIs: Generate a new node witch two children
NodeIs(NodeIs) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeIs: Generate a new node from another one
NodeLabel - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: Class for management terminal nodes witch are evaluated as linguistic fuzzy labels
NodeLabel(Fuzzy) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeLabel: Constructor.
NodeLabel(NodeLabel) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeLabel: Constructor.
nodeLink - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.FPtree.FPgrowthHeaderTable: The forward link to the next node in the link list of nodes.
nodeLink - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.FPtree.FPgrowthHeaderTable: The forward link to the next node in the link list of nodes.
NodeLog - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: Class for management log nodes.
NodeLog(NodeExprArit) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeLog: Constructor.
NodeLog(NodeLog) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeLog: Constructor.
NodeMinus - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: Class for management minus node.
NodeMinus(NodeExprArit, NodeExprArit) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeMinus: Constructor.
NodeMinus(NodeMinus) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeMinus: Constructor.
nodeModel - Variable in class keel.Algorithms.Decision_Trees.C45.Tree: The model of the node.
nodeModel - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Tree: The model of the node.
nodeModel - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree: The model of the node.
nodeModel - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Tree: The model of the node.
nodeModel - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree: The model of the node.
nodeModel - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Tree: The model of the node.
nodeModel - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree: The model of the node.
nodeModel - Variable in class keel.Algorithms.Rule_Learning.PART.Tree: The model of the node.
nodeModel - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: The model of the node.
NodeOr - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: Class for management of or nodes.
NodeOr(NodeAssert, NodeAssert) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeOr: Constructor.
NodeOr(NodeOr) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeOr: Constructor.
NodeOutput(double[], node[]) - Method in class keel.Algorithms.Neural_Networks.gmdh.node: Obtains the node output
NodeProduct - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: Class for management of product nodes.
NodeProduct(NodeExprArit, NodeExprArit) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeProduct: Constructor.
NodeProduct(NodeProduct) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeProduct: Constructor.
NodeRule - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: Class for management a rule node.
NodeRule(NodeAssert, NodeConsequent, double) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeRule: Constructor.
NodeRule(NodeRule) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeRule: Constructor.
NodeRuleBase - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: Class for management of a node rule base.
NodeRuleBase(NodeRule[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeRuleBase: Constructor.
NodeRuleBase(NodeRuleBase) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeRuleBase: Constructor.
NodeSquareRoot - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: Class for management of square root node.
NodeSquareRoot(NodeExprArit) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeSquareRoot: Constructor.
NodeSquareRoot(NodeSquareRoot) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeSquareRoot: Constructor.
nodeSwap(int, int, Tree) - Method in class keel.Algorithms.Decision_Trees.Target.Tree: Swaps the two nodes given with the given tree.
NodeValue - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: Class for management nodes with the number of the variable and the value of the variable as a string.
NodeValue(int, FuzzyAlphaCut[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeValue: Constructor.
NodeValue(NodeValue) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeValue: Constructor.
NodeVariable - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node: Class for management nodes with the number of the variable and the value of the variable.
NodeVariable(int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeVariable: Constructor.
NodeVariable(NodeVariable) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeVariable: Constructor.
Nodo - Class in keel.Algorithms.Decision_Trees.DT_oblicuo: Tree node.
Nodo() - Constructor for class keel.Algorithms.Decision_Trees.DT_oblicuo.Nodo: Default constructor.
Nodo(myDataset, int, int[], int) - Constructor for class keel.Algorithms.Decision_Trees.DT_oblicuo.Nodo: Paramenter constructor.
Nodo - Class in keel.Algorithms.Decision_Trees.Target: Tree node.
Nodo() - Constructor for class keel.Algorithms.Decision_Trees.Target.Nodo: Default constructor.
Nodo(boolean, myDataset, double, double) - Constructor for class keel.Algorithms.Decision_Trees.Target.Nodo: Paramenter constructor.
nodos - Static variable in class keel.Algorithms.Decision_Trees.DT_oblicuo.Tree: Number of Leafs in the tree
nodos - Static variable in class keel.Algorithms.Decision_Trees.Target.Tree: Number of Leafs in the tree
nodosT - Static variable in class keel.Algorithms.Decision_Trees.DT_oblicuo.Tree: Number of Leafs in the tree
nodosT - Static variable in class keel.Algorithms.Decision_Trees.Target.Tree: Number of Leafs in the tree
NOELEGIR - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.Fitness
noEstaCubierta() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Muestra: Sets the example as uncovered.
noEstaCubierta() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Muestra: Sets the example as uncovered.
noEstaCubierta() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Muestra: Sets the example as uncovered.
noEstaCubierta() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Muestra: Sets the example as uncovered.
noEstaCubierta() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Muestra: Sets the example as uncovered.
noEvaluado() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Individuo
nOfHiddenLayers - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Number of hidden layers of the neural nets
nOfInputs - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Number of inputs of the neural nets
nofinputs - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Number of inputs
nOfLinksRelative - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Has the operator a relative added/removed number of links, depending of the neural net
nofobservations - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Number of observations (Matrix columns)
nOfOutputs - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Number of outputs
nofoutputs - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Number of outputs
nOfPartitions - Variable in class keel.GraphInterKeel.datacf.partitionData.HoldOutOptionsJDialog: Number of partitions
nofselSecondMutator - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Number of parents mutated with second mutator
nofvariables - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Number of variables (Matrix rows)
nogbest - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Number of generations without improving best fitness
nogmean - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Number of generations without improving mean fitness
noiseSensitivity - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Parameters: Noise Sensitivity.
noisyInstances - Variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.INFFC_2STEPS
nom - Variable in class keel.Algorithms.Hyperrectangles.EHS_CHC.Hyper
nombreAtributo(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: Returns the name of the attribute with the id given.
nombreClase(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Returns the nominal value for a class represented by the integer given.
nombreClase(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It returns the name of the class of index intValue
nombreClase(int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It returns the name of the class of index intValue
nombreClase(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It returns the name of the class of index intValue
nombreClase(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Returns the nominal value for a class represented by the integer given.
nombres() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It returns the names for all input variables
nombres() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It returns the name of the attributes
nombres() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It returns the name of the attributes
nombres() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It returns the name of every input attributes.
nombres() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It returns the name of the attributes
nombres() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It returns the name of the attributes
nombres() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It returns the names for all input variables
nombreVar(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It returns the attribute name of a given variable
nombreVar(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: Returns the name of the attribute with the id given.
nombreVar(int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: Returns the name of the attribute with the id given.
nombreVar(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: Returns the name of the attribute with the id given.
nombreVar(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It returns the attribute name of a given variable
NOMINAL - Static variable in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Decision_Trees.M5.M5Attribute: Constant set for nominal attributes.
NOMINAL - Static variable in class keel.Algorithms.Decision_Trees.Target.myDataset: Number to represent type of variable nominal.
nominal - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: Identifies the nominal attributes.
NOMINAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constant set for nominal attributes.
NOMINAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Constant set for nominal attributes.
NOMINAL - Static variable in class keel.Algorithms.Instance_Generation.Basic.Prototype: Nominal type of attributes.
NOMINAL - Static variable in class keel.Algorithms.PSO_Learning.CPSO.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.PSO_Learning.REPSO.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: Number to represent type of variable nominal.
nominal - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm: If an attribute is nominal or not.
NOMINAL - Static variable in class keel.Algorithms.Rule_Learning.AQ.Instance: Attribute nominal type flag.
NOMINAL - Static variable in class keel.Algorithms.Rule_Learning.LEM1.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Rule_Learning.LEM2.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Rule_Learning.Ritio.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Rule_Learning.Rules6.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Rule_Learning.SRI.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.Rule_Learning.Swap1.Attribute: Label for NOMINAL values.
NOMINAL - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Nominal type of attributes.
NOMINAL - Static variable in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Gene: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Gene
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: Number to represent type of variable nominal.
NOMINAL - Static variable in class keel.Dataset.Attribute: Label for NOMINAL values.
Nominal2Binary - Class in keel.Algorithms.Preprocess.Transformations.Nominal2Binary: This class performs the nominal to binary transformation.
Nominal2Binary(String) - Constructor for class keel.Algorithms.Preprocess.Transformations.Nominal2Binary.Nominal2Binary: Creates a new instance of min_max
NominalAntd - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: The antecedent with nominal attribute
NominalAntd(AttributeWeka) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.NominalAntd: Constructor
nominalCounts - Variable in class keel.Algorithms.Decision_Trees.M5.M5AttrStats: Counts of each nominal value
nominalCounts - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.AttributeStats: Counts of each nominal value
nominalDistance - Static variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Nominal distance.
nominalDistance - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
nominalDistance - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
nominalDistance - Static variable in class keel.Algorithms.Preprocess.Basic.Metodo: Nominal distances.
nominales - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
nominalList(Attribute) - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
nominalList(Attribute) - Static method in class keel.Dataset.DataParser
nominalTest - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Test Nominal values.
NominalToBinaryFilter - Class in keel.Algorithms.Decision_Trees.M5: Converts all nominal attributes into binary numeric attributes.
NominalToBinaryFilter() - Constructor for class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter
nominalTrain - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Training Nominal values.
nominalTrain - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
nominalTrain - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
nominalTrain - Variable in class keel.Algorithms.Preprocess.Basic.Metodo: Training Nominal input data.
nominalValid(String) - Method in class keel.GraphInterKeel.datacf.util.Attribute: Return a boolean for a given nominal value, true is valid value, false invalid value.
nominalValue(int, double) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: Returns a nominal representation of a attribute's real value given as argument.
non_evaluated() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual: It returns if the individual has been evaluated o not
NONE - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Fuzzy_Ish: Configuration flags.
NONE - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
nonParametric_mouseEntered(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Enter in statistical button
nonParametric_mouseExited(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Exit from statistical button
nonParametric_mouseReleased(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules: Entering in Statistical module
nonVoidClasses() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Return classes with at least one element.
nonVoidClasses() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Return classes with at least one element.
noOutputs - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyDataset: Identifies if there is ouput variables or not.
noOutputs - Variable in class keel.Algorithms.RE_SL_Methods.LEL_TSK.MiDataset
noOutputs - Variable in class keel.Algorithms.RE_SL_Methods.MamWM.MiDataset
noOutputs - Variable in class keel.Algorithms.RE_SL_Methods.mogulHC.MiDataset
noOutputs - Variable in class keel.Algorithms.RE_SL_Methods.mogulIRL.MiDataset
noOutputs - Variable in class keel.Algorithms.RE_SL_Methods.mogulSC.MiDataset
noOutputs - Variable in class keel.Algorithms.RE_SL_Methods.TSK_IRL.MiDataset
noOutputs - Variable in class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.MiDataset
noOutputs - Variable in class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.MiDataset
noOutputs - Variable in class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.MiDataset
noOutputs - Variable in class keel.Algorithms.RE_SL_Postprocess.MamSelect.MiDataset
noOutputs - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWSelect.MiDataset
noOutputs - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWTuning.MiDataset
noOutputs - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.MiDataset
noOutputs - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKSelect.MiDataset
NOr - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node
norm1() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Returns the L1-norm of the vector
norm1() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: One norm
norm2() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Returns the L2-norm of the vector
norm2() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Two norm
norm2() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.SingularValueDecomposition: Two norm
NORMAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Organizacion
Normal(double, double) - Static method in class keel.Algorithms.Neural_Networks.gann.Rand: Generate a normal distributed value for N(m, s)
NormalDistribution - Class in keel.Algorithms.Semi_Supervised_Learning.Basic: Implements a normal distribution function.
NormalDistribution() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.NormalDistribution: Default constructor.
normalDistribution - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Distribution type: noraml
NormalDistribution - Class in keel.GraphInterKeel.statistical.tests: File: NormalDistribution.java.
NormalDistribution() - Constructor for class keel.GraphInterKeel.statistical.tests.NormalDistribution: Default builder
normalInverse(double) - Static method in class keel.Algorithms.Lazy_Learning.Statistics: Returns the value, x, for which the area under the Normal (Gaussian) probability density function (integrated from minus infinity to x) is equal to the argument y (assumes mean is zero, variance is one).
normalInverse(double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics: Returns the value, x, for which the area under the Normal (Gaussian) probability density function (integrated from minus infinity to x) is equal to the argument y (assumes mean is zero, variance is one).
normaliza() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: It converts all values of the data-set to the interval [0,1]
normaliza() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: It converts all values of the data-set to the interval [0,1]
normaliza() - Method in class keel.Algorithms.Rule_Learning.AQ.Dataset: It converts all values of the data-set to the interval [0,1]
normaliza() - Method in class keel.Algorithms.Rule_Learning.CN2.Dataset: It converts all values of the data-set to the interval [0,1]
normaliza() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset: Convert all the values of the set of values in the inetrval[0,1]
normaliza() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset: Convert all the values of the set of values in the inetrval[0,1]
normaliza() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: It converts all values of the data-set to the interval [0,1]
normaliza() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset: It converts all values of the data-set to the interval [0,1]
normaliza() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset: It converts all values of the data-set to the interval [0,1]
normalizar() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.SMOTE: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.ADASYN.ADASYN: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.ADOMS.ADOMS: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.AHCClustering.AHCClustering: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.Borderline_SMOTE.Borderline_SMOTE: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.CNN.CNN: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.CNN_TomekLinks.CNN_TomekLinks: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.CPM.CPM: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.NCL.NCL: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.OSS.OSS: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.RandomOverSampling.RandomOverSampling: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.RandomUnderSampling.RandomUnderSampling: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.Safe_Level_SMOTE.Safe_Level_SMOTE: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SBC.SBC: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE.SMOTE: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_ENN.SMOTE_ENN: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_TomekLinks.SMOTE_TomekLinks: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SPIDER.SPIDER: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.TomekLinks.TomekLinks: This function builds the data matrix for reference data and normalizes inputs values
normalizar() - Method in class keel.Algorithms.Preprocess.Basic.Metodo: This function builds the data matrix for reference data and normalizes inputs values
normalize() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It transforms the input space into the [0,1] range
normalize(double[]) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.OVO: Normalizes the doubles in the array by their sum.
normalize(double[], double) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.OVO: Normalizes the doubles in the array using the given value.
normalize() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It transform the input space into the [0,1] range
normalize(double[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Normalizes the doubles in the array by their sum.
normalize(double[], double) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Normalizes the doubles in the array using the given value.
normalize() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It transform the input space into the [0,1] range
normalize(double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Normalizes the doubles in the array by their sum.
normalize(double[], double) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Normalizes the doubles in the array using the given value.
normalize() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It transform the input space into the [0,1] range
normalize(double[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Normalizes the doubles in the array by their sum.
normalize(double[], double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Normalizes the doubles in the array using the given value.
normalize() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Convert all the values of the set of values in the inetrval[0,1]
normalize() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It transform the input space into the [0,1] range
normalize(double[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Normalizes the doubles in the array by their sum.
normalize(double[], double) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Normalizes the doubles in the array using the given value.
normalize(double, double, double) - Static method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Normalizes a given value using the maximum and minimum value given.
normalize() - Method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm: This function builds the data matrix for training data and normalizes inputs values
normalize() - Method in class keel.Algorithms.Preprocess.Transformations.decimal_scaling.decimal_scaling: Process the training and test files provided in the parameters file to the constructor.
normalize() - Method in class keel.Algorithms.Preprocess.Transformations.min_max.min_max: Process the training and test files provided in the parameters file to the constructor.
normalize() - Method in class keel.Algorithms.Preprocess.Transformations.z_score.z_score: Process the training and test files provided in the parameters file to the constructor.
normalize(double) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It transform the input space into the [0,t] range
normalize(double) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It transform the input space into the [0,t] range
normalize() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Converts all the values of the set into the [0,1] interval
normalize() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It transform the input space into the [0,1] range
normalize() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It transform the input space into the [0,1] range
normalize(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Normalizes the doubles in the array by their sum.
normalize(double[], double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Normalizes the doubles in the array using the given value.
normalize(double, double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Normalizes a value given with its minimum and maximum values of its interval.
normalize(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Normalizes the doubles in the array by their sum.
normalize(double[], double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Normalizes the doubles in the array using the given value.
normalize(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Normalizes the doubles in the array by their sum.
normalize(double[], double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Normalizes the doubles in the array using the given value.
normalize(double[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Normalizes the doubles in the array by their sum.
normalize(double[], double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Normalizes the doubles in the array using the given value.
normalize() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It transform the input space into the [0,1] range
normalize(double[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Normalizes the doubles in the array by their sum.
normalize(double[], double) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Normalizes the doubles in the array using the given value.
normalize(double, Attribute) - Method in class keel.Algorithms.SVM.SMO.SMO: Normalize the input value according to the provided attribute
normalize_statistics() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It transforms the input space into the [0,1] range, but it is stored in X_normalized.
Normalize_String(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Normalize_String(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
normalizeAndCenter() - Method in class keel.Algorithms.Discretizers.UCPD.UCPD: It normalizes continuous attributes and center them on their mean
NORMALIZED_SUM - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.GP_COACH: Configuration flag (NORMALIZED_SUM). also known as additive combination
NORMALIZED_SUM - Static variable in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.GP_COACH_H: Configuration flag (NORMALIZED_SUM)
normalizedKernel(char[], char[]) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: evaluates the normalized kernel between s and t.
NormalizedPolyKernel - Class in keel.Algorithms.SVM.SMO.supportVector: The normalized polynomial kernel.
NormalizedPolyKernel() - Constructor for class keel.Algorithms.SVM.SMO.supportVector.NormalizedPolyKernel: default constructor - does nothing
NormalizedPolyKernel(Instances, int, double, boolean) - Constructor for class keel.Algorithms.SVM.SMO.supportVector.NormalizedPolyKernel: Creates a new NormalizedPolyKernel instance.
normalizer - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Normalizer used to normalizer the trainData
Normalizer - Class in keel.Algorithms.Neural_Networks.NNEP_Common.util.normalizer: Represents a data normalizer
Normalizer() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.util.normalizer.Normalizer: Empty constructor
normalizeReference() - Method in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: This function builds the data matrix for reference data and normalizes inputs values
normalizeReference() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: This function builds the data matrix for reference data and normalizes inputs values
normalizeReference() - Method in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: This function builds the data matrix for reference data and normalizes inputs values
normalizeReference() - Method in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: This function builds the data matrix for reference data and normalizes inputs values
normalizeReference() - Method in class keel.Algorithms.RST_Learning.RSTAlgorithm: This function builds the data matrix for reference data and normalizes inputs values
normalizeTest() - Method in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: This function builds the data matrix for test data and normalizes inputs values
normalizeTest() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: This function builds the data matrix for test data and normalizes inputs values
normalizeTest() - Method in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: This function builds the data matrix for test data and normalizes inputs values
normalizeTest() - Method in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: This function builds the data matrix for test data and normalizes inputs values
normalizeTest() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: This function builds the data matrix for test data and normalizes inputs values
normalizeTest() - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: This function builds the data matrix for test data and normalizes inputs values
normalizeTest() - Method in class keel.Algorithms.RST_Learning.RSTAlgorithm: This function builds the data matrix for test data and normalizes inputs values
normalizeTrain() - Method in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: This function builds the data matrix for training data and normalizes inputs values
normalizeTrain() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: This function builds the data matrix for training data and normalizes inputs values
normalizeTrain() - Method in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: This function builds the data matrix for training data and normalizes inputs values
normalizeTrain() - Method in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: This function builds the data matrix for training data and normalizes inputs values
normalizeTrain() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: This function builds the data matrix for training data and normalizes inputs values
normalizeTrain() - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: This function builds the data matrix for training data and normalizes inputs values
normalizeTrain() - Method in class keel.Algorithms.RST_Learning.RSTAlgorithm: This function builds the data matrix for training data and normalizes inputs values
normalizeValue(double) - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It does normalize a value.
normalizeValue(double) - Method in class keel.Dataset.Attribute: It does normalize a value.
normalProbability(double) - Static method in class keel.Algorithms.Decision_Trees.M5.Distributions: Returns probability that the standardized normal variate Z (mean = 0, standard deviation = 1) is less than z.
normalProbability(double) - Static method in class keel.Algorithms.Lazy_Learning.Statistics: Returns the area under the Normal (Gaussian) probability density function, integrated from minus infinity to x (assumes mean is zero, variance is one).
normalProbability(double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics: Returns the area under the Normal (Gaussian) probability density function, integrated from minus infinity to x (assumes mean is zero, variance is one).
normF() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Frobenius norm
normInf() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Infinity norm
normp(double) - Static method in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.CDF_Normal: This method calculates the normal cumulative distribution function.
normp(double) - Static method in class keel.GraphInterKeel.statistical.tests.CDF_Normal: This method calculates the normal cumulative distribution function.
Not_Covered_Examples() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set: It counts the number of not covered examples
Not_Covered_Examples() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set: Returns the number of examples not covered
Not_Covered_Examples() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set: It counts the number of not covered examples
Not_Covered_Examples() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set: It counts the number of not covered examples
Not_Covered_Training_Example(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
Not_Covered_Training_Example(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set: Returns the number of examples not covered belonging to the partition "particion"
Not_Covered_Training_Example(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set
Not_Covered_Training_Example(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set
not_eval() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Checks if the current rule has been evaluated or not.
not_eval() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC_Chromosome: Checks if the current chromosome has been evaluated or not.
not_eval() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Checks if the current rule has been evaluated or not.
NOT_INVOLVED - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene
NOT_INVOLVED - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene
NOT_INVOLVED - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene: It is used for representing and handling a Gene throughout the evolutionary learning
NOT_INVOLVED - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene: It is used for representing and handling a Gene throughout the evolutionary learning
NOT_INVOLVED - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene: It is used for representing and handling a Gene throughout the evolutionary learning
NoTakeInitGene() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Gene: Initialise the variable which does not take part in the rule
noTakeInitGene() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Gene: Non-intervene Initialization of an existing gene
NoTakeInitGene() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Gene: Initialise the variable which does not take part in the rule
NoTakeInitGene() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Gene: Non-intervene Initialization of an existing gene
NoTakeInitGene() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Gene: Initialise the variable which does not take part in the rule
notMemberOf(short, short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Checks whether a particular element/attribute identified by a column number is not a member of the given item set.
notMemberOf(short, short[]) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Checks whether a particular element/attribute identified by a column number is not a member of the given item set.
notMemberOf(short, short[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Checks whether a particular element/attribute identified by a column number is not a member of the given item set.
notSelectedDataset - Variable in class keel.GraphInterKeel.experiments.Experiments
Noutputs - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network: Number of units in each layer
Noutputs - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters: Number of units in each layer
Noutputs - Variable in class keel.Algorithms.Neural_Networks.gann.Network: Number of units in each layer
Noutputs - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters: Number of units in each layer
Noutputs - Variable in class keel.Algorithms.Neural_Networks.gmdh.Network: Number of units in each layer
Noutputs - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters: Number of units in each layer
Noutputs - Variable in class keel.Algorithms.Neural_Networks.net.Network: Number of units in each layer
Noutputs - Variable in class keel.Algorithms.Neural_Networks.net.Parameters: Number of units in each layer
nOutputs - Variable in class keel.GraphInterKeel.datacf.util.Dataset: Output number
NProduct - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node
NQueue - Class in keel.Algorithms.Lazy_Learning.IDIBL: File: NQueue.java A class modelling a queue of neighbors.It sorts its elements automatically, satrting from the nearest neighbor.
NQueue() - Constructor for class keel.Algorithms.Lazy_Learning.IDIBL.NQueue: Buider.Builts a empty queue.
nr_weight - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
nr_weight - Variable in class org.libsvm.svm_parameter
NRMCS - Class in keel.Algorithms.Instance_Selection.NRMCS: File: NRMCS.java The NRMCS Instance Selection algorithm.
NRMCS(String) - Constructor for class keel.Algorithms.Instance_Selection.NRMCS.NRMCS: Default constructor.
NRMCS - Class in keel.Algorithms.Preprocess.Instance_Selection.NRMCS: File: NRMCS.java The NRMCS Instance Selection algorithm.
NRMCS(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.NRMCS.NRMCS: Default constructor.
NRule - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node
NRuleBase - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node
nrules - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
NS(Instance, int, int, double[], FreqListPair[], Vector[], double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.EventCovering: Returns the NS value of a instance given.
nsalidas - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
NSC - Class in keel.Algorithms.Lazy_Learning.NSC: File: NSC.java The Nearest subclass algorithm.
NSC(String) - Constructor for class keel.Algorithms.Lazy_Learning.NSC.NSC: The main method of the class
nscor2(int, int) - Static method in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Quoted from original Fortran documentation: Calculates approximate expected values of normal order statistics.
Nspecify - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Is the experience average of classifiers in the action set to apply the specify operator.
NSPECIFY - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
NSquareRoot - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node
NSum - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node
nu - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Exponent in the power function for the fitness evaluation
nu - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Exponent in the power function for the fitness evaluation
NU - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
nu - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
nu - Variable in class org.libsvm.svm_parameter
NU_SVC - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
NU_SVC - Static variable in class org.libsvm.svm_parameter
NU_SVR - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
NU_SVR - Static variable in class org.libsvm.svm_parameter
nuevaTabla() - Method in class keel.Algorithms.RE_SL_Methods.mogulIRL.MiDataset
nuevo(double, double) - Method in class keel.Algorithms.Decision_Trees.Target.Nodo: Constructs a new node by spitting the set with the probability given.
NULL - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for null data.
NULL - Static variable in interface keel.Dataset.DataParserConstants
nulls - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Missing values of a instance
nulls - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: Missing values of a instance.
nulls - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Missing values of a instance
nulls - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Missing values of a instance.
nulls - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm: Missing values of a instance
nulls - Variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: Missing values of a instance
nulls - Variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: Missing values of a instance
nulls - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm: Missing values of a instance
nulosTest - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Test Missing values.
nulosTrain - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Training Missing values.
nulosTrain - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
nulosTrain - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
nulosTrain - Variable in class keel.Algorithms.Preprocess.Basic.Metodo: Training null data.
NUM_ALGORITHMS - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters: Number of algorithms considered.
num_reglas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Ecm: Returns the number of rules generated.
num_reglas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Ecm: Returns the number of rules generated.
num_reglas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Ecm: Returns the number of rules generated.
num_reglas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Ecm: Returns the number of rules generated.
num_reglas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Ecm: Returns the number of rules generated.
num_reglas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Ecm: Returns the number of rules generated.
num_reglas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Ecm: Returns the number of rules generated.
num_reglas() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Ecm: Returns the number of rules generated.
num_used - Variable in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Number of individuals really used
num_used - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Population: Number or individuals really used
numAliveRules - Variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
numAliveRules - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
numAllConditions(Instances) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Compute the number of all possible conditions that could appear in a rule of a given data.
numAllConditions() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Compute the number of all possible conditions that could appear in a rule of a given data.
numAllConditions(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Compute the number of all possible conditions that could appear in a rule of a given data.
numAllConditions() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Compute the number of all possible conditions that could appear in a rule of a given data.
numAllConditions(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Compute the number of all possible conditions that could appear in a rule of a given data.
numAllConditions() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: Compute the number of all possible conditions that could appear in a rule of a given data.
numAplicacions - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: This class is a classifier set.
numArcs() - Method in class keel.GraphInterKeel.experiments.Graph: Gets the current number of arcs
numArguments() - Method in class keel.Algorithms.Decision_Trees.M5.Information: Returns the option's number of arguments.
numArguments() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Option: Returns the option's number of arguments.
numArguments() - Method in class keel.Algorithms.SVM.SMO.core.Option: Returns the option's number of arguments.
numAtt - Variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
numAttributes() - Method in class keel.Algorithms.Decision_Trees.C45.Dataset: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.Decision_Trees.ID3.Dataset: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.Decision_Trees.M5.M5SparseInstance: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns the number of attributes.
NumAttributes - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
numAttributes - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
numAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns the number of attributes.
numAttributes - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
numAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance: Returns the number of attributes.
numAttributes - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters: Number of attributes.
numAttributes - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Parameters: Number of attributes.
numAttributes - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters: Number of attributes.
numAttributes - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters: Number of attributes.
numAttributes - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Parameters: Number of attributes.
numAttributes - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters: Number of attributes.
numAttributes - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Parameters: Number of attributes.
numAttributes() - Method in class keel.Algorithms.Rule_Learning.ART.Dataset: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.SVM.SMO.core.Instance: Returns the number of attributes.
numAttributes() - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns the number of attributes.
numBasicIntervals - Variable in class keel.Algorithms.Discretizers.MVD.MVD: Number of basic intervals.
NumberActiveLabels(String, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
NumberActiveLabels(String, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
NumberActiveLabels(String, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
numberAntecedents - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Number of antecedents of the rules.
numberExamples - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Number of examples managed.
numberFeatures - Variable in class keel.Algorithms.MIL.AbstractMIAlgorithm
numberInstances(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Returns the number of instances of the class with index passed as argument ("clas").
numberInstances(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It returns the number of instances in the data set for a given class.
numberInstances(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It returns the number of instances in the data set for a given class.
numberInstances(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It returns the number of instances in the data set for a given class.
numberInstances(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It returns the number of instances in the data set for a given class.
numberInstances(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Returns the number of instances of the class with index passed as argument ("clas").
numberInstances(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It returns the number of examples for a given class
numberInstances(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It returns the number of instances in the data set for a given class.
numberInstances(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It returns the number of instances in the data set for a given class.
numberInstances(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It computes the number of intances for the class "clas"
numberInstances(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It computes the number of intances for the class "clas"
numberInstances(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It computes the number of intances for the class "clas"
numberInstances(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It computes the number of intances for the class "clas"
numberInstances(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It computes the number of intances for the class "clas"
numberInstances(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It computes the number of intances for the class "clas"
numberInstances(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It returns the number of instances in the data set for a given class.
numberInstances(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It returns the number of instances in the data set for a given class.
numberInstances(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It returns the number of instances in the data set for a given class.
numberInstances(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It returns the number of instances in the data set for a given class.
numberInstances(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It returns the number of instances in the data set for a given class.
numberInstances(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It computes the number of intances for the class "clas"
numberInstances(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It returns the number of instances in the data set for a given class.
numberInstances(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It computes the number of intances for the class "clas"
numberInstances(int) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It computes the number of intances for the class "clas"
numberInstances(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It returns the number of instances in the dataset of the given class
numberInstances(int) - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It gets the number of examples of one class
numberKFoldCross - Variable in class keel.GraphInterKeel.experiments.Experiments
numberLine - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Number of line
numberLine - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Line number.
numberLine1 - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Number of line 1
numberLine1 - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Line number 1.
numberLine2 - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Number of line 2
numberLine2 - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Line number 2.
numberLine3 - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Number of line 3
numberLine3 - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Line number 3.
numberOfActions - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Represents the number of actions that a classifier can take.
numberOfActions - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Represents the number of actions that a classifier can take.
numberOfAttributes() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IMetadata: Returns number of mining attributes in mining data specification
numberOfAttributes() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.Metadata: Returns number of mining attributes in mining data specification
numberOfbetters - Variable in class keel.Algorithms.Instance_Generation.JADE.JADEGenerator
numberOfCharacters - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Represents the number of different characters that can take a character representation.
numberOfCharacters - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Represents the number of different characters that can take a character representation.
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.AMPSO.AMPSOGenerator
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.DE.DEGenerator: number of classes
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.DEGL.DEGLGenerator: Number of classes considered
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.DROP3LVQ3.DROP3LVQ3
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.DROP3PSO.DROP3PSO: Class Number.
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.DROP3SFLSDE.DROP3SFLSDE
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.ENPC.ENPCGenerator: Class number.
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.ICFLVQ3.ICFLVQ3
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.ICFPSO.ICFPSO: Class number.
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.ICFSFLSDE.ICFSFLSDE
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.ICPL.ICPLGenerator
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.IPLDE.IPLDEGenerator
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.JADE.JADEGenerator
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.MixtGauss.MixtGaussGenerator
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.MSE.MSEGenerator
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.POC.POCGenerator
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.PSCSA.PSCSAGenerator
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.PSO.PSOGenerator
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.SADE.SADEGenerator
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.SFLSDE.SFLSDEGenerator
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.SGP.SGPGenerator
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.SSMALVQ3.SSMALVQ3
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.SSMAPSO.SSMAPSO: Class Number.
numberOfClass - Variable in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE
numberOfClass - Variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ADE_CoForestGenerator: Number of classes.
numberOfClass - Variable in class keel.Algorithms.Semi_Supervised_Learning.APSSC.APSSCGenerator: Number of classes.
numberOfClass - Variable in class keel.Algorithms.Semi_Supervised_Learning.C45SSL.C45SSLGenerator: Number of classes.
numberOfClass - Variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCGenerator: Number of classes.
numberOfClass - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoBC.CoBCGenerator: Number of classes.
numberOfClass - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.CoForestGenerator: Number of classes.
numberOfClass - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoTraining.CoTrainingGenerator: Number of classes.
numberOfClass - Variable in class keel.Algorithms.Semi_Supervised_Learning.DE_TriTraining.DE_TriTrainingGenerator: Number of classes.
numberOfClass - Variable in class keel.Algorithms.Semi_Supervised_Learning.Democratic.DemocraticGenerator: Number of classes.
numberOfClass - Variable in class keel.Algorithms.Semi_Supervised_Learning.NBSSL.NBSSLGenerator: Number of classes.
numberOfClass - Variable in class keel.Algorithms.Semi_Supervised_Learning.NNSSL.NNSSLGenerator: Number of classes.
numberOfClass - Variable in class keel.Algorithms.Semi_Supervised_Learning.RASCO.RASCOGenerator: Number of classes.
numberOfClass - Variable in class keel.Algorithms.Semi_Supervised_Learning.Rel_RASCO.Rel_RASCOGenerator: Number of classes.
numberOfClass - Variable in class keel.Algorithms.Semi_Supervised_Learning.SelfTraining.SelfTrainingGenerator: Number of classes.
numberOfClass - Variable in class keel.Algorithms.Semi_Supervised_Learning.SETRED.SETREDGenerator: Number of classes.
numberOfClass - Variable in class keel.Algorithms.Semi_Supervised_Learning.SMOSSL.SMOSSLGenerator: Number of classes.
numberOfClass - Variable in class keel.Algorithms.Semi_Supervised_Learning.SNNRCE.SNNRCEGenerator: Number of classes.
numberOfClass - Variable in class keel.Algorithms.Semi_Supervised_Learning.TriTraining.TriTrainingGenerator: Number of classes.
numberOfClassifiers - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoBC.CoBCGenerator: Number of classifiers considered.
numberOfClassifiers - Variable in class keel.Algorithms.Semi_Supervised_Learning.Democratic.DemocraticGenerator: Number of classifiers considered for the ensemble.
numberOfDontCareSymbols() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Returns the number of don't care symbols in the classifier.
numberOfDontCareSymbols() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: Returns the number of don't care symbols in the classifier.
numberOfExamples(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It obtains the number of examples for the i-th class
numberOfExplores - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Represents the number of experiments that have to be made.
numberOfExplores - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Represents the number of experiments that have to be made.
NUMBEROFEXPLORES - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
NumberOfId - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: Number of Leafs currently created in the tree
numberOfInputs() - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Returns the number of attributes that has an input.
numberOfInputs - Static variable in class keel.Algorithms.Instance_Generation.utilities.Distance: Number of inputs of the prototypes (used to optimize calculations).
numberOfInputs() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Returns the number of attributes that has an input.
numberOfInputs - Static variable in class keel.Algorithms.Semi_Supervised_Learning.utilities.Distance: Number of inputs of the prototypes (used to optimize calculations).
numberOfInstances() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset: Get the number of Instances.
numberOfInstances() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IDataset: Get the number of Instances
numberOfIterations - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQPRU: Number of iterations of the LVQPRU algorithm.
numberOfIterations - Variable in class keel.Algorithms.Instance_Generation.VQ.AVQGenerator: Number of iterations of the AVQGenerator.
numberOfIterationsLVQ2_1 - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQPRU: Number of iterations of the internal LVQ2.1 mapping.
NumberOfLeafs - Static variable in class keel.Algorithms.Decision_Trees.C45.Tree: Number of Leafs in the tree
NumberOfLeafs - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Tree: Number of Leafs in the tree
NumberOfLeafs - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree: Number of Leafs in the tree
NumberOfLeafs - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Tree: Number of Leafs in the tree
NumberOfLeafs - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree: Number of Leafs in the tree
NumberOfLeafs - Static variable in class keel.Algorithms.Rule_Learning.C45Rules.Tree: Number of Leafs in the tree
NumberOfLeafs - Static variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree: Number of Leafs in the tree
NumberOfLeafs - Static variable in class keel.Algorithms.Rule_Learning.PART.Tree: Number of Leafs in the tree
NumberOfLeafs - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: Number of Leafs in the tree
numberOfLinearModels() - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode: Counts the number of linear models in the tree.
numberOfLinearModels() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode: Counts the number of linear models in the tree.
numberOfNodes - Static variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TtreeNode: The number of nodes in the T-tree.
NumberOfNodes - Static variable in class keel.Algorithms.Decision_Trees.C45.Tree: Total number of Nodes in the tree
NumberOfNodes - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Tree: Total number of Nodes in the tree
NumberOfNodes - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree: Total number of Nodes in the tree
NumberOfNodes - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Tree: Total number of Nodes in the tree
NumberOfNodes - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree: Total number of Nodes in the tree
NumberOfNodes - Static variable in class keel.Algorithms.Rule_Learning.C45Rules.Tree: Total number of Nodes in the tree
NumberOfNodes - Static variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree: Total number of Nodes in the tree
NumberOfNodes - Static variable in class keel.Algorithms.Rule_Learning.PART.Tree: Total number of Nodes in the tree
NumberOfNodes - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: Total number of Nodes in the tree
numberOfNodes - Static variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TtreeNode: The number of nodes in the T-tree.
numberOfNodes - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TtreeNode: The number of nodes in the T-tree.
numberOfNotUseful() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Returns the number of non useful classifiers in the population.
numberOfOutputs() - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Returns the number of attributes that has an output.
numberOfOutputs() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Returns the number of attributes that has an output.
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.AMPSO.AMPSOGenerator: Swarmsize is the percentage
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.Chen.ChenGenerator: Number of prototypes to be generated.
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.DE.DEGenerator: number of Prototypes
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.DEGL.DEGLGenerator: Number of prototypes considered
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.DROP3LVQ3.DROP3LVQ3
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.DROP3PSO.DROP3PSO: prototypes number.
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.DROP3SFLSDE.DROP3SFLSDE
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.ENPC.ENPCGenerator: Number of prototypes.
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.ICFLVQ3.ICFLVQ3
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.ICFPSO.ICFPSO: Prototypes number.
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.ICFSFLSDE.ICFSFLSDE
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.ICPL.ICPLGenerator
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.IPLDE.IPLDEGenerator
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.JADE.JADEGenerator
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQPRU: Number of prototypes that will be generated.
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.MixtGauss.MixtGaussGenerator
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.MSE.MSEGenerator
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.OBDE.OBDEGenerator
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.PNN.PNNGenerator: Number of prototypes to be generated.
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.POC.POCGenerator
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.PSCSA.PSCSAGenerator
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.PSO.PSOGenerator
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.RSP.RSPGenerator
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.SADE.SADEGenerator
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.SFLSDE.SFLSDEGenerator
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.SSMALVQ3.SSMALVQ3
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.SSMAPSO.SSMAPSO: prototypes number.
numberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE
numberOfPrototypes - Variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ADE_CoForestGenerator: Number of prototypes to generated.
numberOfPrototypes - Variable in class keel.Algorithms.Semi_Supervised_Learning.APSSC.APSSCGenerator: Number of prototypes.
numberOfPrototypes - Variable in class keel.Algorithms.Semi_Supervised_Learning.C45SSL.C45SSLGenerator: Number of prototypes.
numberOfPrototypes - Variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCGenerator: Number of prototypes.
numberOfPrototypes - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoBC.CoBCGenerator: Number of prototypes.
numberOfPrototypes - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.CoForestGenerator: Number of prototypes.
numberOfPrototypes - Variable in class keel.Algorithms.Semi_Supervised_Learning.CoTraining.CoTrainingGenerator: Number of prototypes.
numberOfPrototypes - Variable in class keel.Algorithms.Semi_Supervised_Learning.DE_TriTraining.DE_TriTrainingGenerator: Number of prototypes.
numberOfPrototypes - Variable in class keel.Algorithms.Semi_Supervised_Learning.Democratic.DemocraticGenerator: Number of prototypes.
numberOfPrototypes - Variable in class keel.Algorithms.Semi_Supervised_Learning.NBSSL.NBSSLGenerator: Number of prototypes.
numberOfPrototypes - Variable in class keel.Algorithms.Semi_Supervised_Learning.NNSSL.NNSSLGenerator: Number of prototypes.
numberOfPrototypes - Variable in class keel.Algorithms.Semi_Supervised_Learning.RASCO.RASCOGenerator: Number of prototypes.
numberOfPrototypes - Variable in class keel.Algorithms.Semi_Supervised_Learning.Rel_RASCO.Rel_RASCOGenerator: Number of prototypes.
numberOfPrototypes - Variable in class keel.Algorithms.Semi_Supervised_Learning.SelfTraining.SelfTrainingGenerator: Number of prototypes.
numberOfPrototypes - Variable in class keel.Algorithms.Semi_Supervised_Learning.SETRED.SETREDGenerator: Number of prototypes.
numberOfPrototypes - Variable in class keel.Algorithms.Semi_Supervised_Learning.SMOSSL.SMOSSLGenerator: Number of prototypes.
numberOfPrototypes - Variable in class keel.Algorithms.Semi_Supervised_Learning.SNNRCE.SNNRCEGenerator: Number of prototypes.
numberOfPrototypes - Variable in class keel.Algorithms.Semi_Supervised_Learning.TriTraining.TriTrainingGenerator: Number of prototypes.
numberOfPrototypesGenerated - Variable in class keel.Algorithms.Instance_Generation.BTS3.BTS3Generator: Size of the generated set.
numberOfPrototypesGenerated - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQGenerator: Number of prototypes of the resulting set.
numberOfPrototypesSelected - Variable in class keel.Algorithms.Instance_Generation.BasicMethods.RandomSelector: Number of prototypes that will contain the generated data set (prototypes extracted from the training data set).
numberOfReductExamples - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Is the number of reduct iterations that have to be made in a reduction.
numberOfStrategies - Variable in class keel.Algorithms.Instance_Generation.DROP3LVQ3.DROP3LVQ3
numberOfStrategies - Variable in class keel.Algorithms.Instance_Generation.DROP3PSO.DROP3PSO: number of strategies in the pool.
numberOfStrategies - Variable in class keel.Algorithms.Instance_Generation.DROP3SFLSDE.DROP3SFLSDE
numberOfStrategies - Variable in class keel.Algorithms.Instance_Generation.ICFLVQ3.ICFLVQ3
numberOfStrategies - Variable in class keel.Algorithms.Instance_Generation.ICFPSO.ICFPSO: Number of strategies in the pool
numberOfStrategies - Variable in class keel.Algorithms.Instance_Generation.ICFSFLSDE.ICFSFLSDE
numberOfStrategies - Variable in class keel.Algorithms.Instance_Generation.SADE.SADEGenerator
numberOfStrategies - Variable in class keel.Algorithms.Instance_Generation.SSMALVQ3.SSMALVQ3
numberOfStrategies - Variable in class keel.Algorithms.Instance_Generation.SSMAPSO.SSMAPSO: number of strategies in the pool.
numberOfStrategies - Variable in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE
numberOfTestExamples - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Indicates the number of iterations that has to be made in a test execution.
numberOfTestExamples - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Indicates the number of iterations that has to be made in a test execution.
NUMBEROFTESTEXAMPLES - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
numberOfViolatedConstraints - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Number of Violated Constraints.
numberReferences - Variable in class keel.Algorithms.MIL.Nearest_Neighbour.AbstractNearestNeighbour
numberRules - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Number of rules of the system.
numberUncoveredExamples - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Variables to control the number of uncovered examples.
numberValues(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Returns the number of different values of the attribute with index passed as argument.
numberValues(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: Function to get the number of different feasible values for a given attribute
numberValues(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: Function to get the number of different feasible values for a given attribute
numberValues(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: Function to get the number of different feasible values for a given attribute
numberValues(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: Function to get the number of different feasible values for a given attribute
numberValues(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Returns the number of different values of the attribute with index passed as argument.
numberValues(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It returns the number of labels for a nominal attribute
numberValues(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: Function to get the number of different feasible values for a given attribute
numberValues(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: Function to get the number of different feasible values for a given attribute
numberValues(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It returns the number of nominal values for the atributte "attribute"
numberValues(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It returns the number of nominal values for the atributte "attribute"
numberValues(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It returns the number of nominal values for the atributte "attribute"
numberValues(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It returns the number of nominal values for the atributte "attribute"
numberValues(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It returns the number of nominal values for the atributte "attribute"
numberValues(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It returns the number of nominal values for the atributte "attribute"
numberValues(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: Function to get the number of different feasible values for a given attribute
numberValues(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: Function to get the number of different feasible values for a given attribute
numberValues(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: Function to get the number of different feasible values for a given attribute
numberValues(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: Function to get the number of different feasible values for a given attribute
numberValues(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Function to get the number of different feasible values for a given attribute
numberValues(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It returns the number of nominal values for the atributte "attribute"
numberValues(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Function to get the number of different feasible values for a given attribute
numberValues(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It returns the number of nominal values for the atributte "attribute"
numberValues(int) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It returns the number of nominal values for the atributte "attribute"
numberValues(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It returns the number of different values of an attribute
numberValues(int) - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It gets the number of different values of an specific attribute
numberValues(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: It returns the number of different values in the case of a nominal variable
numberValues(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: It returns the number of different values in the case of a nominal variable
numberValues(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: It returns the number of different values in the case of a nominal variable
numberValues(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: It returns the number of different values in the case of a nominal variable
numberValues(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: It returns the number of different values in the case of a nominal variable
numberValues(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: It returns the number of different values in the case of a nominal variable
numberValues(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: It returns the number of different values in the case of a nominal variable
numberValues(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: It returns the number of different values in the case of a nominal variable
numberValues(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: It returns the number of different values in the case of a nominal variable
numCacheHits() - Method in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: Returns the number of cache hits on dot products.
numCacheHits() - Method in class keel.Algorithms.SVM.SMO.supportVector.Kernel: Returns the number of dot product cache hits.
numCacheHits() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Returns the number of dot product cache hits.
numCategories - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.CategoricalAttribute: Number of Categories
numChildren() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Returns the number of children.
numClassAttributeValues() - Method in class keel.Algorithms.SVM.SMO.SMO: Returns the number of values of the class attribute.
numClasses() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.DataBase: It returns the number of different possible outputs (classes) of the examples
numClasses() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.DataBase: It returns the number of different possible outputs (classes) of the examples
numClasses - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Number of classes in input data set (input by the user).
numClasses() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.DataBase: It returns the number of different possible outputs (classes) of the examples
numClasses() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.DataBase: It returns the number of different possible outputs (classes) of the examples
numClasses() - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Returns number of classes.
numClasses() - Method in class keel.Algorithms.Decision_Trees.C45.Dataset: Returns the number of possible values of the class attribute.
numClasses() - Method in class keel.Algorithms.Decision_Trees.C45.Itemset: Returns the number of class values.
numClasses() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Returns number of classes.
numClasses() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Returns the number of possible values of the class attribute.
numClasses() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset: Returns the number of class values.
numClasses() - Method in class keel.Algorithms.Decision_Trees.ID3.Dataset: Returns the number of possible values of the class attribute.
numClasses() - Method in class keel.Algorithms.Decision_Trees.ID3.Itemset: Returns the number of class values.
numClasses() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Returns the number of class labels.
numClasses() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Returns the number of class labels.
numClasses() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Returns the number of possible values of the class attribute.
numClasses() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Itemset: Returns the number of class values.
numClasses() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Returns the number of class labels.
numClasses() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns the number of class labels.
numClasses - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
numClasses - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
numClasses() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Returns the number of class values.
numClasses() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns the number of possible values of the class attribute.
numClasses - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
numClasses() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Returns the number of possible values of the class attribute.
numClasses() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset: Returns the number of class values.
numClasses() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Returns number of classes.
numClasses() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset: Returns the number of class values.
numClasses() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Returns the number of possible values of the class attribute.
numClasses() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Returns number of classes.
numClasses() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Returns the number of possible values of the class attribute.
numClasses() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset: Returns the number of class values.
numClasses() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Returns number of classes.
numClasses() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Returns the number of possible values of the class attribute.
numClasses() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset: Returns the number of class values.
numClasses() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Returns the number of class labels.
numClasses() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns the number of class labels.
numClasses - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters: Number of classes.
numClasses - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Parameters: Number of classes.
numClasses - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters: Number of classes.
numClasses - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters: Number of classes.
numClasses - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Parameters: Number of classes.
numClasses - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters: Number of classes.
numClasses - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Parameters: Number of classes.
numClasses() - Method in class keel.Algorithms.Rule_Learning.ART.Dataset: Returns the number of possible values of the class attribute.
numClasses() - Method in class keel.Algorithms.Rule_Learning.ART.Itemset: Returns the number of class values.
numClasses() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Returns number of classes.
numClasses() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Itemset: Returns the number of class values.
numClasses() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Returns the number of possible values of the class attribute.
numClasses() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Returns number of classes.
numClasses() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset: Returns the number of class values.
numClasses() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Returns the number of possible values of the class attribute.
numClasses() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Returns the number of possible values of the class attribute.
numClasses() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset: Returns the number of class values.
numClasses() - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Returns number of classes.
numClasses() - Method in class keel.Algorithms.Rule_Learning.PART.Itemset: Returns the number of class values.
numClasses() - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Returns the number of possible values of the class attribute.
numClasses() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Returns number of classes.
numClasses() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Returns the number of possible values of the class attribute.
numClasses() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset: Returns the number of class values.
numClasses - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerCSVM: Number of classes.
numClasses - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerSMO: Number of classes.
numClasses - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.ParametersSMO: Number of classes.
numClasses() - Method in class keel.Algorithms.SVM.SMO.core.Instance: Returns the number of class labels.
numClasses() - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns the number of class labels.
NumClassesNotRemoved() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: Returns the number of classes of the instances not removed.
NumClassesNotRemoved() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: Returns the number of classes of the instances not removed.
NumClassesNotRemoved() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: Returns the number of classes of the instances not removed.
numCols - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Command line argument for number of columns.
numCols - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Number of columns.
numCols - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Number of columns.
numColumns() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Returns the number of columns in the matrix.
numConsequents() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase: Returns the number of consequents in RuleBase.
numCorrect() - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Returns the weight of all itemsets of the class with highest frequency.
numCorrect(int) - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Returns the number of correctly classified itemsets for the given value.
numCorrect() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Returns the weight of all itemsets of the class with highest frequency.
numCorrect(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Returns the number of correctly classified itemsets for the given value.
numCorrect() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Returns the weight of all itemsets of the class with highest frequency.
numCorrect(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Returns the number of correctly classified itemsets for the given value.
numCorrect() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Returns the weight of all itemsets of the class with highest frequency.
numCorrect(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Returns the number of correctly classified itemsets for the given value.
numCorrect() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Returns the weight of all itemsets of the class with highest frequency.
numCorrect(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Returns the number of correctly classified itemsets for the given value.
numCorrect() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Returns the weight of all itemsets of the class with highest frequency.
numCorrect(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Returns the number of correctly classified itemsets for the given value.
numCorrect() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Returns the weight of all itemsets of the class with highest frequency.
numCorrect(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Returns the number of correctly classified itemsets for the given value.
numCorrect() - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Returns the weight of all itemsets of the class with highest frequency.
numCorrect(int) - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Returns the number of correctly classified itemsets for the given value.
numCorrect() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Returns the weight of all itemsets of the class with highest frequency.
numCorrect(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Returns the number of correctly classified itemsets for the given value.
numCuantitativos() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: Returns number of quantitative attributes.
numDataset - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Number of datasets.
numDistinctValues(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Returns the number of distinct values of a given attribute.
numDistinctValues(M5Attribute) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Returns the number of distinct values of a given attribute.
numDistinctValues(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns the number of distinct values of a given attribute.
numDistinctValues(AttributeWeka) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns the number of distinct values of a given attribute.
numDistinctValues(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns the number of distinct values of a given attribute.
numDistinctValues(Attribute) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns the number of distinct values of a given attribute.
numDistinctValues(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Returns the number of distinct values of a given attribute.
numDistinctValues(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Returns the number of distinct values of a given attribute.
numDistinctValues(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: Returns the number of distinct values of a given attribute.
numDistinctValues(int) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns the number of distinct values of a given attribute.
numDistinctValues(Attribute) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns the number of distinct values of a given attribute.
numEjemplos(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It returns the number of instances in the data set for a given class.
numEjemplos(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.Poblacion: Obtains the number of examples for the i-th class.
numEjemplos(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It returns the number of instances in the dataset of the given class
numEjemplos(int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It returns the number of instances in the dataset of the given class
numEjemplos(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It returns the number of instances in the data set for a given class.
numElements() - Method in class keel.Algorithms.SVM.SMO.supportVector.SMOset: Returns the number of elements in the set.
numElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.FreqList: The number of different elements stored
numElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.FreqList: The number of different elements stored
numElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.FreqList: The number of different elements stored
numElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqList: The number of different elements stored
numElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqListPair: Number of different pairs of this list
numElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.FreqList: The number of different elements stored
numElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.FreqList: The number of different elements stored
numElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.FreqList: The number of different elements stored
numElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.FreqList: The number of different elements stored
numElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.FreqList: The number of different elements stored
numElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.FreqList: The number of different elements stored
NUMERIC - Static variable in class keel.Algorithms.Decision_Trees.M5.M5Attribute: Constant set for numeric attributes.
NUMERIC - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constant set for numeric attributes.
Numeric - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat: Class to implements some Numeric operation.
Numeric() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Numeric
NumericalDerivative - Class in keel.Algorithms.Preprocess.Missing_Values.EM.util: approximates numerically the first and second derivatives of a function of a single variable and approximates gradient and diagonal of Hessian for multivariate functions Known bugs and limitations: - the sparse number of function evaluations used can potentially
NumericalDerivative() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EM.util.NumericalDerivative
NumericalNaiveBayes - Class in keel.Algorithms.Semi_Supervised_Learning.Basic: Numerical Naive Bayes algorithm.
NumericalNaiveBayes(double[][], int[], double[][], int[], int) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.NumericalNaiveBayes: Executes the Naive Bayes algorithms with the datasets given.
NumericalNaiveBayes(String, InstanceSet, InstanceSet, InstanceSet) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.NumericalNaiveBayes: Executes the Naive Bayes algorithms with the datasets given.
NumericalNaiveBayes(String) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.NumericalNaiveBayes: The main method of the class
NumericAntd - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: The antecedent with numeric attribute
NumericAntd(AttributeWeka) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.NumericAntd: Constructor
numericClass(String) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: Returns a numeric representation of a class nominal value given as argument.
NumericFunction<IndexType> - Class in keel.Algorithms.Instance_Generation.utilities
NumericFunction<IndexType> - Class in keel.Algorithms.Semi_Supervised_Learning.utilities: Numeric function class
NUMERICO - Static variable in class keel.Algorithms.Rule_Learning.AQ.Instance: Attribute numeric type flag.
numericStats - Variable in class keel.Algorithms.Decision_Trees.M5.M5AttrStats: Stats on numeric value distributions
numeroMuestrasCondiciones(Regla, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Returns the number of examples covered by the conditions stored in the rule given.
numeroMuestrasCondiciones(Regla, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Returns the number of examples covered by the conditions stored in the rule given.
numeroMuestrasCondiciones(Regla, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Returns the number of examples covered by the conditions stored in the rule given.
numeroMuestrasCondiciones(Regla, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Returns the number of examples covered by the conditions stored in the rule given.
numeroMuestrasCubiertas(Regla, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Returns the number of examples covered by the whole rule given (covered by the conditions and with the same class of the rule).
numeroMuestrasCubiertas(Regla, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Returns the number of examples covered by the whole rule given (covered by the conditions and with the same class of the rule).
numeroMuestrasCubiertas(Regla, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Returns the number of examples covered by the whole rule given (covered by the conditions and with the same class of the rule).
numeroMuestrasCubiertas(Regla, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Returns the number of examples covered by the whole rule given (covered by the conditions and with the same class of the rule).
numeroMuestrasCubiertasSinClase(Regla) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Returns the number of examples covered by the whole rule given (covered by the conditions and without the same class of the rule).
numEtiquetas() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseD: It returns the number of fuzzy labels
numEtiquetas() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseR
numEtiquetas() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseR
numEvals() - Method in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel: Returns the number of time Eval has been called.
numEvals() - Method in class keel.Algorithms.SVM.SMO.supportVector.Kernel: Returns the number of kernel evaluation performed.
numEvals() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Returns the number of kernel evaluation performed.
numExecutions - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters: Number of executions.
numFalseNegatives(int) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Calculate number of false negatives with respect to a particular class.
numFalsePositives(int) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Calculate number of false positives with respect to a particular class.
numFoldsTipText() - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Returns the tip text for this property
numFoldsTipText() - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Returns the tip text for this property
numFoldsTipText() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Returns the tip text for this property
numFoldsTipText() - Method in class keel.Algorithms.SVM.SMO.SMO: Returns the tip text for this property
numFrequentsets - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: The number of frequent sets (nodes in t-tree with above minimum support) generated so far.
numFun - Variable in class keel.Algorithms.Preprocess.Missing_Values.EM.util.UnivariateMinimum: total number of function evaluations neccessary
numIncorrect() - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Returns incorrectly classifed
numIncorrect(int) - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Returns the number of incorrectly classified itemsets for the given value.
numIncorrect() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Returns number of incorrectly classified examples
numIncorrect(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Returns the number of incorrectly classified itemsets for the given value.
numIncorrect() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Returns incorrectly classifed
numIncorrect(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Returns the number of incorrectly classified itemsets for the given value.
numIncorrect() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Returns incorrectly classifed items
numIncorrect(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Returns the number of incorrectly classified itemsets for the given value.
numIncorrect() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Returns incorrectly classifed
numIncorrect(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Returns the number of incorrectly classified itemsets for the given value.
numIncorrect() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Returns incorrectly classifed
numIncorrect(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Returns the number of incorrectly classified itemsets for the given value.
numIncorrect() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Returns incorrectly classifed
numIncorrect(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Returns the number of incorrectly classified itemsets for the given value.
numIncorrect() - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Returns incorrectly classifed
numIncorrect(int) - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Returns the number of incorrectly classified itemsets for the given value.
numIncorrect() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Returns incorrectly classifed
numIncorrect(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Returns the number of incorrectly classified itemsets for the given value.
numInputs() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: To get the number of inputs
numInstances() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Gets the number of test instances that had a known class value (actually the sum of the weights of test instances with known class value).
numInstances() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Returns the number of instances in the dataset.
numInstances() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns the number of instances in the dataset.
NumInstances - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
numInstances - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
numInstances - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.matchProfileAgent
numInstances() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns the number of instances in the dataset.
numInstances - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters: Number of instances.
numInstances - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Parameters: Number of instances.
numInstances - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters: Number of instances.
numInstances - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters: Number of instances.
numInstances - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Parameters: Number of instances.
numInstances - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters: Number of instances.
numInstances - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Parameters: Number of instances.
numInstances - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerCSVM: Number of instances.
numInstances - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerSMO: Number of instances.
numInstances - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.ParametersSMO: Number of instances.
numInstances() - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns the number of instances in the dataset.
NumInterv(float, int, TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Returns the number of the interval of the indicated variable to which belongs the value.
NumInterv(float, int, TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndCAN: Returns the number of the interval of the indicated variable to which belongs the value.
NumInterv(float, int, TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndDNF: Returns the number of the interval of the indicated variable to which belongs the value.
NumInterv(float, int, TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Returns the number of the interval of the indicated variable to which belongs the value.
NumInterv(float, int, TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual: Returns the number of the interval of the indicated variable to which belongs the value.
numIntervals - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
numIntervals - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
numIntervals(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.DataB
numIntervals() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
numIntervals(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.DataB
numIntervals(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.DiscreteDataset: It returns the number of intervals in which the domain of an attribute has been decomposed
numIntervals() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
numIntrvls - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
numItemsets() - Method in class keel.Algorithms.Decision_Trees.C45.Dataset: Returns the number of itemsets.
numItemsets() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset: Returns the number of itemsets.
numItemsets() - Method in class keel.Algorithms.Decision_Trees.ID3.Dataset: Returns the number of itemsets.
numItemsets() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Dataset: Returns the number of itemsets.
numItemsets() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Returns the number of itemsets.
numItemsets() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset: Returns the number of itemsets.
numItemsets() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset: Returns the number of itemsets.
numItemsets() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Returns the number of itemsets.
numItemsets() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset: Returns the number of itemsets.
numItemsets() - Method in class keel.Algorithms.Rule_Learning.ART.Dataset: Returns the number of itemsets.
numItemsets() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset: Returns the number of itemsets.
numItemsets() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset: Returns the number of itemsets.
numItemsets() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset: Returns the number of itemsets.
numItemsets() - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset: Returns the number of itemsets.
numItemsets() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset: Returns the number of itemsets.
numIterations - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
numIterations - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
numIterations - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
numIterationsMDL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
numKO - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.matchProfileAgent
numLabels(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.DataBase: It returns the number of different labels that a specific input attribute can hold
numLabels(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.DataBase: It returns the number of different labels that a specific input attribute can hold
numLabels(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.DataBase: It returns the number of different labels that a specific input attribute can hold
numLabels(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.DataBase: It returns the number of different labels that a specific input attribute can hold
numLabels(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.DataBase: It returns the number of different labels that a specific input attribute can hold
numLabels(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.DataBase: It returns the number of different labels that a specific input attribute can hold
numLabels(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.DataBase: It returns the number of different labels that a specific input attribute can hold
numLabels() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.DataBase: It returns the number of fuzzy labels
numLabels() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.DataBase: It returns the number of fuzzy labels
numLabels() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.DataBase: It returns the number of fuzzy labels
numLabels(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.DataBase
numLabels() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.DataBase: It returns the number of fuzzy labels
numLabels(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.DataBase: Returns the number of labels of a given variable.
numLayers - Static variable in class keel.GraphInterKeel.experiments.Layer
numLeaves(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode: Sets the leaves' numbers
numLeaves(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode: Sets the leaves' numbers
numliteralCovers(boolean[][], int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.pnPair: It computes the number of literals that covers the pnPair
numMatched - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.matchProfileAgent
numNeighbors - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters: Number of nearest neighbours considered.
numNeighbors - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters: Number of nearest neighbours considered.
numNodes() - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Computes size of the tree.
numNodes() - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Computes size of the tree.
numNodes() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Computes size of the tree.
numNodes() - Method in class keel.GraphInterKeel.experiments.Graph: Gets the number of nodes of this graph
numObjectives - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
numObjectives - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
numObjectives - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
numOK - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.matchProfileAgent
numOneItemSets - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: The number of one itemsets (singletons).
numOneItemSets - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: The number of one itemsets (singletons).
numOneItemSets - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: The number of one itemsets (singletons).
numOutputs() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: To get the number of outputs
numParentsRSWcrossover - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
numPartitions - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters: Number of partitions.
numPartitions - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Parameters: Number of partitions.
numPartitions - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters: Number of partitions.
numPartitions - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters: Number of partitions.
numPartitions - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Parameters: Number of partitions.
numPartitions - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerCSVM: Number of partitions.
numPartitions - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerSMO: Number of partitions.
numPartitions - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.ParametersSMO: Number of partitions.
numPendingOutput() - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Returns the number of instances pending output
numPendingOutput() - Method in class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter: Returns the number of pending output.
numReglas - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.EscribeBCLing: Number of rules.
numReglas - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.LeerWm: Number of rules.
numReglas - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.EscribeBCLing: Number of rules.
numReglas - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.LeerWm: Number of rules.
numReglas - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.EscribeBCLing: Number of rules.
numReglas - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.LeerWm: Number of rules.
numReglas - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.EscribeBCLing: Number of rules.
numReglas - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.LeerWm: Number of rules.
numReglas - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.EscribeBCLing: Number of rules.
numReglas - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.LeerWm: Number of rules.
numReglas - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.EscribeBCLing: Number of rules.
numReglas - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.LeerWm: Number of rules.
numReglas - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.EscribeBCLing: Number of rules.
numReglas - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.LeerWm: Number of rules.
numReglas - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.EscribeBCLing: Number of rules.
numReglas - Variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.LeerWm: Number of rules.
numRepetitionsLearning - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
numRows - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Command line argument for number of rows.
numRows() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Returns the number of rows in the matrix.
numRows - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Number of rows.
numRows - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Number of rows.
numRowsInInputSet - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Number of rows in input data set, not the same as the number of rows in the classification training set.
numRowsInTestSet - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Number of rows in test set, again not the same as the number of rows in the classification training set.
numRowsInTrainingSet - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Number of rows in training set, also not the same as the number of rows in the classification training set.
numRules - Variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
numRules - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
numSamplesLeft() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Sampling
numSamplesLeft() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Sampling
numSpecialStages() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
numSpecialStages() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list
numSpecialStages() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
numSpecialStages() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
numSpecialStages() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierADI
numSpecialStages() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierGABIL
numSpecialStages() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierUBR
numSpecialStages() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
numSpecialStages() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
numSpecialStages() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
numStages - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
numStrata - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
numStrata - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
numStrataWindowing - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
numSubsets - Variable in class keel.Algorithms.Decision_Trees.C45.Cut: Number of subsets.
numSubsets() - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Returns the number of created subsets for the cut.
numSubsets - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Number of subsets.
numSubsets() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Returns the number of created subsets for the cut.
numSubsets - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Number of subsets.
numSubsets() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Returns the number of created subsets for the cut.
numSubsets - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Number of subsets.
numSubsets() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Returns the number of created subsets for the cut.
numSubsets - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Number of subsets.
numSubsets() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Returns the number of created subsets for the cut.
numSubsets - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Number of subsets.
numSubsets() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Returns the number of created subsets for the cut.
numSubsets - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Number of subsets.
numSubsets() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Returns the number of created subsets for the cut.
numSubsets - Variable in class keel.Algorithms.Rule_Learning.PART.Cut: Number of subsets.
numSubsets() - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Returns the number of created subsets for the cut.
numSubsets - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Number of subsets.
numSubsets() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Returns the number of created subsets for the cut.
numTrueNegatives(int) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Calculate the number of true negatives with respect to a particular class.
numTruePositives(int) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Calculate the number of true positives with respect to a particular class.
numUpdates - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: The number of updates required to generate the T-tree.
numUpdates - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TotalSupportTree: The number of updates required to generate the T-tree.
numUpdates - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TotalSupportTree: The number of updates required to generate the T-tree.
numValues() - Method in class keel.Algorithms.Decision_Trees.C45.Attribute: Function to get the number of values of a discret attribute.
numValues() - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Returns number of values.
numValues() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Attribute: Function to get the number of values of a discret attribute.
numValues() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Returns number of values.
numValues() - Method in class keel.Algorithms.Decision_Trees.ID3.Attribute: Function to get the number of values of a discret attribute.
numValues() - Method in class keel.Algorithms.Decision_Trees.M5.M5Attribute: Returns the number of attribute values.
numValues() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Returns the number of values present.
numValues() - Method in class keel.Algorithms.Decision_Trees.M5.M5SparseInstance: Returns the number of values in the sparse vector.
numValues() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Attribute: Function to get the number of values of a discret attribute.
numValues() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Returns the number of attribute values.
numValues() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Returns the number of values present.
numValues() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Returns the number of values.
numValues() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute: Function to get the number of values of a discret attribute.
numValues() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA_Attribute: Function to get the number of values of a discret attribute.
numValues() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Returns number of values.
numValues() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyAttribute: Function to get the number of values of a discret attribute.
numValues() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Attribute: Function to get the number of values of a discret attribute.
numValues() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Returns number of values.
numValues() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Attribute: Function to get the number of values of a discret attribute.
numValues() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Returns number of values.
numValues() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Returns the number of attribute values.
numValues() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Returns the number of values present.
numValues() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance: Returns the number of values in the sparse vector.
numValues() - Method in class keel.Algorithms.Rule_Learning.ART.Attribute: Function to get the number of values of a discret attribute.
numValues() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Returns number of values.
numValues() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyAttribute: Function to get the number of values of a discret attribute.
numValues() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Returns number of values.
numValues() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyAttribute: Function to get the number of values of a discret attribute.
numValues() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Attribute: Function to get the number of values of a discret attribute.
numValues() - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Returns number of values.
numValues() - Method in class keel.Algorithms.Rule_Learning.PART.MyAttribute: Function to get the number of values of a discret attribute.
numValues() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Returns number of values.
numValues() - Method in class keel.Algorithms.SVM.SMO.core.Instance: Returns the number of values present.
numVariables() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.DataBase: It returns the number of input attributes in the examples
numVariables() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.DataBase: It returns the number of input attributes in the examples
numVariables() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.DataBase: It returns the number of input attributes in the examples
numVariables() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.DataBase: It returns the number of input attributes in the examples
numVariables() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.DataBase: It returns the number of input attributes in the examples
numVariables() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.DataBase: It returns the number of input attributes in the examples
numVariables() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.DataBase: It returns the number of input attributes in the examples
numVariables() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.DataBase: It returns the number of input variables
numVariables() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseD: It returns the number of input variables
numVariables() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.DataBase: It returns the number of variables of the problem
numVariables() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.DataBase: It returns the number of input variables
numVariables() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseD
numVariables() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseD
numVariables() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.DataBase: It returns the number of input variables
numVariables() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.DataBase
numVariables() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.DataBase: It returns the number of input variables
numVariables() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseD
numVariables() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.DataBase: Returns the number of variables.
numVariables() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.DataB
numVariables() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.DataB
numVariablesUsed() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.DataBase: It returns the number of input attributes which has been used
numVariablesUsed() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.DataBase: It returns the number of input attributes which has been used
numVariablesUsed() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.DataBase: It returns the number of input attributes which has been used
numVariablesUsed() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.DataBase: It returns the number of input attributes which has been used
numVersions() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowing
numVersions() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowingGWS
numVersions() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowingILAS
numVersions() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper
numVersions() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Windowing
numVersions() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper
numVersions() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Windowing
NValue - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node
nValueNominal(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It returns the number of different values that can take a nominal attribute.
NVariable - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node: The class defines the characteristics of a node
nVec - Variable in class keel.Algorithms.Instance_Selection.CPruner.Trio: Vector number.
nVec - Variable in class keel.Algorithms.Preprocess.Instance_Selection.CPruner.Trio: Vector number.

O

OBDEAlgorithm - Class in keel.Algorithms.Instance_Generation.OBDE: OBDE algorithm calling.
OBDEAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.OBDE.OBDEAlgorithm
OBDEGenerator - Class in keel.Algorithms.Instance_Generation.OBDE: OBDEGenerator.
OBDEGenerator(PrototypeSet, int, int, int, int, double, double, double, int) - Constructor for class keel.Algorithms.Instance_Generation.OBDE.OBDEGenerator: Build a new OBDEGenerator Algorithm
OBDEGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.OBDE.OBDEGenerator: Build a new OBDEGenerator Algorithm
objective - Variable in class keel.GraphInterKeel.experiments.Graph
objectiveFunction(double[]) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization: Subclass should implement this procedure to evaluate objective function to be minimized
objectives - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
objectives - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
objectives - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
objetiveFunction(PrototypeSet[], PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCGenerator: Returns the objective function value of the clusters given.
objFun() - Method in class keel.Algorithms.SVM.SMO.SMOreg: Debuggage function.
objFun(int, int, double, double, double, double) - Method in class keel.Algorithms.SVM.SMO.SMOreg: Debuggage function.
objType - Variable in class keel.GraphInterKeel.experiments.Experiments
obtain_rule(fuzzy[][], Vector<Vector<Float>>, Vector<fuzzyPartition>, int, int, int, Vector<Float>, Vector<Vector<fuzzy>>, int, String, Vector<fuzzy>) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
obtain_rule(fuzzy[][], Vector<Vector<Float>>, Vector<fuzzyPartition>, int, int, int, Vector<Float>, Vector<Vector<fuzzy>>, int, String, Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
obtain_rule(Interval[][], Vector<Vector<Float>>, Vector<partition>, int, int, int, Vector<Float>, Vector<Vector<Float>>, int) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.rule
obtain_rule(fuzzy[][], Vector<Vector<Float>>, Vector<partition>, int, int, int, Vector<Float>, Vector<Vector<Float>>, int) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
obtain_rule(fuzzy[][], Vector<Vector<Float>>, Vector<partition>, int, int, int, Vector<Float>, Vector<Vector<Float>>, int) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
obtain_rule_random(fuzzy[][], Vector<Vector<Float>>, Vector<fuzzyPartition>, int, int, int, Vector<Float>, Vector<Vector<fuzzy>>, String, Vector<fuzzy>) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
obtain_rule_random(fuzzy[][], Vector<Vector<Float>>, Vector<fuzzyPartition>, int, int, int, Vector<Float>, Vector<Vector<fuzzy>>, String, Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
obtain_rule_random(Interval[][], Vector<Vector<Float>>, Vector<partition>, int, int, int, Vector<Float>, Vector<Vector<Float>>) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.rule
obtain_rule_random(fuzzy[][], Vector<Vector<Float>>, Vector<partition>, int, int, int, Vector<Float>, Vector<Vector<Float>>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
obtain_rule_random(fuzzy[][], Vector<Vector<Float>>, Vector<partition>, int, int, int, Vector<Float>, Vector<Vector<Float>>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
obtain_rule_random_eje(fuzzy[][], Vector<Vector<Float>>, Vector<fuzzyPartition>, int, int, int, Vector<Float>, Vector<Vector<fuzzy>>, String, Vector<fuzzy>, int) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
obtain_rule_random_eje(fuzzy[][], Vector<Vector<Float>>, Vector<fuzzyPartition>, int, int, int, Vector<Float>, Vector<Vector<fuzzy>>, String, Vector<Float>, int) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
obtainBestClassifier(List, IEvaluator) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.algorithm.classification.CCRElitistNeuralNetAlgorithm: Returns best individual of a list of individuals using the MSEErrorFunction
obtainClass(int, int, double[]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Multiclassifier: It computes the output class according to the learned system
obtainClass(int, double[]) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.multi_C45: It returns the class index of the prediction of an example in the i^{th} classifier
obtainConfidence(int, int, double[]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Multiclassifier: Computes and returns the confidence vector for a given example for the classification class x vs class y.
obtainConfidence(int, double[]) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.multi_C45: It obtains the confidence on the prediction of the example in the i^{th} classifier
obtainConstantsInputs() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Obtain a boolean array with true at these inputs that are constants
obtainExhaustive(Vector<Integer>) - Static method in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Multiple: Obtain all exhaustive comparisons possible from an array of indexes
obtainExhaustive(Vector<Integer>) - Static method in class keel.GraphInterKeel.statistical.tests.Multiple: Obtain all exhaustive comparisons possible from an array of indexes
obtainIntervals(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.Chi2_Discretizer.Chi2Discretizer
obtainIntervals(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.Chi2_Discretizer.Discretizer: Returns the discretized intervals of the given attribute.
obtainIntervals(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Discretizer: Returns the discretized intervals of the given attribute.
obtainIntervals(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.ExtendedChi2Discretizer
obtainIntervals(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Discretizer: Returns the discretized intervals of the given attribute.
obtainIntervals(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.ModifiedChi2Discretizer
obtainLateralTuning() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC: Obtains the best lateral tuning from the genetic tuning process
obtainLateralTuning() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC_Chromosome: Obtains the lateral tuning of the data base associated to the current chromosome
obtainNewNeuron() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.ExpLayer: New neuron for the layer
obtainNewNeuron() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinearLayer: New neuron for the layer
obtainNewNeuron() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: New neuron for the layer
obtainNewNeuron() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.SigmLayer: New neuron for the layer
obtainNewRuleBase() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC: Obtains the best set of rules from the genetic rule selection process
obtainPathDot() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.AdministrativeStaff
obtainPathPathfinder() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.AdministrativeStaff
obtainPathResultFilesTxt() - Method in class keel.GraphInterKeel.experiments.EducationalDiscretizerReport: Return paths of result.txt files
obtainReportFilePath() - Method in class keel.GraphInterKeel.experiments.EducationalReport: This method return path of report file
obtainReportFilePath() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: Return the Report File Path
obtainSelectedRules() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC_Chromosome: Obtains the selected rules of the rule base associated to the current chromosome
obtainVotes(Instance) - Method in class keel.Algorithms.SVM.SMO.SMO: Returns an array of votes for the given instance.
obtenerCalidad() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Returns the quality of the rule.
obtenerCalidad() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Returns the quality of the rule.
obtenerCalidad() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Returns the quality of the rule.
obtenerCalidad() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Returns the quality of the rule.
obtenerCalidad() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Returns the quality of the rule.
obtenerCalidadMejorPosicion() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Returns the quality of the best position.
obtenerCalidadPosicionActual() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Returns the quality of the actual position.
obtenerCercano(int[], int, double[][], int, double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma: Obtains the nearest neighbour given a mask (chromosome)
obtenerCercano(int[], int, double[][], int, double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma: Obtains the nearest neighbour given a mask (chromosome)
obtenerCercano(int[], int, double[][], int, double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma: Obtains the nearest neighbour given a mask (chromosome)
obtenerCercano(int[], int, double[][], int, double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Instance_Selection.SSMA.Cromosoma: Obtain the nearest neighbour given a mask (cromosome)
obtenerCercano(int[], int, double[][], int, double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma: Obtain the nearest neighbour given a mask (cromosome)
obtenerComparador() - Static method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Atributo: Returns the compare condition to compare two attributes.
obtenerComparador() - Static method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Returns the rules comparative method.
obtenerComparador() - Static method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Atributo: Returns the compare condition to compare two attributes.
obtenerComparador() - Static method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Returns the rules comparative method.
obtenerComparador() - Static method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Atributo: Returns the compare condition to compare two attributes.
obtenerComparador() - Static method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Returns the rules comparative method.
obtenerComparador() - Static method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Atributo: Returns the compare condition to compare two attributes.
obtenerComparador() - Static method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Returns the rules comparative method.
obtenerComparador() - Static method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Atributo: Returns the compare condition to compare two attributes.
obtenerComparador() - Static method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Returns the rules comparative method.
obtenerIMVars() - Method in class keel.Algorithms.Preprocess.Feature_Selection.Datos: Computes the mutual information between every two variables.
obtenerIMVarsClase() - Method in class keel.Algorithms.Preprocess.Feature_Selection.Datos: Calculates the mutual information measure between the variables and the class.
obtenerMayorClase(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Returns the majority class of the dataset (most frequent class).
obtenerMayorClase(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Returns the majority class of the dataset (most frequent class).
obtenerMayorClase(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ConjuntoDatos: Returns the majority class of the dataset (most frequent class).
obtenerMayorClase(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Returns the majority class of the dataset (most frequent class).
obtenerMayorClase(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Returns the majority class of the dataset (most frequent class).
obtenerMuestra() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Returns the first example of the dataset.
obtenerMuestra(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Returns the example in the position given.
obtenerMuestra() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Returns the first example of the dataset.
obtenerMuestra(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Returns the example in the position given.
obtenerMuestra() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ConjuntoDatos: Returns the first example of the dataset.
obtenerMuestra(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ConjuntoDatos: Returns the example in the position given.
obtenerMuestra() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Returns the first example of the dataset.
obtenerMuestra(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Returns the example in the position given.
obtenerMuestra() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Returns the first example of the dataset.
obtenerMuestra(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Returns the example in the position given.
obtenerMuestras() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Returns all the examples in the dataset.
obtenerMuestras() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Returns all the examples in the dataset.
obtenerMuestras() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ConjuntoDatos: Returns all the examples in the dataset.
obtenerMuestras() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Returns all the examples in the dataset.
obtenerMuestras() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Returns all the examples in the dataset.
obtenerMuestrasCubiertas() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Returns the number of covered samples.
obtenerMuestrasCubiertas() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Returns the number of covered samples.
obtenerMuestrasCubiertas() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Returns the number of covered samples.
obtenerMuestrasCubiertas() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Returns the number of covered samples.
obtenerNumCondiciones() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Returns the number of conditions in the rule.
obtenerNumCondiciones() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Returns the number of conditions in the rule.
obtenerNumCondiciones() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Returns the number of conditions in the rule.
obtenerNumCondiciones() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Returns the number of conditions in the rule.
obtenerNumCondicionesContinuas() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Returns the number of continuous conditions of the rule.
obtenerNumCondicionesNominales() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Returns the number of nominal conditions of the rule.
obtenerNumCondicionesReales() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Returns the number of real conditions.
obtenerNumCondicionesReales() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Returns the number of real conditions.
obtenerNumCondicionesReales() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Returns the number of real conditions.
obtenerNumeroMuestrasCubiertas() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Returns the number of examples covered by the rule.
obtenerReglaPredicha() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Returns the predicted class for the rule.
obtenerReglaPredicha() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Returns the predicted class for the rule.
obtenerReglaPredicha() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Returns the predicted class for the rule.
obtenerReglaPredicha() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Returns the predicted class for the rule.
obtenerReglaPredicha() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Returns the predicted class for the rule.
obtenerVecinos(int, int, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Returns a vector with the neighbours of the particle.
obtieneCabecera(Dataset) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
OCEC - Class in keel.Algorithms.Genetic_Rule_Learning.OCEC: Title: Algorithm OCEC Description: It contains the implementation of the algorithm OCEC Company: KEEL
OCEC() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OCEC.OCEC: Default constructor
OCEC(parseParameters) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OCEC.OCEC: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
offMark() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: Function to unmark the rule.
offMark() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: Function to unmark the rule
offNew() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual: Modifies the new flag to false.
offNew() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule: Sets this rule as "non-new"
offNew() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual
offset - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_ADI
offset - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_GABIL
offset - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_UBR
offset - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_ADI
offset - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_GABIL
offsetPredicates - Variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
offspring1 - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
offspring2 - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
offUsed(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
offUsed(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
Oiga - Class in keel.Algorithms.Genetic_Rule_Learning.OIGA: This class implements the OIGA algorithm from: Zhu, F., Guan, S.
Oiga() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OIGA.Oiga: Default constructor
Oiga(String) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OIGA.Oiga: Constructor for the KEEL parameter file
OK() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ACO: All is OK if the data-sets have not got any continuos values
OK() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ACO: All is OK is the data-sets have not got any continuos values
OK() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ACO: All is OK is the data-sets have not got any continuos values
OK() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ACO: All is OK is the data-sets have not got any continuos values
ok - Variable in class keel.GraphInterKeel.datacf.util.OptionsDialog: OK button
okClass - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PerformanceAgent
okClass - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PerformanceAgent
oldClassificationProcess(String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: Process a old format dataset file for a modelling problem.
oldClassificationProcess(String) - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Process a old format dataset file for a modelling problem.
oldClusteringProcess(String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: Process an old format dataset file for a classification problem.
oldClusteringProcess(String) - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Process an old format dataset file for a classification problem.
oldEntropy(Classification) - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Function to compute entropy of classification before cutting.
oldEntropy(Classification) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to compute entropy of classification before cutting.
oldEntropy(Classification) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Function to compute entropy of classification before cutting.
oldEntropy(Classification) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Function to compute entropy of classification before cutting.
oldEntropy(Classification) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to compute entropy of classification before cutting.
oldEntropy(Classification) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Function to compute entropy of classification before cutting.
oldEntropy(Classification) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Function to compute entropy of classification before cutting.
oldEntropy(Classification) - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Function to compute entropy of classification before cutting.
oldEntropy(Classification) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Function to compute entropy of classification before cutting.
OlexGA - Class in keel.Algorithms.Genetic_Rule_Learning.olexGA
OlexGA() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
OlexGA(String) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
OlexGA_Attribute - Class in keel.Algorithms.Genetic_Rule_Learning.olexGA: Class to implement an attribute
OlexGA_Attribute(String, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA_Attribute: Constructor for continuous attributes.
OlexGA_Attribute(String, Vector, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA_Attribute: Constructor for discret attributes.
olexGAParams - Variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
OlexResult - Class in keel.Algorithms.Genetic_Rule_Learning.olexGA: Class to implement the results given by the algorithm olexGA
OlexResult(String, String, String) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexResult
omega - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters
omega - Variable in class keel.Algorithms.Neural_Networks.gmdh.SetupParameters
omegaTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.Puk: Returns the tip text for this property
ONE_CLASS - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
ONE_CLASS - Static variable in class org.libsvm.svm_parameter
onePointCrossover() - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Cogin
onePointCrossover(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Ilga: One-point crossover
onePointCrossover(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.SEM: One-point crossover
onePointCrossover(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Oiga: One-point crossover
onePointCrossover(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.SEM: One-point crossover
OneR - Class in keel.Algorithms.Discretizers.OneR: This class implements the OneR discretizer
OneR(int) - Constructor for class keel.Algorithms.Discretizers.OneR.OneR: Parameter constructor.
ones(int, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Creates a new matrix filled with ones
OneSideFloatMatrix<IndexType> - Class in keel.Algorithms.Instance_Generation.utilities
OneSideFloatMatrix() - Constructor for class keel.Algorithms.Instance_Generation.utilities.OneSideFloatMatrix
OneSideFloatMatrix(ArrayList<IndexType>, ArrayList<IndexType>) - Constructor for class keel.Algorithms.Instance_Generation.utilities.OneSideFloatMatrix
OneSideFloatMatrix<IndexType> - Class in keel.Algorithms.Semi_Supervised_Learning.utilities: Implements a one side float matrix.
OneSideFloatMatrix() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.utilities.OneSideFloatMatrix: Default constructor.
OneSideFloatMatrix(ArrayList<IndexType>, ArrayList<IndexType>) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.utilities.OneSideFloatMatrix: Initializes the HashMap matrix with indeces given.
onlyOneLiteral(boolean[][], Vector<Integer>, int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.pnPair: It checks if only the literal lit covers the pnPair and the other do not
onMark() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: Function to mark the rule.
onMark() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: Function to mark the rule
onNew() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual: Modifies the new flag to true.
onNew() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule: Sets this rule as new
onNew() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual
onUsed(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
onUsed(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
open() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset: Open dataset
open() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArffDataSet: Open dataset
open() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArrayDataset: Open dataset
open() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.FileDataset: Open dataset
open() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IDataset: Open dataset
open() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.KeelDataSet: Open dataset
OpenDataset - Class in keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS: Dataset interface simplification
OpenDataset() - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.OpenDataset: Empty Constructor
OpenDataset - Class in keel.Algorithms.Neural_Networks.gann: Dataset interface simplification
OpenDataset() - Constructor for class keel.Algorithms.Neural_Networks.gann.OpenDataset: Constructor
OpenDataset - Class in keel.Algorithms.Neural_Networks.gmdh: Dataset interface simplification
OpenDataset() - Constructor for class keel.Algorithms.Neural_Networks.gmdh.OpenDataset: Constructor
OpenDataset - Class in keel.Algorithms.Neural_Networks.net: Dataset interface simplification
OpenDataset() - Constructor for class keel.Algorithms.Neural_Networks.net.OpenDataset: Empty Constructor
Operacion - Class in keel.Algorithms.Genetic_Rule_Learning.Hider: Binary operations (2 operands)
Operacion(String) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
OperacionHandler - Class in keel.Algorithms.Genetic_Rule_Learning.Hider
OperacionHandler(XMLReader, FuncionEvaluacionBeanHandler, Operacion) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.OperacionHandler
OperacionHandler(XMLReader, FuncionEvaluacionBeanHandler, OperacionHandler, Operacion, Operacion, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.OperacionHandler
operate(double[]) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuron: Operates this neuron using an array of inputs for the inputs neurons
operate(double[][]) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuron: Operates this neuron using an input matrix for the inputs neurons
operate(double[]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputNeuron: Operates this neuron.
operate(DoubleTransposedDataSet) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputNeuron: Operates this neuron.
operate(double[][]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputNeuron: Operates this neuron.
operate(double[]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron: Operates this neuron, using an input array.
operate(double[][]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron: Operates this neuron using an input matrix as argument
operate(double[]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.neuralnet.NeuralNetRegressor: Estimates output value of a observation, through its inputs values
operate(double[][]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.neuralnet.NeuralNetRegressor: Estimates output values of a set of observations, through their inputs values
operate(double[]) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Regr.problem.regression.IRegressor: Estimates output value of a observation, through its inputs values
operate(double[][]) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Regr.problem.regression.IRegressor: Estimates output values of a set of observations, through their inputs values
operations - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE
OperatorIdent - Class in keel.Algorithms.Preprocess.NoiseFilters.ANR: Class with constant identifiers of operators and methods.
OperatorIdent() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.ANR.OperatorIdent
OperatorIdent - Class in keel.Algorithms.Shared.Parsing: Class with constant identifiers of operators and methods.
OperatorIdent() - Constructor for class keel.Algorithms.Shared.Parsing.OperatorIdent
Operators - Class in keel.Algorithms.RST_Learning: File: Operators.java Auxiliar class with several fuzzy operators
Operators() - Constructor for class keel.Algorithms.RST_Learning.Operators
opposite() - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Computes inverse prototype, element by element. (1 - value)
opposite() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Computes inverse set, element by element. (1 - value)
opposite() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Opuesto de un prototipo
opposite() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Calculaa el opuesto de un conjunto .
Opt - Class in keel.Algorithms.Discretizers.OneR: This class represents the optimum class for a given explanatory value
Opt() - Constructor for class keel.Algorithms.Discretizers.OneR.Opt: Dafault constructor.
Opt(double, int) - Constructor for class keel.Algorithms.Discretizers.OneR.Opt: Creates a new object with the given elements
optimalClass(int, long, boolean) - Method in class keel.Algorithms.Rule_Learning.UnoR.Interval: Finds the optimal class for the interval.
optimalMODL(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.MODL.MODL: It seachs for the best possible optimization scheme.
optimalPopulationFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It's the file name that contains the optimal population.
OPTIMALPOPULATIONFILE - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
optimalPopulationPercentage(Population) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Looks for the percentage of optimal classifiers present in the current population.
optimizacionLocal(int, int, int[], double[][], double, double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma: Local optimization for the chromosome as Memetic algorithm
optimizacionLocal(int, int, int[], double[][], double, double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma: Local optimization for the chromosome as Memetic algorithm
optimizacionLocal(int, int, int[], double[][], double, double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma: Local optimization for the chromosome as Memetic algorithm
optimizacionLocal(int, int, int[], double[][], double, double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Instance_Selection.SSMA.Cromosoma: Performs the local search procedure of SSMA
optimizacionLocal(int, int, int[], double[][], double, double[][], double[][], int[][], boolean[][], boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma: Performs the local search procedure of SSMA
optimization - Variable in class keel.Algorithms.MIL.Diverse_Density.DD.DD
optimization - Variable in class keel.Algorithms.MIL.Diverse_Density.EMDD.EMDD
Optimization - Class in keel.Algorithms.MIL.Diverse_Density.Optimization: Optimization auxiliary methods
Optimization() - Constructor for class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
Optimization - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core: Implementation of Active-sets method with BFGS update to solve optimization problem with only bounds constraints in multi-dimensions.
Optimization() - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization
optimizationMethod - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
optimizationsTipText() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Returns the tip text for this property
optimize(IOptimizableFunc) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus: One local iRprop plus search in a IOptimizableFunc.
optimize(UnivariateFunction, double, double, double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.util.UnivariateMinimum: The actual optimization routine (Brent's golden section method)
optimize(UnivariateFunction, double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.util.UnivariateMinimum: The actual optimization routine (Brent's golden section method)
optimize(double, UnivariateFunction, double, double, double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.util.UnivariateMinimum: The actual optimization routine (Brent's golden section method)
optimize(double, UnivariateFunction, double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.util.UnivariateMinimum: The actual optimization routine (Brent's golden section method)
optimize(Ruleset, MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ripper: It implements the Ripper2's Optimization Phase: After generating the initial ruleset {Ri}, generate and prune two variants of each rule Ri from randomized data using the grow and prune method.
optimize() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMO: finds alpha and alpha* parameters that optimize the SVM target function
optimize1() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: use variant 1 of Shevade's et al.s paper
optimize2() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved: use variant 2 of Shevade's et al.s paper
option - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters: Option string.
Option - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core: Class to store information about an option.
Option(String, String, int, String) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.Option: Creates new option with the given parameters.
Option - Class in keel.Algorithms.SVM.SMO.core: Class to store information about an option.
Option(String, String, int, String) - Constructor for class keel.Algorithms.SVM.SMO.core.Option: Creates new option with the given parameters.
OptionHandler - Interface in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets: Interface to something that understands options.
OptionHandler - Interface in keel.Algorithms.Statistical_Classifiers.Logistic.core: Interface to something that understands options.
OptionHandler - Interface in keel.Algorithms.SVM.SMO.core: Interface to something that understands options.
OptionsDialog - Class in keel.GraphInterKeel.datacf.util
OptionsDialog(Frame, boolean) - Constructor for class keel.GraphInterKeel.datacf.util.OptionsDialog: Constructor that initializes the panel
optMethod - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
OPV - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal: Class for vector, matrix and cubic matrix operations
OPV() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV
OPV - Class in keel.Algorithms.Preprocess.NoiseFilters.ANR: Vector operations class.
OPV() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV
OPV - Class in keel.Algorithms.Shared.ClassicalOptim: Vector operations class.
OPV() - Constructor for class keel.Algorithms.Shared.ClassicalOptim.OPV
or(Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask: Returns the mask that it's the outcome of the bolean operation 'or' between this and a given mask.
or(Mask[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask: Implements the 'or' whith other Masks
or(Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Returns the mask that it's the outcome of the bolean operation 'or' between this and a given mask.
or(Mask[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Implements the 'or' whith other Masks
or(IncrementalMask) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Returns the mask that it's the outcome of the bolean operation 'or' between this and a given mask.
or(Mask) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask: Returns the mask that it's the outcome of the bolean operation 'or' between this and a given mask.
or(Mask[]) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask: Implements the 'or' whith other Masks
or(IncrementalMask) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask: Returns the mask that it's the outcome of the bolean operation 'or' between this and a given mask.
or(Mask) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Returns the mask that it's the outcome of the bolean operation 'or' between this and a given mask.
or(Mask[]) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Implements the 'or' whith other Masks
or(IncrementalMask) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Returns the mask that it's the outcome of the bolean operation 'or' between this and a given mask.
or(Mask) - Method in class keel.Algorithms.Rule_Learning.PART.Mask: Returns the mask that it's the outcome of the bolean operation 'or' between this and a given mask.
or(Mask[]) - Method in class keel.Algorithms.Rule_Learning.PART.Mask: Implements the 'or' whith other Masks
or(IncrementalMask) - Method in class keel.Algorithms.Rule_Learning.PART.Mask: Returns the mask that it's the outcome of the bolean operation 'or' between this and a given mask.
or(Mask) - Method in class keel.Algorithms.Rule_Learning.Ripper.Mask: Returns the mask that it's the outcome of the bolean operation 'or' between this and a given mask.
or(Mask) - Method in class keel.Algorithms.Rule_Learning.Slipper.Mask: Returns the mask that it's the outcome of the bolean operation 'or' between this and a given mask.
OR_operator(int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary: Given the individuals "indiv1" and "indiv2", it selects two points and makes the OR operation between their central zones.
OR_operator(int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary: Given the individuals "indiv1" and "indiv2", it selects two points and makes the OR operation between their central zones.
OrCrecIndex(float[], int, int, int[]) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Utils: C.A.R, Hoare Quick sort.
OrCrecIndex(float[], int, int, int[]) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Utils: C.A.R, Hoare Quick sort.
OrCrecIndex(float[], int, int, int[]) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Utils: C.A.R, Hoare Quick sort.
OrCrecIndex(double[], int, int, int[]) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Utils: C.A.R, Hoare Quick sort.
OrCrecIndex(float[], int, int, int[]) - Static method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Utils: C.A.R, Hoare Quick sort.
OrCrecIndex(float[], int, int, int[]) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Utils: C.A.R, Hoare Quick sort.
OrCrecIndex(float[], int, int, int[]) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Utils: C.A.R, Hoare Quick sort.
OrDecIndex(float[], int, int, int[]) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Utils: C.A.R, Hoare Quick sort.
OrDecIndex(float[], int, int, int[]) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Utils: C.A.R, Hoare Quick sort.
OrDecIndex(float[], int, int, int[]) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Utils: C.A.R, Hoare Quick sort.
OrDecIndex(double[], int, int, int[]) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Utils: C.A.R, Hoare Quick sort.
OrDecIndex(float[], int, int, int[]) - Static method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Utils: C.A.R, Hoare Quick sort.
OrDecIndex(float[], int, int, int[]) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Utils: C.A.R, Hoare Quick sort.
OrDecIndex(float[], int, int, int[]) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Utils: C.A.R, Hoare Quick sort.
ordenaAtributos() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Sorts the attributs in increasing order.
ordenaCondiciones() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Sorts the rules in increasing order.
ordenaCondiciones() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Sorts the rules in increasing order.
ordenaCondiciones() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Sorts the rules in increasing order.
ordenaCondiciones() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Sorts the rules in increasing order.
ordenar() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.BaseR: Sorts the rules following their relative supports.
ordenar() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.Interval
ordenar() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.Interval
ordenar() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.Interval
ordenar() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.Interval
ordenar() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.Interval
ordenar() - Method in class keel.Algorithms.LQD.preprocess.Expert.interval
ordenar() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.interval
ordenar() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.interval
ordenLexicografico() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset: Sorts the datasets following lexical order.
order(Vector<Float>, Vector<fuzzy>) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling.Main
order(Vector<Float>, Vector<fuzzy>) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.Main
orderByAttb() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
orderDF() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule: It selects to order by DF
Ordered(double[], int, int) - Static method in class keel.Algorithms.Neural_Networks.gann.Selector: Ordered selection method
orderFirstNofCountArray(int[][], int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Bubble sorts first N elements in count array produced by countSingles method so that array is ordered according to frequency of single items.
orderFirstNofCountArray(int[][], int) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Bubble sorts first N elements in count array produced by countSingles method so that array is ordered according to frequency of single items.
orderFirstNofCountArray(int[][], int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Bubble sorts first N elements in count array produced by countSingles method so that array is ordered according to frequency of single items.
ordering() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Returns the ordering of the attribute.
ordering() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Returns the ordering of the attribute.
ORDERING_MODULO - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constant set for modulo-ordered attributes.
ORDERING_MODULO - Static variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Constant set for modulo-ordered attributes.
ORDERING_ORDERED - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constant set for ordered attributes.
ORDERING_ORDERED - Static variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Constant set for ordered attributes.
ORDERING_SYMBOLIC - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constant set for symbolic attributes.
ORDERING_SYMBOLIC - Static variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Constant set for symbolic attributes.
orderPrecede() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule: It selects to order by precede
org.core - package org.core
org.libsvm - package org.libsvm
Organizacion - Class in keel.Algorithms.Genetic_Rule_Learning.OCEC
Organizacion() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OCEC.Organizacion
Organizacion(Attribute, myDataset) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OCEC.Organizacion
Organizacion(int, Attribute, myDataset) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OCEC.Organizacion
Organizacion(Organizacion, Organizacion) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OCEC.Organizacion
origin - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.Link: Origin neuron
OrigSeed - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
orphans - Variable in class keel.Algorithms.Instance_Generation.PNN.MatrixOfDistances: Prototypes added to the set, to be compared both A and B (it is not so much used)
OSS - Class in keel.Algorithms.ImbalancedClassification.Resampling.OSS: File: OSS.java The OSS algorithm is an undersampling method used to deal with the imbalanced problem.
OSS(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.OSS.OSS: Constructor of the class.
out(EachDataSet, boolean) - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleQualityEvaluation: Generates a string with out-put lists
outAttribute - Variable in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanel: Output attribute
outFile - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Output files names
outFile - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: Output files names.
outFile - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Output files names
outFile - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Output files names.
outFile - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm: Output files names
outFile - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm: Output files names
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.FreqList: Test if the iterator is out of the bounds of the list
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.FreqList: Test if the iterator is out of the bounds of the list
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.valueAssociations: Tests if the iterator is out of the bounds of the list
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.FreqList: Test if the iterator is out of the bounds of the list
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.valueAssociations: Tests if the iterator is out of the bounds of the list
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqList: Test if the iterator is out of the bounds of the list
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqListPair: Test if the iterator is out of the bounds of the list
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.valueAssociations: Tests if the iterator is out of the bounds of the list
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.FreqList: Test if the iterator is out of the bounds of the list
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.valueAssociations: Tests if the iterator is out of the bounds of the list
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.FreqList: Test if the iterator is out of the bounds of the list
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.valueAssociations: Tests if the iterator is out of the bounds of the list
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.FreqList: Test if the iterator is out of the bounds of the list
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.valueAssociations: Tests if the iterator is out of the bounds of the list
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.FreqList: Test if the iterator is out of the bounds of the list
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.FreqList: Test if the iterator is out of the bounds of the list
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.valueAssociations: Tests if the iterator is out of the bounds of the list
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.FreqList: Test if the iterator is out of the bounds of the list
outOfBounds() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.valueAssociations: Tests if the iterator is out of the bounds of the list
output - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Output attribute
output() - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Output an instance after filtering and remove from the output queue.
output() - Method in class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter: Returns the outputs.
output - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: Output attribute.
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGP.FuzzyGPClassifier: This method performs the debug operation, which allow to analyze the behaviour of the learning process.
output - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyDataset: Output files names.
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.Classifier: abstract method to print information useful for debugging purposes
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.FuzzyClassifier: This method prints information about the Rule Base useful for debugging purposes
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.FuzzyFGPClassifier: This method prints information about the Rule Base useful for debugging purposes
output(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPModel: This method return the output of the model defuzzified
output(FuzzyAlphaCut[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPRegSymModel: This method return the output of the model like fuzzy alpha cuts
output(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyModel: This method defuzzified the output and return a value
output(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.Model: This abstract method return the output of the model defuzzified
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node: This abstrac method returns the output of the node.
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeAdd: Returns the output of the node.
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeAnd: Returns the output of the node.
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeConsequent: This method is for debug
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeExp: Returns the output of the node.
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeExprHold: Returns the output of the node.
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeIs: Returns the output of the node.
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeLabel: Returns the output of the node.
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeLog: Returns the output of the node.
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeMinus: Returns the output of the node.
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeOr: Returns the output of the node.
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeProduct: Returns the output of the node.
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeRule: This method is for debug
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeRuleBase: This method is for debug
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeSquareRoot: Returns the output of the node.
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeValue: Returns the output of the node.
output() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeVariable: Returns the output of the node.
output(FuzzyAlphaCut[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyRegressor: Creates and returns a fuzzy alpha-cut with result of the run.
output(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase: Returns output (Wang-Mendel) for input x.
output - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Output attribute
Output - Class in keel.Algorithms.ImbalancedClassification.Ensembles.Basic
Output() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Output
Output - Class in keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic: File: Output.java An auxiliary class to write result files for Instance Selection algorithms
Output() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Output
output - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Output attribute.
Output - Class in keel.Algorithms.Preprocess.Basic: File: Output.java An auxiliary class to write result files for Preprocess algorithms
Output() - Constructor for class keel.Algorithms.Preprocess.Basic.Output
output - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm: Output attribute
output - Variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: Output attribute
output - Variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: Output attribute
output - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm: Output attribute
OUTPUT - Static variable in class keel.Algorithms.Rule_Learning.Swap1.Attribute: Label to identify OUTPUT attributes
output - Variable in class keel.Algorithms.Rule_Learning.Swap1.swap1
OUTPUT - Static variable in class keel.Dataset.Attribute: Label to identify OUTPUT attributes
output_file_tra - Variable in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Param: Training output mandatory file
output_file_tra - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Param: Training output mandatory file
output_file_tra - Variable in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Param: Training output mandatory file
output_file_tst - Variable in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Param: Test output mandatory file
output_file_tst - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Param: Test output mandatory file
output_file_tst - Variable in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Param: Test output mandatory file
OUTPUT_LAYER - Static variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Defines output layer type
output_test_name - Variable in class keel.Algorithms.SVM.SMO.SMO: Test output filename.
output_train_name - Variable in class keel.Algorithms.SVM.SMO.SMO: Training output filename.
outputAccuracy() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Outputs classification accuracy.
outputCMARrules(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList: Outputs contents of CMAR rule linked list (if any)
outputCMARrulesWithReconversion() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList: Outputs contents of CMAR rule linked list (if any)
outputConversionArrays() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Outputs conversion array (used to renumber coulmns for input data in terms of frequency of single attributes --- reordering will enhance performance for some ARM algorithms).
OUTPUTDATA - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
outputData - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: NEW: All the output files are stored in this vector (in the doOutputData method)
outputData - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: All the output files are stored in this vector (in the doOutputData method)
outputDataArray() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Outputs stored input data set; initially read from input data file, but may be reirdered or pruned if desired by a particular application.
outputDataArray(short[][]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Outputs the given array of array of short integers.
outputDataArray() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Outputs stored input data set; initially read from input data file, but may be reordered or pruned if desired by a particular application.
outputDataArray() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Outputs stored input data set; initially read from input data file, but may be reordered or pruned if desired by a particular application.
outputDataArraySize() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Ouputs size (number of records and number of elements) of stored input data set read from input data file.
outputDuration(double, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Outputs difference between two given times.
outputFile - Class in keel.Algorithms.Statistical_Tests.Shared: Appends the results of an experiment to a file
outputFile() - Constructor for class keel.Algorithms.Statistical_Tests.Shared.outputFile
outputFiles - Static variable in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm: Name of output files.
outputFiles - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm: Name of output files.
outputFilesPath - Static variable in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm: Complete path of output files.
outputFilesPath - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm: Complete path of output files.
outputFormat() - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Deprecated.
use getOutputFormat() instead.
outputFormatPeek() - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Returns a reference to the current output format without copying it.
outputFrequentSets() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Commences the process of outputting the frequent sets contained in the T-tree.
outputFrequentSets(JTextArea) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Commences the process of outputting the frequent sets contained in the T-tree to a rext area.
outputFrequentSets() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TotalSupportTree: Commences the process of outputting the frequent sets contained in the T-tree.
outputFrequentSets() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TotalSupportTree: Commences the process of outputting the frequent sets contained in the T-tree.
OutputFS - Class in keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic: File: OutputIFS.java General Framework to print results of performing either instance or feature selection
OutputFS() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.OutputFS
OutputFS - Class in keel.Algorithms.Preprocess.Basic: File: OutputFS.java General Framework to print results of performing either instance or feature selection
OutputFS() - Constructor for class keel.Algorithms.Preprocess.Basic.OutputFS
outputFunction(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.ExpNeuron: Output function of the neuron
outputFunction(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinearNeuron: Output function of the neuron
outputFunction(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron: Output function of the neuron
outputFunction(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.SigmNeuron: Output function of the neuron
outputInterval - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Normalization input interval
OutputIS - Class in keel.Algorithms.ImbalancedClassification.Ensembles.Basic
OutputIS() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.OutputIS
OutputIS - Class in keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic: File: OutputIS.java An auxiliary class to write result files for Instance Selection algorithms
OutputIS() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.OutputIS
OutputIS - Class in keel.Algorithms.Preprocess.Basic: File: OutputIS.java An auxiliary class to write result files for Instance Selection algorithms
OutputIS() - Constructor for class keel.Algorithms.Preprocess.Basic.OutputIS
outputItemSet(short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Outputs a given item set.
outputItemSet(short[]) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Outputs a given item set.
outputItemSet(short[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Outputs a given item set.
outputItemSetWithReconversion(short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Outputs a given item set reconverting it to its orginal column number labels (used where input dataset has been reordered and possible pruned).
outputLayer - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet: Ouput layer
outputLine() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FingramsProcess: Returns a String output line.
outputLinksPercentage - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator: Percentage of links to remove in the mutations
outputMeans - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Array with the mean of each output
outputMenu() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Outputs menu for command line arguments.
outputMenu() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Outputs menu for command line arguments.
OutputMissingValue - Static variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo
OutputMissingValue - Static variable in class keel.Dataset.ErrorInfo
outputNumClasses() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Outputs number of classes.
outputNumCMARrules() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList: Outputs number of generated rules (ARs or CARS).
outputNumFreqSets() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Commences the process of counting and outputing number of supported nodes in the T-tree.
outputNumFreqSetsPerBranch() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Outputs the number of supported sets per T-tree branch descending from the top-level of the tree.
outputNumNodes() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree: Outputs total number of P-tree nodes (and the number of support value increments).
outputNumRules() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList: Outputs number of generated rules (ARs or CARS).
outputNumUpdates() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Outputs the number of update and number of nodes created during the generation of the T-tree (the later is not the same as the number of supported nodes).
outputPeek() - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Output an instance after filtering but do not remove from the output queue.
outputPtree() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree: Commences process to output P-tree.
outputPtree1(PtreeNodeTop[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree: Continues process to output P-tree.
outputPtreeStats() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree: Commences the process of outputting P-tree statistics (for diagnostic purposes): (a) Storage, (b) Number of nodes on P-tree, (c) number of partial support increments (updates) and (d) generation time.
outputPtreeStorage() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree: Outputs P-tree storgae requirements in Bytes.
outputPtreeTable() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree: Outputs P-tree table.
outputPtreeTableStats() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree: Outputs storage requirements for P-tree table.
outputRules(RuleList.RuleNodeCMAR) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList: Outputs given CMAR rule list.
outputRules() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Outputs contents of rule linked list (if any) assuming that the list represents a set of ARs.
outputRules(AssocRuleMining.RuleNode) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Outputs given rule list.
outputRules() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Outputs contents of rule linked list (if any) assuming that the list represents a set of ARs.
outputRules(AssocRuleMining.RuleNode) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Outputs given rule list.
outputRulesWithReconversion(RuleList.RuleNodeCMAR) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList: Outputs contents of rule linked list (if any) with reconversion.
outputs - Variable in class keel.Algorithms.Instance_Generation.Basic.Prototype: Normalized outputs of the prototype (values in [0,1]).
OUTPUTS - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for output line.
outputs - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Normalized outputs of the prototype (values in [0,1]).
OUTPUTS - Static variable in interface keel.Dataset.DataParserConstants
outputs_list() - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
outputs_list() - Static method in class keel.Dataset.DataParser
outputs_tra - Variable in class keel.GraphInterKeel.experiments.Parameters
outputs_tst - Variable in class keel.GraphInterKeel.experiments.Parameters
outputSettings() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Outputs command line values provided by user.
outputSettings() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Outputs command line values provided by user.
outputSettings2() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Outputs instance field values.
OutputsInTestNotEquals - Static variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo
OutputsInTestNotEquals - Static variable in class keel.Dataset.ErrorInfo
outputsLine - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
outputsLine - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
outputStorage() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Commences the process of determining and outputting the storage requirements (in bytes) for the T-tree.
outputSuppAndConf() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Outputs current support and confidence settings.
OutputTestAttributeNotDefined - Static variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo
OutputTestAttributeNotDefined - Static variable in class keel.Dataset.ErrorInfo
outputTestDataArray() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Outputs stored input data set read from input data file.
outputTr - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.multi_C45: Training output filename.
OutputTrainAttributeNotDefined - Static variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo
OutputTrainAttributeNotDefined - Static variable in class keel.Dataset.ErrorInfo
outputTtree() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Commences process of outputting T-tree structure contents to screen.
outputTtreeBranch(TtreeNode[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Commences process of outputting contents of a given T-tree branch to screen.
outputTtreeStats() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Commences the process of outputting T-tree statistics (for diagnostic purposes): (a) Storage, (b) Number of nodes on P-tree, (c) number of partial support increments (updates) and (d) generation time.
outputTtreeStats(JTextArea) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Commences the process of outputting T-tree statistics:GUI version.
ova_table(double[]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Multiclassifier: Computes and returns the one-vs-all vector for a given example.
over(int, int) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Combinatoric: Returns the combinatory number of the two number given.
overallConstraintViolation - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Overall Constraint Violation percentage.
overlap(Rule) - Method in class keel.Algorithms.Hyperrectangles.BNGE.Rule: Test if two rules are overlapped
Overlapping() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: This method return the ratio of the average distance between instances blongin to different classes of i and the average distance between instances that are from the same class i.
Overlapping() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: This method return the ratio of the average distance between instances blongin to different classes of i and the average distance between instances that are from the same class i.
OVO - Class in keel.Algorithms.Decision_Trees.C45_Binarization: Title: OVO Description: This class implements the Binarization methodology (OVO and OVO ) Company: KEEL
OVO(Multiclassifier, String, boolean) - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.OVO: It constructs the new OVO instance depending on the aggregation that is going to be used
ovo_table(double[]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Multiclassifier: Computes and returns the one-vs-one matrix for a given example.

P

p - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.T_FRM: a value.
p - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
p - Variable in class org.libsvm.svm_parameter
P0 - Static variable in class keel.Algorithms.Lazy_Learning.Statistics: approximation for 0 <= |y - 0.5| <= 3/8
P0 - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics: approximation for 0 <= |y - 0.5| <= 3/8
P1 - Static variable in class keel.Algorithms.Lazy_Learning.Statistics
P1 - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics: Approximation for interval z = sqrt(-2 log y ) between 2 and 8 i.e., y between exp(-2) = .135 and exp(-32) = 1.27e-14.
P2 - Static variable in class keel.Algorithms.Lazy_Learning.Statistics
P2 - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics: Approximation for interval z = sqrt(-2 log y ) between 8 and 64 i.e., y between exp(-32) = 1.27e-14 and exp(-2048) = 3.67e-890.
p_bp - Variable in class keel.Algorithms.Neural_Networks.gann.SetupParameters
p_param - Variable in class keel.Algorithms.Neural_Networks.gann.SetupParameters
P_REDUCT - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
p_struct - Variable in class keel.Algorithms.Neural_Networks.gann.SetupParameters
padLeft(String, int) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Pads a string to a specified length, inserting spaces on the left as required.
padLeft(String, int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Pads a string to a specified length, inserting spaces on the left as required.
padLeft(String, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Pads a string to a specified length, inserting spaces on the left as required.
padLeft(String, int) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Pads a string to a specified length, inserting spaces on the left as required.
padLeft(String, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Pads a string to a specified length, inserting spaces on the left as required.
padLeft(String, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Pads a string to a specified length, inserting spaces on the left as required.
padLeft(String, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Pads a string to a specified length, inserting spaces on the left as required.
padLeft(String, int) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Pads a string to a specified length, inserting spaces on the left as required.
padLeft(String, int) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Pads a string to a specified length, inserting spaces on the left as required.
padRight(String, int) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Pads a string to a specified length, inserting spaces on the right as required.
padRight(String, int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Pads a string to a specified length, inserting spaces on the right as required.
padRight(String, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Pads a string to a specified length, inserting spaces on the right as required.
padRight(String, int) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Pads a string to a specified length, inserting spaces on the right as required.
padRight(String, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Pads a string to a specified length, inserting spaces on the right as required.
padRight(String, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Pads a string to a specified length, inserting spaces on the right as required.
padRight(String, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Pads a string to a specified length, inserting spaces on the right as required.
padRight(String, int) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Pads a string to a specified length, inserting spaces on the right as required.
padRight(String, int) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Pads a string to a specified length, inserting spaces on the right as required.
Paint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
Paint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
Paint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.fuzzy_t
Paint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
Paint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
Paint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary
Paint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger
Paint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal
Paint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
Paint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Paint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
Paint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
Paint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
Paint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.fuzzy_t
Paint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
Paint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
Paint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
Paint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationinteger
Paint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal
Paint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
Paint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
Paint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
Paint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
Paint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
Paint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.fuzzy_t
Paint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
Paint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
Paint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
Paint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationinteger
Paint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationreal
Paint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
Paint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
Paint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
paint() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbf: Prints neuron on std out
paint(String) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbf: Prints neuron on a file
paint() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Prints net on a stdout
paint(String) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Prints net on a file.
paint() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation: Prints the ppulation on a stdout
paint(String) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation: Prints the pipulation on a file.
paint_sort(int) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation: Shows the _size first individuals (sorted by fitness)
PaintBin(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal
PaintBin(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal
PaintBin(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationreal
PaintBinInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal
PaintBinInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal
PaintBinInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationreal
paintComponent(Graphics) - Method in class keel.GraphInterKeel.experiments.GraphPanel: Draw graph
PaintFitness(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
PaintFitness(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary
PaintFitness(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger
PaintFitness(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
PaintFitness(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
PaintFitness(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationinteger
PaintFitness(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
PaintFitness(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
PaintFitness(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationinteger
PaintFitness_Stationary(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
PaintFitness_Stationary(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
PaintFitness_Stationary(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
PaintFitnessInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
PaintFitnessInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary
PaintFitnessInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger
PaintFitnessInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
PaintFitnessInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
PaintFitnessInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationinteger
PaintFitnessInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
PaintFitnessInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
PaintFitnessInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationinteger
paintGrid - Variable in class keel.GraphInterKeel.experiments.GraphPanel
PaintIndividual(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
PaintIndividual(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
PaintIndividual(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
PaintInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary
PaintInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger
PaintInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal
PaintInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
PaintInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationinteger
PaintInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal
PaintInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
PaintInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationinteger
PaintInFile(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationreal
Pair - Class in keel.Algorithms.Genetic_Rule_Learning.PART: Auxiliary class - Representation of a pair of integers (key/value).
Pair() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.Pair: Default constructor.
Pair - Class in keel.Algorithms.Hyperrectangles.INNER: File: Pair.java Auxiliary class to repressent pairs of rules and its distance
Pair(int, int, double) - Constructor for class keel.Algorithms.Hyperrectangles.INNER.Pair: Builder.
Pair<F,S> - Class in keel.Algorithms.Instance_Generation.utilities: Implements a simple pair.
Pair(F, S) - Constructor for class keel.Algorithms.Instance_Generation.utilities.Pair: Constructor of the pair
Pair - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering: This class is a pair of ints
Pair() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Pair: Creates a new instance of Pair
Pair(int, int) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Pair: Creates a pair with the provided integers
Pair - Class in keel.Algorithms.Rule_Learning.PART: Auxiliar class.
Pair() - Constructor for class keel.Algorithms.Rule_Learning.PART.Pair: Default constructor.
Pair - Class in keel.Algorithms.Rule_Learning.Ripper: Auxiliar class.
Pair() - Constructor for class keel.Algorithms.Rule_Learning.Ripper.Pair: Default constructor.
Pair - Class in keel.Algorithms.Rule_Learning.Slipper: Auxiliar class.Representation of a pair of integers (key/value).
Pair() - Constructor for class keel.Algorithms.Rule_Learning.Slipper.Pair: Default constructor.
Pair<F,S> - Class in keel.Algorithms.Semi_Supervised_Learning.utilities: Implements a simple pair.
Pair(F, S) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.utilities.Pair: Constructor of the pair
Pair - Class in keel.Algorithms.Statistical_Tests.Shared.nonParametric: File: Pair.java This class defines a comparable pair of two double values.
Pair() - Constructor for class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Pair: Default builder
Pair(double, double) - Constructor for class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Pair: Builder
Pair - Class in keel.GraphInterKeel.statistical.tests: File: Pair.java This class defines a comparable pair of two double values.
Pair() - Constructor for class keel.GraphInterKeel.statistical.tests.Pair: Default builder
Pair(double, double) - Constructor for class keel.GraphInterKeel.statistical.tests.Pair: Builder
pair_fi - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting
pair_fi() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.pair_fi
pair_fi(double, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.pair_fi
pair_gf - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting
pair_gf(GenotypeBoosting, double) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.pair_gf
pair_gg - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting
pair_gg(GenotypeBoosting, GenotypeBoosting) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.pair_gg
pairwiseCoupling(double[][], double[][]) - Method in class keel.Algorithms.SVM.SMO.SMO: Implements pairwise coupling.
palfa - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.T_FRM: a value.
PANDA - Class in keel.Algorithms.Preprocess.NoiseFilters.PANDA: This noise detection algorithm, PANDA, seeks to identify those instances with a large deviation from normal given the values of a pair of attributes.
PANDA() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.PANDA.PANDA: Constructor of the class
panelDatasets - Variable in class keel.GraphInterKeel.experiments.Experiments
par - Static variable in class keel.Algorithms.LQD.methods.FGFS_Original.Main: Contains all the parameters of this algorithm.
par - Variable in class keel.GraphInterKeel.experiments.Node
parAlgorithmType - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Algorithm to execute
parAlgorithmType - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Algorithm to execute identifier.
Param - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate: This class manages the values of the variables defined by the parameters file
Param(String, String, String, String, String, String, int) - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Param: Create an instance Param
Param - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate: This class manages the values of the variables defined by the parameters file
Param(String, String, String, String, String, String, int) - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Param: Create an instance Param
Param - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate
Param(String, String, String, String, String, String, int) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Param: Create an instance Param
param - Variable in class keel.GraphInterKeel.experiments.Jclec
parameter_data - Variable in class keel.GraphInterKeel.experiments.DinamicParameter
Parameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV: Parameters.java.
Parameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.Parameters
Parameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2
Parameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.Parameters
Parameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0
Parameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.Parameters
Parameters - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL: Main class to parse the parameters of the algorithm
Parameters() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
Parameters - Class in keel.Algorithms.Genetic_Rule_Learning.Globals: Parameters.java This class contains all the parameters of the system
Parameters() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
Parameters - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals
Parameters() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
Parameters - Class in keel.Algorithms.Genetic_Rule_Learning.UCS: In this class there are all the classifier parameters of UCS.
Parameters() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters: Constructs a Parameters Object.
Parameters(int, double, double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters: It creates an instance of Parameters.
Parameters(Parameters, Parameters, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters: It creates a new parameter object making a crossover between the parents parametres.
Parameters(Parameters, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters: It creates a new parameter object copying the parameters from a parent
PARAMETERS - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
Parameters - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: In this class there are all the classifier parameters of XCS.
Parameters() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Constructs a Parameters Object.
Parameters(int, double, double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: It creates an instance of Parameters.
Parameters(Parameters, Parameters, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: It creates a new parameter object making a crossover between the parents parametres.
Parameters(Parameters, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Copy constructor Constructs a Parameters Object initializing its attributes from another Parameters object.
Parameters(StringTokenizer) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Constructs a Parameters Object initializing its attributes
Parameters - Class in keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS: Class representing the parameters of the algorithm
Parameters() - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters: Empty constructor
parameters - Static variable in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm: Parameters given by the console.
Parameters - Class in keel.Algorithms.Instance_Generation.utilities: Implements operations of getting and setting the algorithm parameters.
Parameters(int) - Constructor for class keel.Algorithms.Instance_Generation.utilities.Parameters: Constructor.
Parameters(String[]) - Constructor for class keel.Algorithms.Instance_Generation.utilities.Parameters: Constructor.
Parameters(ArrayList<String>) - Constructor for class keel.Algorithms.Instance_Generation.utilities.Parameters: Constructor.
Parameters(String[], String[]) - Constructor for class keel.Algorithms.Instance_Generation.utilities.Parameters: Constructor.
parameters - Class in keel.Algorithms.LQD.methods.FGFS_costInstances: File: parameters.java Read the parameters given by the usuary
parameters(String) - Constructor for class keel.Algorithms.LQD.methods.FGFS_costInstances.parameters: Constructor.
parameters - Class in keel.Algorithms.LQD.methods.FGFS_Minimum_Risk: File: parameters.java Read the parameters given by the usuary
parameters(String) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.parameters: Constructor.
parameters - Class in keel.Algorithms.LQD.methods.FGFS_Original: File: parameters.java Read the parameters given by the usuary
parameters(String) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Original.parameters: Constructor.
parameters - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight: File: parameters.java Read the parameters given by the usuary
parameters(String) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.parameters: Constructor.
parameters - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty: File: parameters.java Read the parameters given by the usuary
parameters(String) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.parameters: Constructor.
parameters - Class in keel.Algorithms.LQD.preprocess.Expert: File: parameters.java Read the parameters given by the usuary
parameters(String) - Constructor for class keel.Algorithms.LQD.preprocess.Expert.parameters: Constructor.
parameters - Class in keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE: File: parameters.java Read the parameters given by the usuary
parameters(String) - Constructor for class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.parameters: Constructor.
parameters - Class in keel.Algorithms.LQD.preprocess.Prelabelling: File: parameters.java Read the parameters given by the usuary
parameters(String) - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling.parameters: Constructor.
parameters - Class in keel.Algorithms.LQD.preprocess.Prelabelling_Expert: File: parameters.java Read the parameters given by the usuary
parameters(String) - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.parameters: Constructor.
parameters - Class in keel.Algorithms.LQD.tests.IntermediateBoost: File: parameters.java Read the parameters original dataset
parameters(String) - Constructor for class keel.Algorithms.LQD.tests.IntermediateBoost.parameters: Constructor.
parameters - Class in keel.Algorithms.LQD.tests.Results: File: parameters.java Read the parameters given by the user.
parameters(String) - Constructor for class keel.Algorithms.LQD.tests.Results.parameters: Constructor.
parameters(String) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils: Reads a text file with parameters of the form name=value and returns a hastable containing them.
Parameters - Class in keel.Algorithms.Neural_Networks.gann: Class Parameters
Parameters() - Constructor for class keel.Algorithms.Neural_Networks.gann.Parameters: Empty constructor
Parameters - Class in keel.Algorithms.Neural_Networks.gmdh: Class Parameters
Parameters() - Constructor for class keel.Algorithms.Neural_Networks.gmdh.Parameters: Empty constructor
Parameters - Class in keel.Algorithms.Neural_Networks.net: Class representing the parameters of the algorithm
Parameters() - Constructor for class keel.Algorithms.Neural_Networks.net.Parameters: Empty constructor
parameters(String) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils: Reads a text file with parameters of the form name=value and returns a hastable containing them.
parameters(String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils: Reads a text file with parameters of the form name=value and returns a hastable containing them.
parameters(String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils: Reads a text file with parameters of the form name=value and returns a hastable containing them.
parameters(String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils: Reads a text file with parameters of the form name=value and returns a hastable containing them.
parameters(String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils: Reads a text file with parameters of the form name=value and returns a hastable containing them.
parameters(String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils: Reads a text file with parameters of the form name=value and returns a hastable containing them.
Parameters - Class in keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter: Main class to parse the parameters of the algorithm
Parameters() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters
Parameters - Class in keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter: Main class to parse the parameters of the algorithm
Parameters() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Parameters
Parameters - Class in keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter: Main class to parse the parameters of the algorithm
Parameters() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters
Parameters - Class in keel.Algorithms.Preprocess.NoiseFilters.INFFC: Main class to parse the parameters of the algorithm
Parameters() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters
Parameters - Class in keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter: Main class to parse the parameters of the algorithm
Parameters() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Parameters
Parameters - Class in keel.Algorithms.Preprocess.NoiseFilters.PANDA: Main class to parse the parameters of the algorithm
Parameters() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters
Parameters - Class in keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter: Main class to parse the parameters of the algorithm
Parameters() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Parameters
Parameters - Class in keel.Algorithms.Rule_Learning.Swap1: Main class to parse the parameters of the algorithm
Parameters() - Constructor for class keel.Algorithms.Rule_Learning.Swap1.Parameters
parameters - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm: Parameters given by the console.
Parameters - Class in keel.Algorithms.Semi_Supervised_Learning.utilities: Implements operations of getting and setting the algorithm parameters.
Parameters(int) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Constructs the Parameters class (the String's array) with the size given.
Parameters(String[]) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Constructs the Parameters class (the String's array) with the array given.
Parameters(ArrayList<String>) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Constructs the Parameters class (the String's array) with the array given.
Parameters(String[], String[]) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Constructs the Parameters class (the String's array) with the parameters array given and their names.
parameters - Variable in class keel.GraphInterKeel.experiments.Joint
Parameters - Class in keel.GraphInterKeel.experiments
Parameters() - Constructor for class keel.GraphInterKeel.experiments.Parameters: Default builder
Parameters(Parameters) - Constructor for class keel.GraphInterKeel.experiments.Parameters: Builder that copies another Parametros object
Parameters(String, boolean) - Constructor for class keel.GraphInterKeel.experiments.Parameters: Read pattern file
ParametersC45 - Class in keel.Algorithms.Semi_Supervised_Learning.Basic.C45: Class to implement Parameters the C4.5 algorithm
ParametersC45() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.ParametersC45
ParametersC45 - Class in keel.Algorithms.Semi_Supervised_Learning.Basic: Class to implement Parameters the C4.5 algorithm.
ParametersC45() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.ParametersC45
ParametersDialog - Class in keel.GraphInterKeel.experiments
ParametersDialog(Experiments, String, boolean, Vector, ExternalObjectDescription) - Constructor for class keel.GraphInterKeel.experiments.ParametersDialog: Builder
ParametersDialog() - Constructor for class keel.GraphInterKeel.experiments.ParametersDialog: Default builder
ParametersDialog2 - Class in keel.GraphInterKeel.experiments
ParametersDialog2(Frame, String, boolean, UserMethod) - Constructor for class keel.GraphInterKeel.experiments.ParametersDialog2: Builder
ParametersDialog2() - Constructor for class keel.GraphInterKeel.experiments.ParametersDialog2: Default builder
parametersFromGenotype() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGAP.FuzzyGAPClassifier: This method sets the current classifier of according to it's genotype.
parametersFromGenotype() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGP.FuzzyGPClassifier: This method sets the current classifier of according to it's genotype.
parametersFromGenotype() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyPittsBurgh.PittsburghClassifier: This method sets the current classifier of according to it's genotype.
parametersFromGenotype() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySAP.FuzzySAPClassifier: This method sets the current classifier of according to it's genotype.
parametersFromGenotype() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividual: This abstract method sets parameters from a genotype
parametersFromGenotype() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGAPModelIndividual: This method obtain the parameters of a genetic individual from the genotype
parametersFromGenotype() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPModelIndividual: This method obtain the parameters of a genetic individual from the genotype
parametersFromGenotype() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.PittsburghModel: This method obtain the parameters of a genetic individual from the genotype
parametersFromGenotype() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.RegSymFuzzyGP: This method obtain the parameters of a genetic individual from the genotype
parametersName - Static variable in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm: Name of the parameters.
parametersName - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm: Name of the parameters.
ParametersParser - Class in keel.Algorithms.Genetic_Rule_Learning.olexGA: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
ParametersParser() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.olexGA.ParametersParser: Default constructor
ParametersSMO - Class in keel.Algorithms.Semi_Supervised_Learning.Basic: Class to implement Parameters the SMO algorithm
ParametersSMO() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.ParametersSMO
ParametersTable - Class in keel.GraphInterKeel.experiments
ParametersTable(Parameters, JDialog) - Constructor for class keel.GraphInterKeel.experiments.ParametersTable: EDUCATIONAL KEEL ****************************
parametersUser - Variable in class keel.GraphInterKeel.experiments.UserMethod
parameterType - Variable in class keel.GraphInterKeel.experiments.Parameters
parametricMutation(ExpNeuron, LinkedLayer, LinkedLayer, int, double, double, double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ExpNeuronParametricMutator: Do the parametric mutation over the links of a specific neuron in a specific layer
parametricMutation(N, LinkedLayer, LinkedLayer, int, double, double, double) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.INeuronParametricMutator: Do the parametric mutation over the links of a specific neuron in a specific layer
parametricMutation(LinearNeuron, LinkedLayer, LinkedLayer, int, double, double, double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.LinearNeuronParametricMutator: Do the parametric mutation over the links of a specific neuron in a specific layer
parametricMutation(SigmNeuron, LinkedLayer, LinkedLayer, int, double, double, double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.SigmNeuronParametricMutator: Do the parametric mutation over the links of a specific neuron in a specific layer
ParametricMutator - Class in keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric: Parametric mutator for neural nets, mutate the weights of the neural nets mutated.
ParametricMutator() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Empty Constructor
ParametricSAMutator - Class in keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric: Parametric mutator for neural nets, mutate the weights of the neural nets mutated.
ParametricSAMutator() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricSAMutator: Empty Constructor
ParametricSRMutator - Class in keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric: Parametric mutator for neural nets, mutate the weights of the neural nets mutated.
ParametricSRMutator() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricSRMutator: Empty Constructor
ParametrosPreEvaluacion() - Method in class keel.Algorithms.PSO_Learning.CPSO.Particle
ParametrosPreEvaluacion() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
ParametrosPreEvaluacion() - Method in class keel.Algorithms.PSO_Learning.REPSO.Particle
pararRun() - Method in class keel.GraphInterKeel.experiments.EducationalPartitionsRun: Thread is stop in natural way, but the partitions finish
parCrGAProb - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: GA Cross probability
parCrGAProb - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: GA Cross probability.
parCrossId1 - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Type of cross operator
parCrossId1 - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Type of cross operator.
parCrossId2 - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Type of cross operator GAP
parCrossId2 - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Type of cross operator GAP.
parCrossId3 - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Type of cross operator GAP
parCrossId3 - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Type of cross operator GAP.
parDeltaFit - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Waited fitness increment for a SAP overcrossing
parDeltaFit - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Waited fitness increment for a SAP overcrossing.
Pareja - Class in keel.Algorithms.Instance_Selection.CCIS: Pair implementation.
Pareja() - Constructor for class keel.Algorithms.Instance_Selection.CCIS.Pareja: Default constructor.
Pareja(int, double) - Constructor for class keel.Algorithms.Instance_Selection.CCIS.Pareja: Parameter constructor.
Pareja - Class in keel.Algorithms.Preprocess.Instance_Selection.CCIS: Pair implementation.
Pareja() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.CCIS.Pareja: Default constructor.
Pareja(int, double) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.CCIS.Pareja: Parameter constructor.
parent - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Lists: Parent list.
parent - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Lists: Parent list.
parent - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Lists: Parent list.
parent - Variable in class keel.GraphInterKeel.datacf.DataCFFrame: Parent frame
parent - Variable in class keel.GraphInterKeel.datacf.editData.EditPanel
parent - Variable in class keel.GraphInterKeel.datacf.exportData.ExportPanel: Parent frame
parent - Variable in class keel.GraphInterKeel.datacf.importData.ImportPanel: Parent frame
parent - Variable in class keel.GraphInterKeel.datacf.partitionData.PartitionPanel: Parent frame
parent - Variable in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanel: Parent Frame
parent - Variable in class keel.GraphInterKeel.experiments.Credits
parent - Variable in class keel.GraphInterKeel.experiments.GraphPanel
parent - Variable in class keel.GraphInterKeel.menu.FrameModules: Parent frame
parent - Variable in class keel.GraphInterKeel.statistical.StatisticalF
parentRule() - Method in class keel.Algorithms.Rule_Learning.Rules6.Regla: Returns the father of this rule, its ancestor.
parentRule() - Method in class keel.Algorithms.Rule_Learning.SRI.Regla: Returns the father of this rule, its ancestor.
PAREnvironment - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: This is the typical example for a single step problem, the parity problem.
PAREnvironment() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.PAREnvironment: It is the constructor of the class.
parFitnessType - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Type of fitness
parFitnessType - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Type of fitness.
parGALen - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Number of parameters for GA string (GAP)
parGALen - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Number of parameters for GA string (GAP).
parInputData - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Input Train and Test names
parInputData - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Train name.
parIntraNicheProb - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Crossing probability intra-niche
parIntraNicheProb - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Crossing probability intra-niche.
parIslandNumber - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Number of populations
parIslandNumber - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Number of populations
parIterNumber - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Number of iterations (generations of crosses)
parIterNumber - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Number of iterations (generations of crosses).
parKernel - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Kernel parameter
parKernel - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Kernel parameter.
parLoId - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Local Optimization Algorithm
parLoId - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Local Optimization Algorithm identifier.
parLoIterNumber - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: NUmber of iterations for Local Optimization * used nvar
parLoIterNumber - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: NUmber of iterations for Local Optimization * used nvar.
parLoProb - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Local Optimization probability
parLoProb - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Local Optimization probability.
parMaxHeigth - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Maximum height for each individual
parMaxHeigth - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Maximum height for each individual
parMaxNiche - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Maximum number of individuls by niche
parMaxNiche - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Maximum number of individuls by niche.
parMigProb - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Migration probability
parMigProb - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Migration probability.
parMuGAProb - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: GA Mutation probability
parMuGAProb - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: GA Mutation probability.
parMutaId1 - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Type of mutation operator
parMutaId1 - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Type of mutation operator.
parMutaId2 - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Type of mutation operator GAP
parMutaId2 - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Type of mutation operator GAP.
parMutaId3 - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Type of mutation operator GAP
parMutaId3 - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Type of mutation operator GAP.
parMutAmpl - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Mutation amplitude
parMutAmpl - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Mutation amplitude.
parMutProb - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Mutation probability
parMutProb - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Mutation probability.
parNClusters - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Number of cluster in clustering problems
parNClusters - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Number of cluster in clustering problems.
parNetTopo - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Neural Network topology
parNetTopo - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Neural Network topology.
parNewFormat - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Keel format or not
parNewFormat - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Keel format or not flag.
parNiche - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Using GA-P niches
parNiche - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Using GA-P niches flag.
parNMeans - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Number of means
parNMeans - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Number of means.
parNSUB - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Number of iterations for each temperature SAP
parNSUB - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Number of iterations for each temperature SAP.
parOutputData - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Output Train and Test names
parOutputData - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Test name.
parP0 - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Accepting probability -deltafit on 0 iteration SAP
parP0 - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Accepting probability -deltafit on 0 iteration SAP.
parP1 - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Accepting probability -deltafit on iteration SAP
parP1 - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Accepting probability -deltafit on iteration SAP.
parPartitionLabelNum - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Partition label
parPartitionLabelNum - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Partition label number.
parPopSize - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Population size
parPopSize - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Population size
parResultLabel - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Results label
parResultLabel - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Results label.
parResultName - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Global results file
parResultName - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Test results file
parResultTrainName - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Result file name for Trail file
parResultTrainName - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Result file name for Trail file
parRuleNumber - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Number of rules (Boosting and FSS98)
parRuleNumber - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Number of rules (Boosting and FSS98).
parse(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractAttribute: Parse an external value to obtain the internal value of the Attribute
parse(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.CategoricalAttribute: Parse an external value to obtain the internal value of the Attribute
parse(String) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IAttribute: Parse an external value to obtain the internal value of the Attribute
parse(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.IntegerNumericalAttribute: Parse an external value to obtain the internal value of the Attribute
parse(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.RealNumericalAttribute: Parse an external value to obtain the internal value of the Attribute
parseAttribute(StreamTokenizer) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Parses the attribute declaration.
parseAttribute(String) - Method in class keel.GraphInterKeel.datacf.importData.ImportPanel: Obtains an Attribute object from a text line representing the attribute in KEEL format
parseConfigurationFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Decision_Trees.DT_GA.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Decision_Trees.Target.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.parseParameters: It obtains all the necessary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.ParametersParser: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.parseParameters: It obtains all the necessary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.PSO_Learning.CPSO.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.PSO_Learning.REPSO.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Rule_Learning.C45Rules.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Rule_Learning.LEM1.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Rule_Learning.LEM2.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Rule_Learning.PART.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Rule_Learning.Ripper.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Rule_Learning.Ritio.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Rule_Learning.Rules6.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Rule_Learning.Slipper.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Rule_Learning.SRI.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.SVM.C_SVM.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.SVM.EPSILON_SVR.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.SVM.NU_SVM.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.SVM.NU_SVR.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.parseParameters: It obtains all the necesary information from the configuration file.
parseConfigurationFile(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.parseParameters: It obtains all the necesary information from the configuration file.
parseDate(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Parses the given String as Date, according to the current format and returns the corresponding amount of milliseconds.
parseDate(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Parse a date from a string given.
parSeed - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Random seed
parSeed - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Random seed.
ParseException - Exception in keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser: This exception is thrown when parse errors are encountered.
ParseException(Token, int[][], String[]) - Constructor for exception keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParseException: This constructor is used by the method "generateParseException" in the generated parser.
ParseException() - Constructor for exception keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParseException: The following constructors are for use by you for whatever purpose you can think of.
ParseException(String) - Constructor for exception keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParseException: Parameters contructor.
ParseException - Exception in keel.Algorithms.Rule_Learning.Swap1: This exception is thrown when parse errors are encountered.
ParseException(Token, int[][], String[]) - Constructor for exception keel.Algorithms.Rule_Learning.Swap1.ParseException: This constructor is used by the method "generateParseException" in the generated parser.
ParseException() - Constructor for exception keel.Algorithms.Rule_Learning.Swap1.ParseException: The following constructors are for use by you for whatever purpose you can think of.
ParseException(String) - Constructor for exception keel.Algorithms.Rule_Learning.Swap1.ParseException
ParseException - Exception in keel.Dataset: This exception is thrown when parse errors are encountered.
ParseException(Token, int[][], String[]) - Constructor for exception keel.Dataset.ParseException: This constructor is used by the method "generateParseException" in the generated parser.
ParseException() - Constructor for exception keel.Dataset.ParseException: The following constructors are for use by you for whatever purpose you can think of.
ParseException(String) - Constructor for exception keel.Dataset.ParseException
ParseFileList - Class in keel.Algorithms.Statistical_Tests.Shared: Parse a list of files and perform certain the statistical test.
ParseFileList() - Constructor for class keel.Algorithms.Statistical_Tests.Shared.ParseFileList
ParseFileRegSym - Class in keel.Algorithms.Symbolic_Regression.Shared: This class obtains a symbolic model of training input data using algorithms: -GAP (Genetic Algorithm Programming). method symbolicRegressionFuzzyGAP.
ParseFileRegSym() - Constructor for class keel.Algorithms.Symbolic_Regression.Shared.ParseFileRegSym
parseHeader(InstanceParser, boolean) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: It reads the information in the header of the file.
parseHeader(InstanceParser, boolean) - Method in class keel.Dataset.InstanceSet: It reads the information in the header of the file.
parseMatlab(String) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: creates a matrix from the given Matlab string.
parseMatlab(String) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

creates a matrix from the given Matlab string.
parseParameters - Class in keel.Algorithms.Associative_Classification.ClassifierCBA: It reads the configuration file (data-set files and parameters).
parseParameters() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Associative_Classification.ClassifierCBA2: It reads the configuration file (data-set files and parameters)
parseParameters() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Associative_Classification.ClassifierCMAR: It reads the configuration file (data-set files and parameters)
parseParameters() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCMAR.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Associative_Classification.ClassifierCPAR: It reads the configuration file (data-set files and parameters)
parseParameters() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCPAR.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR: It reads the configuration file (data-set files and parameters)
parseParameters() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA: It reads the configuration file (data-set files and parameters)
parseParameters() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Decision_Trees.C45_Binarization: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Decision_Trees.DT_GA: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Decision_Trees.DT_oblicuo: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Decision_Trees.DT_oblicuo.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Decision_Trees.Target: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Decision_Trees.Target.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW: It reads the configuration file (data-set files and parameters)
parseParameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted: It reads the configuration file (data-set files and parameters)
parseParameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD: It reads the configuration file (data-set files and parameters)
parseParameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99: It reads the configuration file (data-set files and parameters)
parseParameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV: It reads the configuration file (data-set files and parameters).
parseParameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen
parseParameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE: It reads the configuration file (data-set files and parameters).
parseParameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2
parseParameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0
parseParameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Genetic_Rule_Learning.DMEL: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.DMEL.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Genetic_Rule_Learning.GIL: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GIL.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Genetic_Rule_Learning.LogenPro: It reads the configuration file (data-set files and parameters)
parseParameters() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.LogenPro.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Genetic_Rule_Learning.OCEC: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OCEC.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Genetic_Rule_Learning.PART: Reads the configuration file (data-set files and parameters)
parseParameters() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Genetic_Rule_Learning.RMini: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.RMini.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.ImbalancedClassification.Ensembles: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.PSO_Learning.CPSO: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.PSO_Learning.CPSO.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.PSO_Learning.LDWPSO: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.PSO_Learning.LDWPSO.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.PSO_Learning.PSOLDA: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.PSO_Learning.PSOLDA.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.PSO_Learning.REPSO: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.PSO_Learning.REPSO.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.RE_SL_Methods.P_FCS1: It reads the configuration file (data-set files and parameters)
parseParameters() - Constructor for class keel.Algorithms.RE_SL_Methods.P_FCS1.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.RE_SL_Methods.SEFC
parseParameters() - Constructor for class keel.Algorithms.RE_SL_Methods.SEFC.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Rule_Learning.C45Rules: Title: Parse Configuration File Reads the configuration file (data-set files and parameters).
parseParameters() - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Rule_Learning.C45RulesSA
parseParameters() - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Rule_Learning.LEM1: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Rule_Learning.LEM1.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Rule_Learning.LEM2: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Rule_Learning.LEM2.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Rule_Learning.PART
parseParameters() - Constructor for class keel.Algorithms.Rule_Learning.PART.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Rule_Learning.Ripper
parseParameters() - Constructor for class keel.Algorithms.Rule_Learning.Ripper.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Rule_Learning.Ritio: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Rule_Learning.Ritio.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Rule_Learning.Rules6: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Rule_Learning.Rules6.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Rule_Learning.Slipper
parseParameters() - Constructor for class keel.Algorithms.Rule_Learning.Slipper.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Rule_Learning.SRI: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Rule_Learning.SRI.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Statistical_Classifiers.Naive_Bayes: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.Subgroup_Discovery.SDMap.SDMap: It reads the configuration file (data-set files and parameters)
parseParameters() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.SVM.C_SVM: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.SVM.C_SVM.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.SVM.EPSILON_SVR: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.SVM.EPSILON_SVR.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.SVM.NU_SVM: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.SVM.NU_SVM.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.SVM.NU_SVR: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.SVM.NU_SVR.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal
parseParameters() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams
parseParameters() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori
parseParameters() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori
parseParameters() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC
parseParameters() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal
parseParameters() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth: It reads the configuration file (data-set files and parameters)
parseParameters() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR: Title: Parse Configuration File Description: It reads the configuration file (data-set files and parameters) Company: KEEL
parseParameters() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh: It reads the configuration file (data-set files and parameters)
parseParameters() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR: It reads the configuration file (data-set files and parameters)
parseParameters() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.parseParameters: Default constructor
parseParameters - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII: It reads the configuration file (data-set files and parameters)
parseParameters() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.parseParameters: Default constructor
Parser - Class in keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser: Parser class of the parameters for the algorithm XCS
Parser(InputStream) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
Parser(Reader) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
Parser(ParserTokenManager) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
ParserConstants - Interface in keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser: ParserConstants.java
ParserParameters - Class in keel.Algorithms.Discretizers.Basic: Parser for the package Discretizers.
ParserParameters() - Constructor for class keel.Algorithms.Discretizers.Basic.ParserParameters
ParserParameters - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist: ParserParameters.java
ParserParameters() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.ParserParameters
ParserParameters - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Basic: ParserParameters.java
ParserParameters() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Basic.ParserParameters
ParserParameters - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS: Reads the configuration file using the KEEL format.
ParserParameters() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ParserParameters
ParserTokenManager - Class in keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser: ParserTokenManager.java
ParserTokenManager(SimpleCharStream) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserTokenManager
ParserTokenManager(SimpleCharStream, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserTokenManager
parSigma - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Initial Covariance in FSS98
parSigma - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Initial Covariance in FSS98.
parSignificanceLevel - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Contrast Significance Level
parSignificanceLevel - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Contrast Significance Level.
parSteady - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Steady or not
parSteady - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Steady or not flag.
Parte - Class in keel.Algorithms.Genetic_Rule_Learning.RMini
PartialSupportTree - Class in keel.Algorithms.Associative_Classification.ClassifierCMAR: Methods to implement the "Apriori-TFP" (Total From Partial) ARM algorithm using both the T-tree (Total support tree) and P-tree (Partial support tree data structures.
PartialSupportTree(double, double, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree: Processes command line arguments.
PartialSupportTree.PtreeRecord - Class in keel.Algorithms.Associative_Classification.ClassifierCMAR: Structurte to contain P-tree data in tabular form for improved computational efficiency when creating T-tree.
Particle - Class in keel.Algorithms.PSO_Learning.CPSO: Title: Particle Company: KEEL
Particle(int, int) - Constructor for class keel.Algorithms.PSO_Learning.CPSO.Particle
Particle - Class in keel.Algorithms.PSO_Learning.LDWPSO: Title: Particle Company: KEEL
Particle(int, int) - Constructor for class keel.Algorithms.PSO_Learning.LDWPSO.Particle
Particle - Class in keel.Algorithms.PSO_Learning.PSOLDA: Title: Particle Company: KEEL
Particle(int) - Constructor for class keel.Algorithms.PSO_Learning.PSOLDA.Particle
Particle - Class in keel.Algorithms.PSO_Learning.REPSO: Title: Particle Company: KEEL
Particle(int) - Constructor for class keel.Algorithms.PSO_Learning.REPSO.Particle
Particula - Class in keel.Algorithms.Genetic_Rule_Learning.PSO_ACO: Title: Particula (Particle) Description: Particle class: Implements the particles needed on the Particle Swarm Optimization algorithm (PSO).
Particula() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Default constructor.
Particula(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Paramater Constructor.
partIn(int) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Make a partition of a set. numberOfSets sets with the same number of prototypes will be generated.
partIn(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Make a partition of a set. numberOfSets sets with the same number of prototypes will be generated.
partIntoSubsetsOverlappingDegree(Prototype, Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Generate two subsets of the set t.
partIntoSubsetsOverlappingDegree(Pair<Prototype, Prototype>) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Generate two subsets of the set t.
partIntoSubsetsOverlappingDegree(Prototype, Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Generate two subsets of the set t.
partIntoSubsetsOverlappingDegree(Pair<Prototype, Prototype>) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Generate two subsets of the set t.
partIntoSubsetsWhichSeedPointsAre(Prototype, Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Generate two subsets of the set t.
partIntoSubsetsWhichSeedPointsAre(Pair<Prototype, Prototype>) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Generate two subsets of the set t.
partIntoSubsetsWhichSeedPointsAre(Prototype, Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Generate two subsets of the set t.
partIntoSubsetsWhichSeedPointsAre(Pair<Prototype, Prototype>) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Generate two subsets of the set t.
partition(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.DataBase: It translates the antecedent id to a valid partition
partition(int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Partitions the instances around a pivot.
partition(Instances, int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Patition the data into 2, first of which has (numFolds-1)/numFolds of the data and the second has 1/numFolds of the data
partition(int, int, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Partitions the instances around a pivot.
partition - Class in keel.Algorithms.LQD.methods.FGFS_Original: File: partitions.java Properties and functions of the partitions of the fuzzy number
partition() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Original.partition
partition(float, float, int) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Original.partition
partition - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight: File: partitions.java Properties and functions of the partitions of the fuzzy number
partition() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.partition
partition(float, float, int) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.partition
partition - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty: File: fuzzy.java Properties and functions of fuzzy partitions
partition() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.partition
partition(float, float, int) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.partition
partition(int, int, int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Partitions the instances around a pivot.
partition(int, int, int) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Partitions the instances around a pivot.
partition(int, String, String, long, int, int) - Method in class keel.GraphInterKeel.datacf.partitionData.PartitionGenerator: Partitions a dataset
partitionCost(int, int) - Method in class keel.Algorithms.Discretizers.MODL.MODL: Computes the cost of the partition
PartitionCreator - Class in keel.GraphInterKeel.experiments
PartitionGenerator - Class in keel.GraphInterKeel.datacf.partitionData
PartitionGenerator() - Constructor for class keel.GraphInterKeel.datacf.partitionData.PartitionGenerator
partitionOptions(String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Returns the secondary set of options (if any) contained in the supplied options array.
partitionOptions(String[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Returns the secondary set of options (if any) contained in the supplied options array.
partitionOptions(String[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Returns the secondary set of options (if any) contained in the supplied options array.
partitionOptions(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Returns the secondary set of options (if any) contained in the supplied options array.
partitionOptions(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Returns the secondary set of options (if any) contained in the supplied options array.
partitionOptions(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Returns the secondary set of options (if any) contained in the supplied options array.
partitionOptions(String[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Returns the secondary set of options (if any) contained in the supplied options array.
partitionOptions(String[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Returns the secondary set of options (if any) contained in the supplied options array.
partitionOptions(String[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Returns the secondary set of options (if any) contained in the supplied options array.
PartitionPanel - Class in keel.GraphInterKeel.datacf.partitionData
PartitionPanel() - Constructor for class keel.GraphInterKeel.datacf.partitionData.PartitionPanel: Constructor that initializes the panel
partitionPercentTraining - Variable in class keel.Algorithms.Instance_Generation.HYB.HYBGenerator: % of training data set used as training in the search process.
partitions(Vector<Vector<fuzzy>>, int, int, interval[]) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling.Main
partitions(Vector<Vector<fuzzy>>, int, int, interval[]) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.Main
partitions - Variable in class keel.GraphInterKeel.experiments.DatasetXML
PartitionScheme - Class in keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter: This class implements a stratified scheme (equal number of examples of each class in each partition) to partition a dataset
PartitionScheme() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.PartitionScheme: It reads the training set and creates the partitions
PartitionScheme - Class in keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter: This class implements a stratified scheme (equal number of examples of each class in each partition) to partition a dataset
PartitionScheme(String, int) - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.PartitionScheme: It reads the training set and creates the partitions
PartitionScheme - Class in keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter: This class implements a stratified scheme (equal number of examples of each class in each partition) to partition a dataset
PartitionScheme() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.PartitionScheme: It reads the training set and creates the partitions
PartitionScheme - Class in keel.Algorithms.Preprocess.NoiseFilters.INFFC: This class implements a stratified scheme (equal number of examples of each class in each partition) to partition a dataset
PartitionScheme(String, int) - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.INFFC.PartitionScheme: It reads the training set and creates the partitions
PartitionScheme - Class in keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter: This class implements a stratified scheme (equal number of examples of each class in each partition) to partition a dataset
PartitionScheme(String, int) - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.PartitionScheme: It reads the training set and creates the partitions
partitionType() - Method in class keel.GraphInterKeel.experiments.Experiments: Gets the partition type of the experiment
partititonWhoseCentersAre(Prototype, Prototype) - Method in class keel.Algorithms.Instance_Generation.VQ.Cluster: Use the nearest neighbor condition to make a partition in two cluster which have got this centers.
parTourSize - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Tournament size
parTourSize - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Tournament size.
Pasar(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
Pass(int, populationbinary, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary
Pass(int, populationinteger, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger
Pass(int, populationreal, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal
Pass(int, populationbinary, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
Pass(int, populationinteger, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationinteger
Pass(int, populationreal, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal
Pass(int, populationbinary, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
Pass(int, populationinteger, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationinteger
Pass(int, populationreal, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationreal
path - Variable in class keel.GraphInterKeel.experiments.ExternalObjectDescription
Path - Class in keel.GraphInterKeel.util
Path() - Constructor for class keel.GraphInterKeel.util.Path
path - Static variable in class keel.GraphInterKeel.util.Path: File path.
pathDatasetFiles - Variable in class keel.GraphInterKeel.experiments.EducationalReport
pathfinder(List<List<Double>>, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.AdministrativeStaff
pathOutputFiles - Variable in class keel.GraphInterKeel.experiments.EducationalReport
pathReporFile - Variable in class keel.GraphInterKeel.experiments.EducationalReport
pathResultFilesTxt - Variable in class keel.GraphInterKeel.experiments.EducationalDiscretizerReport: Path for result files
pathResultFilesTxt - Variable in class keel.GraphInterKeel.experiments.EducationalFSReport: This class creates a report in the experiment directory.
pathResultFilesTxt - Variable in class keel.GraphInterKeel.experiments.EducationalISReport: This class creates a report in the experiment directory.
pattern(int, int) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FloatingPointFormat: Generates a pattern with the given width and decimal size parameters.
patternFile - Variable in class keel.GraphInterKeel.experiments.UserMethod
PBIL - Class in keel.Algorithms.Instance_Selection.PBIL: File: PBIL.java Population Based Incremental Learning for Instance Selection.
PBIL(String) - Constructor for class keel.Algorithms.Instance_Selection.PBIL.PBIL: Default builder.
PBIL - Class in keel.Algorithms.Preprocess.Instance_Selection.PBIL: File: PBIL.java Population Based Incremental Learning for Instance Selection.
PBIL(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.PBIL.PBIL: Default builder.
PCA - Class in keel.Algorithms.Discretizers.UCPD: This class implements the PCA algorithm
PCA(double[][]) - Constructor for class keel.Algorithms.Discretizers.UCPD.PCA: Constructor of the class
PCF_II - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Fuzzy_Chi: Configuration flags.
PCF_II - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Fuzzy_Ish: Configuration flags.
PCF_II - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Fuzzy_Ish: Configuration flag (PCF_II).
PCF_II - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Fuzzy_Ish
PCF_II - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.GP_COACH: Configuration flag (PCF_II).
PCF_II - Static variable in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.GP_COACH_H: Configuration flag (PCF_II)
PCF_IV - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Fuzzy_Chi: Configuration flags.
PCF_IV - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Fuzzy_Ish: Configuration flags.
PCF_IV - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Fuzzy_Ish: Configuration flag (PCF_IV).
PCF_IV - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Fuzzy_Ish
PCF_IV - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.GP_COACH: Configuration flag (PCF_IV).
PCF_IV - Static variable in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.GP_COACH_H: Configuration flag (PCF_IV)
pchisq(double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Returns the cumulative probability of the Chi-squared distribution
pchisq(double, double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Returns the cumulative probability of the noncentral Chi-squared distribution.
pchisq(double, DoubleVector) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Returns the cumulative probability of a set of noncentral Chi-squared distributions.
PCOMA - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
pctCorrect() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Gets the percentage of instances correctly classified (that is, for which a correct prediction was made).
pctIncorrect() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Gets the percentage of instances incorrectly classified (that is, for which an incorrect prediction was made).
pctUnclassified() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Gets the percentage of instances not classified (that is, for which no prediction was made by the classifier).
pd - Variable in class keel.GraphInterKeel.experiments.Node
PDFC - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS: This class implements the Positive Definite Fuzzy Classifier from Chen and Wang's paper: Ref: Y.
PDFC(String) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.PDFC: Creates a new instance of PDFC
PDFC() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.PDFC: Default constructor
pdfNormal(double) - Method in class keel.Algorithms.Instance_Generation.MixtGauss.MixtGaussGenerator: Return the value of PDF normal.
pDontCare - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Is the probability of using # in one attribute when covering.
pDontCare - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Is the probability of using # in one attribute when covering.
PDONTCARE - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
PDRFKernel - Class in keel.Algorithms.SVM.SMO.supportVector: Positive-Definite Reference Function Kernel To be used with PDFS algorithm
PDRFKernel() - Constructor for class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: default constructor - does nothing.
PDRFKernel(Instances, int, double) - Constructor for class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel: Constructor.
peek() - Method in class keel.Algorithms.Decision_Trees.M5.Queue: Gets object from the front of the queue.
peek() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Queue: Gets object from the front of the queue.
peek() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue: Gets object from the front of the queue.
peek() - Method in class keel.Algorithms.SVM.SMO.core.Queue: Gets object from the front of the queue.
peigs(DenseMatrix, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Finds positive eigenvalues and corresponding eigenvectors.
PENAL_CUB - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
penalize(Prototype, Prototype) - Method in class keel.Algorithms.Instance_Generation.DSM.DSMGenerator: Applies a DSMGenerator-reward to prototype m.
penalize(Prototype, Prototype) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQ1: Applies a penalization to prototype m
penalize(Prototype, Prototype) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQ3: Applies LVQ3-penalization to prototype m
penalize(Prototype, Prototype) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQGenerator: Applies a penalization to prototype m
penalize(Prototype, Prototype) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQTC: Applies LVQTC penalization to prototype m
penalty(int, int) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCGenerator: Returns the penalty for the cluster given.
PERC_SIZE_TXT - Static variable in class keel.Algorithms.Instance_Generation.utilities.Parameters: Text flag (size).
PERC_SIZE_TXT - Static variable in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters: Configuration text (reduction size).
percentageInitPartition - Variable in class keel.Algorithms.Instance_Generation.VQ.AVQGenerator: Percentage of the original set used in the initial partition.
percentageOfLearning - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
percentageOfLearning - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
percentageOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQPRU: Percentage of prototypes.
percentageOfPrototypesPerClass - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQPRU: Percentage of prototypes for each class in initial reduction.
percentages - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies: Percentages of each neuron type for hibrid layers
percentageSecondMutator - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Percentage of parents mutated with second mutator
perClass(int) - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Returns number of itemsets of given class.
perClass(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Returns number of itemsets of given class.
perClass(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Returns number of itemsets of given class.
perClass(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Returns number of itemsets of given class.
perClass(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Returns number of itemsets of given class.
perClass(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Returns number of itemsets of given class.
perClass(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Returns number of itemsets of given class.
perClass(int) - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Returns number of itemsets of given class.
perClass(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Returns number of itemsets of given class.
perClassPerValue(int, int) - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Returns number of itemsets of given class in given value.
perClassPerValue(int, int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Returns number of itemsets of given class in given value.
perClassPerValue(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Returns number of itemsets of given class in given value.
perClassPerValue(int, int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Returns number of itemsets of given class in given value.
perClassPerValue(int, int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Returns number of itemsets of given class in given value.
perClassPerValue(int, int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Returns number of itemsets of given class in given value.
perClassPerValue(int, int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Returns number of itemsets of given class in given value.
perClassPerValue(int, int) - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Returns number of itemsets of given class in given value.
perClassPerValue(int, int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Returns number of itemsets of given class in given value.
Perf - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Individuo
perf() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Cromosoma: Get the performance of a chromosome
perf() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Cromosoma: Get the performance of a chromosome
perf() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Cromosoma: Get the performance of a chromosome
perf() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Cromosoma: Get the performance of a chromosome
perf() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Cromosoma: Get the performance of a chromosome
perf() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Cromosoma: Get the performance of a chromosome
perf() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Cromosoma: Get the performance of a chromosome
perf() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Cromosoma: Get the performance of a chromosome
perfectRule(Complejo, Dataset) - Method in class keel.Algorithms.Rule_Learning.Prism.Prism: Returns True if the rule is perfect for the data set.
PerformanceAgent - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist: This class computes several performance measures taken from a fitness computation of an individual
PerformanceAgent() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.PerformanceAgent
PerformanceAgent - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS: This class computes several performance measures taken from a fitness computation of an individual
PerformanceAgent() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PerformanceAgent
performFeatureSelectionTipText() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
permitWithinCrossover - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It indicates if the within crossover is permitted in case of using a real representation.
PERMITWITHINCROSSOVER - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
perOneGenerated - Variable in class keel.Algorithms.Instance_Generation.HYB.HYBGenerator: Per one prototypes generated in the LVQ3.
perrorln(String) - Static method in class keel.Algorithms.Instance_Generation.utilities.Debug: Prints a message in the error console.
perrorln(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Debug: Prints a message in the error console.
pertenece(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Checks the existence of the prototype given as argument in this set.
pertenece(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Comprueba si existen en el conjunto....
perteneceSinClass(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet
pertenencia(int, int, int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseD
pertenencia(int, int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseD
pertenencia(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyPartition
pertenencia(int, int, double) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseD
pertenenecia_intervalo(int, Interval) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.partition
perValue(int) - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Returns number of (possibly fractional) itemsets in given value.
perValue(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Returns number of (possibly fractional) itemsets in given value.
perValue(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Returns number of (possibly fractional) itemsets in given value.
perValue(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Returns number of (possibly fractional) itemsets in given value.
perValue(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Returns number of (possibly fractional) itemsets in given value.
perValue(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Returns number of (possibly fractional) itemsets in given value.
perValue(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Returns number of (possibly fractional) itemsets in given value.
perValue(int) - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Returns number of (possibly fractional) itemsets in given value.
perValue(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Returns number of (possibly fractional) itemsets in given value.
pf(double, double, double) - Static method in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Computes cumulative Snedecor F distribution
PFKNN - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.PFKNN: File: PFKNN.java The PFKNN algorithm.
PFKNN(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.PFKNN.PFKNN: Main builder.
pinv(DenseMatrix) - Static method in class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.LLSImpute: Computes the pseudoinverse of matrix A -> pinv(A) = V * pinv(S) * U' That is, Moore-Penrose pseudoinverse of a matrix If A is square and not singular, then pinv(A) is an expensive way to compute inv(A)
PittsburghClassifier - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyPittsBurgh: PittsburghClassifier is designed to allow a Fuzzy Classifier evolve by means of an Genetic Algorithm.
PittsburghClassifier() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyPittsBurgh.PittsburghClassifier: Default constructor
PittsburghClassifier(FuzzyClassifier, int, Randomize) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyPittsBurgh.PittsburghClassifier: A constructor of the class specifying the Fuzzy classifier o be train, the fitness type and the Randomize object to use.
PittsburghClassifier(PittsburghClassifier) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyPittsBurgh.PittsburghClassifier: The copy constructor for this class.
PittsburghModel - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model: PittsburghModel
PittsburghModel() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.PittsburghModel: Constructor.
PittsburghModel(FuzzyModel, int, Randomize) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.PittsburghModel: Constructor.
PittsburghModel(PittsburghModel) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.PittsburghModel: Constructor.
platform() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.AdministrativeStaff
plus(double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Adds a value to all the elements
plus(DoubleVector) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Adds another vector element by element
plus(Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: C = A + B
plusEquals(double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Adds a value to all the elements in place
plusEquals(DoubleVector) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Adds another vector in place element by element
plusEquals(Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: A = A + B
pM - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Probability of mutate an allele in the offspring.
pM - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Probability of mutate an allele in the offspring.
PM - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
PNArray - Class in keel.Algorithms.Associative_Classification.ClassifierCPAR: This class contains the representation of a structure that stores some information corresponding to rule r.
PNArray() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCPAR.PNArray: Default Constructor
PNArray(myDataset, DataBase) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCPAR.PNArray: Parameters Constructor
PNN - Static variable in class keel.Algorithms.Instance_Generation.HYB.HYBGenerator: PNNGenerator title text
PNNAlgorithm - Class in keel.Algorithms.Instance_Generation.PNN: PNN algorithm calling.
PNNAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.PNN.PNNAlgorithm
PNNGenerator - Class in keel.Algorithms.Instance_Generation.PNN: Prototypes for Nearest Neighbor Classifiers
PNNGenerator(PrototypeSet) - Constructor for class keel.Algorithms.Instance_Generation.PNN.PNNGenerator: Build a new algorithm PNNGenerator that will reduce a prototype set.
PNNGenerator(PrototypeSet, int) - Constructor for class keel.Algorithms.Instance_Generation.PNN.PNNGenerator: Build a new algorithm PNNGenerator that will reduce a prototype set.
PNNGenerator(PrototypeSet, double) - Constructor for class keel.Algorithms.Instance_Generation.PNN.PNNGenerator: Build a new algorithm PNNGenerator that will reduce a prototype set.
PNNGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.PNN.PNNGenerator: Build a new algorithm PNNGenerator that will reduce a prototype set.
pnorm(double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Returns the cumulative probability of the standard normal.
pnorm(double, double, double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Returns the cumulative probability of a normal distribution.
pnorm(double, DoubleVector, double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Returns the cumulative probability of a set of normal distributions with different means.
pnorm(double, boolean) - Static method in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Computes cumulative N(0,1) distribution.
pnorm(double, boolean, double, double) - Static method in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Computes cumulative N(mu,sigma) distribution.
pnPair - Class in keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter: This class lets to handle pnPair structure
pnPair(int, int) - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.pnPair: Constructor of the class
Poblacion - Class in keel.Algorithms.Decision_Trees.DT_GA: Title: Poblacion (Population).
Poblacion() - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.Poblacion: Default Constructor.
Poblacion(int, Regla, int, int, double, double, myDataset, String) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.Poblacion: Paramenter constructor.
Poblacion(boolean[], int, int, double, double, myDataset, double[]) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.Poblacion: Paramenter constructor.
Poblacion - Class in keel.Algorithms.Decision_Trees.DT_oblicuo: Title: Poblacion (Population).
Poblacion() - Constructor for class keel.Algorithms.Decision_Trees.DT_oblicuo.Poblacion: Default Constructor.
Poblacion(myDataset, int, int[], int, double[], double) - Constructor for class keel.Algorithms.Decision_Trees.DT_oblicuo.Poblacion: Paramenter constructor.
Poblacion - Class in keel.Algorithms.Decision_Trees.Target: Title: Poblacion (Population).
Poblacion() - Constructor for class keel.Algorithms.Decision_Trees.Target.Poblacion: Default Constructor.
Poblacion(myDataset, double, int, int, int, int, int) - Constructor for class keel.Algorithms.Decision_Trees.Target.Poblacion: Paramenter constructor.
Poblacion - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec
Poblacion() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Poblacion
Poblacion(BaseR, double, double, int, double, double) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Poblacion
Poblacion - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF: Title: Poblacion (Population).
Poblacion(int, BaseR, myDataset) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Poblacion: Paramenter constructor.
Poblacion - Class in keel.Algorithms.Genetic_Rule_Learning.OCEC
Poblacion() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OCEC.Poblacion
Poblacion(int, Attribute, myDataset) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OCEC.Poblacion
Poblacion - Class in keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM: Title: Poblacion (Population).
Poblacion(int, BaseR, myDataset) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Poblacion: Paramenter constructor.
Poblacion - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs: The population class
Poblacion(int, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion: It is the constructor of the class poblacion
Poblacion - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs: The population class
Poblacion(int, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion: It is the constructor of the class poblacion
Poblacion - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs: The population class
Poblacion(int, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion: It is the constructor of the class poblacion
Poblacion - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs: The population class
Poblacion(int, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion: It is the constructor of the class poblacion
Poblacion - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs: The population class
Poblacion(int, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion: It is the constructor of the class poblacion
Poblacion - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs: The population class
Poblacion(int, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion: It is the constructor of the class poblacion
Poblacion - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs: The population class
Poblacion(int, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion: It is the constructor of the class poblacion
Poblacion - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs: The population class
Poblacion(int, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion: It is the constructor of the class poblacion
POCAlgorithm - Class in keel.Algorithms.Instance_Generation.POC: PSO algorithm calling.
POCAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.POC.POCAlgorithm
POCGenerator - Class in keel.Algorithms.Instance_Generation.POC
POCGenerator(PrototypeSet, double, String) - Constructor for class keel.Algorithms.Instance_Generation.POC.POCGenerator: Build a new POCGenerator Algorithm
POCGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.POC.POCGenerator: Build a new RSPGenerator Algorithm
podaArbol() - Method in class keel.Algorithms.Decision_Trees.SLIQ.SLIQ: Prunes the tree.
podaNodoCompleto(Node) - Method in class keel.Algorithms.Decision_Trees.SLIQ.SLIQ: Prunes the node given as parameter, deleting its children.
podaNodoParcial(Node, int) - Method in class keel.Algorithms.Decision_Trees.SLIQ.SLIQ: Prunes the node given as parameter, deleting the given child.
POLY - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
poly(double[], int, double) - Static method in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Quoted from the original Fortran documentation: Calculates the algebraic polynomial of order nord-1 with array of coefficients c.
POLY - Static variable in class org.libsvm.svm_parameter
PolyKernel - Class in keel.Algorithms.SVM.SMO.supportVector: The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p
PolyKernel() - Constructor for class keel.Algorithms.SVM.SMO.supportVector.PolyKernel: default constructor - does nothing.
PolyKernel(Instances, int, double, boolean) - Constructor for class keel.Algorithms.SVM.SMO.supportVector.PolyKernel: Creates a new PolyKernel instance.
ponBaseDatosInicial() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseD
ponBaseDatosInicial() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseD
ponValor(int, int, String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Sets a value to an attribute of a given instance.
pop() - Method in class keel.Algorithms.Decision_Trees.M5.Queue: Pops an object from the front of the queue.
pop() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Queue: Pops an object from the front of the queue.
pop() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue: Pops an object from the front of the queue.
POP - Class in keel.Algorithms.Instance_Selection.POP: File: POP.java The POP Instance Selection algorithm.
POP(String) - Constructor for class keel.Algorithms.Instance_Selection.POP.POP: Default constructor.
POP - Class in keel.Algorithms.Preprocess.Instance_Selection.POP: File: POP.java The POP Instance Selection algorithm.
POP(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.POP.POP: Default constructor.
pop() - Method in class keel.Algorithms.SVM.SMO.core.Queue: Pops an object from the front of the queue.
popSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
popSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
popSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
popSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Represents the maximum number of microclassifiers in the population.
popSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Represents the maximum number of microclassifiers in the population.
POPSIZE - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
populateFromResultSet(ResultSet) - Method in class keel.GraphInterKeel.datacf.util.AttributeTable: Fills data in the table with a ResultSet
Population - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD: Title: Population Description: Class for the CHC algorithm Copyright: KEEL Copyright (c) 2010 Company: KEEL
Population() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Population: Default constructor.
Population(myDataset, DataBase, RuleBase, int, int, int, double) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Population: Builder
Population - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA: This class contains the population for the genetic algorithm
Population() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Population: Default Constructor
Population(myDataset, DataBase, int, double, double, int, double, double, double, double, int, Apriori) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Population: Constructor with parameters
Population - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD: This class contains the population for the genetic algorithm
Population() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Population: Default Constructor
Population(RuleBase, int, myDataset, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Population: Constructor with parameters
Population - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid: Title: Populatoin Description: This class contains the population for the genetic algorithm Copyright: KEEL Copyright (c) 2008 Company: KEEL
Population() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Population: Default Constructor
Population(myDataset, DataBase, int, int, double, int, int, int, double, double) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Population: Constructor with parameters
Population - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS: Title: Population Description: Class for the CHC algorithm Copyright: KEEL Copyright (c) 2010 Company: KEEL
Population() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Population: Default constructor.
Population(myDataset, DataBase, RuleBase, int, int, int, double, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Population: Builder
Population - Class in keel.Algorithms.Genetic_Rule_Learning.CORE: This class represents the population of chromosomes in the CORE algorithm.
Population() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.CORE.Population: Constructor which initializes the memory allocations.
Population - Class in keel.Algorithms.Genetic_Rule_Learning.UCS: This class contains a rule-set.
Population(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Population constructor: it creates an empty population.
Population(Population, double[]) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Math set [M] constructor.
Population(Population, double[], int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Correct set [C] constructor.
Population - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: This class is a classifier set.
Population(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: It creates a new empty population (it's used in the beginning of each experiment to initialize the population).
Population(double[], Population, int, boolean) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: This constructor creates the match set of the population.
Population(Population, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: It creates the action set using the match set and the chosen action.
Population - Class in keel.Algorithms.Neural_Networks.gann: Class Population, which represents a population of individuals
Population(SetupParameters) - Constructor for class keel.Algorithms.Neural_Networks.gann.Population: Constructor
Population - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate: Population of candidate rules
Population(int, int, TypeVar[]) - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population: Creates a new instance of Population
Population - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF: Population of candidate rules
Population(Genetic, int, int, String, TableVar, int, int) - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Creates a population of Individual
Population - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate: Population of candidate rules
Population(int, int, TypeVar[]) - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population: Creates a new instance of Population
Population - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD: Population of candidate rules
Population(int, int, int, int, String, TableVar) - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Population: Creates a population of Individual
Population - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate: Population of candidate rules
Population(int, int, TypeVar[]) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population: Creates a new instance of Population
Population - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA: Population of candidate rules
Population(int, int, String, TableVar) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Population: Creates a population of Individual
population_size - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
POPULATION_SIZE - Static variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
populationbinary - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV
populationBinary - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE
populationbinary - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2: It contains the methods for handling the binary population of individuals
populationbinary - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0: It contains the methods for handling the binary population of individuals
populationFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: If not null, it represents the name of the file where the population has been writen
populationFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: If not null, it represents the name of the file where the population has been writen
POPULATIONFILE - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
populationInt - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE
populationinteger - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV
populationinteger - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2
populationinteger - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0
populationreal - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV
populationReal - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE
populationreal - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2
populationreal - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0
populationSize - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Population size
populationSizeTipText() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
populationWrapper - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
populationWrapper(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.populationWrapper
PopulationWrapper - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist: Contains methods that manipulate the population in various ways: classifying the training set for the fitness computations, checking if there are improved solutions in the population and performing the test stage (generating the output files)
PopulationWrapper() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper
PopulationWrapper - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS: Contains methods that manipulate the population in various ways: classifying the training set for the fitness computations, checking if there are improved solutions in the population and performing the test stage (generating the output files)
PopulationWrapper() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper
porcentajeMuestrasClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Returns the percentage of examples with the given class.
porcentajeMuestrasClase(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Returns the percentage of examples for each class.
porcentajeMuestrasClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Returns the percentage of examples with the given class.
porcentajeMuestrasClase(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Returns the percentage of examples for each class.
porcentajeMuestrasClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Returns the percentage of examples with the given class.
porcentajeMuestrasClase(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Returns the percentage of examples for each class.
porcentajeMuestrasClase(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Returns the percentage of examples with the given class.
porcentajeMuestrasClase(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Returns the percentage of examples for each class.
porcentajeMuestrasCondicion(Condicion, Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Returns the percentage of examples with the given class covered by the given condition.
porcentajeMuestrasCondicion(Condicion, Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Returns the percentage of examples with the given class covered by the given condition.
porcentajeMuestrasCondicion(Condicion, Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Returns the percentage of examples with the given class covered by the given condition.
porcentajeMuestrasCondicion(Condicion, Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Returns the percentage of examples with the given class covered by the given condition.
porcentajeMuestrasCubiertas(Regla, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Computes the percentage of examples of the dataset covered by the given rule.
porcentajeMuestrasCubiertas(Regla, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Computes the percentage of examples of the dataset covered by the given rule.
porcentajeMuestrasCubiertas(Regla) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ConjuntoDatos: Computes the percentage of examples of the dataset covered by the given rule.
porcentajeMuestrasCubiertas(Regla, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Computes the percentage of examples of the dataset covered by the given rule.
porcentajeMuestrasCubiertas(Regla, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Computes the percentage of examples of the dataset covered by the given rule.
porcentajeMuestrasVacias(Atributo, Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos: Returns the percentage of examples with the given class and the attribute as null.
porcentajeMuestrasVacias(Atributo, Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos: Returns the percentage of examples with the given class and the attribute as null.
porcentajeMuestrasVacias(Atributo, Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos: Returns the percentage of examples with the given class and the attribute as null.
porcentajeMuestrasVacias(Atributo, Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos: Returns the percentage of examples with the given class and the attribute as null.
pos - Variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.rank
POSEnvironment - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: Single step problem: the position problem.
POSEnvironment() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.POSEnvironment: It is the constructor of the class.
PosIBL - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.PosIBL: File: PosIBL.java The PosIBL algorithm.
PosIBL(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.PosIBL.PosIBL: Main builder.
position_name(String) - Method in class keel.GraphInterKeel.experiments.Joint
positionRuleMatch - Variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
positionRuleMatch - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
positive_class() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Obtains the index of the positive class in the dataset
positive_cost() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset: Computes the cost for a instance from the positive class
POSITIVE_TERMS_SIZE - Static variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
positiveCovered() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Rule: Returns the instances covered by the rule and are correctly classified.
positiveCovered() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Rule: Returns the instances covered by the rule and are correctly classified.
positiveEta - Variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus: Value of positive Eta
PosMax() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.vectordouble: Returns the position in the vector for maximum value of the vector
PosMin() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.vectordouble: Returns the position in the vector for minimum value of the vector
PosProb - Class in keel.Algorithms.ImbalancedClassification.Auxiliar.AUC: Class to compute the positive probabilities
PosProb(boolean, double) - Constructor for class keel.Algorithms.ImbalancedClassification.Auxiliar.AUC.PosProb: Constructor
possibleLabelConclusions - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Set of label conclusions that can appear.
possibleValuesOfOutput() - Static method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Return all the existing classes in our universe.
possibleValuesOfOutput() - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Return all the existing classes in our universe.
postOptimizationMODL(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.MODL.MODL: Implements the post-optimization procedure for MODL, after obtaining the best initial interval division.
postprocess() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
postprocess() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list
postprocess() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
postProcess() - Method in class keel.Algorithms.Hyperrectangles.INNER.INNER: Performs the postprocessing phase of INNER
postprocessSelectionTree - Variable in class keel.GraphInterKeel.experiments.Experiments
posValorAtt(String, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: Returns the position of the value given in the attribute given.
posValue(int, String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.DataBase: Returns the position of a given value of a given variable.
posValueNominal(int, String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: Returns the integer representation (id or position) of the given attribute's value.
posVariable(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: Returns the id (position) of an attribute with the given name.
potential(int, double, double[], double[], boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Calculate the potential to decrease DL of the ruleset, i.e. the possible DL that could be decreased by deleting the rule whose index and simple statstics are given.
pow(fuzzy, float) - Static method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
ppal() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
ppal() - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
ppal() - Static method in class keel.Dataset.DataParser
ppnd16(double) - Static method in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Quoted from original Fortran documentation: Produces the normal deviate Z corresponding to a given lower tail area of P; Z is accurate to about 1 part in 10**16.
precalculateParameters() - Method in class keel.Algorithms.Lazy_Learning.CamNN.CamNN: Calculates A, B and TAU Values for each training instance.
precalculateParameters() - Method in class keel.Algorithms.Lazy_Learning.CenterNN.CenterNN: Calculates centers of each class, and vectors from each train instance to its class center
precision(int) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Calculate the precision with respect to a particular class.
precision - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
precision - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal
PRECISION - Static variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Numeric: Precision on the numeric operations.
precompute() - Method in class keel.Algorithms.Lazy_Learning.LazyDT.LazyDT: Does some previous computations to the beginning of the algorithm, this means, getting the number of different values of the categorical attributes and the denormalization of the dataset
PRECOMPUTED - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
PRECOMPUTED - Static variable in class org.libsvm.svm_parameter
predicates - Variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
prediccion(Muestra, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Returns the predicted class of the given sample it they have compatible conditions, null otherwise.
prediccion(Muestra, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Returns the predicted class of the given sample it they have compatible conditions, null otherwise.
prediccion(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Returns the predicted class of the given sample it they have compatible conditions, null otherwise.
prediccion(Muestra, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Returns the predicted class of the given sample it they have compatible conditions, null otherwise.
prediccion(Muestra) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Returns the predicted class of the given sample it they have compatible conditions, null otherwise.
predict(M5Instance) - Method in class keel.Algorithms.Decision_Trees.M5.Function: Returns the predicted value of instance i by a function
predict(M5Instance, boolean) - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode: Predicts the class value of an instance by the tree
predict(Itemset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Function: Returns the predicted value of itemset i by a function
predict(Itemset, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode: Predicts the class value of an itemset by the tree
prediction - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: predictions for test
prediction - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Predited classes of the test instances.
prediction - Variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: Test predicted classes.
prediction - Variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: Test predicted classes.
PredictionArray - Class in keel.Algorithms.Genetic_Rule_Learning.UCS: Prediction array.
PredictionArray(Population) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.PredictionArray: This function builds the prediction arry
PredictionArray - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: This class will construct the prediction array from a given match set.
PredictionArray(Population) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.PredictionArray: Constructs the prediction array.
predictionErrorReduction - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: The factor by which the prediction error is reduced when a new classifier is generated in the AG.
PREDICTIONERRORREDUCTION - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
predictions - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Class predictions for the examples.
predictionsToString(M5Instances, int, boolean) - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode: Converts the predictions by the tree under this node to a string
predictionsToString(MyDataset, int, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode: Converts the predictions by the tree under this node to a string
PredPair - Class in keel.Algorithms.ImbalancedClassification.Auxiliar.AUC: This class represents pairs holding the class prediction and value of the voting procedure.
PredPair(String, double) - Constructor for class keel.Algorithms.ImbalancedClassification.Auxiliar.AUC.PredPair: Constructor
Preduct - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It's the reduction applied to the max reward to decide if a classifier is accurate enough to participate in the reduction.
prepare(int, int, double[][], double[][], int[][], boolean[][], int[], int, boolean) - Method in class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Construct a random chromosome of specified size and it is prepared to be evaluated.
prepare(int, int, double[][], double[][], int[][], boolean[][], int[], int, boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.Cromosoma: Construct a random chromosome of specified size and it is prepared to be evaluated.
prepareCreation() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetCreator: Prepares the creation initiators
prepareFileDirectories(String, String) - Static method in class keel.GraphInterKeel.experiments.FileUtils: Creates the necessary directory to build a KEEL experiment
prepareInitiation() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.initiators.PureLayerInitiator: Prepare initiation process
prepareInitiation() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.initiators.RandomInitiator: Prepare initiation process
prepareMutation() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.NeuralNetMutator: This method prepares the mutation, stablising the species
prepareMutation() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator: Prepares the mutation and actualize the alpha value
preprocessTree - Variable in class keel.GraphInterKeel.experiments.Experiments
presence(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.IntegerSet: It returns true if the element e is present in the set
presentAttsActual() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.Particle
presentAttsBest() - Method in class keel.Algorithms.PSO_Learning.CPSO.Particle
presentAttsBest() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
presentAttsBest() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.Particle
presentAttsBest() - Method in class keel.Algorithms.PSO_Learning.REPSO.Particle
prev - Variable in class keel.Algorithms.Discretizers.Khiops.DeltaValue: item for the previous merge of intervals
prev - Variable in class keel.Algorithms.Discretizers.MODL.DeltaValue: item for the previous bound of intervals
prevCharIsCR - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
prevCharIsCR - Static variable in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
prevCharIsCR - Static variable in class keel.Dataset.SimpleCharStream
prevCharIsLF - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
prevCharIsLF - Static variable in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
prevCharIsLF - Static variable in class keel.Dataset.SimpleCharStream
previousBest - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Previous best fitness
previousMean - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Previous mean fitness
print(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.DataBase: It prints an attribute with its label in a string way
print(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.DataBase: It prints an attribute with its label in a string way
print(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.DataBase: It prints an attribute with its label in a string way
print(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.DataBase: It prints an attribute with its label in a string way
print(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.DataBase: It prints an attribute with its label in a string way
print(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.DataBase: It prints an attribute with its label in a string way
print(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.DataBase: It prints an attribute with its label in a string way
print(myDataset, String, int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.FunctionalTrees: Prints in a file the result of the classification made with the tree generated by the PUBLIC algorithm.
print(myDataset, String, int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.PUBLIC: Prints in a file the result of the classification made with the tree generated by the PUBLIC algorithm.
print() - Method in class keel.Algorithms.Discretizers.UCPD.Itemset: It prints the itemset
print(String) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.Util: Prints a text in the standard output
print(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseD: It prints a single label of the Data Base into an string
print(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.DataBase: It prints the name of a fuzzy label
Print(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
Print() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.fuzzy_t
Print(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Prints in the standard output the name of the label number i in the domain
Print() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.fuzzy_t: Prints in the standard output the name of the label
Print(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
Print() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.fuzzy_t
Print(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
Print() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.fuzzy_t
print(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.DataBase: It prints the name of a fuzzy label
print(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseD
print() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Individuo
print(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.DataBase
print() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Individuo
print(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.LogManager
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Instance: It shows the content of the example
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.myDataset: It prints the examples
print(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Globals.LogManager
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Prints the String representation given by printString.
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Condition: Create a string according to the type of the condition
print(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.LogManager
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Attribute
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Poblacion
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Instance: It shows the content of the example
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.myDataset: It prints the examples
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Rule: It prints the rule
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.ruleSet: It prints all rules by screen
print() - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Attribute: Prints the classifier representation
print(PrintWriter) - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Attribute: Prints the classifier representation on the PrintWriter object given.
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Prints the classifier.
print(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Prints the classifier to the specified file.
print(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters: Prints the classifier to the specified file.
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters: Prints the classifier statistics to the standard output.
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Prints on standard output the population representation.
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.PredictionArray: Prints the prediction array to the standard output.
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep
print(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep: Prints the classifier representation on the PrintWriter object given.
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Roulette: Prints the roulette
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep: Prints the allele.
print(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep: Prints the allele.
print(BufferedReader) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.UCS: Does print the population and stops the execution.
print() - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute: Prints the classifier representation
print(PrintWriter) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute: Prints the classifier representation on the PrintWriter object given.
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Prints the classifier.
print(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Prints the classifier to the specified file.
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: This methods prints the attribute bounds
print(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: This method prints to the attribute bounds to the specified PrintWriter
print(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Prints the classifier to the specified file.
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters: Prints the classifier into standard output.
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Prints on standard output the population representation.
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.PredictionArray: Prints the prediction array to the standard output.
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: Prints the classifier representation on standard output.
print(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: Prints the classifier representation on the PrintWriter object given.
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: Prints on the standard output a String representation of the Representation object.
print(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: Prints the classifier to the specified file.
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Roulette: Prints the roulette
print() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep: Prints the allele.
print(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep: Prints the allele.
print(BufferedReader) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.XCS: Does print the population and stops the execution.
print(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Prints on the standard output the content of this complex object.
print() - Method in class keel.Algorithms.Hyperrectangles.EACH.EachDataSet: Shows the examples on the screen
print() - Method in class keel.Algorithms.Hyperrectangles.EACH.Hyperrectangle: Prints an instance
print(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleSet: Prints on the screen the set of rules
print() - Method in class keel.Algorithms.Hyperrectangles.EACH.Sample: Prints on the screen the example's content
print() - Method in class keel.Algorithms.Hyperrectangles.EACH.Selector: Shows the content of a selector: attribute-operator-value
print() - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Print the prototype.
print() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Prints the prototype in the terminal
print(String, ArrayList<Double>) - Static method in class keel.Algorithms.Instance_Generation.utilities.Debug: Prints a message and a array in the standard console.
print() - Method in class keel.Algorithms.Instance_Selection.CoCoIS.Subpopulation: Prints the population
print() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.Cromosoma: it prints a chromosome, gene by gene.
print() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaBinario: it prints a chromosome, gene by gene
print() - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.CromosomaEntero: it prints a chromosome, gene by gene
print() - Method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.Subpopulation: Prints the population
print() - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.IntegerSet: It prints the set
print() - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.pnPair: Prints on the standard output a representation of the pnPair.
print() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Individuo
print() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.MatrizR
print(String) - Static method in class keel.Algorithms.RST_Learning.Util: Prints a text in the standard output
print() - Method in class keel.Algorithms.Rule_Learning.AQ.Complex: It prints the complex content
print() - Method in class keel.Algorithms.Rule_Learning.AQ.Instance: It shows the content of the example
print() - Method in class keel.Algorithms.Rule_Learning.AQ.myDataset: It prints the examples
print() - Method in class keel.Algorithms.Rule_Learning.AQ.ruleSet: It prints the rule-set
print() - Method in class keel.Algorithms.Rule_Learning.AQ.Selector: It shows the content of the selector attribute - operator - value.
print() - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: It prints the complex content
print() - Method in class keel.Algorithms.Rule_Learning.CN2.Instance: It shows the content of the example
print() - Method in class keel.Algorithms.Rule_Learning.CN2.myDataset: It prints the examples
print() - Method in class keel.Algorithms.Rule_Learning.CN2.ruleSet: It prints the rule-set
print() - Method in class keel.Algorithms.Rule_Learning.CN2.Selector: It shows the content of the selector attribute - operator - value.
print() - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Prints on the screen the content of the complex(Lista -> Attribute operator value)
print() - Method in class keel.Algorithms.Rule_Learning.Prism.ConjDatos: Prints the examples on the screen
print() - Method in class keel.Algorithms.Rule_Learning.Prism.ConjReglas: Prints on the screen the set of rules
print() - Method in class keel.Algorithms.Rule_Learning.Prism.Muestra: Prints on the screen the example's content
print() - Method in class keel.Algorithms.Rule_Learning.Prism.Selector: Shows the content of a selector: attribute-operator-value
print() - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Print the content of the complex (List->Attribute operator value)
print() - Method in class keel.Algorithms.Rule_Learning.Riona.Selector: Shows the content of the selector selector: Attribute - operator - value
print() - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: This method prints the attribute information.
print() - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It does print the attributes information
print() - Method in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo: It does print an understable message about the error
print(PrintWriter) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: It prints the instance to the specified PrintWriter.
print() - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: It does print the instance information
print(InstanceAttributes, PrintWriter) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance
print(InstanceAttributes) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: It does print the instance information
print(PrintWriter) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: It prints the dataset to the specified PrintWriter
print() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet
print(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Print the content of the complex (List->Attribute operator value)
print() - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjDatos: Prints the examples on the screen
print(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjReglas: Prints on the screen the set of rules
print() - Method in class keel.Algorithms.Rule_Learning.UnoR.Interval: Prints on the standard output the interval.
print() - Method in class keel.Algorithms.Rule_Learning.UnoR.Muestra: Prints on the screen the example's content
print() - Method in class keel.Algorithms.Rule_Learning.UnoR.Selector: Shows the content of a selector: attribute-operator-value
print() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Print the prototype.
print() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Prints the prototype in the terminal
print(String, ArrayList<Double>) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Debug: Prints a message and a array of doubles in the standard console.
print(int, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Print the matrix to stdout.
print(PrintWriter, int, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Print the matrix to the output stream.
print(NumberFormat, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Print the matrix to stdout.
print(PrintWriter, NumberFormat, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Print the matrix to the output stream.
print() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjDatos: Prints the examples on the screen
print() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjReglas: Prints on the screen the set of rules
print() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Item: Prints on the standard output the item information.
print() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Muestra: Prints on the screen the example's content
print() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Prints the rule information on the standard output.
print() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Print the content of the complex (List->Attribute operator value)
print() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjDatos: Prints the examples on the screen
print() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjReglas: Prints on the screen the set of rules
print() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Muestra: Prints on the screen the example's content
print() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Selector: Shows the content of a selector: attribute-operator-value
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Chromosome: Prints the chromosome genes
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Gene: Prints the gene
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual: Method to Print the contents of the individual
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population: Prints population individuals
print(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.CromCAN: Prints the chromosome genes
print(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.CromDNF: Prints the chromosome genes
print() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.CromDNF: Gets the chromosome genes
print(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Gene: Prints the gene
print() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Gene: Gets the genes of the variable
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndCAN: Method to print the contents of the individual
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndDNF: Method to print the contents of the individual
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual: Method to print the contents of the individual
print(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Prints population individuals
print(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Prints the measures
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Chromosome: Prints the chromosome genes
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Gene: Prints the gene
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual: Method to Print the contents of the individual
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population: Prints population individuals
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.CromCAN: Prints the chromosome genes
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.CromDNF: Prints the chromosome genes
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Gene: Prints the gene
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndCAN: Method to Print the contents of the individual
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndDNF: Method to Print the contents of the individual
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Method to print the contents of the individual
Print(String, Vector) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Population: Prints population individuals
Print(String, Genetic) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.QualityMeasures: Prints the measures
print() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Print the content of the complex
print() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Instance: It shows the content of the example
print() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Selector: Show the selector
print() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetData: Show an instance
print() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetRules: Show the set of rules
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Chromosome: Prints the chromosome genes
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Gene: Prints the gene
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual: Method to Print the contents of the individual
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population: Prints population individuals
print(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.CromCAN: Prints the chromosome genes
print(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.CromDNF: Prints the chromosome genes
print() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.CromDNF: Gets the chromosome genes
print(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Gene: Prints the gene
print() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Gene: Gets the genes of the variable
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndCAN: Method to print the contents of the individual
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndDNF: Method to print the contents of the individual
Print(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual: Method to print the contents of the individual
print(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Population: Prints population individuals
print(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.QualityMeasures: Prints the measures
print() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.StopWatch: Prints out the lap times and the total time.
print(Object) - Method in class keel.Algorithms.SVM.SMO.core.Check: prints the given message to stdout, if not silent mode
print() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.StopWatch: Prints out the lap times and the total time.
print(int, int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.DataBase: Returns the name of a given label id of a given variable id.
print() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.StopWatch: Prints out the lap times and the total time.
print() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.StopWatch: Prints out the lap times and the total time.
print() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.StopWatch: Prints out the lap times and the total time.
print() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.StopWatch: Prints out the lap times and the total time.
print() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.StopWatch: Prints out the lap times and the total time.
print() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.StopWatch: Prints out the lap times and the total time.
print() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.StopWatch: Prints out the lap times and the total time.
print() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.StopWatch: Prints out the lap times and the total time.
print() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.StopWatch: Prints out the lap times and the total time.
print() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.StopWatch: Prints out the lap times and the total time.
print() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.StopWatch: Prints out the lap times and the total time.
print() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.StopWatch: Prints out the lap times and the total time.
print() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.StopWatch: Prints out the lap times and the total time.
print() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.StopWatch: Prints out the lap times and the total time.
print() - Method in class keel.Dataset.Attribute: This method prints the attribute information.
print() - Static method in class keel.Dataset.Attributes: It does print the attributes information
print() - Method in class keel.Dataset.ErrorInfo: It does print an understable message about the error
print(PrintWriter) - Method in class keel.Dataset.Instance: It prints the instance to the specified PrintWriter.
print() - Method in class keel.Dataset.Instance: It does print the instance information
print(InstanceAttributes, PrintWriter) - Method in class keel.Dataset.Instance
print(InstanceAttributes) - Method in class keel.Dataset.Instance: It does print the instance information
print(PrintWriter) - Method in class keel.Dataset.InstanceSet: It prints the dataset to the specified PrintWriter
print() - Method in class keel.Dataset.InstanceSet
Print_Population(Individual) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.Algorithm: It prints the current RB encoded in the individual "indiv" as a String
Print_Population() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.Algorithm: It prints the current population as a String
Print_Population() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.Algorithm: It prints the current population as a String
Print_SteadyState_Fitness(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.multiPopulation: It prints the steady state fitness for the individual "i"
print_triangle(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.DataBase: Return a String representation of the Triangular Membership Functions of the variable and its label given as arguments.
print_triangle(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.DataBase: Return a String representation of the Triangular Membership Functions of the variable and its label given as arguments.
print_triangle(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.DataBase: It prints the values of the fuzzy label of the given variable
print_triangle(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.DataBase: It prints the points of a fuzzy label
print_triangle(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.DataBase: It prints the points of a fuzzy label
print_triangle(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseD
print_triangle(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.DataBase
printAllErrors() - Method in exception keel.Algorithms.Rule_Learning.Swap1.DatasetException: It does print all the errors.
printAllErrors() - Method in exception keel.Dataset.DatasetException: It does print all the errors.
printArray(double[]) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils: Prints on console the elements of an array of doubles
printArray(int[]) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils: Prints on console the elements of an array of integers
printArray(double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils: Prints on console the elements of a double array.
printArray(double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils: Prints on console the elements of a double array.
printArray(double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils: Prints on console the elements of a double array.
printArray(double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils: Prints on console the elements of a double array.
printArray(double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils: Prints on console the elements of a double array.
printArray(double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils: Prints on console the elements of a double array.
printAsOriginal(PrintWriter) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: It prints the instance to the specified PrintWriter.
printAsOriginal(InstanceAttributes, PrintWriter) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance: It prints the instance to the specified PrintWriter.
printAsOriginal(PrintWriter, int) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: It prints the dataset to the specified PrintWriter.
printAsOriginal(PrintWriter) - Method in class keel.Dataset.Instance: It prints the instance to the specified PrintWriter.
printAsOriginal(InstanceAttributes, PrintWriter) - Method in class keel.Dataset.Instance: It prints the instance to the specified PrintWriter.
printAsOriginal(PrintWriter, int) - Method in class keel.Dataset.InstanceSet: It prints the dataset to the specified PrintWriter.
printC(int) - Method in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Friedman: Prints as many "c" as desired
printC(int) - Method in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Multiple: Prints as many "c" as desired
printC(int) - Static method in class keel.GraphInterKeel.statistical.tests.Friedman: Prints as many "c" as desired
printC(int) - Static method in class keel.GraphInterKeel.statistical.tests.Multiple: Prints as many "c" as desired
printClassifier() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
printClassifier() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierADI
printClassifier() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierGABIL
printClassifier() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ClassifierUBR
printClassifier() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
printClassifier() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierADI
printClassifier() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
printContents() - Static method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: It does print all the parameters value to check their correct initialization
printContents() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It does print all the parameters value to check their correct initialization
printDataSet(boolean) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It prints the dataset into an string
printDataSet() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Returns a string representation of the dataset.
PrintDefinition() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Prints in the standard output the definition of the domain
PrintDefinition(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Prints in the standard output the label number i in the domain
PrintDefinition() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.fuzzy_t: Prints in the standard output the definition of the label
PrintDefinition() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t: Prints in the standard output the definition of the variable
PrintDefinition() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.vectordouble: Prints in the standard output the vector
PrintDefinition(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Prints in the standard output the definition of the variable in position "variable" in the list
PrintDefinition() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Prints in the standard output the definition of all the variables in the list
PrintDefinitionToString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t: Prints in a String the definition of each label in the domain
PrintDefinitionToString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.fuzzy_t: Prints in a String the definition of the label
PrintDefinitionToString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t: Prints in a String the definition of the variable
PrintDefinitionToString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Prints in a String the definition of all the variables in the list
printDistrib() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Print the distribution
printDistribucion() - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Prints on screen the distribution of the classes for the complex
printDistribucion() - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Print the classes distribution for the complex
printDistribucion() - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Print the classes distribution for the complex
printDistribucion() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Print the classes distribution for the rule
printDistribucion() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Print the classes distribution for the complex
printDistribucionString() - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: prints on a string the distribution of the classes for the complex
printDistribucionString() - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Print the classes distribution for the complex
printDistribucionString() - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Print the classes distribution for the complex
printDistribucionString() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Print the classes distribution for the rule
printDistribucionString() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Print the classes distribution for the complex
printDistribucionString() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Print a String with the distribution
printDistribucionString() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.AssociationRule: Print in a string the distribution values of the class
printDistribution() - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Prints on the screen the class distribution of the complex
printDistribution() - Method in class keel.Algorithms.Rule_Learning.AQ.Complex: It prints the class distribution
printDistribution() - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: It prints the class distribution
printDistributionString() - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Prints on the screen the class distribution for the complex
printDistributionString() - Method in class keel.Algorithms.Rule_Learning.AQ.Complex: It prints the class distribution on a string
printDistributionString() - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: It prints the class distribution on a string
PrintDomain(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
PrintDomain(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
PrintDomain(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t: Prints in the standard ouput the name of the label "value" of the variable.
PrintDomain(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Prints in the standard ouput the name of the label "value" of the variable in position "variable" in the list.
PrintDomain(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
PrintDomain(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
PrintDomain(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
PrintDomain(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
printElements() - Method in class keel.Algorithms.SVM.SMO.supportVector.SMOset: Prints all the current elements in the set.
printErr(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.LogManager
printErr(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Globals.LogManager
printErr(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.LogManager
printExitValues() - Method in class keel.Algorithms.Coevolution.CIW_NN.CIW_NN: Prints the additional output file
printExitValues() - Method in class keel.Algorithms.Coevolution.IFS_COCO.IFS_COCO: Prints the additional output file
PrintfApplet - Class in keel.Algorithms.Neural_Networks.gmdh: Applet to print the different configurations and results of the NN.
PrintfApplet() - Constructor for class keel.Algorithms.Neural_Networks.gmdh.PrintfApplet
PrintfFormat - Class in keel.Algorithms.Neural_Networks.gmdh: PrintfFormat allows the formatting of an array of objects embedded within a string.
PrintfFormat(String) - Constructor for class keel.Algorithms.Neural_Networks.gmdh.PrintfFormat: Constructs an array of control specifications possibly preceded, separated, or followed by ordinary strings.
PrintfFormat(Locale, String) - Constructor for class keel.Algorithms.Neural_Networks.gmdh.PrintfFormat: Constructs an array of control specifications possibly preceded, separated, or followed by ordinary strings.
printFichero(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.BaseR
printFingramsLegend() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.FARFingrams
printFingramsLegend() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams
printFS() - Static method in class keel.Algorithms.RST_Learning.KNNClassifier: Returns a string representation of the features selection vector
printGeneration(int, <any>, <any>, ParametricMutator<NeuralNetIndividual>, IEvaluator) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.listener.NeuralNetReporterClas: Outputs the information of a generation to System.out
printGeneration(int, <any>, <any>, ParametricMutator<NeuralNetIndividual>, IEvaluator) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.listener.NeuralNetReporterRegr: Outputs the information of a generation to System.out
printIndividual() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual: Prints the tree encoded in the individual as a string
printIndividual(int) - Method in class keel.Algorithms.Instance_Selection.CoCoIS.Subpopulation: Prints an individual of the population
printIndividual(int) - Method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.Subpopulation: Prints an individual of the population
printIndividual_bk() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual: Prints the tree encoded in the individual as a string
printInstance(int[]) - Static method in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Generates a string with the contents of the instance
printInstance(double[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.Util: Generates a string with the contents of the instance
printInstance(int[]) - Static method in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Generates a string with the contents of the instance
printInstance(int[]) - Static method in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Generates a string with the contents of the instance
printInstance(int[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm: Generates a string with the contents of the instance
printInstance(int[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: Generates a string with the contents of the instance
printInstance(int[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: Generates a string with the contents of the instance
printInstance(double[]) - Static method in class keel.Algorithms.RST_Learning.Util: Prints on the standard output the contents of the instance
printLap() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.StopWatch: Prints out the lap time of the last recorded lap.
printLap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.StopWatch: Prints out the lap time of the last recorded lap.
printLap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.StopWatch: Prints out the lap time of the last recorded lap.
printLap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.StopWatch: Prints out the lap time of the last recorded lap.
printLap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.StopWatch: Prints out the lap time of the last recorded lap.
printLap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.StopWatch: Prints out the lap time of the last recorded lap.
printLap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.StopWatch: Prints out the lap time of the last recorded lap.
printLap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.StopWatch: Prints out the lap time of the last recorded lap.
printLap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.StopWatch: Prints out the lap time of the last recorded lap.
printLap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.StopWatch: Prints out the lap time of the last recorded lap.
printLap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.StopWatch: Prints out the lap time of the last recorded lap.
printLap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.StopWatch: Prints out the lap time of the last recorded lap.
printLap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.StopWatch: Prints out the lap time of the last recorded lap.
printLap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.StopWatch: Prints out the lap time of the last recorded lap.
printLap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.StopWatch: Prints out the lap time of the last recorded lap.
printLap() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.StopWatch: Prints out the lap time of the last recorded lap.
printlit() - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Literals: It prints the matrix of literals
println(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.LogManager
println(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Globals.LogManager
println(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.LogManager
println(String) - Static method in class keel.Algorithms.Instance_Generation.utilities.Debug: Prints a message in the standard console.
println(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Debug: Prints a message in the standard console.
println(Object) - Method in class keel.Algorithms.SVM.SMO.core.Check: prints the given message (+ LF) to stdout, if not silent mode
println() - Method in class keel.Algorithms.SVM.SMO.core.Check: prints a LF to stdout, if not silent mode
printlnError(String) - Static method in class keel.Algorithms.Instance_Generation.utilities.Debug: Prints a message in the error console.
printlnError(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Debug: Prints a message in the error console.
printMatrix(double[][]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.Util: Generates a string with the contents of a data matrix
printMatrix(double[][]) - Static method in class keel.Algorithms.RST_Learning.Util: Prints on the standard output a string with the contents of a data matrix
printMeasure(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.EvaluateRules: Print the quality measures in the measure file
printModelFile(Object) - Method in class keel.Algorithms.Decision_Trees.CART.ResultPrinter: Print result model tree
printNet() - Method in class keel.Algorithms.Shared.ClassicalOptim.GCNet: Returns the weights as a String for printing or storing purposes
printNetworkToFile(String, String) - Method in class keel.Algorithms.Neural_Networks.LVQ.LVQ: Save network weights to a file
printNotNorm(PrintWriter, double) - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Attribute: Prints the classifier representation not normalized on the PrintWriter object given.
printNotNorm(PrintWriter, double, double) - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Attribute: Prints the classifier representation not normalized on the PrintWriter object given.
printNotNorm(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier: Prints the desnormalized classifier to the specified file.
printNotNorm(PrintWriter, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep: Prints the classifier representation not normalized on the PrintWriter object given.
printNotNorm(PrintWriter, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep: Prints the classifier representation not normalized on the PrintWriter object given.
printNotNorm(PrintWriter, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep: Prints the classifier representation not normalized on the PrintWriter object given.
printNotNorm(PrintWriter, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep: Prints the classifier representation not normalized on the PrintWriter object given.
printNotNorm(PrintWriter, Vector) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute: Prints the classifier representation on the PrintWriter object given.
printNotNorm(PrintWriter, int) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute: Prints the classifier representation not normalized on the PrintWriter object given.
printNotNorm(PrintWriter, double, double) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute: Prints the classifier representation not normalized on the PrintWriter object given.
printNotNorm(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier: Prints the desnormalized classifier to the specified file.
printNotNorm(PrintWriter, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: Prints the classifier representation on the PrintWriter object given.
printNotNorm(PrintWriter, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: Prints the classifier representation not normalized on the PrintWriter object given.
printNotNorm(PrintWriter, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep: Prints the classifier representation not normalized on the PrintWriter object given.
printNotNorm(PrintWriter, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: Prints the classifier representation not normalized on the PrintWriter object given.
printNotNorm(PrintWriter, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: Prints the classifier representation not normalized on the PrintWriter object given.
printNotNorm(PrintWriter, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: Prints the classifier representation on the PrintWriter object given.
printNotNorm(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: Prints the classifier to the specified file.
printNotNorm(PrintWriter, Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep: Prints the classifier representation on the PrintWriter object given.
printNotNorm(PrintWriter, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep: Prints the classifier representation not normalized on the PrintWriter object given.
printNotNorm(PrintWriter, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep: Prints the classifier representation not normalized on the PrintWriter object given.
printNotNormPopToFile(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Prints the desnormalized population into the specified file.
PrintOutputFunctions() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.PSOLDA: Prints on the ouput fuctions file the fuctions inferenced by the algorithm.
PrintOutputRules() - Method in class keel.Algorithms.PSO_Learning.CPSO.CPSO: Prints on the ouput rules file the rules inferenced by the algorithm.
PrintOutputRules() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.LDWPSO: Prints on the ouput rules file the rules inferenced by the algorithm.
PrintOutputRules() - Method in class keel.Algorithms.PSO_Learning.REPSO.REPSO: Prints on the ouput rules file the rules inferenced by the algorithm.
printParameters() - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm: Print the parameters of the algorithm.
printParameters() - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm: Print the parameters of the algorithm.
printPareto(ArrayList<AssociationRule>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess
printPareto() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_GhoshProcess
printPareto() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_GoshProcess
printPareto() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.MOPNARProcess
printPareto() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.QAR_CIP_NSGAIIProcess
printPopulation(Population) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Statistic: It writes the population to a file.
printPopulation(Population) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Statistic: It writes the population to a file.
printPopulationToFile(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Prints the population into the specified file.
printPopulationToFile(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Prints the population into the file specified.
printPopulationToFile(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Prints the population into the specified file.
printPopulationToFile(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Prints the population into the file specified.
printRbf() - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbf: Prints neuron on std out
printRbf(String) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbf: Prints neuron on a file.
printRbf() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbf: Prints neuron on std out
printRbf(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbf: Prints neuron on a file.
printRbf() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbf: Prints neuron on std out
printRbf(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbf: Prints neuron on a file.
printRbf() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf: Prints neuron on std out
printRbf(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf: Prints neuron on a file.
printRbf() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbf: Prints neuron on std out
printRbf(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbf: Prints neuron on a file.
printRbf() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf: Prints neuron on std out
printRbf(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf: Prints neuron on a file.
printRbfn() - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbfn: Prints net on a stdout
printRbfn(String) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbfn: Prints net on a file.
printRbfn() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn: Prints net on a stdout
printRbfn(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn: Prints net on a file.
printRbfn() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn: Prints net on a stdout
printRbfn(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn: Prints net on a file.
printRbfn() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn: Prints net on a stdout
printRbfn(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn: Prints net on a file.
printRbfn() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn: Prints net on a stdout
printRbfn(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn: Prints net on a file.
printRbfn() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn: Prints net on a stdout
printRbfn(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn: Prints net on a file.
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.CFKNN.CFKNN: Reports the results obtained
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.D_SKNN.D_SKNN: Reports the results obtained
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FCMKNN.FCMKNN: Reports the results obtained
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FENN.FENN: Reports the results obtained
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRKNNA.FRKNNA: Reports the results obtained
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN.FRNN: Reports the results obtained
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_FRS.FRNN_FRS: Reports the results obtained
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_VQRS.FRNN_VQRS: Reports the results obtained
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyKNN.FuzzyKNN: Reports the results obtained
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyNPC.FuzzyNPC: Reports the results obtained
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.GAFuzzyKNN.GAFuzzyKNN: Reports the results obtained
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IF_KNN.IF_KNN: Reports the results obtained
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFSKNN.IFSKNN: Reports the results obtained
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFV_NP.IFV_NP: Reports the results obtained
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IT2FKNN.IT2FKNN: Reports the results obtained
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN.JFKNN: Reports the results obtained
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.PFKNN.PFKNN: Reports the results obtained
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.PosIBL.PosIBL: Reports the results obtained
printReport() - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.ReportTool: Prints the output report
printReport() - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.VWFuzzyKNN.VWFuzzyKNN: Reports the results obtained
printReport() - Method in class keel.Algorithms.RST_Learning.EFS_RPS.EFS_RPS: Reports the results obtained
printReport() - Method in class keel.Algorithms.RST_Learning.EIS_RFS.EIS_RFS: Reports the results obtained
printReport() - Static method in class keel.Algorithms.RST_Learning.ReportTool: Prints the output report
printReport(ArrayList<AssociationRule>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.AlcalaetalProcess: It prints out on screen relevant information regarding the mined association rules
printReport(ArrayList<AssociationRule>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyAprioriProcess: It prints out on screen relevant information regarding the mined association rules
printReport(ArrayList<AssociationRule>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.GeneticFuzzyAprioriProcess: It prints out on screen relevant information regarding the mined association rules
printReport(ArrayList<AssociationRule>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.GeneticFuzzyAprioriDCProcess: It prints out on screen relevant information regarding the mined association rules
printReport(ArrayList<AssociationRule>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AlatasetalProcess: It prints out on screen relevant information regarding the mined association rules
printReport(ArrayList<AssociationRule>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AprioriProcess: It prints out on screen relevant information regarding the mined association rules
printReport(double, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.EARMGAProcess
printReport(ArrayList<AssociationRule>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.EclatProcess: It prints out on screen relevant information regarding the mined association rules
printReport(ArrayList<AssociationRule>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.FPgrowthProcess: It prints out on screen relevant information regarding the mined association rules
printReport(double, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.GARProcess: It prints out on screen relevant information regarding the mined association rules which have their confidence and support values higher than the minimum ones given.
printReport(double, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.GENARProcess: It prints out on screen relevant information regarding the mined association rules which have their confidence and support values higher than the minimum ones given.
printResult() - Method in class keel.Algorithms.Decision_Trees.C45.Algorithm: Evaluates the algorithm and writes the results in the file.
printResult() - Method in class keel.Algorithms.Decision_Trees.C45.C45: Writes the tree and the results of the training and the test in the file.
printResult() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm: Evaluates the algorithm and writes the results in the file.
printResult() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.C45: Writes the tree and the results of the training and the test in the file.
printResult() - Method in class keel.Algorithms.Decision_Trees.ID3.Algorithm: Evaluates the algorithm and writes the results in the file.
printResult() - Method in class keel.Algorithms.Decision_Trees.ID3.ID3: Writes the tree and the results of the training and the test in the file.
printResult() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm: Evaluates the algorithm and writes the results in the file.
printResult() - Method in class keel.Algorithms.Decision_Trees.SLIQ.SLIQ: Writes the statistical measurements obtained on the output file.
printResult() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm: Evaluates the algorithm and writes the results in the file.
printResult() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.C45: Writes the tree and the results of the training and the test in the file.
printResult() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm: Evaluates the algorithm and writes the results in the file.
printResult() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.C45CS: Writes the tree and the results of the training and the test in the file.
printResult() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm: Evaluates the algorithm and writes the results in the file.
printResult() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.C45: Writes the tree and the results of the training and the test in the file.
printResult() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.C45: Writes the tree and the results of the training and the test in the file.
printResult() - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.C45: Writes the tree and the results of the training and the test in the file.
printResult() - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.C45: Writes the tree and the results of the training and the test in the file.
printResult() - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.C45: Writes the tree and the results of the training and the test in the file.
printResult() - Method in class keel.Algorithms.Rule_Learning.ART.Algorithm: Evaluates the algorithm and writes the results in the file.
printResult() - Method in class keel.Algorithms.Rule_Learning.ART.ART: Writes the tree and the results of the training and the test in the file.
printResult() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm: Evaluates the algorithm and writes the results in the file.
printResult() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.DataSqueezer: Writes the rules and the results of the training and the test in the file.
printResult() - Method in class keel.Algorithms.Rule_Learning.PART.Algorithm: Evaluates the algorithm and writes the results in the file.
printResult() - Method in class keel.Algorithms.Rule_Learning.PART.C45: Writes the tree and the results of the training and the test in the file.
printResult() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45: Writes the tree and the results of the training and the test in the file.
printResults(myDataset, String) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.FunctionalTrees: Prints in a file the result of the classification made with the tree generated by the PUBLIC algorithm, this means, the tree itself and the general information about it
printResults(myDataset, String) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.PUBLIC: Prints in a file the result of the classification made with the tree generated by the PUBLIC algorithm, this means, the tree itself and the general information about it
printRule(AssociationRule) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.Apriori: Returns a String with relevant information regarding the mined association rule given
printRule(AssociationRule) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.Eclat: Returns a String with relevant information regarding the mined association rule given
printRuleBase() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.PDFC: This method prints the actual Rule Base stored in m_ruleSet to a file
printRules() - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Cogin
printRules() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Core: Print the final set of rules to disk
printRules() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Ilga: Print the rules to the file passed as parameters in the configuration file
printRules() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Oiga: Print the current rules to the file specified by KEEL .
printRules(ArrayList<AssociationRule>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AlatasetalProcess: Returns a String with relevant information regarding the mined association rules
printRules(ArrayList<AssociationRule>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.ARMMGAProcess: Returns a String with relevant information regarding the mined association rules
printRules(ArrayList<AssociationRule>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.EARMGAProcess
printRules(ArrayList<AssociationRule>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.EclatProcess
printRules(ArrayList<AssociationRule>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.GARProcess: Returns a String with relevant information regarding the mined association rules
printRules(ArrayList<AssociationRule>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.GENARProcess: Returns a String with relevant information regarding the mined association rules
printSet() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Print Prototype Set.
printSet() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Print Prototype Set.
printStateAndClass(double[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Statistic: It prints the environmental state and the correct associated action.
printStateAndClass(double[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Statistic: It prints the environmental state and the correct associated action.
printStateAndClassNoCov(double[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Statistic: It prints the environmental state and for those examples with no action.
printStateAndClassNoCov(double[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Statistic: It prints the environmental state and for those examples with no action.
printString() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.DataBase: It prints the whole database
printString() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.RuleBase: It prints the whole rulebase
printString() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.DataBase: It prints the whole database
printString() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.RuleBase: It prints the whole rulebase
printString() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.DataBase: It prints the whole database
printString() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.DataBase: It prints the whole database
printString() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.RuleBase: It prints the whole rulebase
printString() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.DataBase: It prints the whole database
printString() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.RuleBase: It prints the whole rulebase
printString() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.DataBase: It prints the whole database
printString() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: It prints the whole rulebase
printString() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.DataBase: It prints the whole database
printString() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.RuleBase: It prints the whole rulebase
printString() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Rule: Returns the rule as a String
printString() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.RuleBase: It prints the whole rulebase
printString() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Selector: Returns a String representation of the selector.
printString() - Method in class keel.Algorithms.Decision_Trees.DT_GA.BaseR: Returns a String representation of all the rules collected.
printString() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.C45: It prints the information related to the decision tree
printString() - Method in class keel.Algorithms.Decision_Trees.DT_GA.Individuo: Returns a String representation of the Individual.
printString() - Method in class keel.Algorithms.Decision_Trees.DT_GA.Poblacion: Returns a String representation of the population.
printString() - Method in class keel.Algorithms.Decision_Trees.DT_GA.Regla: Returns a String representation of the rule.
printString() - Method in class keel.Algorithms.Decision_Trees.DT_GA.Selector: Returns String representation of the Selector.
printString() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.Nodo: Returns a String representation of the node.
printString() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.Tree: Returns a String representation of the tree.
printString() - Method in class keel.Algorithms.Decision_Trees.Target.Nodo: Returns a String representation of the node.
printString() - Method in class keel.Algorithms.Decision_Trees.Target.Tree: Returns a String representation of the tree.
printString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.DataBase: It prints the Data Base into an string
printString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.RuleBase: It prints the rule base into an string
printString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseD: It prints the Data Base into an string
printString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseR: It prints the rule base into an string
printString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.DataBase: It prints the Data Base into an string
printString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase: It prints the rule base into an string
printString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.DataBase: It prints the Data Base into an string
printString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.RuleBase: Prints the Rule Base into a String object
printString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseD
printString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseR
printString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseD
printString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseR
printString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Individuo
printString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.DataBase: It prints the Data Base into an string
printString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.FuzzyAntecedent: String representation of a Fuzzy Antecedent in the GP-COACH algorithm.
printString(String[], String[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: String representation of a Fuzzy Rule in the GP-COACH algorithm.
printString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.DataBase
printString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
printString() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Returns a String representation of the dataset (A list of all the instances).
printString() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Attribute
printString() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.BaseR
printString() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Organizacion
printString() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Poblacion
printString() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Regla
printString() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.evaluateRuleQuality: It returns a string with the accuracy percentage in training and test
printString() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Rule: It prints the rule into a string
printString() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.ruleSet: It prints all rules into a string
printString(int[]) - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Prints as a String the complex content (List->Attribute)
printString() - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleQualityEvaluation: Prints on a string the statistical results
printString(int[]) - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleSet: Prints on a string the set of rules
printString() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.C45: Returns a String representation of the tree.
printString() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Rule: Returns the rule as a String
printString() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.RuleBase: It prints the whole rulebase
printString() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Selector: Returns a String representation of the selector.
printString() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.DataBase: It prints the Data Base into an string
printString() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.FuzzyAntecedent: String representation of a Fuzzy Antecedent in the GP-COACH algorithm.
printString(String[], String[]) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: String representation of a Fuzzy Rule in the GP-COACH-H algorithm.
printString() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseD
printString() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseR
printString() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Individuo
printString() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.MatrizR
printString() - Method in class keel.Algorithms.Rule_Learning.AQ.Complex: It prints on a string the content of the complex
printString() - Method in class keel.Algorithms.Rule_Learning.AQ.evaluateRuleQuality: It prints on a string the statistics
printString() - Method in class keel.Algorithms.Rule_Learning.AQ.ruleSet: It prints on a string the rule-set
printString() - Method in class keel.Algorithms.Rule_Learning.CN2.Complex: It prints on a string the content of the complex
printString() - Method in class keel.Algorithms.Rule_Learning.CN2.evaluateRuleQuality: It prints on a string the statistics
printString() - Method in class keel.Algorithms.Rule_Learning.CN2.ruleSet: It prints on a string the rule-set
printString() - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Prints on a string the content of the complex(List -> Attribute operator value)
printString() - Method in class keel.Algorithms.Rule_Learning.Prism.ConjReglas: Prints on a string the set of rules
printString() - Method in class keel.Algorithms.Rule_Learning.Prism.EvaluaCalidadReglas: Prints on a string the statistical(for test)
printString(int[]) - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Print the complex content using a string (List->Attribute operator value)
printString(int, double, double, boolean) - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Returns a string with the complex contents (List -> Attribute operator value).
printString(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjReglas: Prints on a string the set of rules
printString() - Method in class keel.Algorithms.Rule_Learning.UnoR.EvaluaCalidadReglas: Prints on a string the statistical(for test)
printString() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjReglas: Prints on a string the set of rules
printString() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.EvaluaCalidadReglas: Prints on a string the statistical(for test)
printString() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Returns a string with the rule information.
printString() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo: Returns a string with the complex contents (List -> Attribute operator value).
printString() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjReglas: Prints on a string the set of rules
printString() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.EvaluaCalidadReglas: Prints on a string the statistical(for test)
printString() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex: Print a String the content of the complex
printString() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.EvaluateRules: Print the results in a String
printString() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetRules: Print string the set of rules
printStringF() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Rule: Returns the rule as a String (float representation of covers instances).
printStringF() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.RuleBase: It prints the whole rulebase with float representation.
printStringF() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Rule: Returns the rule as a String (float representation of covers instances).
printStringF() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.RuleBase: It prints the whole rulebase with float representation.
printStringOVO() - Method in class keel.Algorithms.Decision_Trees.C45.C45: Function to print the tree (OVO code).
printSupport() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Returns the support of the rule as string.
printSVs() - Method in class keel.Algorithms.SVM.SMO.SMO: Prints the Support vectors to file
printTest() - Method in class keel.Algorithms.Decision_Trees.C45.Algorithm: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Decision_Trees.C45.C45: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.C45: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Decision_Trees.ID3.Algorithm: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Decision_Trees.ID3.ID3: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Decision_Trees.SLIQ.SLIQ: Evaluates the test dataset and writes the results on the output file.
printTest() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.C45: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.C45CS: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.C45: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.C45: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.C45: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.C45: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.C45: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Rule_Learning.ART.Algorithm: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Rule_Learning.ART.ART: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.DataSqueezer: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Rule_Learning.PART.Algorithm: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Rule_Learning.PART.C45: Evaluates the test dataset and writes the results in the file.
printTest() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45: Evaluates the test dataset and writes the results in the file.
printTimes(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TimeControl: It prints the average time wasted in every kind of run
printTimes() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TimeControl: It prints the average time wasted in every kind of run
printTimes(PrintWriter) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TimeControl: It prints the average time wasted in every kind of run
printTimes() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TimeControl: It prints the average time wasted in every kind of run
printTrain() - Method in class keel.Algorithms.Decision_Trees.C45.Algorithm: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Decision_Trees.C45.C45: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.C45: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Decision_Trees.ID3.Algorithm: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Decision_Trees.ID3.ID3: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Decision_Trees.SLIQ.SLIQ: Evaluates the training dataset and writes the results on the output file.
printTrain() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.C45: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.C45CS: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.C45: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.C45: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.C45: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.C45: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.C45: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Rule_Learning.ART.Algorithm: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Rule_Learning.ART.ART: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.DataSqueezer: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Rule_Learning.PART.Algorithm: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Rule_Learning.PART.C45: Evaluates the training dataset and writes the results in the file.
printTrain() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45: Evaluates the training dataset and writes the results in the file.
printTree() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Prints the tree in a String with all the information that makes it human readable
printTree(ArrayList<myAttribute>, myAttribute) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode: Prints the tree in a String with all the information that makes it human readable
printValidOptions() - Method in class keel.Algorithms.Decision_Trees.M5.InformationHandler: Prints valid command line options and simply explains the output
PrintVar() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
PrintVar(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
PrintVar() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t: Prints in the standard output the name of the variable
PrintVar(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar: Prints in the standard output the name of the variable in position "variable" in the list
PrintVar() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
PrintVar(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
PrintVar() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
PrintVar(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
PrintWeights() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network: Print weights to screen.
PrintWeights() - Method in class keel.Algorithms.Neural_Networks.gann.Network: Print weights to screen.
PrintWeights() - Method in class keel.Algorithms.Neural_Networks.gmdh.Network: Print weights to screen.
PrintWeights() - Method in class keel.Algorithms.Neural_Networks.net.Network: Print weights to screen.
printWeightsAtributes(String[]) - Method in class keel.Algorithms.Hyperrectangles.EACH.HyperrectangleSet: Returns on a string the name and its weight of each attribute
priorEntropy() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Calculate the entropy of the prior distribution
priorsProbabilities() - Method in class keel.Algorithms.Decision_Trees.C45.C45: Sets the class prior probabilities.
priorsProbabilities() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.C45: Sets the class prior probabilities.
priorsProbabilities() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.C45: Sets the class prior probabilities.
priorsProbabilities() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.C45CS: Sets the class prior probabilities.
priorsProbabilities() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.C45: Sets the class prior probabilities.
priorsProbabilities() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.C45: Sets the class prior probabilities.
priorsProbabilities() - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.C45: Sets the class prior probabilities.
priorsProbabilities() - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.C45: Sets the class prior probabilities.
priorsProbabilities() - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.C45: Sets the class prior probabilities.
priorsProbabilities() - Method in class keel.Algorithms.Rule_Learning.C45Rules.C45: Sets the class prior probabilities.
priorsProbabilities() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.C45: Sets the class prior probabilities.
priorsProbabilities() - Method in class keel.Algorithms.Rule_Learning.PART.C45: Sets the class prior probabilities.
priorsProbabilities() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45: Sets the class prior probabilities.
priorVal(double[][]) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree: Computes value of splitting criterion before split.
priorVal(double[][]) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree: Computes value of splitting criterion before split.
priorVal(double[][]) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree: Computes value of splitting criterion before split.
Prism - Class in keel.Algorithms.Rule_Learning.Prism: Contents the principal methods of the Prismsd algorithm
Prism() - Constructor for class keel.Algorithms.Rule_Learning.Prism.Prism: Default constructor.
Prism(String, String, String, String, String, long) - Constructor for class keel.Algorithms.Rule_Learning.Prism.Prism: Constructor with all the attributes to initialize
PRM - Class in keel.Algorithms.Associative_Classification.ClassifierCPAR: This class stores information to manage the PRM procedure.
PRM() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCPAR.PRM: Default Constructor
PRM(DataBase, myDataset, RuleBase, double, double, double) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCPAR.PRM: Parameters Constructor
PrnToKeel - Class in keel.Algorithms.Preprocess.Converter: PrnToKeel This class extends from the Importer class.
PrnToKeel(String) - Constructor for class keel.Algorithms.Preprocess.Converter.PrnToKeel: PrnToKeel class Constructor.
Prob(int[][], int[], Condition, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.RuleSet
prob_cross - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.Parameters: Cross probability.
prob_cross_bin - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.Parameters
prob_cross_bin - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.Parameters
prob_cross_real - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.Parameters
prob_mut_bin - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.Parameters: Mutation binary probability.
prob_mut_bin - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.Parameters
prob_mut_bin - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.Parameters
prob_mut_int - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.Parameters: Mutation integer probability.
prob_mut_real - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.Parameters: Mutation real probability.
prob_mut_real - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.Parameters
PROBABILISTIC_SUM - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.GP_COACH: Configuration flag (PROBABILISTIC_SUM).
PROBABILISTIC_SUM - Static variable in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.GP_COACH_H: Configuration flag (PROBABILISTIC_SUM)
probabilities - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Class probabilities for the examples.
probabilities - Variable in class keel.Algorithms.SVM.C_SVM.svmClassifier: SVM probabilities.
probabilities - Variable in class keel.Algorithms.SVM.SMO.SMO: SMO probabilities.
probabilitiesPerClass - Static variable in class org.libsvm.svm
probability(int) - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Returns relative frequency of class over all values.
probability(int, int) - Method in class keel.Algorithms.Decision_Trees.C45.Classification: Returns relative frequency of class for given value.
probability(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Returns relative frequency of class over all values.
probability(int, int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification: Returns relative frequency of class for given value.
probability(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Returns relative frequency of class over all values.
probability(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification: Returns relative frequency of class for given value.
probability(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Returns relative frequency of class over all values.
probability(int, int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification: Returns relative frequency of class for given value.
probability - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
probability(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Returns relative frequency of class over all values.
probability(int, int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification: Returns relative frequency of class for given value.
probability(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Returns relative frequency of class over all values.
probability(int, int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification: Returns relative frequency of class for given value.
probability(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Returns relative frequency of class over all values.
probability(int, int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification: Returns relative frequency of class for given value.
probability(int) - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Returns relative frequency of class over all values.
probability(int, int) - Method in class keel.Algorithms.Rule_Learning.PART.Classification: Returns relative frequency of class for given value.
probability(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Returns relative frequency of class over all values.
probability(int, int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification: Returns relative frequency of class for given value.
probability(PrototypeSet, int, int, boolean) - Method in class keel.Algorithms.Semi_Supervised_Learning.Rel_RASCO.Rel_RASCOGenerator: Computes the reduction probability of a set for a certain value of the given instance and feature.
probability - Variable in class org.libsvm.svm_parameter
probabilityBelongCluster(Prototype, PrototypeSet, int) - Method in class keel.Algorithms.Instance_Generation.MSE.MSEGenerator: Zindex probability, di = x - conjuntoi
ProbabilityManagement - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist
ProbabilityManagement(double, double, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.ProbabilityManagement
ProbabilityManagement - Class in keel.Algorithms.Genetic_Rule_Learning.Globals
ProbabilityManagement(double, double, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Globals.ProbabilityManagement
ProbabilityManagement - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals
ProbabilityManagement(double, double, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.ProbabilityManagement
ProbabilityManagement - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS
ProbabilityManagement(double, double, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ProbabilityManagement
ProbClass(int[][], int[], Condition, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.RuleSet
ProbCondNegative(int[][], int[], Condition, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.RuleSet
ProbCondPositive(int[][], int[], Condition, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.RuleSet
probCrossover - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
probCrossover - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
PROBLEM - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
problem - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters: Type of the problem ( CLASSIFICATION | REGRESSION )
problem - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters: Type of the problem ( CLASSIFICATION | REGRESSION )
problem - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters: Type of the problem ( CLASSIFICATION | REGRESSION )
problem - Variable in class keel.Algorithms.Neural_Networks.net.Parameters: Type of the problem ( CLASSIFICATION | REGRESSION )
problem - Variable in class keel.GraphInterKeel.experiments.Joint
ProblemEvaluator - Class in keel.Algorithms.Neural_Networks.NNEP_Common.problem: Abstract implementation of an individuals evaluator of a dataset problem
ProblemEvaluator() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Empty constructor
PROBLEMTYPE - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
problemType - Variable in class keel.GraphInterKeel.experiments.AlgorithmXML
problemType - Variable in class keel.GraphInterKeel.experiments.DatasetXML
probLocalSearch - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
probMerge - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
probMerge - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
probMutationInd - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
probMutationInd - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
probOne - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
probOne - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
probOne - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
probReinit - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_ADI
probReinit - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_ADI
probReinitialize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
probReinitialize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
probReinitializeBegin - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
probReinitializeBegin - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
probReinitializeEnd - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
probReinitializeEnd - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
probRound(double, Random) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Rounds a double to the next nearest integer value in a probabilistic fashion (e.g. 0.8 has a 20% chance of being rounded down to 0 and a 80% chance of being rounded up to 1).
probRound(double, Random) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Rounds a double to the next nearest integer value in a probabilistic fashion (e.g. 0.8 has a 20% chance of being rounded down to 0 and a 80% chance of being rounded up to 1).
probRound(double, Random) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Rounds a double to the next nearest integer value in a probabilistic fashion (e.g. 0.8 has a 20% chance of being rounded down to 0 and a 80% chance of being rounded up to 1).
probRound(double, Random) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Rounds a double to the next nearest integer value in a probabilistic fashion (e.g. 0.8 has a 20% chance of being rounded down to 0 and a 80% chance of being rounded up to 1).
probRound(double, Random) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Rounds a double to the next nearest integer value in a probabilistic fashion (e.g. 0.8 has a 20% chance of being rounded down to 0 and a 80% chance of being rounded up to 1).
probRound(double, Random) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Rounds a double to the next nearest integer value in a probabilistic fashion (e.g. 0.8 has a 20% chance of being rounded down to 0 and a 80% chance of being rounded up to 1).
probRound(double, Random) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Rounds a double to the next nearest integer value in a probabilistic fashion (e.g. 0.8 has a 20% chance of being rounded down to 0 and a 80% chance of being rounded up to 1).
probRSWcrossover - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
probSplit - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
probSplit - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
probToLogOdds(double) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Returns the log-odds for a given probabilitiy.
probToLogOdds(double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Returns the log-odds for a given probabilitiy.
probToLogOdds(double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Returns the log-odds for a given probabilitiy.
probToLogOdds(double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Returns the log-odds for a given probabilitiy.
probToLogOdds(double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Returns the log-odds for a given probabilitiy.
probToLogOdds(double) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Returns the log-odds for a given probabilitiy.
proc - Variable in class keel.GraphInterKeel.experiments.EducationalPartitionsRun
ProcDataset - Class in keel.Algorithms.Neural_Networks.EvRBF_CL: Process the KEEL dataset.
ProcDataset(String, boolean) - Constructor for class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Init a new set of instances
ProcDataset - Class in keel.Algorithms.Neural_Networks.RBFN: Process the KEEL dataset.
ProcDataset(String, boolean) - Constructor for class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Init a new set of instances
ProcDataset - Class in keel.Algorithms.Neural_Networks.RBFN_CL: Process the KEEL dataset.
ProcDataset(String, boolean) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Init a new set of instances
ProcDataset - Class in keel.Algorithms.Neural_Networks.RBFN_decremental: Process the KEEL dataset.
ProcDataset(String, boolean) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Init a new set of instances
ProcDataset - Class in keel.Algorithms.Neural_Networks.RBFN_decremental_CL: Process the KEEL dataset
ProcDataset(String, boolean) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Init a new set of instances
ProcDataset - Class in keel.Algorithms.Neural_Networks.RBFN_incremental: Process the KEEL dataset
ProcDataset(String, boolean) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Init a new set of instances
ProcDataset - Class in keel.Algorithms.Neural_Networks.RBFN_incremental_CL: Process the KEEL dataset
ProcDataset(String, boolean) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Init a new set of instances
procesa_clustering_old(String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: Process an old format dataset file for a clustering problem.
procesa_clustering_old(String) - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Process an old format dataset file for a clustering problem.
procesaBaseDatos(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider: Process the database file 'filename'
procesoGenetico(int, double, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Poblacion: Performs the GA to generate the different solutions.
procesoGenetico(int, double, double) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Poblacion: Performs the GA to generate the different solutions.
process() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svmClassifierCost: Process the training and test files provided in the parameters file to the constructor.
process() - Method in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.AllPossibleValues: Process the training and test files provided in the parameters file to the constructor.
process() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.ConceptAllPossibleValues: Process the training and test files provided in the parameters file to the constructor.
process() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.ConceptMostCommonValue: Process the training and test files provided in the parameters file to the constructor.
process() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.EventCovering: Process the training and test files provided in the parameters file to the constructor.
process() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.fkmeans: Process the training and test files provided in the parameters file to the constructor.
process() - Method in class keel.Algorithms.Preprocess.Missing_Values.ignore_missing.ignore_missing: Process the training and test files provided in the parameters file to the constructor.
process() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.kmeansImpute: Process the training and test files provided in the parameters file to the constructor.
process() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.knnImpute: Process the training and test files provided in the parameters file to the constructor.
process() - Method in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.MostCommonValue: Process the training and test files provided in the parameters file to the constructor.
process() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.svmImpute: Process the training and test files provided in the parameters file to the constructor.
process() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.wknnImpute: Process the training and test files provided in the parameters file to the constructor.
process() - Method in class keel.Algorithms.SVM.C_SVM.svmClassifier: Process the training and test files provided in the parameters file to the constructor.
process(InstanceSet, InstanceSet) - Method in class keel.Algorithms.SVM.C_SVM.svmClassifier: Process the training and test files provided in the parameters file to the constructor.
process() - Method in class keel.Algorithms.SVM.EPSILON_SVR.svmRegression: Process the training and test files provided in the parameters file to the constructor.
process() - Method in class keel.Algorithms.SVM.NU_SVM.svmClassifier: Process the training and test files provided in the parameters file to the constructor.
process() - Method in class keel.Algorithms.SVM.NU_SVR.svmRegression: Process the training and test files provided in the parameters file to the constructor.
process(List<Integer>) - Method in class keel.GraphInterKeel.experiments.PartitionCreator: Set process
processClassifierDataset(String, boolean) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.OpenDataset: Load a file and parse it
processClassifierDataset() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Process a dataset for classification
processClassifierDataset(String, boolean) - Method in class keel.Algorithms.Neural_Networks.gann.OpenDataset: Load nfejemplos file and parse it
processClassifierDataset(String, boolean) - Method in class keel.Algorithms.Neural_Networks.gmdh.OpenDataset: Load a file and parse it.
processClassifierDataset(String, boolean) - Method in class keel.Algorithms.Neural_Networks.net.OpenDataset: Load a file and parse it
processClassifierDataset() - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Process a dataset for classification
processClassifierDataset() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Process a dataset for classification
processClassifierDataset() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Process a dataset for classification
processClassifierDataset() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Process a dataset for classification
processClassifierDataset() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Process a dataset for classification
processClassifierDataset() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Process a dataset for classification
processClassifierDataset(String, boolean) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: Process a dataset file for a classification problem.
processClassifierDataset(String, boolean) - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Process a dataset file for a classification problem.
ProcessCluster(PrototypeSet[], PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCGenerator: Process the cluster given to obtain the best cluster evaluated with the data given.
processClusterDataset(String, boolean) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Process a Dataset for clustering
processClusterDataset(String, boolean) - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Process a Dataset for clustering
processClusterDataset(String, boolean) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Process a Dataset for clustering
processClusterDataset(String, boolean) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Process a Dataset for clustering
processClusterDataset(String, boolean) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Process a Dataset for clustering
processClusterDataset(String, boolean) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Process a Dataset for clustering
processClusterDataset(String, boolean) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Process a Dataset for clustering
processClusterDataset(String, boolean) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: Process a dataset file for a clustering problem.
processClusterDataset(String, boolean) - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Process a dataset file for a clustering problem.
ProcessConfig - Class in keel.Algorithms.Preprocess.NoiseFilters.ANR
ProcessConfig() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Constructor that initializes input/output parameters.
ProcessConfig - Class in keel.Algorithms.Shared.Parsing: Class that process the configuration file for KEEL algorithms.
ProcessConfig() - Constructor for class keel.Algorithms.Shared.Parsing.ProcessConfig: Constructor that initializes input/output parameters.
ProcessDataset - Class in keel.Algorithms.Preprocess.NoiseFilters.ANR: Wrapper for KEEL's Dataset class.
ProcessDataset() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: A constructor that inits a new set of instances
ProcessDataset - Class in keel.Algorithms.Shared.Parsing: Wrapper for KEEL's Dataset class.
ProcessDataset() - Constructor for class keel.Algorithms.Shared.Parsing.ProcessDataset: A constructor that inits a new set of instances
processHeader - Variable in class keel.Algorithms.Preprocess.Converter.Importer: Property for considering or not the header with the attributes names only used for CSV, TXT, Excel, HTML and PRN formats).
processModelDataset(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyDataset: Reads the Data Sets
processModelDataset() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.ProcDataset: Process a dataset for modelling
processModelDataset() - Method in class keel.Algorithms.Neural_Networks.RBFN.ProcDataset: Process a dataset for modelling
processModelDataset() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.ProcDataset: Process a dataset for modelling
processModelDataset() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.ProcDataset: Process a dataset for modelling
processModelDataset() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.ProcDataset: Process a dataset for modelling
processModelDataset() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.ProcDataset: Process a dataset for modelling
processModelDataset() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.ProcDataset: Process a dataset for modelling
processModelDataset(String, boolean) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset: Process a dataset file for a modelling problem.
processModelDataset(String, boolean) - Method in class keel.Algorithms.RE_SL_Methods.LEL_TSK.MiDataset
processModelDataset(String, boolean) - Method in class keel.Algorithms.RE_SL_Methods.MamWM.MiDataset
processModelDataset(String, boolean) - Method in class keel.Algorithms.RE_SL_Methods.mogulHC.MiDataset
processModelDataset(String, boolean) - Method in class keel.Algorithms.RE_SL_Methods.mogulIRL.MiDataset
processModelDataset(String, boolean) - Method in class keel.Algorithms.RE_SL_Methods.mogulSC.MiDataset
processModelDataset(String, boolean) - Method in class keel.Algorithms.RE_SL_Methods.TSK_IRL.MiDataset
processModelDataset(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.MiDataset
processModelDataset(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.MiDataset
processModelDataset(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.MiDataset
processModelDataset(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.MamSelect.MiDataset
processModelDataset(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.MamWSelect.MiDataset
processModelDataset(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.MamWTuning.MiDataset
processModelDataset(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.MiDataset
processModelDataset(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.TSKSelect.MiDataset
processModelDataset(String, boolean) - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset: Process a dataset file for a modelling problem.
processType - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
processWindowEvent(WindowEvent) - Method in class keel.GraphInterKeel.datacf.help.HelpFrame: Overriden so we can close the frame
processWindowEvent(WindowEvent) - Method in class keel.GraphInterKeel.experiments.Experiments: Overridden so we can exit when window is closed, or cancel the process
processWindowEvent(WindowEvent) - Method in class keel.GraphInterKeel.help.HelpFrame
processWindowEvent(WindowEvent) - Method in class keel.GraphInterKeel.menu.Frame
processWindowEvent(WindowEvent) - Method in class keel.GraphInterKeel.menu.FrameModules
processWindowEvent(WindowEvent) - Method in class keel.RunKeelGraph.Frame1: Overridden so we can exit when window is closed
prod_interval(Interval) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.Interval
prod_interval(Interval) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.Interval
PRODUCT - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Fuzzy_Chi: Configuration flags.
PRODUCT - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Fuzzy_Ish: Configuration flags.
PRODUCT - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Fuzzy_Ish: Configuration flag (PRODUCT).
PRODUCT - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.GP_COACH: Configuration flag (PRODUCT).
product - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase: Product configuration flag.
PRODUCT - Static variable in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.GP_COACH_H: Configuration flag (PRODUCT)
PRODUCT - Static variable in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
PRODUCTO - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Fuzzy_Ish
properties(ActionEvent, JComboBox, Joint, String) - Method in class keel.GraphInterKeel.experiments.Container_Selected
properties - Variable in class keel.GraphInterKeel.experiments.DatasetXML
PropertyListToKeel - Class in keel.Algorithms.Preprocess.Converter: PropertyListToKeel This class extends from the Importer class.
PropertyListToKeel() - Constructor for class keel.Algorithms.Preprocess.Converter.PropertyListToKeel
PROPORTIONAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Fuzzy_Ish: Configuration flags.
ProportionalDiscretizer - Class in keel.Algorithms.Discretizers.Proportional_Discretizer: This class implements the Proportional discretizer.
ProportionalDiscretizer() - Constructor for class keel.Algorithms.Discretizers.Proportional_Discretizer.ProportionalDiscretizer: Constructor of the class, initializes the numInt attribute
ProtectedProperties - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: Simple class that extends the Properties class so that the properties are unable to be modified.
ProtectedProperties(Properties) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.ProtectedProperties: Creates a set of protected properties from a set of normal ones.
ProtectedProperties - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets: Simple class that extends the Properties class so that the properties are unable to be modified.
ProtectedProperties(Properties) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ProtectedProperties: Creates a set of protected properties from a set of normal ones.
Prototype - Class in keel.Algorithms.Instance_Generation.Basic: Representation of a prototype.
Prototype() - Constructor for class keel.Algorithms.Instance_Generation.Basic.Prototype: Constructs a null prototype.
Prototype(int, int) - Constructor for class keel.Algorithms.Instance_Generation.Basic.Prototype: Parameter Constructor.
Prototype(double[], double[]) - Constructor for class keel.Algorithms.Instance_Generation.Basic.Prototype: Constructs a Prototype.
Prototype(Instance) - Constructor for class keel.Algorithms.Instance_Generation.Basic.Prototype: Constructs a Prototype from an instance.
Prototype(Prototype) - Constructor for class keel.Algorithms.Instance_Generation.Basic.Prototype: Constructs a Prototype from another protoype.
Prototype - Class in keel.Algorithms.Semi_Supervised_Learning.Basic: Representation of a prototype.
Prototype() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Default constructor.
Prototype(int, int) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Parameter constructor.
Prototype(double[], double[]) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Constructs a Prototype.
Prototype(Instance) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Constructs a Prototype from an instance.
Prototype(Prototype) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Constructs a Prototype from another protoype.
PrototypeGenerationAlgorithm<T extends PrototypeGenerator> - Class in keel.Algorithms.Instance_Generation.Basic: Template used by children classes to executes its algorithms.
PrototypeGenerationAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm
PrototypeGenerationAlgorithm<T extends PrototypeGenerator> - Class in keel.Algorithms.Semi_Supervised_Learning.Basic: Template used by children classes to executes its algorithms.
PrototypeGenerationAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm
PrototypeGenerator - Class in keel.Algorithms.Instance_Generation.Basic: Implements a generic Prototype Generator
PrototypeGenerator(PrototypeSet) - Constructor for class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Construct the PrototypeGenerator
PrototypeGenerator(PrototypeSet, int) - Constructor for class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Construct the PrototypeGenerator
PrototypeGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Construct the PrototypeGenerator
PrototypeGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.Basic: Implements a generic Semi-supervised method
PrototypeGenerator(PrototypeSet) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Construct the PrototypeGenerator
PrototypeGenerator(PrototypeSet, int) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Construct the PrototypeGenerator
PrototypeGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Construct the PrototypeGenerator
PrototypeGenerator(PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Construct the PrototypeGenerator for Supervised Learning (pos-processing)
PrototypeGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator: Construct the PrototypeGenerator
PrototypeSet - Class in keel.Algorithms.Instance_Generation.Basic: Represents a prototype set.
PrototypeSet() - Constructor for class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Empty constructor
PrototypeSet(int) - Constructor for class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Constructs a void set with a number of elements.
PrototypeSet(InstanceSet) - Constructor for class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Constructs the set based on a instance set
PrototypeSet(ArrayList<PrototypeSet>) - Constructor for class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Build a set using a partition of other.
PrototypeSet(PrototypeSet) - Constructor for class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Copy constructor.
PrototypeSet - Class in keel.Algorithms.Semi_Supervised_Learning.Basic: Represents a prototype set.
PrototypeSet() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Empty constructor
PrototypeSet(int) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Constructs a void set with a number of elements.
PrototypeSet(InstanceSet) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Constructs the set based on a instance set
PrototypeSet(PrototypeSet, int, int) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Creates a new set of instances by copying a subset of another set.
PrototypeSet(ArrayList<PrototypeSet>) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Build a set using a partition of other.
PrototypeSet(PrototypeSet) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Copy constructor.
prototypeSetClasses() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet
prototypeSetTodouble() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: PrototypeSet to double.
prototypeSetTodouble() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: PrototypeSet to double.
provider - Static variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.KEELIRPropPlusWrapperClas: Individuals provider
provider - Static variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.KEELIRPropPlusWrapperRegr: Individuals provider
provider - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Individuals provider
pruebaCorte(int, ListaClases[], Attribute) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Checks a given cut and computes a possible improvement.
pruebaCorte(int, ListaClases[], double, double) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node: Checks a given cut and computes a possible improvement.
prune - Variable in class keel.Algorithms.Decision_Trees.C45.Tree: Is pruned the tree or not.
prune() - Method in class keel.Algorithms.Decision_Trees.C45.Tree: Function to prune a tree.
prune - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Tree: Is pruned the tree or not.
prune() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Tree: Function to prune a tree.
prune(double) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Prunes this leave or its descendants according to the error given, making it a leave with a classifier on it
prune() - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode: Prunes the model tree
prune(Instances, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule: Prune all the possible final sequences of the rule using the pruning data.
prune() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode: Prunes the model tree
prune(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule: Deletes a simple rule from this chain
prune(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule: Deletes a simple rule from this chain
prune - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree: Is pruned the tree or not.
prune() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree: Function to prune a tree.
prune - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Tree: Is pruned the tree or not.
prune() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Tree: Function to prune a tree.
prune - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree: Is pruned the tree or not.
prune() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree: Function to prune a tree.
prune - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters: Prune flag (True, prune method will be executed).
prune - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Parameters: Prune flag (True, prune method will be executed).
prune - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters: Prune flag (True, prune method will be executed).
prune - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Parameters: Prune flag (True, prune method will be executed).
prune(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Rule: Deletes a simple rule from this chain
prune - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Tree: Is pruned the tree or not.
prune() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Tree: Function to prune a tree.
prune(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule: Deletes a simple rule from this chain
prune - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree: Is pruned the tree or not.
prune() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree: Function to prune a tree.
prune(int) - Method in class keel.Algorithms.Rule_Learning.PART.Rule: Deletes a simple rule from this chain
prune - Variable in class keel.Algorithms.Rule_Learning.PART.Tree: Is pruned the tree or not.
prune() - Method in class keel.Algorithms.Rule_Learning.PART.Tree: Function to prune a tree.
prune(Rule, MyDataset, Mask, Mask, int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ripper: It prunes a rule, according with one of two heuristics: W= (p+1)/(t+2) A= (p+n')/T p/t: number of positive/total instances covered by the current rule n': number of negative instances not covered by the current rule (true negatives) T: number of total instances
prune(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Rule: Deletes a simple rule from this chain
prune(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Rule: Deletes a simple rule from this chain
prune(Rule, MyDataset, Mask, Mask, Mask, Mask, double[]) - Static method in class keel.Algorithms.Rule_Learning.Slipper.Slipper: It prunes a rule, minimizing with the heuristic: 1 - V+ + V_ + V+·exp(-Cr) + V_·exp(Cr) V+: sum of the weights of the positive instances of the prune set that are covered by the current rule V_: sum of the weights of the negative instances of the prune set that are covered by the current rule Cr: rule confidence (computed in the grow set)
prune - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.ParametersC45: Pruned flag, true if prune is used.
prune - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: Is pruned the tree or not.
prune() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree: Function to prune a tree.
prune - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.ParametersC45: Pruned flag, true if prune is used.
prune_tree() - Method in class keel.Algorithms.Decision_Trees.CART.CART: Prune decision tree
pruneAllLeaves() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Prunes all the leaves in the tree, this means, that all the leaves disappear and the ascendants of those leaves become the new leaves of the tree with the classifier at leaves
pruneConditions() - Method in class keel.Algorithms.Hyperrectangles.INNER.Rule: Prune redundant conditions of a rule, if it decreases its impurity level
pruneInstanceSet() - Method in class keel.Algorithms.Lazy_Learning.IDIBL.IDIBL: Prune instance set using drop 4 algorithm
pruneLevelN(TtreeNode[], int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree: Prunes the given level in the T-tree.
pruneTree() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.FunctionalTrees: Prunes the tree accordingly to the prune criteria, this means, makes some of the non-leaf nodes as leaves and deletes its descendants
pruneUnsupportedAtts() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Removes single attributes (not classifiers) from input data set which do not meet the minimum support requirement.
pruneUsingCover(short[][]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList: Prunes the current CMAR list of rules according to the "cover" principle.
pruneWithError() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode: Prunes the leaves in the tree that have greater error than the general error of the tree, making them leaves with classifiers on them
pruning(double, int, int[]) - Method in class keel.Algorithms.Decision_Trees.DT_GA.Individuo: Prune the individual using the information gain and the examples passed as argument.
pruning(double[]) - Method in class keel.Algorithms.Decision_Trees.DT_GA.Individuo: Prune the individual using accuracy given as parameter.
PRUNING_LAMBDA - Static variable in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Pruning method: Lambda See [2] for details.
PRUNING_NONE - Static variable in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Pruning method: No Pruning
pruningMethodTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel: Returns the tip text for this property
PSC - Class in keel.Algorithms.Instance_Selection.PSC: File: PSC.java The PSC Instance Selection algorithm.
PSC(String) - Constructor for class keel.Algorithms.Instance_Selection.PSC.PSC: Default constructor.
PSC - Class in keel.Algorithms.Preprocess.Instance_Selection.PSC: File: PSC.java The PSC Instance Selection algorithm.
PSC(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.PSC.PSC: Default constructor.
PSCSAAlgorithm - Class in keel.Algorithms.Instance_Generation.PSCSA: PSCSA algorithm calling.
PSCSAAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.PSCSA.PSCSAAlgorithm
PSCSAGenerator - Class in keel.Algorithms.Instance_Generation.PSCSA
PSCSAGenerator(PrototypeSet, int, int, int, int, double, double, int) - Constructor for class keel.Algorithms.Instance_Generation.PSCSA.PSCSAGenerator: Build a new PSCSAGenerator Algorithm
PSCSAGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.PSCSA.PSCSAGenerator: Build a new PSCSAGenerator Algorithm
pset1 - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Individuals selected as parents for first mutator
pset2 - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Individuals selected as parents for second mutator
PSI - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: The constant 1 / sqrt(2 pi)
psings(DenseMatrix, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.SVDimpute.SVDimpute: Computes the rmax eigenvalues of a given matrix (with greater absolute value)
PsoAco - Class in keel.Algorithms.Genetic_Rule_Learning.PSO_ACO: Title: PsoAco (Particle Swarm Optimization and Ant Colony Optimization) Description: Hybridization of the PSO and ACO algorithms to conform a rules based classification algorithm.
PsoAco() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.PsoAco: Default constructor.
PsoAco(String, String, String, String, String, String, long, int, int, int, int, int, float, float, float, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.PsoAco: Parameter constructor.
PSOAlgorithm - Class in keel.Algorithms.Instance_Generation.PSO: PSO algorithm calling.
PSOAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.PSO.PSOAlgorithm
PSOGenerator - Class in keel.Algorithms.Instance_Generation.PSO
PSOGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Instance_Generation.PSO.PSOGenerator: Build a new PSOGenerator Algorithm
PSOGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.PSO.PSOGenerator: Build a new PSOGenerator Algorithm
PSOLDA - Class in keel.Algorithms.PSO_Learning.PSOLDA: Title: Algorithm PSOLDA Description: It contains the implementation of the algorithm PSOLDA Company: KEEL
PSOLDA() - Constructor for class keel.Algorithms.PSO_Learning.PSOLDA.PSOLDA: Default constructor
PSOLDA(parseParameters) - Constructor for class keel.Algorithms.PSO_Learning.PSOLDA.PSOLDA: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Pspecify - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Is the probability of don't care allele to be changed for the environmental value.
PSPECIFY - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
PSRCG - Class in keel.Algorithms.Instance_Selection.PSRCG: File: PSRCG.java The PSRCG Instance Selection algorithm.
PSRCG(String) - Constructor for class keel.Algorithms.Instance_Selection.PSRCG.PSRCG: Default constructor.
PSRCG - Class in keel.Algorithms.Preprocess.Instance_Selection.PSRCG: File: PSRCG.java The PSRCG Instance Selection algorithm.
PSRCG(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.PSRCG.PSRCG: Default constructor.
PtreeNode - Class in keel.Algorithms.Associative_Classification.ClassifierCMAR: Class to store the node of a P-tree
PtreeNode(short[]) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCMAR.PtreeNode: Create P-tree node (other than top-level node)
PtreeNodeTop - Class in keel.Algorithms.Associative_Classification.ClassifierCMAR: Class to store the top node of the P-tree
PtreeNodeTop() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCMAR.PtreeNodeTop
pttls(DenseMatrix, DenseVector, int[], int[], int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: MATLAB - Truncated TLS regularization with permuted columns.
PUBLIC - Class in keel.Algorithms.Decision_Trees.PUBLIC: File: PUBLIC.java The PUBLIC algorithm builds a decision tree model integrating the steps of building and pruning in one phase, and after the model is built, the classification is done according to that model.
PUBLIC(String) - Constructor for class keel.Algorithms.Decision_Trees.PUBLIC.PUBLIC: Creates a PUBLIC instance by reading the script file that contains all the information needed for running the algorithm
Puk - Class in keel.Algorithms.SVM.SMO.supportVector: The Pearson VII function-based universal kernel.
Puk() - Constructor for class keel.Algorithms.SVM.SMO.supportVector.Puk: default constructor - does nothing.
Puk(Instances, int, double, double) - Constructor for class keel.Algorithms.SVM.SMO.supportVector.Puk: Constructor.
pulish(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Ruleset: Remove the rules that increase the DL value of the set.
pulish(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset: Remove the rules that increase the DL value of the set.
pulish(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset: Remove the rules that increase the DL value of the set.
puntuacion(int, Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.AlgGenetic
puntuacion(int, Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.AlgGenetic
puntuation(int, Vector<Float>, int) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.AlgGenetic
puntuation(int, Vector<Float>, int) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.AlgGenetic
puntuation(int, Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.AlgGenetic
puntuation_ltf(int, Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.AlgGenetic
puntuation_ltf(int, Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.AlgGenetic
PureLayerInitiator - Class in keel.Algorithms.Neural_Networks.NNEP_Common.initiators: Abstract implementation for IInitiator
PureLayerInitiator() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.initiators.PureLayerInitiator: Empty constructor
push(M5Instance) - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Adds an output instance to the queue.
push(Object) - Method in class keel.Algorithms.Decision_Trees.M5.Queue: Appends an object to the back of the queue.
push(M5Instance) - Method in class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter: Pushes an instance format into the output queue.
push(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Queue: Appends an object to the back of the queue.
push(Object) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue: Appends an object to the back of the queue.
push(Object) - Method in class keel.Algorithms.SVM.SMO.core.Queue: Appends an object to the back of the queue.
pushMissingIntoTable(int) - Method in class keel.GraphInterKeel.experiments.DataSet: Updates table of missing partitions
Put(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectordouble
Put(double[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectordouble
Put(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.vectordouble: Set the value to the vector in position "pos" to the value "x" (overwritting the previous value)
Put(double[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.vectordouble: Set the values of the first "tamano" elements of the vector to the values in vector "x"
Put(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectordouble
Put(double[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectordouble
Put(double, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectordouble
Put(double[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectordouble
put(Object, Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.ProtectedProperties: Overrides a method to prevent the properties from being modified.
put(Object, Object) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ProtectedProperties: Overrides a method to prevent the properties from being modified.
Put_NotModified() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
Put_NotModified() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
Put_NotModified() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal
Put_NotModified() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
Put_NotModified() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
putAll(Map) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.ProtectedProperties: Overrides a method to prevent the properties from being modified.
putAll(Map) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ProtectedProperties: Overrides a method to prevent the properties from being modified.
PutBinary(int, int[], char[][]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
PutBinary(int, int[], char[][]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
PutBinary(int, int[], char[][]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
PutCode(int, genetcode) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
PutCode(int, genetcode) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
PutCode(int, genetcode) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
PutInteger(int, int[], int[][]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
PutInteger(int, int[], int[][]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
PutInteger(int, int[], int[][]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
PutModified(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
PutModified(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
PutModified(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
PutReal(int, int[], double[][]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
PutReal(int, int[], double[][]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
PutReal(int, int[], double[][]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
PutValue(int, int, char) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary
PutValue(int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger
PutValue(int, int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal
PutValue(int, int, char) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
PutValue(int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationinteger
PutValue(int, int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal
PutValue(int, int, char) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
PutValue(int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationinteger
PutValue(int, int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationreal
PutValueBinary(int, int, char) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
PutValueBinary(int, int, char) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
PutValueBinary(int, int, char) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
PutValueInteger(int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
PutValueInteger(int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
PutValueInteger(int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
PutValueReal(int, int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
PutValueReal(int, int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
PutValueReal(int, int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
pw - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
PW - Class in keel.Algorithms.Lazy_Learning.PW: File: PW.java Prototipe weigthed learning.
PW(String) - Constructor for class keel.Algorithms.Lazy_Learning.PW.PW: The main method of the class
pX - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Probability of applying crossover in the GA.
pX - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Probability of applying crossover in the GA.
PX - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
Px - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.InstanceP: The p value associated.

Q

Q(PrototypeSet, Prototype) - Method in class keel.Algorithms.Instance_Generation.VQ.AVQGenerator: Generate the Q-count: prototypes which its nearest is the center of the cluster, and not any of the other reduced-prototypes.
Q0 - Static variable in class keel.Algorithms.Lazy_Learning.Statistics
Q0 - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics: approximation for 0 <= |y - 0.5| <= 3/8
Q1 - Static variable in class keel.Algorithms.Lazy_Learning.Statistics
Q1 - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics: Approximation for interval z = sqrt(-2 log y ) between 2 and 8 i.e., y between exp(-2) = .135 and exp(-32) = 1.27e-14.
Q2 - Static variable in class keel.Algorithms.Lazy_Learning.Statistics
Q2 - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics: Approximation for interval z = sqrt(-2 log y ) between 8 and 64 i.e., y between exp(-32) = 1.27e-14 and exp(-2048) = 3.67e-890.
q_sort_dist(ArrayList<Chromosome>, int[], int, int) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Sort: Actual implementation of the randomized quick sort used to sort a population based on a crowding distance
q_sort_front_obj(ArrayList<Chromosome>, int, int[], int, int) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Sort: Actual implementation of the randomized quick sort used to sort a population based on a particular objective chosen
q_sort_front_obj(ArrayList<Chromosome>, int, int[], int, int) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Sort: Actual implementation of the randomized quick sort used to sort a population based on a particular objective chosen
QAR_CIP_NSGAII - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII
QAR_CIP_NSGAII() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.QAR_CIP_NSGAII: Default constructor
QAR_CIP_NSGAII(parseParameters) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.QAR_CIP_NSGAII: It reads the data from the input files and parse all the parameters from the parameters array
QAR_CIP_NSGAIIProcess - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII
QAR_CIP_NSGAIIProcess(myDataset, int, int, int, double, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.QAR_CIP_NSGAIIProcess: It creates a new process for the algorithm by setting up its parameters
qr() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: QR Decomposition
QRDecomposition - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix: QR Decomposition.
QRDecomposition(Matrix) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.QRDecomposition: QR Decomposition, computed by Householder reflections.
QuadeC - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Classification Quade Stat-test identifier.
QuadeR - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest: Regression Quade Stat-test identifier.
quadraticModelOutput(double[], double[][][]) - Method in class keel.Algorithms.Shared.ClassicalOptim.ConjGradQUAD: Returns the output of the perceptron with weights W for input example x.
quadSpline(double[], double[], double[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.Interpolation: finds the piecewise second degree function that interpolates given nodes (xi,yi)
quadSplineCoeff(double[], double[], double) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.Interpolation: gives the derivatives at the nodes of the quadratic spline functions that interpolates the nodes (ti,xi)
QualityMeasures - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF: Defines the quality measures of the individual
QualityMeasures(Genetic, int) - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures: Creates a new instance of QualityMeasures
QualityMeasures - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD: Defines the quality measures of the individual
QualityMeasures(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.QualityMeasures: Creates a new instance of QualityMeasures
QualityMeasures - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA: Defines the quality measurements of the individual
QualityMeasures() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.QualityMeasures: Creates a new instance of QualityMeasures
QualitySubgroup - Class in keel.Algorithms.Subgroup_Discovery.SDMap.SDMap: Class with the quality measures need for obtaining the best subgroups
QualitySubgroup() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.QualitySubgroup: It creates a new object empty
QualitySubgroup(double, double, double, double, double) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.QualitySubgroup: It creates a new object with the values of the quality measures
QuedanMasInstancias(double) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: Checks if there are more instances to remove, depending on a threshold.
QuedanMasInstancias(double) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: Checks if there are more instances to remove, depending on a threshold.
QuedanMasInstancias(double) - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: Checks if there are more instances to remove, depending on a threshold.
question - Variable in class keel.GraphInterKeel.experiments.Experiments
Queue - Class in keel.Algorithms.Decision_Trees.M5: Class representing a FIFO queue.
Queue() - Constructor for class keel.Algorithms.Decision_Trees.M5.Queue
Queue - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: Class representing a FIFO queue.
Queue() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Queue
Queue - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules: Class representing a FIFO queue.
Queue() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue
Queue - Class in keel.Algorithms.SVM.SMO.core: Class representing a FIFO queue.
Queue() - Constructor for class keel.Algorithms.SVM.SMO.core.Queue
Queue.QueueNode - Class in keel.Algorithms.Decision_Trees.M5: Represents one node in the queue.
Queue.QueueNode - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: Represents one node in the queue.
Queue.QueueNode - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules: Represents one node in the queue.
Queue.QueueNode - Class in keel.Algorithms.SVM.SMO.core: Represents one node in the queue.
QueueNode(Object) - Constructor for class keel.Algorithms.Decision_Trees.M5.Queue.QueueNode: Creates a queue node with the given contents
QueueNode(Object) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Queue.QueueNode: Creates a queue node with the given contents
QueueNode(Object) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue.QueueNode: Creates a queue node with the given contents
QueueNode(Object) - Constructor for class keel.Algorithms.SVM.SMO.core.Queue.QueueNode: Creates a queue node with the given contents
Quicksort - Class in keel.Algorithms.Discretizers.HellingerBD: This class implements the Quicksort algorithm.
Quicksort() - Constructor for class keel.Algorithms.Discretizers.HellingerBD.Quicksort
Quicksort - Class in keel.Algorithms.Discretizers.UCPD: This class implements the Quicksort algorithm.
Quicksort() - Constructor for class keel.Algorithms.Discretizers.UCPD.Quicksort
quickSort(int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Implements quicksort according to Manber's "Introduction to Algorithms".
Quicksort - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL: This class implements the Quicksort algorithm.
Quicksort() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Quicksort
quickSort(int, int, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Implements quicksort according to Manber's "Introduction to Algorithms".
Quicksort - Class in keel.Algorithms.Preprocess.NoiseFilters.INFFC: This class implements the Quicksort algorithm.
Quicksort() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Quicksort
Quicksort - Class in keel.Algorithms.Preprocess.NoiseFilters.PANDA: This class implements the Quicksort algorithm.
Quicksort() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Quicksort
Quicksort - Class in keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter: This class implements the Quicksort algorithm.
Quicksort() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Quicksort
quicksort(ArrayList<Integer>) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Sorted a ArrayList of Integers
quickSort(int, int, int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Implements quicksort according to Manber's "Introduction to Algorithms".
quicksort(double[], int[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Utilidades: Performs a quicksort over the vectors given.
quicksort(double[], int[], int, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Utilidades: Performs a quicksort over the vectors given as recursive method.
quickSort(int, int, int) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Implements quicksort according to Manber's "Introduction to Algorithms".
quicksort_dist(ArrayList<Chromosome>, int[], int) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Sort: Randomized quick sort routine to sort a population based on crowding distance
quicksort_front_obj(ArrayList<Chromosome>, int, int[], int) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Sort: Randomized quick sort routine to sort a population based on a particular objective chosen
quicksort_front_obj(ArrayList<Chromosome>, int, int[], int) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Sort: Randomized quick sort routine to sort a population based on a particular objective chosen
quote(String) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Quotes a string if it contains special characters.
quote(String) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Quotes a string if it contains special characters.
quote(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Quotes a string if it contains special characters.
quote(String) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Quotes a string if it contains special characters.
quote(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Quotes a string if it contains special characters.
quote(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Quotes a string if it contains special characters.
quote(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Quotes a string if it contains special characters.
quote(String) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Quotes a string if it contains special characters.
quote(String) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Quotes a string if it contains special characters.

R

r - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoosting
R(Cluster) - Method in class keel.Algorithms.Instance_Generation.VQ.AVQGenerator: Generate the R-count: prototypes which its centroid is its nearest prototypes.
r_0 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Represents the interval of random numbers that will be generated to be added in the real mutation.
r_0 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Represents the interval of random numbers that will be generated to be added in the real mutation.
R_0 - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
r_random(Rbfn) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation: Performs the R_RANDOM mutator operator: modifies 50% of the Radius of the net
radius(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Returns the greatest distance between center and other prototype and that prototype.
radius(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Returns the greatest distance between center and other prototype and that prototype.
raiz - Variable in class keel.GraphInterKeel.menu.Frame: Root directory.
raiz - Variable in class keel.GraphInterKeel.menu.FrameModules: Root directory.
RamaLqd - Variable in class keel.GraphInterKeel.experiments.Experiments
RAN(double[][], double[][], int, double, double, double) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Uses RAN algorithm to build a net
RAN(double[][], double[][], int, double, double, double) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbfn: Uses RAN algorithm to build a net
RAN(double[][], double[][], int, double, double, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn: Uses RAN algorithm to build a net
RAN(double[][], double[][], int, double, double, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn: Uses RAN algorithm to build a net
RAN(double[][], double[][], int, double, double, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn: Uses RAN algorithm to build a net
RAN(double[][], double[][], int, double, double, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn: Uses RAN algorithm to build a net
RAN(double[][], double[][], int, double, double, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn: Uses RAN algorithm to build a net
rand - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.Genotype: Genotype is the base clase to represent the genotype of any GeneticIndividual.
Rand - Class in keel.Algorithms.Genetic_Rule_Learning.Globals: Rand.java Created on 29 de marzo de 2004, 23:31
Rand() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Globals.Rand
Rand - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals
Rand() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Rand
rand() - Static method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Returns a number between [0, 1), so, 0 inclusive and 1 exclusive.
rand() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Returns a number between [0, 1), so, 0 inclusive and 1 exclusive.
Rand - Class in keel.Algorithms.Neural_Networks.gann: Class to obtain random values
Rand() - Constructor for class keel.Algorithms.Neural_Networks.gann.Rand: Empty constructor
Rand() - Static method in class org.core.Randomize: Rand computes a psuedo-random float value between 0 and 1, excluding 1
randAct() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene
randAct() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene
randAct() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene
RandClosed() - Static method in class org.core.Randomize: RandClosed computes a psuedo-random float value between 0 and 1 inclusive
Randdouble(double, double) - Static method in class org.core.Randomize: Randdouble gives an double value between low and high, excluding high
RanddoubleClosed(double, double) - Static method in class org.core.Randomize: RanddoubleClosed gives an double value between low and high inclusive
RanddoubleOpen(double, double) - Static method in class org.core.Randomize: RanddoubleOpen gives an double value between low and high, excluding low and high
RandGaussian() - Static method in class org.core.Randomize: RandGaussian generates a standardized gaussian random number
randGen - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.initiators.PureLayerInitiator: Random generator used in creation
randgen - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ExpNeuronParametricMutator: Random generator used in mutation
randgen - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.LinearNeuronParametricMutator: Random generator used in mutation
randgen - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.SigmNeuronParametricMutator: Random generator used in mutation
randgen - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.ExpNeuronStructuralMutator: Random generator used in mutation
randgen - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.LinearNeuronStructuralMutator: Random generator used in mutation
randgen - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.SigmNeuronStructuralMutator: Random generator used in mutation
randGenFactory - Static variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.KEELIRPropPlusWrapperClas: Random generators factory
randGenFactory - Static variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.KEELIRPropPlusWrapperRegr: Random generators factory
randGenFactory - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Random generators factory
Randint(int, int) - Static method in class org.core.Randomize: Randint gives an integer value between low and high, excluding high
RandintClosed(int, int) - Static method in class org.core.Randomize: RandintClosed gives an integer value between low and high inclusive
RandintOpen(int, int) - Static method in class org.core.Randomize: RandintOpen gives an integer value between low and high, excluding 0 and 1
Random() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGAP.FuzzyGAPClassifier: This method initialize the current object randomly.
Random() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGP.FuzzyGPClassifier: This method initialize the current object randomly.
Random() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyPittsBurgh.PittsburghClassifier: This method initialize the current object randomly.
Random() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySAP.FuzzySAPClassifier: This method initialize the current object randomly.
Random() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.Genotype: abstract method to randomly initialize a Genotype and then the corresponding individual.
Random() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGAP: The method intended to randomly initialize a Genotype and then the corresponding individual.
Random() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGP: The method intended to randomly initialize a Genotype and then the corresponding individual.
Random() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGPRegSym: The method intended to randomly initialize a Genotype and then the corresponding individual.
Random() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypePitts: The method intended to randomly initialize a Genotype and then the corresponding individual.
Random() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividual: This abstract method is for random generation
Random() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGAPModelIndividual: This method generate a random genotype and obtain the parameters from another one
Random() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPModelIndividual: This method generate a random genotype and obtain the parameters from another one
Random() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.PittsburghModel: This method generate a random genotype and obtain the parameters from another one
Random() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.RegSymFuzzyGP: This method generate a random genotype and obtain the parameters from another one
random - Static variable in class keel.Algorithms.Instance_Generation.utilities.RandomGenerator: Object random used int the number generators
random - Static variable in class keel.Algorithms.Semi_Supervised_Learning.utilities.RandomGenerator: Object random used int the number generators
random(int) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Returns a random vector of uniform distribution
random(int, int) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Generate matrix with random elements
RandomDiscretizer - Class in keel.Algorithms.Discretizers.Random_Discretizer: This class implements the Random Discretizer
RandomDiscretizer() - Constructor for class keel.Algorithms.Discretizers.Random_Discretizer.RandomDiscretizer
RandomGenerator - Class in keel.Algorithms.Instance_Generation.utilities: Random Number Generator class to be used in the package Prototype_Generation.
RandomGenerator() - Constructor for class keel.Algorithms.Instance_Generation.utilities.RandomGenerator
RandomGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.utilities: Random Number Generator class to be used in the package Prototype_Generation.
RandomGenerator() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.utilities.RandomGenerator
randomIndexes - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQ1: Random number list of indexes
randomInitialization() - Method in class keel.Algorithms.PSO_Learning.CPSO.Particle
randomInitialization() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
randomInitialization() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.Particle
randomInitialization() - Method in class keel.Algorithms.PSO_Learning.REPSO.Particle
RandomInitialPopulation() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary
RandomInitialPopulation() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger
RandomInitialPopulation(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger
RandomInitialPopulation(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal
RandomInitialPopulation(double[][], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal
RandomInitialPopulation() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal
RandomInitialPopulation(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal
RandomInitialPopulation(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationreal
RandomInitiator - Class in keel.Algorithms.Neural_Networks.NNEP_Common.initiators: Random initiator both conections and weights
RandomInitiator() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.initiators.RandomInitiator: Empty constructor
randomize(Random) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Shuffles the instances in the set so that they are ordered randomly.
randomize(Randomize) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Shuffles the instances in the set so that they are ordered randomly.
randomize(Random) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Shuffles the instances in the set so that they are ordered randomly.
randomize() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Shuffle the set.
randomize(long) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Shuffle the set.
randomize() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Shuffle the set.
randomize(long) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Shuffle the set.
randomize(Random) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Shuffles the instances in the set so that they are ordered randomly.
randomize() - Method in class keel.Algorithms.SVM.SMO.core.Instances: Shuffles the instances in the set so that they are ordered randomly.
Randomize - Class in org.core
Randomize() - Constructor for class org.core.Randomize
randomizeAttribute(int, Randomize, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Shuffles the values of a given attribute in all instances.
randomizeRules(InstanceSet) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.RuleSet: Initialize the set of rules
randomizeRules(InstanceSet) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.RuleSet: Initialize the set of rules
RandomOverSampling - Class in keel.Algorithms.ImbalancedClassification.Resampling.RandomOverSampling: File: RandomOverSampling.java The Random Over Sampling algorithm is an oversampling method used to deal with the imbalanced problem that generates positive instances randomly.
RandomOverSampling(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.RandomOverSampling.RandomOverSampling: Constructor of the class.
randomSampling(myDataset, int, int, int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Random undersampling of the dataset given.
randomSampling(myDataset, int, int, int, int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Random undersampling of the dataset given.
randomSeedTipText() - Method in class keel.Algorithms.SVM.SMO.SMO: Returns the tip text for this property
randomSelection() - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Cogin
RandomSelector - Class in keel.Algorithms.Instance_Generation.BasicMethods: Implements a random selection of the training data to the edited data set.
RandomSelector(PrototypeSet, int) - Constructor for class keel.Algorithms.Instance_Generation.BasicMethods.RandomSelector: Creates a new RandomSelector
RandomSelector(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.BasicMethods.RandomSelector: Creates a new RandomSelector
randomSelector(T, T) - Static method in class keel.Algorithms.Instance_Generation.utilities.RandomGenerator: Select by random method between two objects
randomSelector(T, T) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.RandomGenerator: Select by random method between two objects
RandomTree - Class in keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest: RandomTree
RandomTree() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree
RandomTree - Class in keel.Algorithms.Semi_Supervised_Learning.CLCC: Class for constructing a tree that considers K randomly chosen attributes at each node.
RandomTree() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree
RandomTree - Class in keel.Algorithms.Semi_Supervised_Learning.CoForest: Class for constructing a tree that considers K randomly chosen attributes at each node.
RandomTree() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree
randomTrials - Variable in class keel.Algorithms.Instance_Generation.BTS3.BTS3Generator: Number of bootstrapings of the algorithm.
randomUnderSampling(myDataset, int, int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Original dataset to take examples from and the % of majority class in the new data set
RandomUnderSampling - Class in keel.Algorithms.ImbalancedClassification.Resampling.RandomUnderSampling: File: RandomUnderSampling.java The Random Under Sampling algorithm is an undersampling method used to deal with the imbalanced problem that deletes negative instances randomly.
RandomUnderSampling(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.RandomUnderSampling.RandomUnderSampling: Constructor of the class.
randomValues() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual: Initialization of the individual with random values.
randomValues() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Individual: Initialization of the individual with random values
randomValues() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual
randomWeight(IRandGen, double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron: Returns a random weight for a link
randomWeights(double) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ConjGradNN: Initializes the matrix of weights with random valued in the range [-x,x].
randomWeights(double) - Method in class keel.Algorithms.Shared.ClassicalOptim.ConjGradNN: Initializes the matrix of weights with random valued in the range [-x,x].
RandOpen() - Static method in class org.core.Randomize: RandOpen computes a psuedo-random float value between 0 and 1, excluding 0 and 1
randOperatorNominal() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene
randOperatorNumeric() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene
Range - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: Class representing a range of cardinal numbers.
Range() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Range: Default constructor.
Range(String) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Range: Constructor to set initial range.
rangeLower(String) - Method in class keel.Algorithms.Decision_Trees.M5.Interval: Translates a range into it's lower index.
rangeLower(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Range: Translates a range into it's lower index.
rangeSingle(String) - Method in class keel.Algorithms.Decision_Trees.M5.Interval: Translates a single string selection into it's internal 0-based equivalent
rangeSingle(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Range: Translates a single string selection into it's internal 0-based equivalent
rangeUpper(String) - Method in class keel.Algorithms.Decision_Trees.M5.Interval: Translates a range into it's upper index.
rangeUpper(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Range: Translates a range into it's upper index.
rank - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
rank() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.rank
rank() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Matrix rank
rank() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.SingularValueDecomposition: Effective numerical matrix rank
rank - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Rank.
rank - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
rank - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
rank - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
Ranking - Class in keel.Algorithms.LQD.methods.FGFS_costInstances: File: ranking.java Properties and function to ranking the fuzzy number.
Ranking() - Constructor for class keel.Algorithms.LQD.methods.FGFS_costInstances.Ranking
Ranking - Class in keel.Algorithms.LQD.methods.FGFS_Minimum_Risk: File: ranking.java Properties and function to ranking the fuzzy number.
Ranking() - Constructor for class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.Ranking
ranking(Vector<fuzzy>, Vector<Float>) - Static method in class keel.Algorithms.LQD.preprocess.Expert.Main
Ranking - Class in keel.Algorithms.LQD.preprocess.Expert: File: ranking.java Properties and function to ranking the fuzzy number.
Ranking() - Constructor for class keel.Algorithms.LQD.preprocess.Expert.Ranking
Ranking - Class in keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE: File: ranking.java Properties and function to ranking the fuzzy number.
Ranking() - Constructor for class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.Ranking
ranking(Vector<fuzzy>, Vector<Float>) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling.Main
Ranking - Class in keel.Algorithms.LQD.preprocess.Prelabelling: File: ranking.java Properties and function to ranking the fuzzy number.
Ranking() - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling.Ranking
ranking(Vector<fuzzy>, Vector<Float>) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.Main
Ranking - Class in keel.Algorithms.LQD.preprocess.Prelabelling_Expert: File: ranking.java Properties and function to ranking the fuzzy number.
Ranking() - Constructor for class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.Ranking
Ranking - Class in keel.Algorithms.Neural_Networks.gann: Class Ranking.
Ranking() - Constructor for class keel.Algorithms.Neural_Networks.gann.Ranking: Empty constructor
Ranking - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD: Algorithm for the discovery of rules describing subgroups.
Ranking(Population, TableVar, int, int, String, String) - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Ranking: Constructor of the Ranking
RanNnep - Class in keel.Algorithms.Neural_Networks.NNEP_Common.util.random: RandNnep is a random number generator proposed in "Turbo C.
RanNnep() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.util.random.RanNnep: Empty constructor
RanNnep(int) - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.util.random.RanNnep: Default constructor (used by RandGenFactory)
RanNnepFactory - Class in keel.Algorithms.Neural_Networks.NNEP_Common.util.random: Factory for RanNnep random generators
RanNnepFactory() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.util.random.RanNnepFactory: Empty constructor.
RASCOAlgorithm - Class in keel.Algorithms.Semi_Supervised_Learning.RASCO: RASCO algorithm calling.
RASCOAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.RASCO.RASCOAlgorithm
RASCOGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.RASCO: This class implements the RASCO algorithm.
RASCOGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.RASCO.RASCOGenerator: Build a new RASCOGenerator Algorithm
RASCOGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.RASCO.RASCOGenerator: Build a new RASCOGenerator Algorithm
raw() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.util.random.RanNnep: Return a double value in the range [0,1].
rawA() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.util.random.RanNnep: Returns a random number between 0 and 1 using "a" property
rawB() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.util.random.RanNnep: Returns a random number between 0 and 1 using "b" property
rawOutputs(double[]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.neuralnet.NeuralNetClassifier: Obtain the raw output of the classifier for each class
rawOutputs(double[][]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.neuralnet.NeuralNetClassifier: Obtain the raw outputs of classes of a set of observations, through their inputs values
rawOutputs(double[]) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.ISoftmaxClassifier: Obtain the raw output of the classifier for each class
rawOutputs(double[][]) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.ISoftmaxClassifier: Obtain the raw outputs of classes of a set of observations, through their inputs values
RBF - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
Rbf - Class in keel.Algorithms.Neural_Networks.EvRBF_CL: Class representing a Radial Basis Function Neuron.
Rbf(int, int) - Constructor for class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbf: Creates a new instance of neuron/rbf
Rbf - Class in keel.Algorithms.Neural_Networks.RBFN: This class codified a neuron or a RBF
Rbf(int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN.Rbf: Creates a new instance of neuron/rbf
Rbf(int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN.Rbf: Creates a new instance of neuron/rbf
Rbf - Class in keel.Algorithms.Neural_Networks.RBFN_CL: This class codified a neuron or a RBF.
Rbf(int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_CL.Rbf: Creates a new instance of neuron/rbf
Rbf(int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_CL.Rbf: Creates a new instance of neuron/rbf
Rbf - Class in keel.Algorithms.Neural_Networks.RBFN_decremental: This class codified a neuron or a RBF
Rbf(int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbf: Creates a new instance of neuron/rbf
Rbf(int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbf: Creates a new instance of neuron/rbf
Rbf - Class in keel.Algorithms.Neural_Networks.RBFN_decremental_CL: This class codified a neuron or a RBF
Rbf(int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf: Creates a new instance of neuron/rbf
Rbf(int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf: Creates a new instance of neuron/rbf
Rbf - Class in keel.Algorithms.Neural_Networks.RBFN_incremental: This class codified a neuron or a RBF
Rbf(int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbf: Creates a new instance of neuron/rbf
Rbf(int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbf: Creates a new instance of neuron/rbf
Rbf - Class in keel.Algorithms.Neural_Networks.RBFN_incremental_CL: This class codified a neuron or a RBF
Rbf(int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf: Creates a new instance of neuron/rbf
Rbf(int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf: Creates a new instance of neuron/rbf
RBF - Static variable in class org.libsvm.svm_parameter
RBFKernel - Class in keel.Algorithms.SVM.SMO.supportVector: The RBF kernel.
RBFKernel() - Constructor for class keel.Algorithms.SVM.SMO.supportVector.RBFKernel: default constructor - does nothing.
RBFKernel(Instances, int, double) - Constructor for class keel.Algorithms.SVM.SMO.supportVector.RBFKernel: Constructor.
Rbfn - Class in keel.Algorithms.Neural_Networks.EvRBF_CL: Class representing a Radial Basis Function Neural Network for the EvRBF_CL algorithm
Rbfn() - Constructor for class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Creates a instance of rbnf of fixed structure just for test.
Rbfn(int, int) - Constructor for class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Creates a new instance of rbfn
Rbfn(double[][], int, int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Creates a new instance of rbfn from a matrix of instances.
Rbfn(int, double[][], int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Creates a new instance of rbfn from a matrix of instances.
Rbfn - Class in keel.Algorithms.Neural_Networks.RBFN: This class codified a Radial Basis Function Network
Rbfn() - Constructor for class keel.Algorithms.Neural_Networks.RBFN.Rbfn: Creates a instance of rbnf of fixed structure just for test.
Rbfn(int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN.Rbfn: Creates a new instance of rbfn
Rbfn(double[][], int, int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN.Rbfn: Creates a new instance of rbfn from a matrix of instances.
Rbfn(int, double[][], int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN.Rbfn: Creates a new instance of rbfn from a matrix of instances.
Rbfn - Class in keel.Algorithms.Neural_Networks.RBFN_CL: This class codified a Radial Basis Function Network
Rbfn() - Constructor for class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn: Creates a instance of rbnf of fixed structure just for test.
Rbfn(int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn: Creates a new instance of rbfn
Rbfn(double[][], int, int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn: Creates a new instance of rbfn from a matrix of instances.
Rbfn(int, double[][], int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn: Creates a new instance of rbfn from a matrix of instances.
Rbfn - Class in keel.Algorithms.Neural_Networks.RBFN_decremental: This class codified a Radial Basis Function Network
Rbfn() - Constructor for class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn: Creates a instance of rbnf of fixed structure just for test.
Rbfn(int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn: Creates a new instance of rbfn
Rbfn(double[][], int, int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn: Creates a new instance of rbfn from a matrix of instances.
Rbfn(int, double[][], int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn: Creates a new instance of rbfn from a matrix of instances.
Rbfn - Class in keel.Algorithms.Neural_Networks.RBFN_decremental_CL: This class codified a Radial Basis Function Network
Rbfn() - Constructor for class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn: Creates a instance of rbnf of fixed structure just for test.
Rbfn(int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn: Creates a new instance of rbfn
Rbfn(double[][], int, int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn: Creates a new instance of rbfn from a matrix of instances.
Rbfn(int, double[][], int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn: Creates a new instance of rbfn from a matrix of instances.
Rbfn - Class in keel.Algorithms.Neural_Networks.RBFN_incremental: This class codified a Radial Basis Function Network
Rbfn() - Constructor for class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn: Creates a instance of rbnf of fixed structure just for test.
Rbfn(int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn: Creates a new instance of rbfn
Rbfn(double[][], int, int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn: Creates a new instance of rbfn from a matrix of instances.
Rbfn(int, double[][], int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn: Creates a new instance of rbfn from a matrix of instances.
Rbfn - Class in keel.Algorithms.Neural_Networks.RBFN_incremental_CL: This class codified a Radial Basis Function Network
Rbfn() - Constructor for class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn: Creates a instance of rbnf of fixed structure just for test.
Rbfn(int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn: Creates a new instance of rbfn
Rbfn(double[][], int, int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn: Creates a new instance of rbfn from a matrix of instances.
Rbfn(int, double[][], int, int, int) - Constructor for class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn: Creates a new instance of rbfn from a matrix of instances.
rbfNearest(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbfn: Returns the nearest rbf/neuron to a vector v (patron)
rbfNearest(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn: Returns the nearest rbf/neuron to a vector v (patron)
rbfNearest(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn: Returns the nearest rbf/neuron to a vector v (patron)
rbfNearest(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn: Returns the nearest rbf/neuron to a vector v (patron)
rbfNearest(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn: Returns the nearest rbf/neuron to a vector v (patron)
rbfNearest(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn: Returns the nearest rbf/neuron to a vector v (patron)
RbfnPopulation - Class in keel.Algorithms.Neural_Networks.EvRBF_CL: Implements a population of Radial basis Function Neural Networks to be evolved with EvRBFN_CL.
RbfnPopulation(int) - Constructor for class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation: Creates a new instance of RbfnPopulation
RbfnPopulation(int, double[][], int, int, int, double) - Constructor for class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation: Creates and initializes a population using a set of data.
rbfSize() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Returns the number of neurons in the net
RBFUtils - Class in keel.Algorithms.Neural_Networks.EvRBF_CL: A set of useful functions to be used in EvRBF_CL.
RBFUtils() - Constructor for class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils
RBFUtils - Class in keel.Algorithms.Neural_Networks.RBFN: Offers several utilities
RBFUtils() - Constructor for class keel.Algorithms.Neural_Networks.RBFN.RBFUtils
RBFUtils - Class in keel.Algorithms.Neural_Networks.RBFN_CL: Offers several utilities
RBFUtils() - Constructor for class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils
RBFUtils - Class in keel.Algorithms.Neural_Networks.RBFN_decremental: Offers several utilities
RBFUtils() - Constructor for class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils
RBFUtils - Class in keel.Algorithms.Neural_Networks.RBFN_decremental_CL: Offers several utilities
RBFUtils() - Constructor for class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils
RBFUtils - Class in keel.Algorithms.Neural_Networks.RBFN_incremental: Offers several utilities
RBFUtils() - Constructor for class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils
RBFUtils - Class in keel.Algorithms.Neural_Networks.RBFN_incremental_CL: Offers several utilities
RBFUtils() - Constructor for class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils
rchisq(int, double, Random) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Generates a sample of a Chi-square distribution.
read(String) - Static method in class keel.Algorithms.Instance_Generation.utilities.KeelFile: Read a Keel-style file.
read(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset: Reads instance at specified row number
read(IDataset.IInstance[]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset: Reads some number of instances from the dataset and stores them into a buffer array.
read(IDataset.IInstance[], int, int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset: Returns cursor instance
read() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArffDataSet: Returns cursor instance
read() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArrayDataset: Returns cursor instance
read(byte[], IDataset) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Init the DoubleTransposedDataSet using a normal IDataset
read() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Init the array stored in the DataSet
read() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.FileDataset: Returns cursor instance
read() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IDataset: Returns cursor instance
read(int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IDataset: Reads instance at specified row number
read(IDataset.IInstance[]) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IDataset: Reads some number of instances from the dataset and stores them into a buffer array.
read(IDataset.IInstance[], int, int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IDataset: Reads up to length instances from the input stream into an array of IInstances.
read() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.KeelDataSet: Returns cursor instance
read(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KeelFile: Read a Keel-style file.
read(BufferedReader) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Read a matrix from a stream.
read(String) - Static method in class keel.Algorithms.SVM.SMO.core.SerializationHelper: deserializes the given file and returns the object from it
read(InputStream) - Static method in class keel.Algorithms.SVM.SMO.core.SerializationHelper: deserializes from the given stream and returns the object from it
readAlgorithm() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
readAllFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.FileManagement: Reads a whole file
readAllFile() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.FileManagement: Reads a whole file
readAttributes(int) - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
readAttributes(int) - Static method in class keel.Dataset.DataParser
readChar() - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.FileManagement: Reades a char from file.
readChar() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.FileManagement: Reades a char from file.
readChar() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
readChar() - Static method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
readChar() - Static method in class keel.Dataset.SimpleCharStream
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readClassificationSet(String, boolean) - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readConfiguracion(String) - Method in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Reads configuration script, and extracts its contents.
readConfiguracion(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: Reads configuration script, and extracts its contents.
readConfiguracion(String) - Method in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Reads configuration script, and extracts its contents.
readConfiguracion(String) - Method in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Reads configuration script, and extracts its contents.
readConfiguracion(String) - Method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm: Reads configuration script, and extracts its contents.
readConfiguracion(String) - Method in class keel.Algorithms.RST_Learning.RSTAlgorithm: Reads configuration script, and extracts its contents.
readConfiguration(String) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.FunctionalTrees: Reads configuration script, and extracts its contents.
readConfiguration(String) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.PUBLIC: Reads the configuration script, and extracts its contents.
readConfiguration(String) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo: Reads the parameters of the algorithm.
readConfiguration(String) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.EUSCHCQstat: It reads the configuration file for performing the EUS-CHC method
readData() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Read and normalize evaluator datasets
readData(byte[], IDataset, IDataset) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator: Read and normalize evaluator datasets
readDataFiles(String) - Method in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Read the configuration and data files, and process it.
readDataFiles(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: Read the configuration and data files, and process it.
readDataFiles(String) - Method in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Read the configuration and data files, and process it.
readDataFiles(double[][], int[], double[][], int[], int) - Method in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Set the training and the test dataset with their classes given as argument.
readDataFiles(String, InstanceSet, InstanceSet, InstanceSet) - Method in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Read the configuration and data files, process it.
readDataFiles(String) - Method in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Read the configuration and data files, and process it.
readDataFiles(String) - Method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm: Read the configuration and data files, and process it.
readDataFiles(String) - Method in class keel.Algorithms.RST_Learning.RSTAlgorithm: Read the configuration and data files, and process it.
readDataSet(String, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: It reads the whole input data-set and it stores each transaction in local array
readDataSet(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: It reads the whole input data-set and it stores each transaction in local array
readDataSet(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: It reads the whole input data-set and it stores each transaction in local array
readDataSet(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: It reads the whole input data-set and it stores each transaction in local array
readDataSet(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: It reads the whole input data-set and it stores each transaction in local array
readDataSet(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: It reads the whole input data-set and it stores each transaction in local array
readDataSet(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: It reads the whole input data-set and it stores each transaction in local array
readDataSet(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: It reads the whole input data-set and it stores each transaction in local array
readDataSet(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: It reads the whole input data-set and it stores each transaction in local array
readDataSet(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: It reads the whole input data-set and it stores each transaction in local array
readDataSet(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: It reads the whole input data-set and it stores each transaction in local array
readDataSet(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: It reads the whole input data-set and it stores each transaction in local array
readDataSet(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: It reads the whole input data-set and it stores each transaction in local array
readDataSet(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: It reads the whole input data-set and it stores each transaction in local array
readDataSet(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: It reads the whole input data-set and it stores each transaction in local array
readDataSet(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: It reads the whole input data-set and it stores each transaction in local array
readDataSet(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: It reads the whole input data-set and it stores each transaction in local array
readDataSet(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: It reads the whole input data-set and it stores each transaction in local array
readFile(myDataset, DataBase) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Reads input data from file specified in command line argument (GUI version also exists).
readFile(String) - Static method in class keel.GraphInterKeel.experiments.Files: Read of a file
readFile(String) - Static method in class keel.GraphInterKeel.statistical.Files: Read a file and returns the content
readFile(String) - Static method in class org.core.Files: Read a file and returns the content
readFileAsString(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.AdministrativeStaff
readHeader(StreamTokenizer) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Reads and stores header of an file.
readHeader(StreamTokenizer) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Reads and stores header of an ARFF file.
readInputData() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
readInputs() - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
readInputs() - Static method in class keel.Dataset.DataParser
readInstance(Reader) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Reads a single instance from the reader and appends it to the dataset.
readInstance(Reader) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Reads a single instance from the reader and appends it to the dataset.
readInstanceSet(InstanceSet) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readLine() - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.FileManagement: Reads a line from the file.
readLine() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.FileManagement: Reads a line from the file.
ReadMyFile(String) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyFile: Function for reading a data file in a String Object
readOneParameter() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
readOutputData() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
readOutputs() - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
readOutputs() - Static method in class keel.Dataset.DataParser
readParameters(String) - Method in class keel.Algorithms.Coevolution.CIW_NN.CIW_NN: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Coevolution.IFS_COCO.IFS_COCO: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.FunctionalTrees: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.PUBLIC: Reads the configuration script, to extract the parameter's values
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.CFKNN.CFKNN: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.D_SKNN.D_SKNN: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FCMKNN.FCMKNN: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FENN.FENN: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRKNNA.FRKNNA: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN.FRNN: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_FRS.FRNN_FRS: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FRNN_VQRS.FRNN_VQRS: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyKNN.FuzzyKNN: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyNPC.FuzzyNPC: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.GAFuzzyKNN.GAFuzzyKNN: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IF_KNN.IF_KNN: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFSKNN.IFSKNN: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IFV_NP.IFV_NP: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.IT2FKNN.IT2FKNN: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN.JFKNN: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.PFKNN.PFKNN: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.PosIBL.PosIBL: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.VWFuzzyKNN.VWFuzzyKNN: Reads the parameters of the algorithm.
readParameters() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
readParameters(String) - Method in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Hyperrectangles.BNGE.BNGE: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Hyperrectangles.INNER.INNER: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Hyperrectangles.RISE.RISE: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Lazy_Learning.CamNN.CamNN: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Lazy_Learning.CenterNN.CenterNN: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Lazy_Learning.CPW.CPW: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Lazy_Learning.CW.CW: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Lazy_Learning.Deeps.Deeps: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Lazy_Learning.DeepsNN.DeepsNN: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Lazy_Learning.IDIBL.IDIBL: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Lazy_Learning.KNN.KNN: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Lazy_Learning.KNNAdaptive.KNNAdaptive: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Lazy_Learning.KSNN.KSNN: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Lazy_Learning.KStar.KStar: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Lazy_Learning.LazyDT.LazyDT: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Lazy_Learning.LBR.LBR: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Lazy_Learning.NM.NM: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Lazy_Learning.NSC.NSC: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Lazy_Learning.PW.PW: Reads configuration script, to extract the parameter's values.
readParameters(String) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GGA.GGA: Read the parameters
readParameters(String) - Method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.RST_Learning.EFS_RPS.EFS_RPS: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.RST_Learning.EIS_RFS.EIS_RFS: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.RST_Learning.RSTAlgorithm: Reads the parameters of the algorithm.
readParameters(String) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.NumericalNaiveBayes: Reads configuration script, to extract the parameter's values.
ReadParameters(String) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.MESDIF: Auxiliar Gets the name for the input files, eliminating "" and skiping "="
ReadParameters(String) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.NMEEFSD: Reads the parameters from the file specified and stores the values
ReadParameters(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Main: Auxiliar Gets the name for the input files, eliminating "" and skiping "="
ReadParameters(String) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.SDIGA: Auxiliar Gets the name for the input files, eliminating "" and skiping "="
readParametersFile(String) - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm: Read the keel parameters file.
readParametersFile(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm: Read the keel parameters file.
readPopulationFromFile(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Reads the population from a file.
readProperties(String) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Reads properties that inherit from three locations.
readProperties(String) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Reads properties that inherit from three locations.
readProperties(String) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Reads properties that inherit from three locations.
readProperties(String) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Reads properties that inherit from three locations.
readProperties(String) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Reads properties that inherit from three locations.
readPrototypeSet(String) - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm: Reads the prototype set from a data file.
readPrototypeSet(String) - Static method in class keel.Algorithms.Instance_Generation.DROP3LVQ3.DROP3LVQ3: Reads the prototype set from a data file.
readPrototypeSet(String) - Static method in class keel.Algorithms.Instance_Generation.DROP3PSO.DROP3PSO: Reads the prototype set from a data file.
readPrototypeSet(String) - Static method in class keel.Algorithms.Instance_Generation.DROP3SFLSDE.DROP3SFLSDE: Reads the prototype set from a data file.
readPrototypeSet(String) - Static method in class keel.Algorithms.Instance_Generation.ICFLVQ3.ICFLVQ3: Reads the prototype set from a data file.
readPrototypeSet(String) - Static method in class keel.Algorithms.Instance_Generation.ICFPSO.ICFPSO: Reads the prototype set from a data file.
readPrototypeSet(String) - Static method in class keel.Algorithms.Instance_Generation.ICFSFLSDE.ICFSFLSDE: Reads the prototype set from a data file.
readPrototypeSet(String) - Static method in class keel.Algorithms.Instance_Generation.SSMALVQ3.SSMALVQ3: Reads the prototype set from a data file.
readPrototypeSet(String) - Static method in class keel.Algorithms.Instance_Generation.SSMAPSO.SSMAPSO: Reads the prototype set from a data file.
readPrototypeSet(String) - Static method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE: Reads the prototype set from a data file.
readPrototypeSet(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm: Reads the prototype set from a data file.
readPrototypeSet(String, String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm: Reads the prototype set from a given file name.
readPrototypeSet2(InstanceSet) - Static method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRegressionSet(String, boolean) - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset: It reads the whole input data-set and it stores each example and its associated output value in local arrays to ease their use.
readRelation() - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
readRelation() - Static method in class keel.Dataset.DataParser
readSchema(String) - Static method in class keel.Algorithms.Decision_Trees.CART.dataset.DataSetManager: Reads schema from the KEEL file
readSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset: It reads the examples file (training or test)
readSet(String, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset: It reads the examples file (training or test)
readSet(String, boolean) - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset: Reads the file of examples(Train&Test)
readSet(String, boolean) - Method in class keel.Algorithms.Rule_Learning.AQ.Dataset: It reads the examples file (training or test)
readSet(String, boolean) - Method in class keel.Algorithms.Rule_Learning.CN2.Dataset: It reads the examples file (training or test)
readSet(String, boolean) - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset: Reads the file of examples (Train&Test)
readSet(String, boolean) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: This method reads all the information in a DB and load it to memory.
readSet(String, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset: It reads the examples file (training or test)
readSet(String, boolean) - Method in class keel.Dataset.InstanceSet: This method reads all the information in a DB and load it to memory.
readTillEOL(StreamTokenizer) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Reads and skips all tokens before next end of line token.
readXMLUseCase(String) - Method in class keel.GraphInterKeel.experiments.Experiments: Reads a XML use case
REAL - Static variable in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Decision_Trees.Target.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset: Number to represent type of variable real or double.
real - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Referencia
REAL - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset: Number to represent type of variable real or double.
real - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Referencia: Real value.
REAL - Static variable in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Constant set for numeric attributes.
real - Variable in class keel.Algorithms.Instance_Selection.CCIS.Pareja: Real element of the pair.
real - Variable in class keel.Algorithms.Instance_Selection.MNV.ReferenciaMNV: Real value.
real - Variable in class keel.Algorithms.Preprocess.Basic.Referencia: Reference value (real).
real - Variable in class keel.Algorithms.Preprocess.Instance_Selection.CCIS.Pareja: Real element of the pair.
real - Variable in class keel.Algorithms.Preprocess.Instance_Selection.MNV.ReferenciaMNV: Real value.
REAL - Static variable in class keel.Algorithms.PSO_Learning.CPSO.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.PSO_Learning.REPSO.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Rule_Learning.LEM1.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Rule_Learning.LEM2.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Rule_Learning.Ripper.MyDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Rule_Learning.Ritio.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Rule_Learning.Rules6.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Rule_Learning.Slipper.MyDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Rule_Learning.SRI.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.Rule_Learning.Swap1.Attribute: Label for REAL VALUES
REAL - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for real data.
REAL - Static variable in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Gene: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Gene
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset: Number to represent type of variable real or double.
REAL - Static variable in class keel.Dataset.Attribute: Label for REAL VALUES
REAL - Static variable in interface keel.Dataset.DataParserConstants
REAL_CONST - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
REAL_CONST - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for real constant.
REAL_CONST - Static variable in interface keel.Dataset.DataParserConstants
realAttributes - Variable in class keel.Algorithms.Discretizers.Basic.Discretizer: Boolean array identifying which attribute is continuous.
realAttributes - Variable in class keel.Algorithms.Discretizers.Chi2_Discretizer.Discretizer: Boolean array identifying which attribute is continuous.
realAttributes - Variable in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Discretizer: Boolean array identifying which attribute is continuous.
realAttributes - Variable in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Discretizer: Boolean array identifying which attribute is continuous.
realAttributes - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Basic.Discretizer
realAttributes - Variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Discretizer
realBoundaries(Attribute) - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
realBoundaries(Attribute) - Static method in class keel.Dataset.DataParser
realClass - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: real classes values for test
realClass - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Real classes of the test instances.
realClass - Variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: Real test classes.
realClass - Variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: Real test classes.
RealConst() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
realConst() - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
realConst() - Static method in class keel.Dataset.DataParser
realCount - Variable in class keel.Algorithms.Decision_Trees.M5.M5AttrStats: The number of real-like values (i.e. have a fractional part)
realCount - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.AttributeStats: The number of real-like values (i.e. have a fractional part)
realDrawnPrecision - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It represents the precision wanted by the user to draw an interval.
REALDRAWNPRECISION - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
Realloc() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectordouble
Realloc() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.vectordouble: Increases the memory for the vector to the double
Realloc() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectordouble
Realloc() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectordouble
realloc1(short[], short) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Resizes given item set so that its length is increased by one and appends new element (identical to append method)
realloc1(short[], short) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Resizes given item set so that its length is increased by one and appends new element (identical to append method)
realloc1(short[], short) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Resizes given item set so that its length is increased by one and appends new element (identical to append method)
realloc2(short[], short) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Resizes given array so that its length is increased by one element and new element added to front
realloc2(short[], short) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Resizes given array so that its length is increased by one element and new element added to front
realloc2(short[], short) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Resizes given array so that its length is increased by one element and new element added to front
realloc2_new(short[], short) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Resizes given array so that its length is increased by one element and new element added to front
realloc3(short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Resizes given array so that its length is decreased by one element and first element removed
realloc4(short[], short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Resize given array so that its length is decreased by size of second array (which is expected to be a leading subset of the first) and remove second array.
reallocInsert(short[], short) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Resizes given item set so that its length is increased by one and new element inserted.
reallocInsert(short[], short) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Resizes given item set so that its length is increased by one and new element inserted.
reallocInsert(short[], short) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Resizes given item set so that its length is increased by one and new element inserted.
RealMutation - Interface in keel.Algorithms.Genetic_Rule_Learning.UCS: Real Mutation.
RealMutation - Interface in keel.Algorithms.Genetic_Rule_Learning.XCS: Real Mutation.
RealNumericalAttribute - Class in keel.Algorithms.Neural_Networks.NNEP_Common.data: Real attributes
RealNumericalAttribute() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.data.RealNumericalAttribute: Empty constructor
RealRep - Class in keel.Algorithms.Genetic_Rule_Learning.UCS: This class implements the Lower-Upper Bound Representation, i.e., an attribute is represented by [l_i, u_i].
RealRep() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep: Default constructor of the class.
RealRep(double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep: Does construct a real representation allele from the environmental state
RealRep(double, double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep: It is the constructor of the class.
RealRep(Attribute) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep: It's a constructor for the class.
RealRep - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: It's the real lower-upper bound representation.
RealRep() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: It's the default constructor of the class.
RealRep(double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: Does construct a real representation allele from the environmental state
RealRep(double, double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: It is the constructor of the class.
RealRep(Attribute) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep: It's a constructor for the class.
realStats - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.AttributeStats: Stats on numeric value distributions
realTest - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Test Real values.
realTest - Variable in class keel.Algorithms.Preprocess.Basic.Metodo: Test input data.
realTrain - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Training Real values.
realTrain - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
realTrain - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
realTrain - Variable in class keel.Algorithms.Preprocess.Basic.Metodo: Training input data.
realValid(String) - Method in class keel.GraphInterKeel.datacf.util.Attribute: Return a boolean for a given real value, true is valid value, false invalid value.
realValue(int, String) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset: Returns a real representation of a attribute's nominal value given as argument.
realValues - Variable in class keel.Algorithms.Discretizers.Basic.Discretizer: For each attribute, stores its values of every instances.
realValues - Variable in class keel.Algorithms.Discretizers.Chi2_Discretizer.Discretizer: For each attribute, stores its values of every instances.
realValues - Variable in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Discretizer: For each attribute, stores its values of every instances.
realValues - Variable in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Discretizer: For each attribute, stores its values of every instances.
realValues - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Basic.Discretizer
realValues - Variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Discretizer
reanudeProcess() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt: To invoque previously stopProcess.
rebuild(Genotype) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGAP: This method is intended to rebuild the current object if needed.
rebuild(Genotype) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGP: This method is intended to rebuild the current object if needed.
rebuild(Genotype) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGPRegSym: This method is intended to rebuild the current object if needed.
recalculateCenters(InstanceSet) - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.fuzzygCenter: Recalculates all the centroids using a given InstanceSet, to reduce the sum of the distances for each object from the centroid of the cluster to which the object belongs
recalculateCenters(InstanceSet) - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.gCenter: Recalculates all the centroids using a given InstanceSet, in order to reduce the total sum of distances for each object to the centroid of the cluster, which the object belongs to
recall(int) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Calculate the recall with respect to a particular class.
recastInputData() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Recasts the contents of the data array so that each record is ordered according to conversion array.
recastInputData() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Recasts the contents of the data array so that each record is ordered according to conversion array.
recastInputData() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Recasts the contents of the data array so that each record is ordered according to conversion array.
recastInputDataAndPruneUnsupportedAtts() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Recasts the contents of the data array so that each record is ordered according to ColumnCounts array and excludes non-supported elements.
recastInputDataAndPruneUnsupportedAtts() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Recasts the contents of the data array so that each record is ordered according to ColumnCounts array and excludes non-supported elements.
recastInputDataAndPruneUnsupportedAtts() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Recasts the contents of the data array so that each record is ordered according to ColumnCounts array and excludes non-supported elements.
reconfigure(String, String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.SMOTE_RSB
Reconsistent - Class in keel.Algorithms.Instance_Selection.Reconsistent: File: Reconsistent.java The Reconsistent Instance Selection algorithm.
Reconsistent(String) - Constructor for class keel.Algorithms.Instance_Selection.Reconsistent.Reconsistent: Default constructor.
Reconsistent - Class in keel.Algorithms.Preprocess.Instance_Selection.Reconsistent: File: Reconsistent.java The Reconsistent Instance Selection algorithm.
Reconsistent(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.Reconsistent.Reconsistent: Default constructor.
reconstructInputData() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass: Reconstructs the input data set by appending the test set to the training sets.
reconversionArray - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: 1-D array used to reconvert input data column numbers to their original numbering where the input data has been ordered to enhance computational efficienvy.
reconversionArray - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: 1-D array used to reconvert input data column numbers to their original numbering where the input data has been ordered to enhance computational efficiency.
reconversionArray - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: 1-D array used to reconvert input data column numbers to their original numbering where the input data has been ordered to enhance computational efficiency.
reconvertItem(short) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Reconvert single item if appropriate.
reconvertItem(short) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Reconvert single item if appropriate.
reconvertItemSet(short[]) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Reconverts given item set according to contents of reconversion array.
reconvertItemSet(short[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Reconverts given item set according to contents of reconversion array.
rectifyValueInBounds(double) - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute: It update an integer or real value read for an attribute in the test set if it doesn't match with the bounds defined in the train set.
rectifyValueInBounds(double) - Method in class keel.Dataset.Attribute: It update an integer or real value read for an attribute in the test set if it doesn't match with the bounds defined in the train set.
RecuperaParametros(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyClassifier
RecuperaParametros(double[]) - Method in class keel.Algorithms.Statistical_Classifiers.Shared.DiscrAnalysis.AD: Recovers the parameters of the LDA or QDA stored in the array given.
recursiveDelete(File) - Static method in class keel.GraphInterKeel.datacf.util.FileUtils: Utilities for Files.
redim(int) - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription: Resize the object to the new capacity indicated
redimAllNodes(DefaultMutableTreeNode) - Method in class keel.GraphInterKeel.experiments.Experiments: Resizes the ExternalObjectDescription of all nodes.
reduced - Variable in class keel.Algorithms.Instance_Generation.VQ.AVQGenerator: Reduced data set.
reduceDL(double, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Try to reduce the DL of the ruleset by testing removing the rules one by one in reverse order and update all the stats
reducedStepSize - Variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus: Reduced step size array
reduceHyper(Hyper[], boolean[]) - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.EHS_CHC
reduceMatrix(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ContingencyTables: Reduces a matrix by deleting all zero rows and columns.
reduceMatrix(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.ContingencyTables: Reduces a matrix by deleting all zero rows and columns.
reduceMatrix(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.ContingencyTables: Reduces a matrix by deleting all zero rows and columns.
reduceNegative(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.PNArray: It reduces the negative value P of the PNArray by extracting the weight of a training example given by its position in the training dataset
reducePositive(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.PNArray: It reduces the positive value P of the PNArray by extracting the weight of a training example given by its position in the training dataset
reduceRules(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: Function to eliminate the rules that are not needed (Redundant, not enough accurate,...) for a given class.
reduceRules() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.RuleBase: Function to eliminate the redundant rules
reduceRules(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
reduceSet() - Method in class keel.Algorithms.Instance_Generation.AMPSO.AMPSOGenerator: Generate a reduced prototype set by the AMPSOGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator: Makes the trivial reduction.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.BasicMethods.ARS: Extract prototypes from the training data and returns them in a new data set.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.BasicMethods.AVG: Reduces the set by adding centroid prototype of each class to reduced set.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.BasicMethods.CNN
reduceSet() - Method in class keel.Algorithms.Instance_Generation.BasicMethods.RandomSelector: Extract prototypes from the training data and returns them in a new data set.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.BasicMethods.SAVG: Reduce the set.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.BTS3.BTS3Generator: Reduce the training data set by the Hamamoto et al.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.Chen.ChenGenerator: Generate a reduced prototype set by the ChenGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.DE.DEGenerator: Generate a reduced prototype set by the DEGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.DEGL.DEGLGenerator: Generate a reduced prototype set by the DEGLGenerator method.
reduceSet(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.DROP3PSO.DROP3PSO: Generate a reduced prototype set by the PSOGenerator method.
reduceSet(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.DROP3SFLSDE.DROP3SFLSDE: Generate a reduced prototype set by the SADEGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.ENPC.ENPCGenerator: Generate a reduced prototype set by the ENPCGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.GENN.GENNGenerator: Reduce the set by the GENNGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.GMCA.GMCAGenerator
reduceSet() - Method in class keel.Algorithms.Instance_Generation.HYB.HYBGenerator: Reduce the set by the Hybrid method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.HYB.SVMSEL: Reduction of the original prototype set by the SVM.
reduceSet(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.ICFPSO.ICFPSO: Generate a reduced prototype set by the PSOGenerator method.
reduceSet(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.ICFSFLSDE.ICFSFLSDE: Generate a reduced prototype set by the SADEGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.ICPL.ICPLGenerator
reduceSet() - Method in class keel.Algorithms.Instance_Generation.IPLDE.IPLDEGenerator: Generate a reduced prototype set by the IPLDEGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.JADE.JADEGenerator: Generate a reduced prototype set by the JADEGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQGenerator: Execute the method and returns the condensed prototype set.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQPRU: Execute the method LVQPRU and returns the condensed set
reduceSet() - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQTC: Execute the method and returns the output instance set
reduceSet() - Method in class keel.Algorithms.Instance_Generation.MCA.MCAGenerator: Reduce the set by the MCAGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.MixtGauss.MixtGaussGenerator: Generate a reduced prototype set by the MixtGaussGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.MSE.MSEGenerator: Generate a reduced prototype set by the RSPGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.OBDE.OBDEGenerator: Generate a reduced prototype set by the DEGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.PNN.PNNGenerator: Performs a reduction of the training data set by the PNNGenerator (aka Chang) method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.POC.POCGenerator
reduceSet() - Method in class keel.Algorithms.Instance_Generation.PSCSA.PSCSAGenerator: Generate a reduced prototype set by the PSCSAGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.PSO.PSOGenerator: Generate a reduced prototype set by the PSOGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.RSP.RSPGenerator
reduceSet() - Method in class keel.Algorithms.Instance_Generation.SADE.SADEGenerator: Generate a reduced prototype set by the SADEGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.SFLSDE.SFLSDEGenerator: Generate a reduced prototype set by the SFLSDEGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.SGP.SGPGenerator
reduceSet(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.SSMAPSO: Generate a reduced prototype set by the PSOGenerator method.
reduceSet(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE: Generate a reduced prototype set by the SADEGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.VQ.AVQGenerator: Reduce the data set by the AVQGenerator method.
reduceSet() - Method in class keel.Algorithms.Instance_Generation.VQ.VQGenerator: Execute the method and returns the output instance set
reduceWeight(int, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It reduces the weight of the given example in a factor alfa
reduceWeight(myDataset, ArrayList<ExampleWeight>) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Rule: Reduces the weight of the examples that match with the rule (the rule correctly classifies them)
reduceWeight(myDataset, ArrayList<ExampleWeight>) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule
reducir(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Attribute
reductedRulesFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It's the name where the reductes rules have to be written.
REDUCTEDRULESFILE - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
REDUCTION - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
Reduction - Interface in keel.Algorithms.Genetic_Rule_Learning.XCS: This is the interface for all the reduction methods classes in the XCS.
REDUCTIONTYPE - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
reductWindow - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: It indicates the interval where the execution window will be applied.
REDUCTWINDOW - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
reemplazo(Vector, double, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion: Build the next generation
reference - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Reference dataset
reference - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: Reference dataset.
reference - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Reference dataset
reference - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Reference dataset.
reference - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm: Reference dataset
referenceChange() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Condition
referenceData - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Reference input data.
referenceData - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: Reference input data.
referenceData - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Reference input data.
referenceData - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Reference input data.
referenceData - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm: Reference input data.
referenceExtension(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Condition
referenceFile - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Reference file name
referenceFile - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: Reference file name.
referenceFile - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Reference file name
referenceFile - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Reference file name.
referenceFile - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm: Reference file name
referenceFile - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm: Reference file name
referenceOutput - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Reference output data.
referenceOutput - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: Reference output data.
referenceOutput - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Reference output data.
referenceOutput - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Reference output data.
referenceOutput - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm: Reference output data.
referenceRestriction(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Condition
references(ArrayList<IInstance>, int) - Method in class keel.Algorithms.MIL.Nearest_Neighbour.AbstractNearestNeighbour
referencia - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.KNN: Data structures
referencia - Variable in class keel.Algorithms.Hyperrectangles.EHS_CHC.EHS_CHC
Referencia - Class in keel.Algorithms.ImbalancedClassification.Ensembles.Basic
Referencia() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Referencia
Referencia(int, double) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Referencia
Referencia - Class in keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic: File: Referencia.java An auxiliary class to manage references between two values
Referencia() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Referencia: Default builder
Referencia(int, double) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Referencia: Builder
Referencia - Class in keel.Algorithms.Preprocess.Basic: File: Referencia.java An auxiliary class to manage references between two values
Referencia() - Constructor for class keel.Algorithms.Preprocess.Basic.Referencia: Default builder
Referencia(int, double) - Constructor for class keel.Algorithms.Preprocess.Basic.Referencia: Builder
ReferenciaMNV - Class in keel.Algorithms.Instance_Selection.MNV: File: ReferenciaMNV.java Simple structure used on MNV algorithm.
ReferenciaMNV() - Constructor for class keel.Algorithms.Instance_Selection.MNV.ReferenciaMNV: Default constructor.
ReferenciaMNV(int, double, double) - Constructor for class keel.Algorithms.Instance_Selection.MNV.ReferenciaMNV: Parameters constructor.
ReferenciaMNV - Class in keel.Algorithms.Preprocess.Instance_Selection.MNV: File: ReferenciaMNV.java Simple structure used on MNV algorithm.
ReferenciaMNV() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.MNV.ReferenciaMNV: Default constructor.
ReferenciaMNV(int, double, double) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.MNV.ReferenciaMNV: Parameters constructor.
refineClusters(Vector) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.EventCovering: This method refines the initial clusters obtained by clusterInitiation()
refineRuleSet() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ruleOrderAgent
refreshDataPanel(Dataset) - Method in class keel.GraphInterKeel.datacf.editData.EditDataPanel: Change in EditVariablePanel
refreshVariablePanel(Dataset) - Method in class keel.GraphInterKeel.datacf.editData.EditVariablePanel: Refreshes the variable panel
regem(DenseMatrix, InstanceSet) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Imputation of missing values with regularized EM algorithm
regenerateDatasetPartitions(DataSet) - Method in class keel.GraphInterKeel.experiments.GraphPanel: This method checks if any partition of the selected data sets is missing.
regeneration() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Core: Randomize the rules with fitness below the average, to obtain diversity.
regions(double, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.ENPC.ENPCGenerator: Function regions, see equation (2).
Register - Class in keel.Algorithms.Decision_Trees.PUBLIC: File: Register.java Data structure that is used in the construction of the decision tree.
Register(int, double, int) - Constructor for class keel.Algorithms.Decision_Trees.PUBLIC.Register: Creates a register from a id, a value attribute and an output class of the item.
Register(Register) - Constructor for class keel.Algorithms.Decision_Trees.PUBLIC.Register: Creates a register from another existing register
Regla - Class in keel.Algorithms.Decision_Trees.DT_GA: Title: Regla (Rule).
Regla() - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.Regla: Default Constructor.
Regla(String, myDataset) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.Regla: Paramenter constructor.
Regla(myDataset, String) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.Regla: Paramenter constructor.
Regla - Class in keel.Algorithms.Decision_Trees.Target: Title: Regla Description: Rule in the decision tree target Company: KEEL
Regla() - Constructor for class keel.Algorithms.Decision_Trees.Target.Regla: Default constructor.
Regla - Class in keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted: Title: Regla Description: Contains the definition of a fuzzy rule Copyright: Copyright (c) 2009 Company: KEEL
Regla(Regla) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Regla: Copy constructor
Regla(BaseD, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Regla: Constructor with parameters
Regla - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec
Regla(Regla) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Regla
Regla(BaseD, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Regla
Regla - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF: Title: Description: Copyright: Copyright (c) 2007 Company:
Regla(Regla) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Regla
Regla(BaseD) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Regla
Regla - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner: Title: Regla (Rule) Description:Rule class.
Regla() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Default constructor.
Regla(Regla) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Copy Constructor.
Regla(Vector, Atributo) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla: Parameter Constructor.
Regla - Class in keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus: Title: Regla (Rule) Description:Rule class.
Regla() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Default constructor.
Regla(Regla) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Copy Constructor.
Regla(Vector, Atributo) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla: Parameter Constructor.
Regla - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner: Title: Regla (Rule) Description:Rule class.
Regla() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Default constructor.
Regla(Regla) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Copy Constructor.
Regla(Vector, Atributo) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla: Parameter Constructor.
Regla - Class in keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus: Title: Regla (Rule) Description:Rule class.
Regla() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Default constructor.
Regla(Regla) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Copy Constructor.
Regla(Vector, Atributo) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla: Parameter Constructor.
Regla - Class in keel.Algorithms.Genetic_Rule_Learning.OCEC: Title: Description: Copyright: Copyright (c) 2007 Company:
Regla() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OCEC.Regla
Regla(Organizacion) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OCEC.Regla
Regla - Class in keel.Algorithms.Genetic_Rule_Learning.PSO_ACO: Title: Regla (Rule) Description:Rule class.
Regla() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Default constructor.
Regla(Regla) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Copy Constructor.
Regla(Vector, Vector, Atributo) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla: Parameter Constructor.
Regla - Class in keel.Algorithms.Genetic_Rule_Learning.RMini
Regla - Class in keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM: Title: Description: Copyright: Copyright (c) 2007 Company:
Regla(Regla) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Regla
Regla(BaseD) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Regla
Regla - Class in keel.Algorithms.Rule_Learning.Rules6: Title: Regla (Rule).
Regla(String, int) - Constructor for class keel.Algorithms.Rule_Learning.Rules6.Regla: Parameter constructor.
Regla(Regla) - Constructor for class keel.Algorithms.Rule_Learning.Rules6.Regla: Copy Constructor.
Regla - Class in keel.Algorithms.Rule_Learning.SRI: Title: Regla (Rule).
Regla(String, int) - Constructor for class keel.Algorithms.Rule_Learning.SRI.Regla: Parameter constructor.
Regla(Regla) - Constructor for class keel.Algorithms.Rule_Learning.SRI.Regla: Copy Constructor.
Regla - Class in keel.Algorithms.Subgroup_Discovery.aprioriSD: Title: Regla (Rule).
Regla() - Constructor for class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Default constructor.
Regla(int, int) - Constructor for class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Parameter Constructor.
Regla(Item, int) - Constructor for class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla: Parameter Constructor.
reglaCubreInstancia(Instance) - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Checks if the rule gets the parameter instance
reglaCubreInstancia(Instance) - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Checks if the rule covers the given instance.
RegOptimizer - Class in keel.Algorithms.SVM.SMO.supportVector: Base class implementation for learning algorithm of SVMreg
RegOptimizer() - Constructor for class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer: the default constructor
regOptimizerTipText() - Method in class keel.Algorithms.SVM.SMO.SVMreg: Returns the tip text for this property
regression(M5Matrix, int, int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Matrix: Linear regression
regression(Function) - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode: Computes the coefficients of a linear model using the instances at this node
regression(M5Matrix, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5Matrix: Linear regression
regression(Function) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode: Computes the coefficients of a linear model using the itemsets at this node
regression(Matrix, double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Performs a (ridged) linear regression.
regression(Matrix, double[], double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix: Performs a weighted (ridged) linear regression.
regression(Matrix, double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Performs a (ridged) linear regression.
regression(Matrix, double[], double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix: Deprecated.

Performs a weighted (ridged) linear regression.
RegressionFunction - Interface in keel.Algorithms.Preprocess.Missing_Values.SVDimpute: Interface for Regression class Sum of squares function for non-linear regression methods
RegressionProblemEvaluator - Class in keel.Algorithms.Neural_Networks.NNEP_Regr.problem.regression: Regression problem evaluator
RegressionProblemEvaluator() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Regr.problem.regression.RegressionProblemEvaluator: Empty constructor
RegSMO - Class in keel.Algorithms.SVM.SMO.supportVector: Implementation of SMO for support vector regression as described in :

A.J.
RegSMO() - Constructor for class keel.Algorithms.SVM.SMO.supportVector.RegSMO: default constructor
RegSMOImproved - Class in keel.Algorithms.SVM.SMO.supportVector: Learn SVM for regression using SMO with Shevade, Keerthi, et al. adaption of the stopping criterion.
RegSMOImproved() - Constructor for class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved
RegSymFuzzyGP - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model: fuzzy system of symbolic regression
RegSymFuzzyGP(double, double, int, int, int, int, int, Randomize) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.RegSymFuzzyGP: Constructor.
RegSymFuzzyGP(RegSymFuzzyGP) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.RegSymFuzzyGP: Constructor.
reinit() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.timerMDL
reinit() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.timersManagement
ReInit(InputStream) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
ReInit(Reader) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
ReInit(ParserTokenManager) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
ReInit(SimpleCharStream) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserTokenManager
ReInit(SimpleCharStream, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserTokenManager
ReInit(Reader, int, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
ReInit(Reader, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
ReInit(Reader) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
ReInit(InputStream, int, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
ReInit(InputStream) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
ReInit(InputStream, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
ReInit(InputStream) - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
ReInit(InputStream, String) - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
ReInit(Reader) - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
ReInit(DataParserTokenManager) - Method in class keel.Algorithms.Rule_Learning.Swap1.DataParser
ReInit(SimpleCharStream) - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParserTokenManager
ReInit(SimpleCharStream, int) - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParserTokenManager
ReInit(Reader, int, int, int) - Method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
ReInit(Reader, int, int) - Method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
ReInit(Reader) - Method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
ReInit(InputStream, String, int, int, int) - Method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
ReInit(InputStream, int, int, int) - Method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
ReInit(InputStream, String) - Method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
ReInit(InputStream) - Method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
ReInit(InputStream, String, int, int) - Method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
ReInit(InputStream, int, int) - Method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
ReInit(InputStream) - Static method in class keel.Dataset.DataParser
ReInit(InputStream, String) - Static method in class keel.Dataset.DataParser
ReInit(Reader) - Static method in class keel.Dataset.DataParser
ReInit(DataParserTokenManager) - Method in class keel.Dataset.DataParser
ReInit(SimpleCharStream) - Static method in class keel.Dataset.DataParserTokenManager
ReInit(SimpleCharStream, int) - Static method in class keel.Dataset.DataParserTokenManager
ReInit(Reader, int, int, int) - Method in class keel.Dataset.SimpleCharStream
ReInit(Reader, int, int) - Method in class keel.Dataset.SimpleCharStream
ReInit(Reader) - Method in class keel.Dataset.SimpleCharStream
ReInit(InputStream, String, int, int, int) - Method in class keel.Dataset.SimpleCharStream
ReInit(InputStream, int, int, int) - Method in class keel.Dataset.SimpleCharStream
ReInit(InputStream, String) - Method in class keel.Dataset.SimpleCharStream
ReInit(InputStream) - Method in class keel.Dataset.SimpleCharStream
ReInit(InputStream, String, int, int) - Method in class keel.Dataset.SimpleCharStream
ReInit(InputStream, int, int) - Method in class keel.Dataset.SimpleCharStream
Rel_RASCOAlgorithm - Class in keel.Algorithms.Semi_Supervised_Learning.Rel_RASCO: Rel_RASCO algorithm calling.
Rel_RASCOAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Rel_RASCO.Rel_RASCOAlgorithm
Rel_RASCOGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.Rel_RASCO: This class implements the Rel_RASCO algorithm.
Rel_RASCOGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Rel_RASCO.Rel_RASCOGenerator: Build a new Rel_RASCOGenerator Algorithm
Rel_RASCOGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Rel_RASCO.Rel_RASCOGenerator: Build a new Rel_RASCOGenerator Algorithm
relation - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm: Relation string.
relation - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo: Relation string.
relation - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm: Relation string.
relation() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Returns the header info for a relation-valued attribute, null if the attribute is not relation-valued.
relation(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Returns a value of a relation-valued attribute.
relation - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Relation string.
relation - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
relation - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
relation() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Returns the header info for a relation-valued attribute, null if the attribute is not relation-valued.
relation(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Returns a value of a relation-valued attribute.
relation - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm: Relation string.
relation - Variable in class keel.Algorithms.Preprocess.Basic.Metodo: Relation string.
relation - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm: Relation string.
relation - Variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN: Relation string.
relation - Variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN: Relation string.
relation - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm: Relation string.
RELATION - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants: Data Parser Indentifier for Ralation string.
Relation - Class in keel.Algorithms.Statistical_Tests.Shared.nonParametric: File: Relation.java This class defines a relation between two integers.
Relation() - Constructor for class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Relation: Default builder
Relation(int, int) - Constructor for class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Relation: Builder
relation - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Relation.
RELATION - Static variable in interface keel.Dataset.DataParserConstants
Relation - Class in keel.GraphInterKeel.statistical.tests: File: Relation.java This class defines a relation between two integers.
Relation() - Constructor for class keel.GraphInterKeel.statistical.tests.Relation: Default builder
Relation(int, int) - Constructor for class keel.GraphInterKeel.statistical.tests.Relation: Builder
RELATIONAL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka: Constant set for relation-valued attributes.
RELATIONAL - Static variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute: Constant set for relation-valued attributes.
RelationalLocator - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: This class locates and records the indices of relational attributes,
RelationalLocator(Instances) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RelationalLocator: Initializes the RelationalLocator with the given data.
RelationalLocator(Instances, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RelationalLocator: Initializes the RelationalLocator with the given data.
RelationalLocator(Instances, int[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RelationalLocator: Initializes the RelationalLocator with the given data.
relationalValue(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Returns the relational value of a relational attribute.
relationalValue(AttributeWeka) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Returns the relational value of a relational attribute.
relationalValue(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Returns the relational value of a relational attribute.
relationalValue(Attribute) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Returns the relational value of a relational attribute.
relationDirected - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Type of relation.
relationName() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Returns the relation's name.
relationName() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Returns the relation's name.
relationName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config: Variables to store extra information about attributes
relationName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config: Variables to store extra information about attributes
relationName() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Returns the relation's name.
relationName() - Method in class keel.Algorithms.SVM.SMO.core.Instances: Returns the relation's name.
relationsForInstance(double[][]) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.OVO: Retrieves the preference, conflict and ignorance matrices for a single instance
relativeAbsoluteError() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Returns the relative absolute error.
relativeDL(int, double, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: The description length (DL) of the ruleset relative to if the rule in the given position is deleted, which is obtained by:
MDL if the rule exists - MDL if the rule does not exist
Note the minimal possible DL of the ruleset is calculated(i.e. some other rules may also be deleted) instead of the DL of the current ruleset.
releasePopulation() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.populationWrapper
relevancia(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.Rel_RASCO.Rel_RASCOGenerator: This methods return the mutual information between features and labels of the dataset.
ReliefDiff - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.RELIEF: Main class of relief method using difference between nearest neighbours as evalution measure.
ReliefDiff(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.RELIEF.ReliefDiff: Creates a new instance of ReliefDiff
Relieff - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.RELIEF_F: Main class of relief-F method using difference between nearest neighbours as evalution measure.
Relieff(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.RELIEF_F.Relieff: Creates a new instance of ReliefDiff
reload(int) - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Reload the data set list, given a type of experiment
reload(int) - Method in class keel.GraphInterKeel.experiments.SelectData: Reload the data set list, given the experiment type
reload_algorithms() - Method in class keel.GraphInterKeel.experiments.Experiments: Reload the algorithms trees (all kinds)
reload_crisp(int) - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Reload LQD data sets
reload_crisp() - Method in class keel.GraphInterKeel.experiments.SelectData: Reload the data set list
reload_crisp_lqd() - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Reload LQD data sets
reload_crisp_lqd(JPanel) - Method in class keel.GraphInterKeel.experiments.SelectData: Reload the data set list, given the experiment type
reload_lqd_crisp() - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Reload LQD data sets
reload_lqd_crisp(JPanel) - Method in class keel.GraphInterKeel.experiments.SelectData: Reload the data set list, given the experiment type
reloadPreviousActiveDataSets() - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Once the Buttons has been loaded again, we must set their state as it was previously set by the user <- we take the state saved from saveSelected()
reloadPreviousActiveDataSets() - Method in class keel.GraphInterKeel.experiments.SelectData: Once the Buttons has been loaded again, we must set their state as it was previously set by the user <- we take the state saved from saveSelected()
RelSimil(double[][], int, double) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets: Computes the similarity between this set and the one given.
remove(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset: Function to remove the item located in the given position.
remove(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.RuleBase: It removes the rule stored in the given position
remove(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset: Function to remove the item located in the given position
remove(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.RuleBase: It removes the rule stored in the given position
remove(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.RuleBase: It removes the rule stored in the given position
remove(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Itemset: Function to remove the item located in the given position
remove(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.RuleBase: It removes the rule stored in the given position
remove(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Itemset: Function to remove the item located in the given position
remove(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: It removes the rule stored in the given position
remove(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Itemset: Function to remove the item located in the given position
remove(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.RuleBase: It removes the rule stored in the given position
remove(int) - Method in class keel.Algorithms.Decision_Trees.M5.Function: Removes a term from the function
remove(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase: It removes a given rule from the RB
Remove(int[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
Remove() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.ruleset: Removes the last rule of the set of learned rules.
Remove(int[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set: Removes the examples in "v" from the set of examples
Remove(int[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set
Remove() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.ruleset: Removes the last rule of the set of learned rules.
Remove(int[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set
Remove() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.ruleset: Removes the last rule of the set of learned rules.
remove(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Itemset
remove(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
remove(Object) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.ProtectedProperties: Overrides a method to prevent the properties from being modified.
remove(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Function: Removes a term from the function
remove(Object) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ProtectedProperties: Overrides a method to prevent the properties from being modified.
remove(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Removes a prototype given as parameter from the set.
remove(Prototype) - Method in class keel.Algorithms.Instance_Generation.GMCA.Cluster: Removes a prototype of the cluster.
remove(Cluster) - Method in class keel.Algorithms.Instance_Generation.GMCA.ClusterSet: Remove a cluster of the set.
remove(Prototype, boolean) - Method in class keel.Algorithms.Instance_Generation.MCA.DistanceMatrixByClass
remove(Prototype) - Method in class keel.Algorithms.Instance_Generation.MCA.DistanceMatrixByClass
remove(Prototype) - Method in class keel.Algorithms.Instance_Generation.PNN.MatrixOfDistances: Remove a prototype to the matrix of distances.
remove(int, Vector<pnPair>) - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.SaturationFilter: It runs the algorithm
remove(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Returns a copy of the set without an prototype.
remove(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Returns a copy of the set without an prototype.
remove(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Itemset: It allows to remove the item stored at the index "pos" within an itemset
remove(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Itemset: It allows to remove the item stored at the index "pos" within an itemset
remove(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Itemset: It allows to remove the item stored at the index "pos" within an itemset
remove(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Itemset: It allows to remove the item stored at the index "pos" within an itemset
remove(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Itemset: It allows to remove the item stored at the index "pos" within an itemset
remove(int) - Method in class keel.GraphInterKeel.experiments.DinamicParameter
remove_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectData: Rmove button
removeAllData() - Method in class keel.GraphInterKeel.experiments.DinamicDataset: Clear the vectors which stores the list of data sets
removeAllData() - Method in class keel.GraphInterKeel.experiments.SelectData: Removes all the data sets from the list
removeAllElements() - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector: Removes all components from this vector and sets its size to zero.
removeAllElements() - Method in class keel.Algorithms.Decision_Trees.M5.Queue: Removes all objects from the queue.
removeAllElements() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector: Removes all components from this vector and sets its size to zero.
removeAllElements() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Queue: Removes all objects from the queue m_Tail.m_Next.
removeAllElements() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue: Removes all objects from the queue.
removeAllElements() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector: Removes all components from this vector and sets its size to zero.
removeAllElements() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector: Removes all components from this vector and sets its size to zero.
removeAllElements() - Method in class keel.Algorithms.SVM.SMO.core.FastVector: Removes all components from this vector and sets its size to zero.
removeAllElements() - Method in class keel.Algorithms.SVM.SMO.core.Queue: Removes all objects from the queue m_Tail.m_Next.
removeAttribute(boolean, int) - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes: It does remove an attribute.
removeAttribute(boolean, int) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes: It does remove an attribute.
removeAttribute(InstanceSet, boolean, int) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: It does remove an attribute.
removeAttribute(boolean, int) - Static method in class keel.Dataset.Attributes: It does remove an attribute.
removeAttribute(boolean, int) - Method in class keel.Dataset.InstanceAttributes: It does remove an attribute.
removeAttribute(InstanceSet, boolean, int) - Method in class keel.Dataset.InstanceSet: It does remove an attribute.
removeAttributes(double[], int) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset: For each instance, copies the attribute values whose weight in the vector given is greater than 0.5
removeCellEditorListener(CellEditorListener) - Method in class keel.GraphInterKeel.datacf.util.EachRowEditor: Removes a cell editor listener
removeChromosome(Chromosome) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Cochromosome: Remove the specified chromosome from this object
removeChromosome(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Cochromosome: Removes the chromosome at the specified position
removeData(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.EachDataSet: Borra un dato
removeDataset(int) - Method in class keel.GraphInterKeel.experiments.Joint
removeDistribution() - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Resets the classes distribution to 0.
removeDistribution() - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: reset the distribution value for the complex
removeDuplicated(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleSet: Remove repetitive complex(at1 = 0 ^ at2 = 0 -- at2 = 0 ^ at1 = 0)
removeDuplicates() - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset: Removes the duplicated rules
removeElementAt(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector: Deletes an element from this vector.
removeElementAt(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector: Deletes an element from this vector.
removeElementAt(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector: Deletes an element from this vector.
removeElementAt(int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector: Deletes an element from this vector.
removeElementAt(int) - Method in class keel.Algorithms.SVM.SMO.core.FastVector: Deletes an element from this vector.
removeElementN(short[], int) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining: Removes the nth element/attribute from the given item set.
removeElementN(short[], int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining: Removes the nth element/attribute from the given item set.
removeFirstNelements(short[], int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Removes the first n elements/attributes from the given item set.
removeFromA(Prototype) - Method in class keel.Algorithms.Instance_Generation.PNN.MatrixOfDistances: Remove a prototype to the matrix of distances.
removeFromB(Prototype) - Method in class keel.Algorithms.Instance_Generation.PNN.MatrixOfDistances: Remove a prototype to the matrix of distances.
removeFromCandidates(ArrayList<Pair<Prototype, Prototype>>, Prototype) - Static method in class keel.Algorithms.Instance_Generation.MCA.MCAGenerator: Removes a prototype from the list of pairs of the nearest prototypes.
removeGene(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome: Deletes one gene from the chromosome
removeGene(Gene) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome: Remove a specific gene from the chromosome, using the Object.equals() method (must be the SAME object with SAME id)
removeInput(int, int) - Method in class keel.GraphInterKeel.experiments.Multiplexor: Remove input from the multiplexor
removeInputs(boolean[], int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet: Remove the inputs desired
removeInstance(int) - Method in class keel.Algorithms.Rule_Learning.SRI.Instances: Removes the example in the given position.
removeInstance(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet: It does remove the instance i from the instanceSet.
removeInstance(int) - Method in class keel.Dataset.InstanceSet: It does remove the instance i from the instanceSet.
removeInstances(double[][], int, int) - Method in class keel.Algorithms.MIL.APR.AbstractAPR
removeInstances(double[][], int, int) - Method in class keel.Algorithms.MIL.APR.IteratedDiscrimination.IteratedDiscrimination
removeInstances(LinkedList<Integer>) - Method in class keel.Algorithms.Rule_Learning.SRI.Instances: Removes all the examples whose indeces are given as parameter.
removeInstancesAndRestart(classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.instanceSet
removeLast() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Remove the last rule in the ruleset as well as it's stats.
removeLink(ExpNeuron, int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.ExpNeuronStructuralMutator: Removes a link of a neuron of an specific layer from a specific origin neuron
removeLink(N, int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.INeuronStructuralMutator: Removes a link of a neuron of an specific layer from a specific origin neuron
removeLink(LinearNeuron, int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.LinearNeuronStructuralMutator: Removes a link of a neuron of an specific layer from a specific origin neuron
removeLink(SigmNeuron, int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.SigmNeuronStructuralMutator: Removes a link of a neuron of an specific layer from a specific origin neuron
removeMatched(int[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.matchProfileAgent
removeMatchesOfRule(int, matchProfileAgent) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ruleOrderAgent
removeNeuron(LinkedLayer, LinkedLayer, int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.ExpNeuronStructuralMutator: Removes a neuron of a specific layer
removeNeuron(LinkedLayer, LinkedLayer, int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.INeuronStructuralMutator: Removes a neuron of a specific layer
removeNeuron(LinkedLayer, LinkedLayer, int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.LinearNeuronStructuralMutator: Removes a neuron of a specific layer
removeNeuron(LinkedLayer, LinkedLayer, int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.SigmNeuronStructuralMutator: Removes a neuron of a specific layer
removeNeuron(LinkedNeuron) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Removes a neuron of the layer
removeNeuron(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer: Removes a neuron of the layer using its index
removeNominalValues(ArrayList<String>) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Gene: removes a set of nominal values from the active list (if active)
removeNonUsefulClassifiers() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: It removes all classifiers in population that are not useful (the useful parameter is set to false)
removeNulls() - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleSet: Remove complex with repetitive attributes
removeOnly(PrototypeSet, PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ADE_CoForestGenerator: Depuration algorithm.
removeParameters(int) - Method in class keel.GraphInterKeel.experiments.Joint
removeProblem(int) - Method in class keel.GraphInterKeel.experiments.Joint
removeRandom() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Remove a random prototype (and returns it)
removeRandom() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Remove a random prototype (and returns it)
removeRbf(String) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn: Deletes a neuron from the net
removeRbf(String) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbfn: Deletes a neuron of the net
removeRbf(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn: Deletes a neuron of the net
removeRbf(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn: Deletes a neuron of the net
removeRbf(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn: Deletes a neuron of the net
removeRbf(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn: Deletes a neuron of the net
removeRbf(String) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn: Deletes a neuron of the net
removeRedundant(ArrayList<Chromosome>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AlatasetalProcess: Removes the redundant chromosomes of the population given.
removeRedundant(ArrayList<Chromosome>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.MOPNARProcess
removeRedundant(ArrayList<Chromosome>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.QAR_CIP_NSGAIIProcess
RemoveRepeatedCAN(Population) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Eliminates the repeated individuals for canonical representation
RemoveRepeatedDNF(Population, TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic: Eliminates the repeated individuals for DNF representation
removeRule(int, Chromosome) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Population: Removes one rule of the specified subpopulation
removeRule(Chromosome) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Subpopulation: Removes one rule from the subpopulation list
removeRule(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Ruleset: Deletes a given rule of the ruleset.
removeRule(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ruleOrderAgent
removeRule(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Ruleset: Deletes a given rule of the ruleset.
removeRule(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleSet: Removes a rule from the list
removeRule(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Ruleset: Deletes a given rule of the ruleset.
removeRule(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset: Deletes a given rule of the ruleset.
removeRule(int) - Method in class keel.Algorithms.Rule_Learning.PART.Ruleset: Deletes a given rule of the ruleset.
removeRule(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset: Deletes a given rule of the ruleset.
removeRule(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Ruleset: Deletes a given rule of the ruleset.
removeRules() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.RuleBase: Removes the rules stored.
removeRules(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase: It removes a prespecified number of rules from the rule set
removeSeed(String) - Method in class keel.GraphInterKeel.experiments.Parameters: removes seed
removeSelector(Selector) - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Remove the selector from the list
removeSelector(Selector) - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Remove the selector from the list
removeSelector(Selector) - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Drop the selector of the list selectors
removeSelector(Selector) - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Drop the selector of the list selectors
removeSelectorAtributo(int) - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo: Remove the selectors from the list of the selectors that have the parameter attribute o
removeSelectorAtributo(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo: Removes the selectors that have the attribute given as argument from the proper list
removeSelectorAttribute(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex: Remove the selectors from the list of the selectors that have the parameter attribute o
removeSelectorAttribute(int) - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Removes the selectors that have the attribute given as argument from the proper list
removeSubstring(String, String) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Removes all occurrences of a string from another string.
removeSubstring(String, String) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Removes all occurrences of a string from another string.
removeSubstring(String, String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Removes all occurrences of a string from another string.
removeSubstring(String, String) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Removes all occurrences of a string from another string.
removeSubstring(String, String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Removes all occurrences of a string from another string.
removeSubstring(String, String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Removes all occurrences of a string from another string.
removeSubstring(String, String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Removes all occurrences of a string from another string.
removeSubstring(String, String) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Removes all occurrences of a string from another string.
removeSubstring(String, String) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Removes all occurrences of a string from another string.
removeSubsumed(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleSet: Remove rules are the same ina semantic way(At = 1, At <> 0, At = [0,1])
removeValorInv(Integer, Double) - Method in class keel.Algorithms.Rule_Learning.Rules6.Regla: Removes an attribute with its value to the invalid values list.
removeValorInv(Integer, Double) - Method in class keel.Algorithms.Rule_Learning.SRI.Regla: Removes an attribute with its value to the invalid values list.
removeValue(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.IntegerSet: It removes a value of the set if it is present
removeWithoutClass(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Returns a copy of the set without an prototype without checking the class label
removeWithoutClass(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet
renameAttribute(int, String) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Renames an attribute.
renameAttribute(M5Attribute, String) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Renames an attribute.
renameAttribute(int, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Renames an attribute.
renameAttribute(AttributeWeka, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Renames an attribute.
renameAttribute(int, String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Renames an attribute.
renameAttribute(Attribute, String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Renames an attribute.
renameAttributeValue(int, int, String) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Renames the value of a nominal (or string) attribute value.
renameAttributeValue(M5Attribute, String, String) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Renames the value of a nominal (or string) attribute value.
renameAttributeValue(int, int, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Renames the value of a nominal (or string) attribute value.
renameAttributeValue(AttributeWeka, String, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Renames the value of a nominal (or string) attribute value.
renameAttributeValue(int, int, String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Renames the value of a nominal (or string) attribute value.
renameAttributeValue(Attribute, String, String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Renames the value of a nominal (or string) attribute value.
renderGeneration(int, <any>, <any>, ParametricMutator<NeuralNetIndividual>, IEvaluator) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.listener.NeuralNetReporterClas: Renders a generation of the algorithm to a String
renderGeneration(int, <any>, <any>, ParametricMutator<NeuralNetIndividual>, IEvaluator) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.listener.NeuralNetReporterRegr: Renders a generation of the algorithm to a String
renderNeuralNetIndividual(<any>, IEvaluator) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlusReporterClas: Renders a NeuralNetIndividual to a String
renderNeuralNetIndividual(<any>, IEvaluator) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.IRPropPlusReporterRegr: Renders a NeuralNetIndividual to a String
renderNeuralNetIndividual(<any>, IEvaluator) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.listener.NeuralNetReporterClas: Renders a NeuralNetIndividual to a String
renderNeuralNetIndividual() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividual: Returns a string that represents the individual This method can be used by class that extends NeuralNetIndividual
renderNeuralNetIndividual(<any>, IEvaluator) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.listener.NeuralNetReporterRegr: Renders a NeuralNetIndividual to a String
RENN - Class in keel.Algorithms.Instance_Selection.RENN: File: RENN.java The RENN Instance Selection algorithm.
RENN(String) - Constructor for class keel.Algorithms.Instance_Selection.RENN.RENN: Default constructor.
RENN - Class in keel.Algorithms.Preprocess.Instance_Selection.RENN: File: RENN.java The RENN Instance Selection algorithm.
RENN(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.RENN.RENN: Default constructor.
reorderInstances() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowingILAS
rep - Variable in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.Cromosoma: this vector represents a chromosome
repetitionsRuleOrdering - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
Replace - Class in keel.Algorithms.Associative_Classification.ClassifierCBA: This class contains the representation of the "Replace" structure .
Replace() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.Replace: Default Constructor.
Replace(int, int, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.Replace: Parameters Constructor.
Replace - Class in keel.Algorithms.Associative_Classification.ClassifierCBA2: This class contains the representation of the "Replace" structure
Replace() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.Replace: Default Constructor
Replace(int, int, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.Replace: Parameters Constructor
replace(double[], myDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual: It generates a tree that matches the given example
replaceIndividuals(int) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation: Removes worse _numIndividuals nets from Population and includes individuals from subpopulation
replacementAlgorithm() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.geneticAlgorithm
replacementPolicy(Classifier[], boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.GA
replacementPolicy(Classifier[], boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.GA
replaceMissingValues(double[]) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance: Replaces all missing values in the instance with the values contained in the given array.
replaceMissingValues(double[]) - Method in class keel.Algorithms.Decision_Trees.M5.M5SparseInstance: Replaces all missing values in the instance with the values contained in the given array.
replaceMissingValues(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance: Replaces all missing values in the instance with the values contained in the given array.
replaceMissingValues(double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset: Replaces all missing values in the instance with the values contained in the given array.
replaceMissingValues(double[]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance: Replaces all missing values in the instance with the values contained in the given array.
replaceMissingValues(double[]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance: Replaces all missing values in the instance with the values contained in the given array.
replaceMissingValues(double[]) - Method in class keel.Algorithms.SVM.SMO.core.Instance: Replaces all missing values in the instance with the values contained in the given array.
ReplaceMissingValuesFilter - Class in keel.Algorithms.Decision_Trees.M5: Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
ReplaceMissingValuesFilter() - Constructor for class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter
replaceNode(int, Node) - Method in class keel.GraphInterKeel.experiments.Graph: Replace a node
replaceSubstring(String, String, String) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Replaces with a new string, all occurrences of a string from another string.
replaceSubstring(String, String, String) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Replaces with a new string, all occurrences of a string from another string.
replaceSubstring(String, String, String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Replaces with a new string, all occurrences of a string from another string.
replaceSubstring(String, String, String) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Replaces with a new string, all occurrences of a string from another string.
replaceSubstring(String, String, String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Replaces with a new string, all occurrences of a string from another string.
replaceSubstring(String, String, String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Replaces with a new string, all occurrences of a string from another string.
replaceSubstring(String, String, String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Replaces with a new string, all occurrences of a string from another string.
replaceSubstring(String, String, String) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Replaces with a new string, all occurrences of a string from another string.
replaceSubstring(String, String, String) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Replaces with a new string, all occurrences of a string from another string.
replaceTerminals(FuzzyAlphaCut[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node: This method replace the terminal from fuzzy alpha cuts
replaceTerminals(FuzzyAlphaCut[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeVariable: This method replaces the fuzzy alpla cuts in a especified node
replacing_poc_nn(PrototypeSet, double, double) - Method in class keel.Algorithms.Instance_Generation.POC.POCGenerator
repmat(DenseMatrix, int, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: MATLAB - Replicate and tile an array
repmat(DenseMatrix, int, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.LLSImpute: Replicate and tile an array.
report(String, IDataset, ArrayList<ArrayList<IInstance>>, double[][], int) - Method in class keel.Algorithms.MIL.APR.AbstractAPR
report(String, IDataset, ArrayList<ArrayList<IInstance>>, double[][], int, ArrayList<Integer>) - Method in class keel.Algorithms.MIL.APR.AbstractAPR
ReportTool - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning: File: ReportTool.java Class to print reports about the results of the classification process
ReportTool() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.ReportTool
ReportTool - Class in keel.Algorithms.RST_Learning: File: ReportTool.java Class to print reports about the results of the classification process
ReportTool() - Constructor for class keel.Algorithms.RST_Learning.ReportTool
REPRESENTATION - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
Representation - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: A classifer can contain three types of representations: ternary representatiton (each alelle can take 3 possible values, 0, 1 or don't care), real representation, where each alelle can take any real value, and the mixed one, that can take character or real representation for each alelle.
Representation(double[]) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: It creates a new representation with the specified condition and a random action.
Representation(double[], int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: It creates a new representation with the specified condition and action.
Representation(Representation) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: It creates a new representation that is a clone of the representation passed.
Representation(StringTokenizer) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation: It creates a new representation that is a clone of the representation passed.
Reproduccion(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic: Reproductdion schema Replaces the worst individuals with the generated by cross and mutation
reproduction(PrototypeSet, PrototypeSet[][]) - Method in class keel.Algorithms.Instance_Generation.ENPC.ENPCGenerator: Reproduction operator.
REPSO - Class in keel.Algorithms.PSO_Learning.REPSO: Title: Algorithm REPSO Description: It contains the implementation of the algorithm Company: KEEL
REPSO() - Constructor for class keel.Algorithms.PSO_Learning.REPSO.REPSO: Default constructor
REPSO(parseParameters) - Constructor for class keel.Algorithms.PSO_Learning.REPSO.REPSO: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
RequestReevaluation(int, int) - Static method in class keel.Algorithms.Instance_Selection.CoCoIS.CoCoIS: Deletes the fitness value of every member which contains the given selector in the subpopulation selected
RequestReevaluation(int, int) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.CoCoIS: Deletes the fitness value of every member which contains the given selector in the subpopulation selected
resample(Random) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Creates a new dataset of the same size using random sampling with replacement.
resample(Randomize) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Creates a new dataset of the same size using random sampling with replacement.
resample(Random) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Creates a new dataset of the same size using random sampling with replacement.
resample() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Return a prototype set by Bootstrapping the current PrototypeSet Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
resample(Random) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Creates a new dataset of the same size using random sampling with replacement.
resampleWithWeights(Random) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Creates a new dataset of the same size using random sampling with replacement according to the current instance weights.
resampleWithWeights(Random, double[]) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances: Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
resampleWithWeights(Randomize) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Creates a new dataset of the same size using random sampling with replacement according to the current instance weights.
resampleWithWeights(Randomize, double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances: Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
resampleWithWeights(Random) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Creates a new dataset of the same size using random sampling with replacement according to the current instance weights.
resampleWithWeights(Random, double[]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances: Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
resampleWithWeights(PrototypeSet, int, boolean[]) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ADE_CoForestGenerator: Resample instances w.r.t the weight
resampleWithWeights(PrototypeSet, int, boolean[]) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCGenerator: Resample instances w.r.t the weight
resampleWithWeights(PrototypeSet, int, boolean[]) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.CoForestGenerator: Resample instances w.r.t the weight
resampleWithWeights(Random) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Creates a new dataset of the same size using random sampling with replacement according to the current instance weights.
resampleWithWeights(Random, double[]) - Method in class keel.Algorithms.SVM.SMO.core.Instances: Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
resbundle - Variable in class keel.GraphInterKeel.experiments.Credits
reservaVelocidad(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Particula: Reserves memory enough to manage the particle velocity.
reset() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual: Resets the gene with the same value.
reset() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual
reset() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask: Deactivates the entry pointed by the cursor
reset(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask: Deactivates the value of a given position.
reset() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Deactivates the entry pointed by the cursor
reset(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Deactivates the value of a given position.
reset() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Roulette: Resets the roulette (puts all the probabilities to 0).
reset() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Roulette: Resets the roulette (puts all the probabilities to 0).
reset() - Static method in class keel.Algorithms.Instance_Generation.utilities.Debug: Reset both debug modes (set them to false).
reset() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset: Reset dataset
reset() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArffDataSet: Reset dataset
reset() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArrayDataset: Reset dataset
reset() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.FileDataset: Reset dataset
reset() - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IDataset: Reset dataset
reset() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.KeelDataSet: Reset dataset
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.FreqList: Reset the iterator to the beginning of the list.
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.FreqList: Reset the iterator to the beginning of the list.
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.valueAssociations: Resets the iterator of the list to the beginning
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.FreqList: Reset the iterator to the beginning of the list.
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.valueAssociations: Resets the iterator of the list to the beginning
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqList: Reset the iterator to the beginning of the list.
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqListPair: Reset the iterator to the beginning of the list.
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Histogram: Resets the statisticals variables.
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.LinearRegression: Resets the statisticals variables.
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Sample: Resets the statisticals variables.
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.valueAssociations: Resets the iterator of the list to the beginning
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.FreqList: Reset the iterator to the beginning of the list.
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.valueAssociations: Resets the iterator of the list to the beginning
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.FreqList: Reset the iterator to the beginning of the list.
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.valueAssociations: Resets the iterator of the list to the beginning
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.FreqList: Reset the iterator to the beginning of the list.
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.valueAssociations: Resets the iterator of the list to the beginning
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.FreqList: Reset the iterator to the beginning of the list.
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.FreqList: Reset the iterator to the beginning of the list.
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.valueAssociations: Resets the iterator of the list to the beginning
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.FreqList: Reset the iterator to the beginning of the list.
reset() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.valueAssociations: Resets the iterator of the list to the beginning
reset() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask: Deactivates the entry pointed by the cursor
reset(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask: Deactivates the value of a given position.
reset() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Deactivates the entry pointed by the cursor
reset(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Deactivates the value of a given position.
reset() - Method in class keel.Algorithms.Rule_Learning.PART.Mask: Deactivates the entry pointed by the cursor
reset(int) - Method in class keel.Algorithms.Rule_Learning.PART.Mask: Deactivates the value of a given position.
reset() - Method in class keel.Algorithms.Rule_Learning.Ripper.Mask: Deactivates the entry pointed by the cursor
reset(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Mask: Deactivates the value of a given position.
reset() - Method in class keel.Algorithms.Rule_Learning.Slipper.Mask: Deactivates the entry pointed by the cursor
reset(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Mask: Deactivates the value of a given position.
reset() - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Debug: Reset both debug modes (set them to false).
resetAmp(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual
resetBest() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.geneticAlgorithm
resetBestStats() - Static method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.timerEvolutionStats
resetBestStats() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Statistics
resetBestStats() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Statistics
resetClassification(Dataset) - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Function to reset the classification of the model.
resetClassification(Dataset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to reset the classification of the model.
resetClassification(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Function to reset the classification of the model.
resetClassification(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Function to reset the classification of the model.
resetClassification(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to reset the classification of the model.
resetClassification(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Function to reset the classification of the model.
resetClassification(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Function to reset the classification of the model.
resetClassification(MyDataset) - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Function to reset the classification of the model.
resetClassification(Dataset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Function to reset the classification of the model.
resetData() - Method in class keel.GraphInterKeel.statistical.statTableModel: Resets data of the table to defaukt values
resetIndex() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask: Sets the cursor at atBegin (that's a non valid position and it will be necessary a next() to reach the first active position).
resetIndex() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Mask: Sets the cursor at atBegin (that's a non valid position and it will be necessary a next() to reach the first active position).
resetIndex() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask: Sets the cursor at atBegin (that's a non valid position and it will be necessary a next() to reach the first active position).
resetIndex() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask: Sets the cursor at atBegin (that's a non valid position and it will be necessary a next() to reach the first active position).
resetIndex() - Method in class keel.Algorithms.Rule_Learning.PART.Mask: Sets the cursor at atBegin (that's a non valid position and it will be necessary a next() to reach the first active position).
resetIndex() - Method in class keel.Algorithms.Rule_Learning.Ripper.Mask: Sets the cursor at atBegin (that's a non valid position and it will be necessary a next() to reach the first active position).
resetIndex() - Method in class keel.Algorithms.Rule_Learning.Slipper.Mask: Sets the cursor at atBegin (that's a non valid position and it will be necessary a next() to reach the first active position).
resetPerformance() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
resetPerformance(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PerformanceAgent
resetPerformance() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
resetPerformance(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PerformanceAgent
resetPerformanceTest(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PerformanceAgent: The test stage computes more statistics.
resetPerformanceTest(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PerformanceAgent: The test stage computes more statistics.
resetQueue() - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter: Clears the output queue.
resetQueue() - Method in class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter: Resets the output queue.
resetTime() - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.Timer: Resets the time counter
resetTime() - Static method in class keel.Algorithms.RST_Learning.Timer: Resets the time counter
resetTokens() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome: Reset the count of captured tokens for this rule in the tokens competition
resizeInputData(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining: Recasts the input data sets so that only N percent is used.
resizeTable(int, int) - Method in class keel.GraphInterKeel.statistical.statTableModel: Resizes a table
ResourceAllocation(String[][], double[], String[], double) - Method in class keel.Algorithms.Instance_Generation.PSCSA.PSCSAGenerator: To minimize the computaional cost in generation clones
resta(double, double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Discretizacion: Substracts b to a.
resta(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
resta(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
resta(Interval) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.Interval
resta(Interval) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.Interval
resta(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
resta(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
resta(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
resta(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
resta(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
restar(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet: Subtracts two prototype sets, element by element.
restar(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet: Restar dos conjuntos de prototipos , uno a uno.
restart() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.instanceSet
ReStart() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion: Function which restart the population
ReStart() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion: Function which restart the population
ReStart() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion: Function which restart the population
ReStart() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion: Function which restart the population
ReStart() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion: Function which restart the population
ReStart() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion: Function which restart the population
ReStart() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion: Function which restart the population
ReStart() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion: Function which restart the population
restoreDataSet() - Method in class keel.GraphInterKeel.experiments.Graph: Restore the data set node from a previous backup
Result - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate: Used to store the quality measures for the generated rule
Result() - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Result: Creates a new instance of Result
Result - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate: Used to store the quality measures for the generated rule
Result() - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Result: Creates a new instance of Result
Result - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate: Used to store the quality measures for the generated rule
Result() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Result: Creates a new instance of Result
result_class - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
Resultado - Class in keel.Algorithms.Genetic_Rule_Learning.Hider
Resultado(int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Resultado
resultFileName - Static variable in class keel.Algorithms.Decision_Trees.C45.Algorithm: The name of the result file.
resultFileName - Static variable in class keel.Algorithms.Decision_Trees.CART.RunCART: The name of the result file.
resultFileName - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm: The name of the result file.
resultFileName - Static variable in class keel.Algorithms.Decision_Trees.ID3.Algorithm: The name of the result file.
resultFileName - Static variable in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm: Results file's name.
resultFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm: The name of the result file.
resultFileName - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm: The name of the result file.
resultFileName - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm: The name of the result file.
resultFileName - Static variable in class keel.Algorithms.Rule_Learning.ART.Algorithm: The name of the result file.
resultFileName - Static variable in class keel.Algorithms.Rule_Learning.C45Rules.Algorithm: The name of the result file.
resultFileName - Static variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Algorithm: The name of the result file.
resultFileName - Static variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm: The name of the result file.
resultFileName - Static variable in class keel.Algorithms.Rule_Learning.PART.Algorithm: The name of the result file.
ResultPrinter - Class in keel.Algorithms.Decision_Trees.CART: Class to print the results of the CART algorithm
ResultPrinter() - Constructor for class keel.Algorithms.Decision_Trees.CART.ResultPrinter
Results - Class in keel.Algorithms.Decision_Trees.M5: Class for containing the evaluation results of a model
Results(int, int) - Constructor for class keel.Algorithms.Decision_Trees.M5.Results: Constructs an object which could contain the evaluation results of a model
Results - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules: Class for containing the evaluation results of a model
Results(int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Results: Constructs an object which could contain the evaluation results of a model
results(double[], double[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Writes the result file with expected and obtained data for modelling problems.
results(int[], int[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Writes the result file with expected and obtained data for classification problems.
results(double[][], int[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig: Writes the result file with pattern and obtained data for clustering problems.
results(double[], double[]) - Method in class keel.Algorithms.Shared.Parsing.ProcessConfig: Writes the result file with expected and obtained data for modelling problems.
results(int[], int[]) - Method in class keel.Algorithms.Shared.Parsing.ProcessConfig: Writes the result file with expected and obtained data for classification problems.
results(double[][], int[]) - Method in class keel.Algorithms.Shared.Parsing.ProcessConfig: Writes the result file with pattern and obtained data for clustering problems.
reTuneParameters(int) - Method in class keel.Algorithms.Lazy_Learning.IDIBL.IDIBL: Finds best parameters for classification
returnNumberInstances() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Returns an Array with the number of instances that belong to each class.
returnNumberInstances() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: It returns the number of instances
returnNumberInstances() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It returns the number of instances in the dataset for each class.
returnNumberInstances() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: It returns the number of instances in the dataset for each class.
returnNumFeatures() - Method in class keel.Algorithms.Preprocess.Feature_Selection.Datos: Returns the number of features of the datasets
returnNumInstances() - Method in class keel.Algorithms.Preprocess.Feature_Selection.Datos: Returns the number of instances of the datasets
returnRanks() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
returnRanks() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
returnRanks() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset: It returns the ranks of the input and output variables
returnRanks() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
returnRanks() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
returnRanks() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
returnRanks() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset: Returns the minimum and maximum values of every attributes as a matrix.
rev() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector: Returns the reverse vector
revertNewLines(String) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Reverts \r and \n in a string into carriage returns and new lines.
revertNewLines(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Reverts \r and \n in a string into carriage returns and new lines.
revertNewLines(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Reverts \r and \n in a string into carriage returns and new lines.
revertNewLines(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Reverts \r and \n in a string into carriage returns and new lines.
revertNewLines(String) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Reverts \r and \n in a string into carriage returns and new lines.
revertNewLines(String) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Reverts \r and \n in a string into carriage returns and new lines.
reward(Prototype, Prototype) - Method in class keel.Algorithms.Instance_Generation.DSM.DSMGenerator: Applies a DSMGenerator-reward to prototype m.
reward(Prototype, Prototype) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQ1: Applies a reward to prototype m
reward(Prototype, Prototype) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQ3: Applies LVQ3-reward to prototype m
reward(Prototype, Prototype) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQGenerator: Applies a reward to prototype m
reward(Prototype, Prototype) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQTC: Applies the LVQTC reward to prototype m
reward2(Prototype, Prototype) - Method in class keel.Algorithms.Instance_Generation.LVQ.LVQ3: USING EPSILON parameter.
RFreeMutation - Class in keel.Algorithms.Genetic_Rule_Learning.UCS: This class implements the free mutation.
RFreeMutation() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.RFreeMutation
RFreeMutation - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: This class implements the free mutation.
RFreeMutation() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.RFreeMutation
ridgeTipText() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Returns the tip text for this property
rightChi2Row - Variable in class keel.Algorithms.Discretizers.Khiops.DeltaValue: the chi2 row of the right interval in our boundary
rightInterval - Variable in class keel.Algorithms.Discretizers.Khiops.DeltaValue: the right interval in our boundary
rightInterval - Variable in class keel.Algorithms.Discretizers.MODL.DeltaValue: the right interval in our boundary
rightSide(int, Dataset) - Method in class keel.Algorithms.Decision_Trees.C45.Cut: Function to print the condition satisfied by itemsets in a subset.
rightSide(int, Dataset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to print the condition satisfied by itemsets in a subset.
rightSide(int, MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut: Function to print the condition satisfied by itemsets in a subset.
rightSide(int, Dataset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut: Function to print the condition satisfied by itemsets in a subset.
rightSide(int, Dataset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to print the condition satisfied by itemsets in a subset.
rightSide(int, MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut: Function to print the condition satisfied by itemsets in a subset.
rightSide(int, MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut: Function to print the condition satisfied by itemsets in a subset.
rightSide(int, MyDataset) - Method in class keel.Algorithms.Rule_Learning.PART.Cut: Function to print the condition satisfied by itemsets in a subset.
rightSide(int, Dataset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut: Function to print the condition satisfied by itemsets in a subset.
rightSide2(int, Dataset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut: Function to print the condition satisfied by itemsets in a subset.
rightSide2(int, Dataset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut: Function to print the condition satisfied by itemsets in a subset.
Riona - Class in keel.Algorithms.Rule_Learning.Riona: Main procedures of Rionasd algorithm
Riona() - Constructor for class keel.Algorithms.Rule_Learning.Riona.Riona: Riona default constructor
Riona(String, String, String, String, String, long) - Constructor for class keel.Algorithms.Rule_Learning.Riona.Riona: Riona constructor
Ripper - Class in keel.Algorithms.Rule_Learning.Ripper: Implementation of the classification algorithm Ripper, according to the paper [Cohen95] and the Weka's implementation.
Ripper(parseParameters) - Constructor for class keel.Algorithms.Rule_Learning.Ripper.Ripper: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
ripperK(MyDataset, Mask, Mask) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ripper: It implements the algorithm Ripper2: 1.
ripperMulticlass(MyDataset) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ripper: It implements the algorithm Ripperk itself: 1.
RipperRule - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: This class implements a single rule that predicts specified class.
RipperRule() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule: Constructor
RipperRule(double[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule: Constructor
RISE - Class in keel.Algorithms.Hyperrectangles.RISE: File: RISE.java The RISE Algorithm.
RISE(String) - Constructor for class keel.Algorithms.Hyperrectangles.RISE.RISE: The main method of the class
rmCoveredBySuccessives(Instances, FastVector, int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Static utility function to count the data covered by the rules after the given index in the given rules, and then remove them.
rmdir(String) - Static method in class keel.GraphInterKeel.experiments.FileUtils: Remove all the files in a directory, and then deletes the latter
RMHC - Class in keel.Algorithms.Instance_Selection.RMHC: File: RMHC.java The RMHC Instance Selection algorithm.
RMHC(String) - Constructor for class keel.Algorithms.Instance_Selection.RMHC.RMHC: Default constructor.
RMHC - Class in keel.Algorithms.Preprocess.Instance_Selection.RMHC: File: RMHC.java The RMHC Instance Selection algorithm.
RMHC(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.RMHC.RMHC: Default constructor.
Rmin - Variable in class keel.Algorithms.Instance_Generation.SGP.SGPGenerator
RMini - Class in keel.Algorithms.Genetic_Rule_Learning.RMini
RMini() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.RMini.RMini: Default constructor
RMini(parseParameters) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.RMini.RMini: It reads the data from the input files (training, validation and test) and parse all the parameters from the parameters array.
Rmis - Variable in class keel.Algorithms.Instance_Generation.SGP.SGPGenerator
RMPEnvironment - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: RMPEnvironment.
RMPEnvironment() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.RMPEnvironment: It's the constructor of the class.
RMSDistance(double[], int, double[][], int) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils: Computes the RMSdistance between the given center and the n nearest vectors in a double[][]
RMSDistance(double[], int, double[][], int) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils: Computes the RMSdistance between the given center and the n nearest vectors in a double[][]
RMSDistance(double[], int, double[][], int) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils: Computes the RMSdistance between the given center and the n nearest vectors in a double[][]
RMSDistance(double[], int, double[][], int) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils: Computes the RMSdistance between the given center and the n nearest vectors in a double[][]
RMSDistance(double[], int, double[][], int) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils: Computes the RMSdistance between the given center and the n nearest vectors in a double[][]
RMSDistance(double[], int, double[][], int) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils: Computes the RMSdistance between the given center and the n nearest vectors in a double[][]
RndInitCrom(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.CromCAN: Random initialization of an existing chromosome
RndInitCrom() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.CromDNF: Random initialization of an existing chromosome
RndInitGene() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Gene: Random initialization of an existing gene
RndInitGene() - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Gene: Random initialization of an existing gene
RndInitInd(TableVar, int, String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndCAN: Creates random instance of Canonical individual
RndInitInd(TableVar, int, String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndDNF: Creates rangom instance of DNF individual
RndInitInd(TableVar, int, String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual: Creates random instance of individual
RndInitInd(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndCAN: Creates random instance of Canonical individual
RndInitInd(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndDNF: Creates random instance of DNF individual
RndInitInd(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual: Creates rangom instance of DNF individual
RndInitPop(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population: Random population initialization
RndInitPop(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Population: Biased random population initialization
RNG - Class in keel.Algorithms.Instance_Selection.RNG: File: RNG.java The RNG Instance Selection algorithm.
RNG(String) - Constructor for class keel.Algorithms.Instance_Selection.RNG.RNG: Default constructor.
RNG - Class in keel.Algorithms.Preprocess.Instance_Selection.RNG: File: RNG.java The RNG Instance Selection algorithm.
RNG(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.RNG.RNG: Default constructor.
RNichedMutation - Class in keel.Algorithms.Genetic_Rule_Learning.UCS: This class performs the niched mutation.
RNichedMutation() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.RNichedMutation
RNichedMutation - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: This class performs the niched mutation.
RNichedMutation() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.RNichedMutation
RNN - Class in keel.Algorithms.Instance_Selection.RNN: File: RNN.java The RNN Instance Selection algorithm.
RNN(String) - Constructor for class keel.Algorithms.Instance_Selection.RNN.RNN: Default constructor.
RNN - Class in keel.Algorithms.Preprocess.Instance_Selection.RNN: File: RNN.java The RNN Instance Selection algorithm.
RNN(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.RNN.RNN: Default constructor.
rnorm(int, double, double, Random) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths: Generates a sample of a normal distribution.
Rom - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig: Apply Rom flag.
root - Variable in class keel.GraphInterKeel.experiments.Experiments
rootMeanPriorSquaredError() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Returns the root mean prior squared error.
rootMeanSquaredError() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Returns the root mean squared error.
rootNode - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.FPtree: Start reference for FP-tree.
rootNode - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.FPtree: Start reference for FP-tree.
rootRelativeSquaredError() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: Returns the root relative squared error if the class is numeric.
Rotation(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary: Given the individual "i", a rotation operation is made.
Rotation(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal
Rotation(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary: Given the individual "i", a rotation operation is made.
Rotation(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary: Given the individual "i", a rotation operation is made.
RoughSetsCuttoff - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets
RoughSetsCuttoff(Instances, int, double) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
RoughSetsCuttoff(Instances, int, int, double) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
RoughSetsOriginal - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets
RoughSetsOriginal(Instances, int, double) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal
Roulette - Class in keel.Algorithms.Genetic_Rule_Learning.UCS: This class implements a generic roulette.
Roulette(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.Roulette: Constructs a roulette.
Roulette - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: This class implements a generic roulette.
Roulette(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Roulette: Constructs a roulette.
Roulette(double[], int, SetupParameters) - Static method in class keel.Algorithms.Neural_Networks.gann.Selector: Roulette selection method
RouletteSelection - Class in keel.Algorithms.Genetic_Rule_Learning.UCS: This class implements Roulette Selection.
RouletteSelection() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.RouletteSelection: Creates a RouletteSelection object.
RouletteSelection - Class in keel.Algorithms.Genetic_Rule_Learning.XCS: This class implements Selection roulette.
RouletteSelection() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.RouletteSelection: Creates a RouletteSelection object.
rouletteSelection() - Method in class keel.Algorithms.Instance_Selection.CoCoIS.Subpopulation: Performs a roulette selection process
rouletteSelection() - Method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.Subpopulation: Performs a roulette selection process
round(double) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Rounds a double to the next nearest integer value.
round(double) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Rounds a double to the next nearest integer value.
round(double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Rounds a double to the next nearest integer value.
round(double) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Rounds a double to the next nearest integer value.
round(double) - Static method in class keel.Algorithms.Instance_Generation.Basic.Prototype: Rounds a number given.
round(double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Rounds a double to the next nearest integer value.
round(double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype: Rounds a value given.
round(double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Rounds a double to the next nearest integer value.
round(double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Rounds a double to the next nearest integer value.
round(double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Rounds a double to the next nearest integer value.
Round(float, float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar: Rounds the generated value for the semantics when necesary
Round(float, float) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar: Rounds the generated value for the semantics when necesary
Round(float, float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar: Rounds the generated value for the semantics when necesary
round(double) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Rounds a double to the next nearest integer value.
round(double, int) - Static method in class keel.GraphInterKeel.experiments.CreateInform: Round a double whith a decimal precision
round(double, int) - Static method in class keel.GraphInterKeel.experiments.EducationalReport: Round a double whith a decimal precision
round(double, int) - Static method in class keel.GraphInterKeel.experiments.EducationalRun: Round a double with a decimal precision determined
roundDouble(double) - Static method in class keel.Algorithms.Decision_Trees.M5.M5: Rounds a double
roundDouble(double, int) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils: Rounds a double to the given number of decimal places.
roundDouble(double, int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils: Rounds a double to the given number of decimal places.
roundDouble(double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5: Rounds a double
roundDouble(double, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils: Rounds a double to the given number of decimal places.
roundDouble(double, int) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils: Rounds a double to the given number of decimal places.
roundDouble(double, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils: Rounds a double to the given number of decimal places.
roundDouble(double, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils: Rounds a double to the given number of decimal places.
roundDouble(double, int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils: Rounds a double to the given number of decimal places.
roundDouble(double, int) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils: Rounds a double to the given number of decimal places.
roundDouble(double, int) - Static method in class keel.Algorithms.SVM.SMO.core.Utils: Rounds a double to the given number of decimal places.
roundDouble(double, int) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AlatasetalProcess: Rounds the number applying the BigDecimal rounding mode given.
roundDouble(double, int) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AprioriProcess: Rounds the number applying the BigDecimal rounding mode given.
roundDouble(double, int) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.EARMGAProcess: Rounds the number applying the BigDecimal rounding mode given.
roundDouble(double, int) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.EclatProcess: Rounds the number applying the BigDecimal rounding mode given.
roundDouble(double, int) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.GARProcess: Rounds the number applying the BigDecimal rounding mode given.
roundDouble(double, int) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.GENARProcess: Rounds the number applying the BigDecimal rounding mode given.
roundDouble(double, int) - Static method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess
roundNum(double) - Static method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel: To round to 4 decimal in a double.
rows - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
rows_tst - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
rset - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm: Individuals to replace
RSFSS - Class in keel.Algorithms.Fuzzy_Rule_Learning.Random_Sets.FSS98: RSFSS is the model to be obtained as the regression model using the fuzzy random sets regression algorithm.
RSFSS(double[][], double[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Random_Sets.FSS98.RSFSS: Class constructor.
RSFSSX2(int, Randomize, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Random_Sets.FSS98.RSFSS: This methods carries out the modelling algorithm based on Random Sets FSS 2000 using lables for the clusters.
RSFSSX3(int, Randomize, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Random_Sets.FSS98.RSFSS: This methods carries out the modelling algorithm based on Random Sets FSS 2000 without using lables for the clusters.
RSPAlgorithm - Class in keel.Algorithms.Instance_Generation.RSP: PSO algorithm calling.
RSPAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.RSP.RSPAlgorithm
RSPGenerator - Class in keel.Algorithms.Instance_Generation.RSP
RSPGenerator(PrototypeSet, int, String) - Constructor for class keel.Algorithms.Instance_Generation.RSP.RSPGenerator: Build a new PSOGenerator Algorithm
RSPGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.RSP.RSPGenerator: Build a new RSPGenerator Algorithm
RSTAlgorithm - Class in keel.Algorithms.RST_Learning: File: RSTAlgorithm.java Main class for RST methods.
RSTAlgorithm() - Constructor for class keel.Algorithms.RST_Learning.RSTAlgorithm
RSTData - Class in keel.Algorithms.RST_Learning: File: RSTData.java RSTData utility class
RSTData() - Constructor for class keel.Algorithms.RST_Learning.RSTData
RSW(Classifier[], int[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.GA
rt - Variable in class keel.GraphInterKeel.experiments.EducationalPartitionsRun: This class uses a Runkeetxt object with a thread.
RT2(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.ICPL.ICPLGenerator: Retaining Border instances
rul_file - Variable in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Param: Auxiliary input file with rules
rul_file - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Param: Auxiliary input file with rules
rul_file - Variable in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Param: Auxiliary input file with rules
Rule - Class in keel.Algorithms.Associative_Classification.ClassifierCBA: Class to store a non-fuzzy rule, together with some necessary information to manage the CBA algorithm.
Rule(Rule) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: Copy Constructor.
Rule(DataBase) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule: Parameters Constructor.
Rule - Class in keel.Algorithms.Associative_Classification.ClassifierCBA2: Class to store a non-fuzzy rule, together with some necessary information to manage the CBA algorithm
Rule(Rule) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: Copy Constructor
Rule(DataBase) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule: Parameters Constructor
Rule - Class in keel.Algorithms.Associative_Classification.ClassifierCPAR: Class to store a non-fuzzy rule, together with some necessary information to manage the CBA algorithm
Rule() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCPAR.Rule: Copy Constructor
Rule(DataBase) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCPAR.Rule: Parameters Constructor
Rule(myDataset, DataBase, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCPAR.Rule: Parameters Constructor
Rule - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR: Codifies a Fuzzy Rule
Rule(Rule) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule: Copy Constructor
Rule(DataBase) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule: Parameters Constructor
Rule - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD: Title: Rule Description: This class codes a Fuzzy Rule Copyright: KEEL Copyright (c) 2008 Company: KEEL
Rule(Rule) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Rule: Create a rule with another one
Rule(DataBase) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Rule: Create a new rule
Rule - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA: Codifies a Fuzzy Rule
Rule(Rule) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Rule: Copy Constructor
Rule(DataBase) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Rule: Parameters Constructor
Rule - Class in keel.Algorithms.Decision_Trees.C45_Binarization: Title: Rule Description: Rule representation Company: KEEL
Rule() - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.Rule: Default constructor
Rule(String, myDataset) - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.Rule: Constructor with parameters.
Rule(myDataset, String) - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.Rule: Constructor with parameters.
Rule - Class in keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW: This class contains the structure of a Fuzzy Rule
Rule() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Rule: Default constructor
Rule(int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Rule: Constructor with parameters
Rule - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD: Codifies a Fuzzy Rule
Rule(Rule) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule: Copy Constructor
Rule(DataBase, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule: Constructor with parameters
Rule - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid: Title: Rule Description: Codifies a Fuzzy Rule Copyright: KEEL Copyright (c) 2008 Company: KEEL
Rule(Rule) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Rule: Copy Constructor
Rule(DataBase, int, boolean[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Rule: Constructor with parameters
Rule - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH: Title: Rule Description: Fuzzy Rule in the GP-COACH algorithm Company: KEEL
Rule() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Default constructor
Rule(DataBase, int, int, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Constructor with parameters.
Rule(DataBase, int, int, int, int, double[], int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Constructor with parameters.
Rule(Rule) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule: Copy constructor for a Fuzzy rule from another Fuzzy Rule
Rule - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS: Title: Rule Description: This class codes a Fuzzy Rule Copyright: KEEL Copyright (c) 2008 Company: KEEL
Rule(Rule) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule: Create a rule with another one
Rule(DataBase) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule: Create a new rule
Rule - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: Abstract class of generic rule
Rule() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Rule
Rule - Class in keel.Algorithms.Genetic_Rule_Learning.DMEL
Rule() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.DMEL.Rule
Rule(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.DMEL.Rule: Creates an empty rule
Rule(Condition[]) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.DMEL.Rule: Creates a rule that includes a set of conditions
Rule(Rule) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.DMEL.Rule: Creates a copy of a rule
Rule - Class in keel.Algorithms.Genetic_Rule_Learning.GIL
Rule() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule
Rule(myDataset) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule: Creates a random rule
Rule(myDataset, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule: Creates a rule that matches with a certain example
Rule(Rule) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule: Creates a copy of a rule
Rule - Class in keel.Algorithms.Genetic_Rule_Learning.ILGA: Represents one rule as specified by the OIGA Algorithm
Rule() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.ILGA.Rule: Default constructor.
Rule(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.ILGA.Rule: Constructor with the number of attributes specified
Rule(Rule) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.ILGA.Rule: Deep-copy constructor
Rule - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules: Representation of a string of simple rules chained by 'and's: exemple[a1][=|>|<=]v1 && exemple[a2][=|>=|<=]v2
Rule() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule: Constructs an empty rule.
Rule - Class in keel.Algorithms.Genetic_Rule_Learning.OIGA: Represents one rule as specified by the OIGA Algorithm
Rule() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OIGA.Rule: Default constructor.
Rule(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OIGA.Rule: Constructor with the number of attributes specified
Rule(Rule) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OIGA.Rule: Deep-copy constructor
Rule - Class in keel.Algorithms.Genetic_Rule_Learning.PART: Representation of a string of simple rules chained by 'and's: exemple[a1][=|>|<=]v1 && exemple[a2][=|>=|<=]v2
Rule() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.Rule: Constructs an empty rule.
Rule - Class in keel.Algorithms.Genetic_Rule_Learning.SIA: Title: Regla Description: It defines a Rule for the SIA algorithm Company: KEEL
Rule(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.SIA.Rule: Default builder
Rule(int, int, double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.SIA.Rule: Rule Builder
Rule - Class in keel.Algorithms.Hyperrectangles.BNGE: File: Rule.java Auxiliary class to repressent rules for the BNGE algorithm
Rule() - Constructor for class keel.Algorithms.Hyperrectangles.BNGE.Rule: Default builder.
Rule(double[], int) - Constructor for class keel.Algorithms.Hyperrectangles.BNGE.Rule: Builder.
Rule - Class in keel.Algorithms.Hyperrectangles.INNER: File: Rule.java Auxiliary class to repressent rules for the INNER algorithm
Rule() - Constructor for class keel.Algorithms.Hyperrectangles.INNER.Rule: Default builder.
Rule(double[], int) - Constructor for class keel.Algorithms.Hyperrectangles.INNER.Rule: Builder.
Rule - Class in keel.Algorithms.Hyperrectangles.RISE: File: Rule.java Auxiliary class to repressent rules for the RISE algorithm
Rule() - Constructor for class keel.Algorithms.Hyperrectangles.RISE.Rule: Default builder.
Rule(double[], int) - Constructor for class keel.Algorithms.Hyperrectangles.RISE.Rule: Builder.
Rule - Class in keel.Algorithms.ImbalancedClassification.Ensembles: Title: Rule Description: Rule representation Company: KEEL
Rule() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Rule: Default constructor
Rule(String, myDataset) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Rule: Constructor with parameters.
Rule(myDataset, String) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Rule: Constructor with parameters.
Rule - Class in keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H: Title: Rule Description: Fuzzy Rule in the GP-COACH algorithm Company: KEEL
Rule() - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Default constructor
Rule(DataBase, int, int, int, int) - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Constructor with parameters.
Rule(DataBase, int, int, int, int, double[], int) - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Constructor with parameters.
Rule(Rule) - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule: Copy constructor for a Fuzzy rule from another Fuzzy Rule
rule - Class in keel.Algorithms.LQD.methods.FGFS_Original: File: rule.java Properties and functions of the fuzzy rule as obtain the antecedent and the consequent of the rule from the confidence the this rule with the instances
rule(Vector<partition>, int) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Original.rule
rule - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight: File: rule.java Properties and functions of the fuzzy rule as obtain the antecedent and the consequent of the rule from the confidence the this rule with the instances
rule(Vector<partition>, int, int) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
rule - Class in keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty: File: fuzzyRule.java Properties and functions of the fuzzy rule as obtain the antecedent and the consequent of the rule from the confidence the this rule with the instances
rule(Vector<partition>, int, int) - Constructor for class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
Rule - Class in keel.Algorithms.RE_SL_Methods.P_FCS1
Rule(int) - Constructor for class keel.Algorithms.RE_SL_Methods.P_FCS1.Rule: Creates a rule containing "tam" gaussian fuzzy sets
Rule(Rule) - Constructor for class keel.Algorithms.RE_SL_Methods.P_FCS1.Rule: Creates a fuzzy rule as a copy of another fuzzy rule
Rule - Class in keel.Algorithms.Rule_Learning.ART: Class to store a rule
Rule(Vector<Integer>, Vector<Integer>, int, double) - Constructor for class keel.Algorithms.Rule_Learning.ART.Rule
Rule(Vector<Integer>, Vector<Integer>) - Constructor for class keel.Algorithms.Rule_Learning.ART.Rule
Rule - Class in keel.Algorithms.Rule_Learning.C45Rules: Representation of a string of simple rules chained by 'and's: exemple[a1][=|>|<=]v1 && exemple[a2][=|>=|<=]v2
Rule() - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.Rule: Constructs an empty rule.
Rule - Class in keel.Algorithms.Rule_Learning.C45RulesSA: Representation of a string of simple rules chained by 'and's: exemple[a1][=|>|<=]v1 && exemple[a2][=|>=|<=]v2
Rule() - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.Rule: Constructs an empty rule.
Rule - Class in keel.Algorithms.Rule_Learning.PART: Representation of a string of simple rules chained by 'and's: exemple[a1][=|>|<=]v1 && exemple[a2][=|>=|<=]v2
Rule() - Constructor for class keel.Algorithms.Rule_Learning.PART.Rule: Constructs an empty rule.
Rule - Class in keel.Algorithms.Rule_Learning.Ripper: Representation of a string of simple rules chained by 'and's: exemple[a1][=|>|<=]v1 && exemple[a2][=|>=|<=]v2
Rule() - Constructor for class keel.Algorithms.Rule_Learning.Ripper.Rule: Constructs an empty rule.
Rule - Class in keel.Algorithms.Rule_Learning.Slipper: Representation of a string of simple rules chained by 'and's: exemple[a1][=|>|<=]v1 && exemple[a2][=|>=|<=]v2 The rule has also a positive value (confidence) associated.
Rule() - Constructor for class keel.Algorithms.Rule_Learning.Slipper.Rule: Constructs an empty rule.
RuleBase - Class in keel.Algorithms.Associative_Classification.ClassifierCBA: This class contains the representation of a Rule Set.
RuleBase() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.RuleBase: Default Constructor
RuleBase(DataBase, myDataset) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.RuleBase: Parameters Constructor
RuleBase - Class in keel.Algorithms.Associative_Classification.ClassifierCBA2: This class contains the representation of a Rule Set
RuleBase() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.RuleBase: Default Constructor
RuleBase(DataBase, myDataset) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.RuleBase: Parameters Constructor
RuleBase - Class in keel.Algorithms.Associative_Classification.ClassifierCPAR: This class contains the representation of a Rule Set
RuleBase() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCPAR.RuleBase: Default Constructor
RuleBase(DataBase, myDataset, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCPAR.RuleBase: Parameters Constructor
RuleBase - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR: This class contains the representation of a Rule Set
RuleBase() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.RuleBase: Default Constructor
RuleBase(DataBase, myDataset) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.RuleBase: Parameters Constructor
RuleBase - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD: Title: RuleBase Description: This class contains the representation of a Rule Set Copyright: Copyright KEEL (c) 2007 Company: KEEL
RuleBase() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: Default Constructor.
RuleBase(DataBase, myDataset, int, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase: Builder
RuleBase - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA: This class contains the representation of a Rule Set
RuleBase() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.RuleBase: Default Constructor
RuleBase(DataBase, myDataset) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.RuleBase: Parameters Constructor
RuleBase - Class in keel.Algorithms.Decision_Trees.C45_Binarization: Title: RuleBase Description: A full rule set description Company: KEEL
RuleBase() - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.RuleBase: Default constructor.
RuleBase(myDataset, String) - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.RuleBase: To obtain the rule Base from the rule file (extracted from the C4.5 decision tree)
RuleBase - Class in keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW: This class contains the representation of a Rule Set
RuleBase(DataBase, int, int, int, String[], String[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.RuleBase: Rule Base Constructor
RuleBase - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD: This class contains the representation of a Rule Set
RuleBase() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase: Default constructor
RuleBase(DataBase, myDataset, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase: Builds an object for the Rule Base
RuleBase - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid: Title: RuleBase Description: Fuzzy Rule Base Copyright: KEEL Copyright (c) 2008 Company: KEEL
RuleBase() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.RuleBase: Default builder
RuleBase(DataBase, myDataset, int, int, int, double, double) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.RuleBase: Builds an object for the Rule Base
RuleBase(int[], int, DataBase, myDataset, int, int, int, double, double) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.RuleBase: Builds an object for the Rule Base with an heuristic
RuleBase - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS: Title: RuleBase Description: This class contains the representation of a Rule Set Copyright: Copyright KEEL (c) 2007 Company: KEEL
RuleBase() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
RuleBase(DataBase, myDataset, myDataset, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase: Builder
RuleBase - Class in keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy: Represents a FRBS (Fuzzy Rule Base System).
RuleBase(FuzzyPartition[], FuzzyPartition, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase: A constructor for a RuleBase.
RuleBase(RuleBase) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase: A copy constructor for a RuleBase, given other RuleBase.
RuleBase - Class in keel.Algorithms.ImbalancedClassification.Ensembles: Title: RuleBase Description: A full rule set description Company: KEEL
RuleBase() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.RuleBase: Default constructor.
RuleBase(myDataset, String) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.RuleBase: To obtain the rule Base from the rule file (extracted from the C4.5 decision tree)
RuleBase_Sel - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL: It encodes a Rule Base for the simplification process
RuleBase_Sel(String, MyDataset, int, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Sel: Constructor
RuleBase_Sel(int, MyDataset, int, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Sel: Constructor
RuleBase_Tun - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL: It encodes a Rule Base for the tunning process
RuleBase_Tun(String, MyDataset, int, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Tun: Constructor
ruleClass - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.matchProfileAgent
ruleCleaning(int[], int[], int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
ruleCorrelation(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Computes the third heuristic exposed in [Holmes99]
ruleCoversInstance(double[]) - Method in class keel.Algorithms.Rule_Learning.Riona.Complex: Check if the rule match with the parameter instance
ruleDeletionMinRules - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
ruleDeletionMinRules - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
ruleDeviation(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Computes the deviation of a rule from the predicted class values.
ruleDirectedSplit(myDataset, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule
ruleDistance(Rule) - Method in class keel.Algorithms.Hyperrectangles.INNER.Rule: Computes the distance between two rules
ruleExtraction(int) - Method in class keel.Algorithms.Rule_Learning.ART.TBAR
RuleList - Class in keel.Algorithms.Associative_Classification.ClassifierCMAR: Set of utilities to support various Association Rule Mining (ARM) algorithms included in the LUCS-KDD suite of ARM programs.
RuleList(int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList: Default constructor to create an instance of the class RuleList
RuleList.RuleNode - Class in keel.Algorithms.Associative_Classification.ClassifierCMAR: Rule node in linked list of rules (either ARs or CRs).
RuleList.RuleNodeCMAR - Class in keel.Algorithms.Associative_Classification.ClassifierCMAR: Rule node in linked list of rules (either ARs or CRs) for CMAR algorithm.
ruleMatches(int[], InstanceWrapper) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
ruleMeanAbsoluteError(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset: Computes the mean absolute error of a rule for the predicted class values.
ruleNeedsInstances(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ruleOrderAgent
RuleNode(short[], short[], double, double, double) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining.RuleNode: Three argument constructor
RuleNode(short[], short[], double, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining.RuleNode: Three argument constructor
ruleOrderAgent - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS
ruleOrderAgent(int, int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ruleOrderAgent
RuleQualityEvaluation - Class in keel.Algorithms.Hyperrectangles.EACH: To evaluate the rules
RuleQualityEvaluation(RuleSet, EachDataSet, EachDataSet, int[], int[], String[], String[]) - Constructor for class keel.Algorithms.Hyperrectangles.EACH.RuleQualityEvaluation: Calculates the final statistical for a set of rules and a set of data
rules - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
rulesCopy(RuleSet) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.RuleSet
ruleset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV
ruleset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE
ruleset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2: It contains the methods for handling the set of learned rules
ruleset - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0: It contains the methods for handling the set of learned rules
ruleSet - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
RuleSet - Class in keel.Algorithms.Genetic_Rule_Learning.DMEL
RuleSet() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.DMEL.RuleSet
RuleSet(Vector<Rule>) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.DMEL.RuleSet: It initializates a new rule set or chromosome (using rules from a previous level)
RuleSet(RuleSet) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.DMEL.RuleSet: It creates a copy of the r Rule Set
RuleSet - Class in keel.Algorithms.Genetic_Rule_Learning.GIL
RuleSet() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GIL.RuleSet
RuleSet(myDataset, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GIL.RuleSet: It initializates a new rule set or chromosome (randomly and using positive examples)
RuleSet(RuleSet) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GIL.RuleSet: It creates a copy of the r Rule Set
RuleSet - Class in keel.Algorithms.Genetic_Rule_Learning.ILGA: This class represents a set of rules in the OIGA algorithm
RuleSet() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.ILGA.RuleSet: Default constructor.
RuleSet(int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.ILGA.RuleSet: Constructor for a fixed number of rules and attributes
RuleSet(RuleSet) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.ILGA.RuleSet: Deep-copy constructor
Ruleset - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules: Representation of a disjuction of rules with a common consecuent.
Ruleset() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Ruleset: Constructs an empty ruleset.
RuleSet - Class in keel.Algorithms.Genetic_Rule_Learning.OIGA: This class represents a set of rules in the OIGA algorithm
RuleSet() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OIGA.RuleSet: Default constructor.
RuleSet(int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OIGA.RuleSet: Constructor for a fixed number of rules and attributes
RuleSet(RuleSet) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OIGA.RuleSet: Deep-copy constructor
Ruleset - Class in keel.Algorithms.Genetic_Rule_Learning.PART: Representation of a disjuction of rules with a common consecuent.
Ruleset() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.Ruleset: Constructs an empty ruleset.
ruleSet - Class in keel.Algorithms.Genetic_Rule_Learning.SIA: Title: Reglas Description: It defines a Rule-set Company: KEEL
ruleSet() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.SIA.ruleSet: Default Builder
RuleSet - Class in keel.Algorithms.Hyperrectangles.EACH: Set of rules.
RuleSet() - Constructor for class keel.Algorithms.Hyperrectangles.EACH.RuleSet: Constructor
ruleSet - Class in keel.Algorithms.Rule_Learning.AQ: Title: Rule Set Description: Structure to store a complete rule set
ruleSet() - Constructor for class keel.Algorithms.Rule_Learning.AQ.ruleSet: Builder
Ruleset - Class in keel.Algorithms.Rule_Learning.C45Rules: Representation of a disjuction of rules with a common consecuent.
Ruleset() - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.Ruleset: Constructs an empty ruleset.
Ruleset - Class in keel.Algorithms.Rule_Learning.C45RulesSA: Representation of a disjuction of rules with a common consecuent.
Ruleset() - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset: Constructs an empty ruleset.
ruleSet - Class in keel.Algorithms.Rule_Learning.CN2: Title: Rule Set Description: Structure to store a complete rule set
ruleSet() - Constructor for class keel.Algorithms.Rule_Learning.CN2.ruleSet: Builder
Ruleset - Class in keel.Algorithms.Rule_Learning.PART: Representation of a disjuction of rules with a common consecuent.
Ruleset() - Constructor for class keel.Algorithms.Rule_Learning.PART.Ruleset: Constructs an empty ruleset.
Ruleset - Class in keel.Algorithms.Rule_Learning.Ripper: Representation of a disjuction of rules with a common consecuent.
Ruleset() - Constructor for class keel.Algorithms.Rule_Learning.Ripper.Ruleset: Constructs an empty ruleset.
Ruleset - Class in keel.Algorithms.Rule_Learning.Slipper: Representation of a disjuction of rules with a common consecuent.
Ruleset() - Constructor for class keel.Algorithms.Rule_Learning.Slipper.Ruleset: Constructs an empty ruleset.
rulesetForOneClass(double, Instances, double, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA: Build a ruleset for the given class according to the given data
ruleSetText - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm: Rules set text
rulesExchange(RuleSet, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.RuleSet
rulesGeneralization(myDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.RuleSet
rulesIdentifiers - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Rule identifier.
ruleSize - Variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
ruleSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_ADI
ruleSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_GABIL: Computes and maintains global information for the GABIL KR
ruleSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_UBR
ruleSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_ADI
ruleSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_GABIL
ruleSplit(double, double, myDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule
ruleSplitting(int[], int[], int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
RuleStats - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core: This class implements the statistics functions used in the propositional rule learner, from the simpler ones like count of true/false positive/negatives, filter data based on the ruleset, etc.
RuleStats() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Default constructor
RuleStats(Instances, FastVector) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats: Constructor that provides ruleset and data
ruleUncoveredExamples - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams: Variables to control the uncovered examples.
run(double[][], int[], int, int) - Method in class keel.Algorithms.Complexity_Metrics.Statistics: It computes the statistics for the given parameters
run() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ACO: Executes the algorithm.
run() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ACO: Executes the algorithm.
run() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ACO: Executes the algorithm.
run() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ACO: Executes the algorithm.
run() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.BioHEL: Execute the alforithm BioHEL.
run() - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Cogin: Process the training and test files provided in the parameters file to the constructor.
run() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Core: Process the training and test files provided in the parameters file to the constructor.
run() - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.GA: Executes a number of iterations of GA.
run() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Ilga: Runs the ILGA algorithm, with first creates and evolve a single SEM for each attribute.
run() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.SEM: It runs the Single-attribute Evolution Module (SEM) algorithm to obtain a rule set of ONE attribute
run() - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.GA: Executes a number of iterations of GA.
run() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Oiga: Runs the OIGA algorithm, with first creates and evolve a single SEM for each attribute.
run() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.SEM: It runs the Single-attribute Evolution Module (SEM) algorithm to obtain a rule set of ONE attribute
run() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.PsoAco: Executes the algorithm.
run() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.UCS: It runs the system.
run() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.XCS: Execute the algorithm XCS
run() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
run() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.SMOTE: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.ADASYN.ADASYN: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.ADOMS.ADOMS: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.AHCClustering.AHCClustering: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.Borderline_SMOTE.Borderline_SMOTE: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.CNN.CNN: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.CNN_TomekLinks.CNN_TomekLinks: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.CPM.CPM: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.NCL.NCL: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.OSS.OSS: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.RandomOverSampling.RandomOverSampling: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.RandomUnderSampling.RandomUnderSampling: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.Safe_Level_SMOTE.Safe_Level_SMOTE: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SBC.SBC: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE.SMOTE: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_ENN.SMOTE_ENN: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_TomekLinks.SMOTE_TomekLinks: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SPIDER.SPIDER: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SPIDER2.SPIDER2: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.TomekLinks.TomekLinks: The main method of the class that includes the operations of the algorithm.
run() - Method in class keel.Algorithms.Preprocess.Missing_Values.BPCA.BPCA: Runs the BPCA algorithm.
run() - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM: Runs the EM imputation, once the parameters have been set
run() - Method in class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.LLSImpute: Function that runs the LLSImpute over the data sets given in the pattern file in KEEL format
run() - Method in class keel.Algorithms.Preprocess.Missing_Values.SVDimpute.SVDimpute: It runs the SVDI algorithm once the configuration has been readed
run() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.ClassificationFilter: It initializes the partitions from training set
run() - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.CVCommitteesFilter: It initializes the partitions from training set
run() - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.EnsembleFilter: It initializes the partitions from training set
run() - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.INFFC_2STEPS: It initializes the partitions from training set
run() - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.IterativePartitioningFilter: It initializes the partitions from training set
run() - Method in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.PANDA: Executes the algorithm.
run() - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.SaturationFilter: It runs the noise elimination algorithm for multiclass problems
run() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.FPgrowthProcess: It runs the algorithm for mining association rules
run() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.AlcalaetalProcess: It runs the algorithm for mining association rules
run() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.ThreadedStreamHandler
run() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyAprioriProcess: It runs the algorithm for mining association rules
run() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.GeneticFuzzyAprioriProcess: It runs the algorithm for mining association rules
run() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.GeneticFuzzyAprioriDCProcess: It runs the algorithm for mining association rules
run() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AlatasetalProcess: It runs the evolutionary learning for mining association rules
run() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AprioriProcess: It runs the algorithm for mining association rules
run() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.ARMMGAProcess: It runs the algorithm for mining association rules.
run() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.EARMGAProcess
run() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.EclatProcess: It runs the algorithm for mining association rules
run() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.FPgrowthProcess: It runs the algorithm for mining association rules
run() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.GARProcess: It runs the algorithm for mining association rules.
run() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.GENARProcess: It runs the algorithm for mining association rules.
run() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess: It runs the algorithm for mining association rules.
run() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_GhoshProcess: It runs the evolutionary learning for mining association rules
run() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_GoshProcess: It runs the evolutionary learning for mining association rules
run() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.MOPNARProcess: It runs the evolutionary learning for mining association rules
run() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.QAR_CIP_NSGAIIProcess: It runs the evolutionary learning for mining association rules
run() - Method in class keel.Dataset.Main: Run: Testing the Dataset API
run() - Method in class keel.GraphInterKeel.experiments.EducationalPartitionsRun
run2() - Method in class keel.Dataset.Main: Second prove.
runAlgorithm() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
runAlgorithm() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.EUSCHCQstat: It runs the Qstatistic
runButton - Variable in class keel.GraphInterKeel.experiments.Experiments
RunCART - Class in keel.Algorithms.Decision_Trees.CART: Main class for CART algorithm.
RunCART(String, boolean) - Constructor for class keel.Algorithms.Decision_Trees.CART.RunCART: Default constructor
runCHC() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.CHC: Run the CHC algorithm for the data in this population
runCheck(Check, String[]) - Static method in class keel.Algorithms.SVM.SMO.core.Check: runs the CheckScheme with the given options
RunClassificationCART - Class in keel.Algorithms.Decision_Trees.CART.classification: Class to run the CART algorithm
RunClassificationCART(String) - Constructor for class keel.Algorithms.Decision_Trees.CART.classification.RunClassificationCART: Default constructor
runCrossValidation(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.XCSControl: Creates the configuration file for all cross-validation runs
runExpItem - Variable in class keel.GraphInterKeel.experiments.Experiments
runGA() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.BioHEL: Execute the Genetic Algorithm, returning the classifier that has been optimized
runGA(Population, double[], int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.GA: It makes an iteration of the genetic algorithm.
runGA(int, double[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population: Runs the GA if the time since the last application of the GA is greater than the threshold.
runGA(double[], Population, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.GA: It makes an iteration of the genetic algorithm.
runGA(int, double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Runs the GA if the time since the last application of the GA is greater than the threshold.
runkeel - Variable in class keel.GraphInterKeel.experiments.EducationalRunkeelEvent
runkeel - Variable in class keel.GraphInterKeel.experiments.RunkeelEvent
RunkeelEvent<A extends EducationalRunKeelTxt> - Class in keel.GraphInterKeel.experiments
RunkeelEvent(A) - Constructor for class keel.GraphInterKeel.experiments.RunkeelEvent
RunkeelEvent(A, Exception) - Constructor for class keel.GraphInterKeel.experiments.RunkeelEvent
runKeelFinished(EducationalRunkeelEvent<EducationalRunKeelTxt>) - Method in class keel.GraphInterKeel.experiments.EducationalRun: This method is invoqued when the partitions have finished in natural way or not
runKeelFinished(EducationalRunkeelEvent<A>) - Method in interface keel.GraphInterKeel.experiments.IEducationalRunkeelListener: Is invoqued when all partitions have finished
runKeelFinished(RunkeelEvent<A>) - Method in interface keel.GraphInterKeel.experiments.IRunkeelListener
runKeelIterationCompleted(EducationalRunkeelEvent<EducationalRunKeelTxt>) - Method in class keel.GraphInterKeel.experiments.EducationalRun: This method is invoqued when a partition is finished.
runKeelIterationCompleted(EducationalRunkeelEvent<A>) - Method in interface keel.GraphInterKeel.experiments.IEducationalRunkeelListener: Is invoqued when a partition has finished
runKeelIterationCompleted(RunkeelEvent<A>) - Method in interface keel.GraphInterKeel.experiments.IRunkeelListener
runkeeltxt - Class in keel.RunKeelTxt: File: runkeeltxt.java Class to process the execution of a experiment.
runkeeltxt() - Constructor for class keel.RunKeelTxt.runkeeltxt
runMethod(String, String, Instance[], Instance[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.LDA: In this method, a classifier is estimated using Linear Discriminant Analysis
runModel() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.PDFC: Run the model once the parameters have been set by the method config_read()
runModel() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic: Main method for running this class.
runModel() - Method in class keel.Algorithms.SVM.SMO.SMO: Run the model once the parameters have been set by the method config_read()
runModel(InstanceSet, InstanceSet) - Method in class keel.Algorithms.SVM.SMO.SMO: Run the model once the parameters have been set by the method config_read()
running() - Method in class keel.GraphInterKeel.experiments.EducationalDiscretizerReport: This method has to invoque for to create the report.
running() - Method in class keel.GraphInterKeel.experiments.EducationalFSReport: This method has to invoque for to create the report.
running() - Method in class keel.GraphInterKeel.experiments.EducationalISReport: This method has to invoque for to create the report.
running() - Method in class keel.GraphInterKeel.experiments.EducationalMethodReport: This method has to invoque for to create the report.
running() - Method in class keel.GraphInterKeel.experiments.EducationalReport: This method has to invoque for to create the report.
runOutputTimers(int, Classifier[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Timers
runOutputTimers(int, Classifier[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Timers
runPostHoc(double, int[], String[], String, int) - Method in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Friedman: In this method, all possible post hoc statistical test between more than three algorithms results are executed, according to the configuration file
runPostHoc(double, int[], String[], String) - Method in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Multiple: In this method, all possible post hoc statistical test between more than three algorithms results are executed, according to the configuration file
runProcess(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.AdministrativeStaff
runReduction(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population: Runs the compactation algorithm if the time since the last application of the algorithm is greater than the threshold.
RunRegressionCART - Class in keel.Algorithms.Decision_Trees.CART.regression: Class to run the CART algorithm for regression problems
RunRegressionCART(String) - Constructor for class keel.Algorithms.Decision_Trees.CART.regression.RunRegressionCART: Default constructor
runTimers(int, Classifier[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Timers: Manages timers: flags and parameters that are triggered at certain iterations
runTimers(int, Classifier[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Timers
RunType() - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
RUNTYPE - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants





S

SA - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_IEP_FS

 Simulated Annealing Algorithm

SA(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_IEP_FS.SA


 Creates a new instance of SA
 

SA - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_LIU_FS

Simulated Annealing Algorithm

SA(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_LIU_FS.SA


 Creates a new instance of SA
 

SA - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_MI_FS

Simulated Annealing Algorithm

SA(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SA_MI_FS.SA


 Creates a new instance of SA
 

sacaResultadosAFicheros() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ACO

Prints all the results on the output results files.

sacaResultadosAFicheros() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ACO

Prints all the results on the output results files.

sacaResultadosAFicheros() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ACO

Prints all the results on the output results files.

sacaResultadosAFicheros() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ACO

Prints all the results on the output results files.

sacaResultadosAFicheros(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.PsoAco

Prints all the results on the output results files.

SADEAlgorithm - Class in keel.Algorithms.Instance_Generation.SADE

SADE algorithm calling.

SADEAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.SADE.SADEAlgorithm
 
SADEGenerator - Class in keel.Algorithms.Instance_Generation.SADE
 
SADEGenerator(PrototypeSet, int, int, int, int) - Constructor for class keel.Algorithms.Instance_Generation.SADE.SADEGenerator

Build a new SADEGenerator Algorithm

SADEGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.SADE.SADEGenerator

Build a new SADEGenerator Algorithm

Safe_Level_SMOTE - Class in keel.Algorithms.ImbalancedClassification.Resampling.Safe_Level_SMOTE


 File: Safe_Level_SMOTE.java
 

 The Safe Level SMOTE algorithm is an oversampling method used to deal with
 the imbalanced problem.

Safe_Level_SMOTE(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.Safe_Level_SMOTE.Safe_Level_SMOTE


 Constructor of the class.

salida - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo

Output attribute.

salida(myDataset, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.evaluateRuleQuality

It generates a string with the ouput list, <expected output> <method output>

salida - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
 
salida - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
 
salida - Variable in class keel.Algorithms.Preprocess.Basic.Metodo

Output attribute

salida(myDataset) - Method in class keel.Algorithms.Rule_Learning.AQ.evaluateRuleQuality

It generates a string with the output list, that is, <expected output> <output of the method>

salida(myDataset) - Method in class keel.Algorithms.Rule_Learning.CN2.evaluateRuleQuality

It generates a string with the output list, that is, <expected output> <output of the method>

salida(ConjDatos) - Method in class keel.Algorithms.Rule_Learning.Prism.EvaluaCalidadReglas


 Generates a string with the out-put lists
 

salida(ConjDatos, boolean) - Method in class keel.Algorithms.Rule_Learning.UnoR.EvaluaCalidadReglas


 Generates a string with the out-put lists
 

salida(ConjDatos, int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.EvaluaCalidadReglas


 Generates a string with the out-put lists
 

salida(ConjDatos) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.EvaluaCalidadReglas


 Generates a string with the out-put lists
 

salida_r(int) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.AlgGenetic
 
salida_r(int) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.AlgGenetic
 
salir - Variable in class keel.Algorithms.RE_SL_Methods.LEL_TSK.MiDataset
 
salir - Variable in class keel.Algorithms.RE_SL_Methods.MamWM.MiDataset
 
salir - Variable in class keel.Algorithms.RE_SL_Methods.mogulHC.MiDataset
 
salir - Variable in class keel.Algorithms.RE_SL_Methods.mogulIRL.MiDataset
 
salir - Variable in class keel.Algorithms.RE_SL_Methods.mogulSC.MiDataset
 
salir - Variable in class keel.Algorithms.RE_SL_Methods.TSK_IRL.MiDataset
 
salir - Variable in class keel.Algorithms.RE_SL_Postprocess.Mam2TSK.MiDataset
 
salir - Variable in class keel.Algorithms.RE_SL_Postprocess.MamGlobalTunDB.MiDataset
 
salir - Variable in class keel.Algorithms.RE_SL_Postprocess.MamLocalTunRules.MiDataset
 
salir - Variable in class keel.Algorithms.RE_SL_Postprocess.MamSelect.MiDataset
 
salir - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWSelect.MiDataset
 
salir - Variable in class keel.Algorithms.RE_SL_Postprocess.MamWTuning.MiDataset
 
salir - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKLocalTunRules.MiDataset
 
salir - Variable in class keel.Algorithms.RE_SL_Postprocess.TSKSelect.MiDataset
 
same(double[], double[]) - Method in class keel.Algorithms.Coevolution.CoevolutionAlgorithm

Checks if two instances are the same

same(double[], double[]) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.Util

Checks if two instances are the same

same(Chromosome) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome


 Test if two chromosomes are equals by comparing their values
 

same(Gene) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Gene


 Test if two genes are the same, comparing all their values.

same(double[], double[]) - Method in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

Checks if two instances are the same

same(double[], double[]) - Method in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Checks if two instances are the same

same(double[], double[]) - Method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm

Checks if two instances are the same

same(double, double) - Static method in class keel.Algorithms.Preprocess.Missing_Values.BPCA.MachineAccuracy

Checks if the two numbers given are the same within a certain threshold.

same(double, double) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.util.MachineAccuracy

Checks if the two numbers given are the same within a certain threshold.

same(double[], double[]) - Static method in class keel.Algorithms.RST_Learning.Util

Checks if two instances are the same

same(Complex) - Method in class keel.Algorithms.Rule_Learning.AQ.Complex

It checks if a complex is equal to another.

same(Complex) - Method in class keel.Algorithms.Rule_Learning.CN2.Complex

It checks if a complex is equal to another.

sameDistribution(Complex) - Method in class keel.Algorithms.Rule_Learning.AQ.Complex

It checks if two complexes have the same class distribution

sameDistribution(Complex) - Method in class keel.Algorithms.Rule_Learning.CN2.Complex

It checks if two complexes have the same class distribution

sameMissingInputAttributes(Instance, Instance) - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.knnImpute


        Checks if two instances present MVs for the same attributes
 

sameMissingInputAttributes(Instance, Instance) - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.wknnImpute


        Checks if two instances present MVs for the same attributes
 

sample - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
 
Sample - Class in keel.Algorithms.Hyperrectangles.EACH

Stores one data with the form: attribute attribute class

Sample(double[], int, int) - Constructor for class keel.Algorithms.Hyperrectangles.EACH.Sample


 Constructor
 

Sample(int) - Constructor for class keel.Algorithms.Hyperrectangles.EACH.Sample


 Other constructor, more easy
 

sample - Variable in class keel.Algorithms.Neural_Networks.ensemble.EnsembleNetwork

Training data sample

Sample - Class in keel.Algorithms.Neural_Networks.ensemble


 Class that represents a sample of data
 

Sample(int) - Constructor for class keel.Algorithms.Neural_Networks.ensemble.Sample


 Constructor
 

Sample - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat

Statistics class that works as a Sample and computes statistical values as mean, variace, squares sum,...

Sample() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Sample
 
sample(double[][][]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ConjGradNN


 Prints to standard output N-tier Neural Network x 
 
 

sample(double[][][]) - Method in class keel.Algorithms.Shared.ClassicalOptim.ConjGradNN


 Prints to standard output N-tier Neural Network x 
 
 

samplesOfClasses - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper
 
samplesOfClasses - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper
 
Sampling - Class in keel.Algorithms.Discretizers.UCPD


 This class helps managing a sampling without replacement process 
 

Sampling(int) - Constructor for class keel.Algorithms.Discretizers.UCPD.Sampling


 Class constructor
 

Sampling - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
 
Sampling - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist

Helps managing a sampling without replacement process

Sampling(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.Sampling
 
Sampling - Class in keel.Algorithms.Genetic_Rule_Learning.Globals

This class helps managing a sampling without replacement process

Sampling(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Globals.Sampling
 
Sampling - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals

Sampling.java
 This class helps managing a sampling without replacement process

Sampling(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Sampling

Parameter Constructor.

Sampling - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS

Helps managing a sampling without replacement process

Sampling(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Sampling
 
SaturationFilter - Class in keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter


 This class implements the Gamberger's algorithm to remove class noise
 
 

SaturationFilter() - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.SaturationFilter


 Constructor of the class
 

saturationFilter(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.SaturationFilter


 Constructor of the class
 

save - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters

Save at the end of the output

save(String) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet

Save the data in a file (Keel style)

save - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters

Save at the end of the output

save - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters

Save at the end of the output

save - Variable in class keel.Algorithms.Neural_Networks.net.Parameters

Save at the end of the output

Save(String) - Method in class keel.Algorithms.Preprocess.Converter.Importer

Method that creates the output file with KEEL format given as parameter 
 using all the structures built by the start method of not abstract classes.

Save(String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToCsv

Method that creates the output file with CSV format given as parameter 
 using all the structures built by the start method of the Exporter class.

Save() - Method in class keel.Algorithms.Preprocess.Converter.KeelToDb

Method that creates the new SQL database table 
 using all the structures built by the start method of the Exporter class.

Save(String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToDif

Method that creates the output file with DIF format given as parameter 
 using all the structures built by the start method of the Exporter class.

Save(String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToExcel

Method that creates the output file with Excel format given as parameter 
 using all the structures built by the start method of the Exporter class.

Save(String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToHtml

Method that creates the output file with HTLM format given as parameter 
 using all the structures built by the start method of the Exporter class.

Save(String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToPrn

Method that creates the output file with PRN format given as parameter 
 using all the structures built by the start method of the Exporter class.

Save(String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToPropertyList

Method that creates the output file with PropertyList format given as parameter 
 using all the structures built by the start method of the Exporter class.

Save(String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToTxt

Method that creates the output file with TXT format given as parameter 
 using all the structures built by the start method of the Exporter class.

Save(String, String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToUci

Method that creates the output files with UCI format given as parameter 
 using all the structures built by the start method of the Exporter class.

Save(String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToWeka

Method that creates the output file with Weka format given as parameter 
 using all the structures built by the start method of the Exporter class.

Save(String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToXml

Method that creates the output file with XML format given as parameter 
 using all the structures built by the start method of the Exporter class.

Save(String) - Method in class keel.Algorithms.Preprocess.Converter.UciToKeel

Method that creates the output file with KEEL format given as parameter 
 using all the structures built by the start method.

save(String) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Save the data in a file (Keel style)

saveAsExpItem - Variable in class keel.GraphInterKeel.experiments.Experiments
 
saveButton - Variable in class keel.GraphInterKeel.experiments.Experiments
 
SaveEnsemble(String, String) - Method in class keel.Algorithms.Neural_Networks.ensemble.Ensemble


 Save ensemble at file_name
 

saveExperiment(int) - Method in class keel.GraphInterKeel.experiments.Experiments

Stores the experiment to disk

saveExpItem - Variable in class keel.GraphInterKeel.experiments.Experiments
 
saveFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.DataBase


 It stores the data base in a given file
 

saveFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.RuleBase


 It stores the rule base in a given file
 

saveFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.DataBase


 It stores the data base in a given file
 

saveFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.RuleBase


 It stores the rule base in a given file
 

saveFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.DataBase


 It stores the data base in a given file
 

saveFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.DataBase


 It stores the data base in a given file
 

saveFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.RuleBase


 It stores the rule base in a given file
 

saveFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.DataBase


 It stores the data base in a given file
 

saveFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.RuleBase


 It stores the rule base in a given file
 

saveFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.DataBase


 It stores the data base in a given file
 

saveFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase


 It stores the rule base in a given file
 

saveFile(String) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.DataBase


 It stores the data base in a given file
 

saveFile(String, double, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.RuleBase


 It stores the rule base in a given file
 

saveFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.DataBase

It writes the Data Base into an output file

saveFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase

It writes the rule set into a file

saveFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.DataBase
 
saveFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
 
SaveNetwork(String, boolean) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network


 Save network weights to a file
 

SaveNetwork(String, boolean) - Method in class keel.Algorithms.Neural_Networks.gann.Network


 Save network weights to a file
 

SaveNetwork(String, boolean) - Method in class keel.Algorithms.Neural_Networks.gmdh.Network


 Save network weights
 

SaveNetwork(String, long, boolean) - Method in class keel.Algorithms.Neural_Networks.gmdh.sonn


 Saves the network to a file, including the seed
 

SaveNetwork(String, String, boolean) - Method in class keel.Algorithms.Neural_Networks.net.Network


 Save network weights to a file
 

SaveOutputFile(String, double[][], int, String, int, double, double) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network


 Save output data to file
 

SaveOutputFile(String, double[][], int, String, double[], double[]) - Method in class keel.Algorithms.Neural_Networks.ensemble.Ensemble


 Save data in output file
 

SaveOutputFile(String, double[][], int, String) - Method in class keel.Algorithms.Neural_Networks.gann.Network


 Save output data to a file
 

SaveOutputFile(String, double[][], int, String) - Method in class keel.Algorithms.Neural_Networks.gmdh.Network


 Save output data to file
 

SaveOutputFile(String, double[][], int, SetupParameters) - Method in class keel.Algorithms.Neural_Networks.gmdh.sonn


 Saves the output to a file
 

SaveOutputFile(String, double[][], int, String, double[], double[]) - Method in class keel.Algorithms.Neural_Networks.net.Network


 Save output data to file
 

saveReport(ArrayList<AssociationRule>, PrintWriter) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AlatasetalProcess


 It prints out on the given PrintWriter object. relevant information regarding the mined association rules
 

saveReport(ArrayList<AssociationRule>, PrintWriter) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AprioriProcess


 It prints out on the given PrintWriter object. relevant information regarding the mined association rules
 

saveReport(ArrayList<AssociationRule>, PrintWriter) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.ARMMGAProcess


 It prints out on the given PrintWriter object relevant information regarding the mined association rules
 

saveReport(double, PrintWriter) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.EARMGAProcess
 
saveReport(ArrayList<AssociationRule>, PrintWriter) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.EclatProcess
 
saveReport(double, double, PrintWriter) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.GARProcess


 It prints out on the given PrintWriter object relevant information regarding the mined association rules.

saveReport(double, PrintWriter) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.GENARProcess


 It prints out on the given PrintWriter object relevant information regarding the mined association rules.

saveReport(ArrayList<AssociationRule>, PrintWriter) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess
 
saveReport(ArrayList<AssociationRule>, PrintWriter) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_GhoshProcess


 It prints out on the given PrintWriter object relevant information regarding the mined association rules
 

saveReport(ArrayList<AssociationRule>, PrintWriter) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_GoshProcess


 It prints out on the given PrintWriter object relevant information regarding the mined association rules
 

saveReport(ArrayList<AssociationRule>, PrintWriter) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.MOPNARProcess
 
saveReport(ArrayList<AssociationRule>, PrintWriter) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.QAR_CIP_NSGAIIProcess
 
saveResultsOfAccuracyIn(String, int, PrototypeSet, String) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator

Internal function that shows in the screen the parameters of accuracy of the condensation

saveResultsOfAccuracyIn(String, String, PrototypeSet, PrototypeSet, String, boolean) - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator

Internal function that saves the parameters of accuracy of the condensation.

saveResultsOfAccuracyIn(String, String, PrototypeSet, PrototypeSet, String, boolean) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator

Internal function that saves the parameters of accuracy of the condensation.

SaveRuleInterpreted(char[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.ruleset
 
SaveRuleInterpreted_append(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.ruleset


 Writes each rule of the rule base in file "fich".

SaveRuleInterpreted_append(OutputStream) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.ruleset


 Writes each rule of the rule base in file "fich".

SaveRuleInterpreted_append(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.ruleset


 Writes each rule of the rule base in file "fich".

SaveRuleInterpreted_append(OutputStream) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.ruleset


 Writes each rule of the rule base in file "fich".

SaveRuleInterpreted_append(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.ruleset


 Writes each rule of the rule base in file "fich".

saveSelected() - Method in class keel.GraphInterKeel.experiments.DinamicDataset

Method for saving the selected data sets so we can restore them later

saveSelected() - Method in class keel.GraphInterKeel.experiments.SelectData

Method for saving the selected data sets so we can restore them later

SAVG - Class in keel.Algorithms.Instance_Generation.BasicMethods
 
SAVG(PrototypeSet, double) - Constructor for class keel.Algorithms.Instance_Generation.BasicMethods.SAVG

Creates a new SAVG object.

SAVG(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.BasicMethods.SAVG

Creates a new SAVG object.

SAVGAlgorithm - Class in keel.Algorithms.Instance_Generation.BasicMethods

Main class.

SAVGAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.BasicMethods.SAVGAlgorithm
 
SBC - Class in keel.Algorithms.ImbalancedClassification.Resampling.SBC


 File: SBC.java
 

 The SBC algorithm is an undersampling method used to deal with the imbalanced
 problem.

SBC(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SBC.SBC


 Constructor of the class.

SBS - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_IEP_FS

 SBS Algorithm

SBS(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_IEP_FS.SBS


 Creates a new instance of SA
 

SBS - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_LIU_FS

 SBS Algorithm

SBS(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_LIU_FS.SBS


 Creates a new instance of SA
 

SBS - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_MI_FS

 SBS algorithm

SBS(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SBS_MI_FS.SBS


 Creates a new instance of SA
 

scale(double[], double[], double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV


 This static method scale a vector
 

scale(double[][], double[][], double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV


 This static method scales a matrix
 

scale(double[][][], double[][][], double[][][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV


 This static method scales a cubic matrix
 

scale(double[], double, double, double, double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.util.normalizer.Normalizer


 Scale an array of values from a specific domain [unscaledMin, unscaledMax]
 to other domain [scaledMin, scaledMax]
 

scale(double, double, double, double, double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.util.normalizer.Normalizer


 Scale a value from a specific domain [unscaledMin, unscaledMax] to
 other domain [scaledMin, scaledMax]
 

scale() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ConjGradNN


 Scales the input examples values and expected output valued
 
 

scale(double[], double[], double[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV


 Returns a scaled copy of vector a.

scale(double[][], double[][], double[][]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV


 Returns a scaled copy of matrix a.

scale(double[][][], double[][][], double[][][]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV


 Returns a scaled copy of matrix a.

scale() - Method in class keel.Algorithms.Shared.ClassicalOptim.ConjGradNN


 Scales the input examples values and expected output valued
 
 

scale() - Method in class keel.Algorithms.Shared.ClassicalOptim.GCQuad


 Scales the input examples values and expected output valued
 
 

scale(double[], double[], double[]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV


 Returns a scaled copy of vector a.

scale(double[][], double[][], double[][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV


 Returns a scaled copy of matrix a.

scale(double[][][], double[][][], double[][][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV


 Returns a scaled copy of matrix a.

scaleDS(DoubleTransposedDataSet, double[], double[], double[], double[]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.util.normalizer.Normalizer


 Scales a IDataSet using the the maximum and minimum scaled
 and unscaled values specified
 

scaledSocialNetwork - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams

Matrix that stores the scaled social network

scaledTestData - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator

Scaled test DataSet with data to evaluate the individuals

scaledTrainData - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator

Scaled train DataSet with data to evaluate the individuals

ScaleOutputData(Parameters, double, double) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Data


 Tipify all data outputs
 

ScaleOutputData(Parameters, double, double) - Method in class keel.Algorithms.Neural_Networks.gann.Data


 Scale output data
 

ScaleOutputData(Parameters, double, double) - Method in class keel.Algorithms.Neural_Networks.gmdh.Data


 Scale output
 

ScaleOutputData(Parameters, double, double) - Method in class keel.Algorithms.Neural_Networks.net.Data


 Tipify all data outputs
 

scalingFactor - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
 
Scha - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig

Apply Scha flag.

SCOLON - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants

Data Parser Indentifier for semicolon character.

SCOLON - Static variable in interface keel.Dataset.DataParserConstants
 
score(double[], double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
 
Score - Class in keel.Algorithms.Rule_Learning.Ripper

Representation of a Trio's vector.

Score() - Constructor for class keel.Algorithms.Rule_Learning.Ripper.Score

Default constructor.

score(myDataset) - Method in class keel.Algorithms.Rule_Learning.Rules6.Regla

Computes the quality of the rule with the m-probability-estimation.

Score - Class in keel.Algorithms.Rule_Learning.Slipper

Representation of a Trio's vector.

Score() - Constructor for class keel.Algorithms.Rule_Learning.Slipper.Score

Default constructor.

SCORING_FUNCTION - Static variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
scoringFunctionTipText() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
Script - Variable in class keel.Algorithms.Instance_Generation.DROP3LVQ3.DROP3LVQ3
 
Script - Variable in class keel.Algorithms.Instance_Generation.DROP3PSO.DROP3PSO
 
Script - Variable in class keel.Algorithms.Instance_Generation.DROP3SFLSDE.DROP3SFLSDE
 
Script - Variable in class keel.Algorithms.Instance_Generation.ICFLVQ3.ICFLVQ3
 
Script - Variable in class keel.Algorithms.Instance_Generation.ICFPSO.ICFPSO

Configuration file name.

Script - Variable in class keel.Algorithms.Instance_Generation.ICFSFLSDE.ICFSFLSDE
 
Script - Variable in class keel.Algorithms.Instance_Generation.SSMALVQ3.SSMALVQ3
 
Script - Variable in class keel.Algorithms.Instance_Generation.SSMAPSO.SSMAPSO
 
Script - Variable in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE
 
sd() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet

Performs standard desviation operation of the prototype set.

sd() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Performs standard desviation operation of the prototype set.

SD - Class in keel.Algorithms.Subgroup_Discovery.SDAlgorithm

 It is the main class of the SD algorithm

SD(String, String, String, String, String, String, String, String, int, int, float, int) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SD


 Constructs the object of SD_algorithm
 

SDIGA - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA


 SDIGA
 
 
 Algorithm for the discovery of rules describing subgroups
 
 

SDIGA() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.SDIGA
 
searchingIterations - Variable in class keel.Algorithms.Instance_Generation.HYB.HYBGenerator

Iterations of each LVQ3-search for optimal epsilon and window width.

searchPath(String) - Static method in class keel.RunKeelGraph.TestsResults

Search the path of a experiment

secant(DoubleFunc, double, double, double) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Numeric

Computes the secant between the function and the numbers given.

second - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.pair_fi
 
second - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.pair_gf
 
second - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.pair_gg
 
second - Variable in class keel.Algorithms.Instance_Generation.utilities.Pair

Second element of the pair.

second() - Method in class keel.Algorithms.Instance_Generation.utilities.Pair

Get second element of the pair.

second - Variable in class keel.Algorithms.Semi_Supervised_Learning.utilities.Pair

Second element of the pair.

second() - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Pair

Get second element of the pair.

secondChoiceHeuristic(int) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMO

applies heuristic for finding candidate that is expected to lead to
 good gain when applying takeStep together with second candidate.

secondDerivative(UnivariateFunction, double) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.util.NumericalDerivative

determine second derivative

seed - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm

Random seed.

seed - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm

Random seed.

seed - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
seed - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
 
Seed - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
 
seed - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
 
seed - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
 
seed - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

It's the seed of the run

seed - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 It's the seed of the run
 

SEED - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
seed - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

Random seed.

seed - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters

Random seed

SEED - Static variable in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator

Default seed value to the Random Number Generator

seed - Static variable in class keel.Algorithms.Instance_Generation.utilities.RandomGenerator

Seed value.

seed - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Random seed.

seed - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters

Random seed

seed - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters

Random seed

seed - Variable in class keel.Algorithms.Neural_Networks.net.Parameters

Random seed

seed - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm

Random seed.

seed - Variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN

Random seed.

seed - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters

Seed for random purposes.

seed - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Parameters

Seed for random purposes.

seed - Variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN

Random seed.

seed - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters

Seed for random purposes.

seed - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters

Seed for random purposes.

seed - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Parameters

Seed for random purposes.

seed - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters

Seed for random purposes.

seed - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Parameters

Seed for random purposes.

seed - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm

Random seed.

seed - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerCSVM

Seed for random purposes.

seed - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerSMO

Seed for random purposes.

seed - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.ParametersSMO

Seed for random purpose.

SEED - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator

Default seed value to the Random Number Generator

seed - Static variable in class keel.Algorithms.Semi_Supervised_Learning.utilities.RandomGenerator

Seed value.

SEED_TXT - Static variable in class keel.Algorithms.Instance_Generation.utilities.Parameters

Text flag (Seed).

SEED_TXT - Static variable in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters

Configuration text (seed).

seedDefaultValueList - Variable in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator

Default seed list to the Random Number Generator

seedDefaultValueList - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator

Default seed list to the Random Number Generator

seedTipText() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA

Returns the tip text for this property

seedTipText() - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree

Returns the tip text for this property

seedTipText() - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree

Returns the tip text for this property

seedTipText() - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree

Returns the tip text for this property

seedTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer

Returns the tip text for this property

selecDatasets - Variable in class keel.GraphInterKeel.experiments.Experiments
 
selecRandomSet(int, boolean) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator

Extract a random prototype set of the original traning data set.

selecRandomSet(PrototypeSet, int, boolean) - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet

Returns a random subset of the given one as parameter with the size given.

selecRandomSet(int, boolean) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator

Extract a random prototype set of the original traning data set.

select(int, int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Implements computation of the kth-smallest element according
 to Manber's "Introduction to Algorithms".

select(int, int, int, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Implements computation of the kth-smallest element according to Manber's
 "Introduction to Algorithms".

Select() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion

Function which selects the elements of the population

Select() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion

Function which selects the elements of the population

Select() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion

Function which selects the elements of the population

Select() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion

Function which selects the elements of the population

Select() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion

Function which selects the elements of the population

Select() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion

Function which selects the elements of the population

Select() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion

Function which selects the elements of the population

Select() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion

Function which selects the elements of the population

Select() - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic


 Applies the selection schema of the genetic algorithm.

select(int, int, int, int) - Method in class keel.Algorithms.SVM.SMO.core.Instances

Implements computation of the kth-smallest element according
 to Manber's "Introduction to Algorithms".

selectAlg - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
selectAll_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.DinamicDataset

Select all button

selectAll_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectData

Select all button

selectAllC_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.DinamicDataset

Select all button

selectAllC_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectData

Select all button

selectAllC_LQD_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.DinamicDataset

Select all button

selectAllC_LQD_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectData

Select all button

selectAllLQD_C_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.DinamicDataset

Select all button

selectAllLQD_C_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectData

Select all button

selectAllUser_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.DinamicDataset

Select all button

selectAllUser_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.experiments.SelectData

Select all button

SelectCAN(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic


 Applies the selection schema of the genetic algorithm
 

SelectCut - Class in keel.Algorithms.Decision_Trees.C45

Class to select a cut point in a dataset.

SelectCut(int, Dataset) - Constructor for class keel.Algorithms.Decision_Trees.C45.SelectCut

Creates a new cut model.

SelectCut - Class in keel.Algorithms.Decision_Trees.DT_GA.C45

Class to select a cut point in a dataset.

SelectCut(int, Dataset) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.C45.SelectCut

Creates a new cut model.

SelectCut - Class in keel.Algorithms.Genetic_Rule_Learning.PART


 Class to select a cut point in a dataset.

SelectCut(int, MyDataset) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.SelectCut

Creates a new cut model.

SelectCut - Class in keel.Algorithms.ImbalancedClassification.CSMethods.C45CS

Class to select a cut point in a dataset.

SelectCut(int, Dataset) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.SelectCut

Creates a new cut model.

SelectCut - Class in keel.Algorithms.ImbalancedClassification.Ensembles.C45

Class to select a cut point in a dataset.

SelectCut(int, Dataset) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.SelectCut

Creates a new cut model.

SelectCut - Class in keel.Algorithms.Rule_Learning.C45Rules

 Class to select a cut point in a dataset.

SelectCut(int, MyDataset) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.SelectCut

Creates a new cut model.

SelectCut - Class in keel.Algorithms.Rule_Learning.C45RulesSA

Class to select a cut point in a dataset.

SelectCut(int, MyDataset) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.SelectCut

Creates a new cut model.

SelectCut - Class in keel.Algorithms.Rule_Learning.PART

 Class to select a cut point in a dataset.

SelectCut(int, MyDataset) - Constructor for class keel.Algorithms.Rule_Learning.PART.SelectCut

Creates a new cut model.

SelectCut - Class in keel.Algorithms.Semi_Supervised_Learning.Basic.C45

Class to select a cut point in a dataset.

SelectCut(int, Dataset) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.SelectCut

Creates a new cut model.

SelectData - Class in keel.GraphInterKeel.experiments
 
SelectData() - Constructor for class keel.GraphInterKeel.experiments.SelectData

Builder

SelectData(Experiments) - Constructor for class keel.GraphInterKeel.experiments.SelectData

Builder

SelectDNF(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic


 Applies the selection schema of the genetic algorithm
 

Selected - Class in keel.Algorithms.Associative_Classification.ClassifierCBA

This class contains the representation of the "Selected" structure .

Selected() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.Selected

Default Constructor.

Selected(Rule, int, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.Selected

Parameters Constructor

Selected - Class in keel.Algorithms.Associative_Classification.ClassifierCBA2

This class contains the representation of the "Selected" structure 

Selected() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.Selected

Default Constructor

Selected(Rule, int, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.Selected

Parameters Constructor

Selected - Class in keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA

This class contains the representation to select rules

Selected(double, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Selected


 Parameters Constructor
 

SelectedAssociation - Class in keel.Algorithms.Decision_Trees.M5

Represents a selected value from a finite set of values, where each
 value is a Tag (i.e. has some string associated with it).

SelectedAssociation(int, Association[]) - Constructor for class keel.Algorithms.Decision_Trees.M5.SelectedAssociation

Creates a new SelectedAssociation instance.

selectEditor(MouseEvent) - Method in class keel.GraphInterKeel.datacf.util.EachRowEditor


 Establishes select editor
 

selectedN - Variable in class keel.GraphInterKeel.experiments.GraphPanel
 
SelectedTag - Class in keel.Algorithms.SVM.SMO.core

Represents a selected value from a finite set of values, where each
 value is a Tag (i.e. has some string associated with it).

SelectedTag(int, Tag[]) - Constructor for class keel.Algorithms.SVM.SMO.core.SelectedTag

Creates a new SelectedTag instance.

SelectedTag(String, Tag[]) - Constructor for class keel.Algorithms.SVM.SMO.core.SelectedTag

Creates a new SelectedTag instance.

selectedTrainingInstances() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45

Returns a vector including information about which training instances have been selected.

SelectExp - Class in keel.GraphInterKeel.experiments
 
SelectExp(Experiments) - Constructor for class keel.GraphInterKeel.experiments.SelectExp

Builder

selectIndividuals(int, int) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation


 Creates a subPopulation of individuals using the tournament method.

selecting_poc_nn(PrototypeSet, double, double) - Method in class keel.Algorithms.Instance_Generation.POC.POCGenerator

Prototype Selection by Poc-NN algorithm for TWO class classification problem.

selection() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.RuleBase


 Function to eliminate the redundant rules
 

selection(RuleBase, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase

It adds the best rules to a new rule base

Selection() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation


 Selection operator
 

Selection() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation


 Selection operator
 

selection() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Ilga

Applies a roulette wheel selection

selection() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.SEM

Applies a roulette wheel selection

selection() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Oiga

Applies a roulette wheel selection

selection() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.SEM

Applies a roulette wheel selection

Selection - Interface in keel.Algorithms.Genetic_Rule_Learning.UCS

It is the interface for Selection method.

Selection - Interface in keel.Algorithms.Genetic_Rule_Learning.XCS

It is the interface for Selection method.

SELECTION_ALGO - Static variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
selectionAlg - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
selectionAlgorithm() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.geneticAlgorithm
 
selectionAlgorithmTipText() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
selectionPanel1 - Variable in class keel.GraphInterKeel.experiments.Experiments
 
SELECTIONTYPE - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
selective - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator

Selective mutator

selectMethods - Variable in class keel.GraphInterKeel.experiments.Experiments
 
selectModel(Dataset) - Method in class keel.Algorithms.Decision_Trees.C45.SelectCut

Function to select the cut point.

selectModel(Dataset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.SelectCut

Function to select the cut point.

selectModel(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.SelectCut

Function to select the cut point.

selectModel(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.SelectCut

Function to select the cut point.

selectModel(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.SelectCut

Function to select the cut point.

selectModel(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.SelectCut

Function to select the cut point.

selectModel(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.SelectCut

Function to select the cut point.

selectModel(MyDataset) - Method in class keel.Algorithms.Rule_Learning.PART.SelectCut

Function to select the cut point.

selectModel(Dataset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.SelectCut

Function to select the cut point.

Selector - Class in keel.Algorithms.Decision_Trees.C45_Binarization

Title: Selector
 Description: This class implements an attribute condition of a rule
 Company: KEEL 

Selector() - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.Selector

Defalt constructor

Selector(String, String, String) - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.Selector

Parameter constructor

Selector(int, myDataset) - Constructor for class keel.Algorithms.Decision_Trees.C45_Binarization.Selector

Parameter constructor

Selector - Class in keel.Algorithms.Decision_Trees.DT_GA

Title: Selector (Selector).

Selector() - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.Selector

Default Constructor.

Selector(String, String, String) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.Selector

Paramenter constructor.

Selector(int, myDataset) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.Selector

Paramenter constructor.

Selector - Class in keel.Algorithms.Hyperrectangles.EACH

Class to stores selectors with the form (attribute operator values)

Selector(int, int, String[], double[], int) - Constructor for class keel.Algorithms.Hyperrectangles.EACH.Selector


 Class to stores selectors with the form (attribute operator values
 

Selector(int, int, double[], int) - Constructor for class keel.Algorithms.Hyperrectangles.EACH.Selector


 Class to stores selectors with the form (attribute operator values
 

Selector - Class in keel.Algorithms.ImbalancedClassification.Ensembles

Title: Selector
 Description: This class implements an attribute condition of a rule
 Company: KEEL 

Selector() - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Selector

Defalt constructor

Selector(String, String, String) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Selector

Parameter constructor

Selector(int, myDataset) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.Selector

Parameter constructor

Selector - Class in keel.Algorithms.Neural_Networks.gann


 Class Selector.

Selector() - Constructor for class keel.Algorithms.Neural_Networks.gann.Selector


 Empty constructor
 

Selector - Class in keel.Algorithms.Rule_Learning.AQ

Title: Selector
 Description: This class represents a selector, that is, a structure "attribute op value"

Selector(int, int, double) - Constructor for class keel.Algorithms.Rule_Learning.AQ.Selector

Class employed to store selectors in the form
  (attribute operator values)

Selector(int, int, double[]) - Constructor for class keel.Algorithms.Rule_Learning.AQ.Selector

Class employed to store selectors in the form
  (attribute operator values)

Selector - Class in keel.Algorithms.Rule_Learning.CN2

Title: Selector
 Description: This class represents a selector, that is, a structure "attribute op value"

Selector(int, int, double) - Constructor for class keel.Algorithms.Rule_Learning.CN2.Selector

Class employed to store selectors in the form
  (attribute operator values)

Selector(int, int, double[]) - Constructor for class keel.Algorithms.Rule_Learning.CN2.Selector

Class employed to store selectors in the form
  (attribute operator values)

Selector - Class in keel.Algorithms.Rule_Learning.Prism

Class to stores selectors with the form (attribute operator values)

Selector(int, int, double) - Constructor for class keel.Algorithms.Rule_Learning.Prism.Selector


 Class to stores selectors with the form (attribute operator values)
 

Selector(int, int, String, boolean) - Constructor for class keel.Algorithms.Rule_Learning.Prism.Selector


 Class to stores selectors with the form (attribute operator values), where values are nominal
 

Selector(int, int, double[]) - Constructor for class keel.Algorithms.Rule_Learning.Prism.Selector


 Class to stores selectors with the form (attribute operator values
 

Selector - Class in keel.Algorithms.Rule_Learning.Riona

Structure for storing one condition of the antecedent of a rule

Selector(int, int, String[], double[], int, int) - Constructor for class keel.Algorithms.Rule_Learning.Riona.Selector


 Class that stores selectors with the form: (attribute operator values) where values are nominal
 

Selector(int, int, double[], int, int) - Constructor for class keel.Algorithms.Rule_Learning.Riona.Selector


 Class that stores datasets with the form: (attribute operator values)
 

Selector - Class in keel.Algorithms.Rule_Learning.UnoR

Class to stores selectors with the form (attribute operator values)

Selector(int, int, double) - Constructor for class keel.Algorithms.Rule_Learning.UnoR.Selector


 Class to stores selectors with the form (attribute operator values)
 

Selector(int, int, String, double, boolean) - Constructor for class keel.Algorithms.Rule_Learning.UnoR.Selector


 Class to stores selectors with the form (attribute operator values), where values are nominal
 

Selector(int, int, double[], int) - Constructor for class keel.Algorithms.Rule_Learning.UnoR.Selector


 Class to stores selectors with the form (attribute operator values
 

Selector - Class in keel.Algorithms.Subgroup_Discovery.CN2SD

Class to stores selectors with the form (attribute operator values)

Selector(int, int, double) - Constructor for class keel.Algorithms.Subgroup_Discovery.CN2SD.Selector


 Class to stores selectors with the form (attribute operator values)
 

Selector(int, int, double[]) - Constructor for class keel.Algorithms.Subgroup_Discovery.CN2SD.Selector


 Class to stores selectors with the form (attribute operator values), where values are nominal
 

Selector - Class in keel.Algorithms.Subgroup_Discovery.SDAlgorithm

 Structure with the set of complex for the rules

Selector(int, int, double) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Selector


 Constructor
 

Selector(int, int, double[]) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Selector


 
 Constructor for several values
 

Selectos - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec

Title: 

 Description: 

 Copyright: Copyright (c) 2007

 Company: 

Selectos(double, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Selectos

Parameters constructor.

selectPostprocessMethods - Variable in class keel.GraphInterKeel.experiments.Experiments
 
selectPreprocessMethods - Variable in class keel.GraphInterKeel.experiments.Experiments
 
selectRoulette() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Roulette


 Select on position of the roulette and returns it.

selectRoulette() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Roulette


 Select on position of the roulette and returns it.

selectStrategy(double[]) - Method in class keel.Algorithms.Instance_Generation.SADE.SADEGenerator

I use this function to calculate what strategy we must use with the probability strategy we have.

selectTestMethods - Variable in class keel.GraphInterKeel.experiments.Experiments
 
selectVisualizeMethods - Variable in class keel.GraphInterKeel.experiments.Experiments
 
SelfTrainingAlgorithm - Class in keel.Algorithms.Semi_Supervised_Learning.SelfTraining

SelfTraining algorithm calling.

SelfTrainingAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.SelfTraining.SelfTrainingAlgorithm
 
SelfTrainingGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.SelfTraining

This class implements the Self-traning wrapper.

SelfTrainingGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.SelfTraining.SelfTrainingGenerator

Build a new SelfTrainingGenerator Algorithm

SelfTrainingGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.SelfTraining.SelfTrainingGenerator

Build a new SelfTrainingGenerator Algorithm

SEM - Class in keel.Algorithms.Genetic_Rule_Learning.ILGA


 This class implements the SEM algorithm of the OIGA method, which evolves 
 mono-attribute rules.

SEM() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.ILGA.SEM


 Default constructor.

SEM(int, int, int, InstanceSet) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.ILGA.SEM

Parametrized constructor

SEM - Class in keel.Algorithms.Genetic_Rule_Learning.OIGA


 This class implements the SEM algorithm of the OIGA method, which evolves 
 mono-attribute rules.

SEM() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OIGA.SEM


 Default constructor.

SEM(int, int, int, InstanceSet) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.OIGA.SEM

Parametrized constructor

Semantics - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate

This class is defined to manage de semantics of the linguistic variables

Semantics() - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Semantics
 
Semantics - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate

This class is defined to manage de semantics of the linguistic variables

Semantics() - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Semantics
 
Semantics - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate

 This class is defined to manage de semantics of the linguistic variables

Semantics() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Semantics
 
sentences - Variable in class keel.GraphInterKeel.experiments.EducationalReport
 
separationValue - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.ArffDataSet

Symbol which represents the separation between values

separationValue - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.KeelDataSet

Symbol which represents the separation between values

separatorToString() - Static method in class keel.Algorithms.Decision_Trees.M5.M5

Prints sepearating line

SEPErrorFunction - Class in keel.Algorithms.Neural_Networks.NNEP_Regr.problem.errorfunctions

SEP Error Function.

SEPErrorFunction() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Regr.problem.errorfunctions.SEPErrorFunction


 Empty constructor
 

seq(int, int) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector

Generates an IntVector that stores all integers inclusively between
  two integers.

SequenceActiveLabels(String, int, Int_t, Int_t, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
 
SequenceActiveLabels(String, int, Int_t, Int_t, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
 
SequenceActiveLabels(String, int, Int_t, Int_t, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
 
sequentialTest - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

This parameter indicates if the test execution must be a 
 sequential execution (if the enviornment is a file environment),
 or it mustn't.

sequentialTest - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config

This parameter indicates if the test execution must be a 
 sequential execution (if the enviornment is a file environment),
 or it mustn't.

SEQUENTIALTEST - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
SERIAL_VERSION_UID - Static variable in class keel.Algorithms.SVM.SMO.core.SerializationHelper

the field name of serialVersionUID

SerializationHelper - Class in keel.Algorithms.SVM.SMO.core

A helper class for determining serialVersionUIDs and checking whether
 classes contain one and/or need one.

SerializationHelper() - Constructor for class keel.Algorithms.SVM.SMO.core.SerializationHelper
 
SERIALIZED_OBJ_FILE_EXTENSION - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

The filename extension that should be used for bin. serialized instances files

SERIALIZED_OBJ_FILE_EXTENSION - Static variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

The filename extension that should be used for bin. serialized instances
 files

SERIALIZED_OBJ_FILE_EXTENSION - Static variable in class keel.Algorithms.SVM.SMO.core.Instances

The filename extension that should be used for bin. serialized instances files

SerializedObject - Class in keel.Algorithms.Decision_Trees.M5

This class stores an object serialized in memory.

SerializedObject(Object) - Constructor for class keel.Algorithms.Decision_Trees.M5.SerializedObject

Serializes the supplied object into a byte array without compression.

SerializedObject(Object, boolean) - Constructor for class keel.Algorithms.Decision_Trees.M5.SerializedObject

Serializes the supplied object into a byte array.

SerializedObject - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core

Class for storing an object in serialized form in memory.

SerializedObject(Object) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.SerializedObject

Creates a new serialized object (without compression).

SerializedObject(Object, boolean) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.SerializedObject

Creates a new serialized object.

SerializedObject - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets

Class for storing an object in serialized form in memory.

SerializedObject(Object) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SerializedObject

Creates a new serialized object (without compression).

SerializedObject(Object, boolean) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SerializedObject

Creates a new serialized object.

SerializedObject - Class in keel.Algorithms.SVM.SMO.core

Class for storing an object in serialized form in memory.

SerializedObject(Object) - Constructor for class keel.Algorithms.SVM.SMO.core.SerializedObject

Creates a new serialized object (without compression).

SerializedObject(Object, boolean) - Constructor for class keel.Algorithms.SVM.SMO.core.SerializedObject

Creates a new serialized object.

set(FuzzyGAPClassifier) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGAP.FuzzyGAPClassifier

 
 This method copies the given FuzzyGAPClassifier in the current object.

set(FuzzyGPClassifier) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGP.FuzzyGPClassifier

 
 This method copies the given FuzzyGPClassifier in the current object.

set(PittsburghClassifier) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyPittsBurgh.PittsburghClassifier

 
 This method copies the given PittsburghClassifier in the current object.

set(FuzzySAPClassifier) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySAP.FuzzySAPClassifier

 
 This method copies the given FuzzySAPClassifier in the current object.

Set(int, double, double, boolean, boolean, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
 
Set(String, int, String[], double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
 
Set(int, double, double, boolean, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t


 Creates an uniform domain with n labels from inf to sup, cutting in 0.5
 

Set(int, double, double, double[], double[], double[], double[], String[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t


 Creates a domain with n labels in [inf,sup].

Set(double, double, double, double, String, boolean, boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.fuzzy_t


 Assigns a label
 [a,b,c,d] represents a trapezoidal label and "name" is the name of the label.

Set(double, double, double, double, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.fuzzy_t


 Assigns a label
 [a,b,c,d] represents a trapezoidal label and "name" is the name of the label.

Set(int, double, double, boolean, boolean, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t


 Defines a variable in an automatic way.

Set(String, int, String[], double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t


 Defines a variable in an automatic way.

Set(int, String, int, double, double, double[], double[], double[], double[], String[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t


 Defines a variable based in a domain given as an ordered secuence of labels
 ( [a[i],b[i],c[i],d[i]] expresses the definition of the label i)
 "inf" and "sup" are the lower and upper limits, respectively.

Set(int, variable_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar


 Copies the variable "x" in the position "pos" of the list
 

set(FuzzyClassifier) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.FuzzyClassifier

 
 This method copies the given FuzzyClassifier in the current object.

set(FuzzyFGPClassifier) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.FuzzyFGPClassifier

 
 This method copies the given FuzzyFGPClassifier in the current object.

set(GenotypeFuzzyGAP) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGAP


 This method copies the given parameter into the current object.

set(GenotypePitts) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypePitts


 This method copies the given parameter into the current object.

set(FuzzyGAPModelIndividual) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGAPModelIndividual


 This method assing the properties of a fuzzy individual for GAP model to another one
 

set(FuzzyGPModel) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPModel


 This method assign a fuzzy model for GP to anothe one
 

set(FuzzyGPModelIndividual) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPModelIndividual


 This method assing the properties of a fuzzy individual for GP model to another one
 

set(FuzzyGPRegSymModel) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyGPRegSymModel


 This method assing the properties of a fuzzy system of symbolic regression for
 GP model to another one
 

set(FuzzyModel) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.FuzzyModel


 This method asign a fuzzy mothed to another one
 

set(PittsburghModel) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.PittsburghModel


 This method sets the properties of a Pittsburgh model to another one
 

set(RegSymFuzzyGP) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.RegSymFuzzyGP


 This method assing the properties of a fuzzy system of symbolic regession to another one
 

set(Node) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node


 This method asign a node the porperties from another
 

set(NodeAdd) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeAdd


 This method sets to a NodeAdd the properties from another
 

set(NodeAnd) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeAnd


 This method asign to to a NodeAnd the properties of another
 

set(NodeConsequent) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeConsequent


 This method asign to a NodeConsequente the properties of another
 

set(NodeExp) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeExp


 This method asign to a NodeExp the properties from another 
 

set(NodeExprHold) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeExprHold


 This method sets the node properties to another one
 

set(NodeIs) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeIs


 This method sets to a NodeIs the properties from another 
 

set(NodeLabel) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeLabel


 This method asing to a NodeLabel the properties from another
 

set(NodeLog) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeLog


 This method assign a log node to another one
 

set(NodeMinus) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeMinus


 This method sets a minus node to another one
 

set(NodeOr) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeOr


 This method assign an or node to another one
 

set(NodeProduct) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeProduct


 This method sets a product node to another one
 

set(NodeRule) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeRule


 This method sets a rule node from another one
 

set(NodeRuleBase) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeRuleBase


 This method sets a node rule base to another one
 

set(NodeSquareRoot) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeSquareRoot


 This method sets a square root node to another one
 

set(NodeValue) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeValue


 This method sets a node to another
 

set(NodeVariable) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeVariable


 This method sets a variable node to another one
 

set(FuzzyAlphaCut) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut

 
 Copies the FuzzyAlphaCut parameter over the present instance.

set(FuzzyPartition) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyPartition

 
 Copies the Fuzzy partition parameter over the present instance.

set(FuzzyRule) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyRule

 
 Copies the FuzzyRule parameter over the present instance.

set(FuzzySingleton) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzySingleton

 
 Copies the FuzzySingleton parameter over the present instance.

set(RuleBase) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase

 
 Copies the RuleBase parameter over the present instance.

set(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
 
set(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask

Activates the value of a given position.

set(int, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Mask

Sets the states (activated or deactivated) of a given position.

set(int, double, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleRule

Sets the attribute's id and the attribute's value

set(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Mask

Activates the value of a given position.

set(int, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Mask

Sets the states (activated or deactivated) of a given position.

set(int, double, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.SimpleRule

Sets the attribute's id and the attribute's value

set(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype

Copy the values of a Prototype from another protoype.

set(boolean) - Static method in class keel.Algorithms.Instance_Generation.utilities.Debug

Set both debug modes to a desired state.

set() - Static method in class keel.Algorithms.Instance_Generation.utilities.Debug

Set both debug modes (set them to true).

set(F, S) - Method in class keel.Algorithms.Instance_Generation.utilities.Pair

Set elements of the pair.

set - Variable in class keel.Algorithms.Instance_Generation.VQ.Cluster

Set of prototypes (minus the center) that forms the cluster.

set(int, int) - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster

Set an element into the cluster

set(int, int) - Method in class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome

Sets the value in the chromosome of the given position with the value given.

set(int, int) - Method in class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome

Sets the value in the chromosome of the given position with the value given.

set(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask

Activates the value of a given position.

set(int, boolean) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask

Sets the states (activated or deactivated) of a given position.

set(int, double, int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.SimpleRule

Sets the attribute's id and the attribute's value

set(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask

Activates the value of a given position.

set(int, boolean) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask

Sets the states (activated or deactivated) of a given position.

set(int, double, int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.SimpleRule

Sets the attribute's id and the attribute's value

set(int) - Method in class keel.Algorithms.Rule_Learning.PART.Mask

Activates the value of a given position.

set(int, boolean) - Method in class keel.Algorithms.Rule_Learning.PART.Mask

Sets the states (activated or deactivated) of a given position.

set(int, double, int) - Method in class keel.Algorithms.Rule_Learning.PART.SimpleRule

Sets the attribute's id and the attribute's value

set(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Mask

Activates the value of a given position.

set(int, boolean) - Method in class keel.Algorithms.Rule_Learning.Ripper.Mask

Sets the states (activated or deactivated) of a given position.

set(int, double, int) - Method in class keel.Algorithms.Rule_Learning.Ripper.SimpleRule

Sets the attribute's id and the attribute's value

set(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Mask

Activates the value of a given position.

set(int, boolean) - Method in class keel.Algorithms.Rule_Learning.Slipper.Mask

Sets the states (activated or deactivated) of a given position.

set(int, double, int) - Method in class keel.Algorithms.Rule_Learning.Slipper.SimpleRule

Sets the attribute's id and the attribute's value

set(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype

Copy the values of a Prototype from another protoype.

set(boolean) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Debug

Set both debug modes to a desired state.

set() - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Debug

Set both debug modes (set them to true).

set(F, S) - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Pair

Set elements of the pair.

set(int, double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Set a single element.

set(double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Set all elements to a value

set(int, int, double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Set some elements to a value

set(int, int, double[], int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Set some elements using a 2-D array

set(DoubleVector) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Set the elements using a DoubleVector

set(int, int, DoubleVector, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Set some elements using a DoubleVector.

set(int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector

Sets the value of an element.

set(int, int, int[], int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector

Sets the values of elements from an int array.

set(int, int, IntVector, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector

Sets the values of elements from another IntVector.

set(IntVector) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector

Sets the values of elements from another IntVector.

set(int, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector

Sets a single element.

set(int, int, double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix

Set a single element.

set(int, Vector<String>) - Method in class keel.GraphInterKeel.experiments.DinamicParameter
 
set_difuso(Difuso) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Difuso
 
set_difuso(Difuso) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Difuso
 
set_difuso(Difuso) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Difuso
 
set_difuso(Difuso) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Difuso
 
set_difuso(Difuso) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Difuso
 
set_difuso(Difuso) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Difuso
 
set_difuso(Difuso) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Difuso
 
set_difuso(Difuso) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Difuso
 
set_entrado(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Cromosoma

Sets the flag value that carry the information about if this chromosome has been selected to be crossed or not.

set_entrado(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Cromosoma

Sets the flag value that carry the information about if this chromosome has been selected to be crossed or not.

set_entrado(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Cromosoma

Sets the flag value that carry the information about if this chromosome has been selected to be crossed or not.

set_entrado(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Cromosoma

Sets the flag value that carry the information about if this chromosome has been selected to be crossed or not.

set_entrado(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Cromosoma

Sets the flag value that carry the information about if this chromosome has been selected to be crossed or not.

set_entrado(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Cromosoma

Sets the flag value that carry the information about if this chromosome has been selected to be crossed or not.

set_entrado(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Cromosoma

Sets the flag value that carry the information about if this chromosome has been selected to be crossed or not.

set_entrado(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Cromosoma

Sets the flag value that carry the information about if this chromosome has been selected to be crossed or not.

set_fp(double) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.QualitySubgroup


 Sets the value for the fp quality measure
 

set_FPm(double) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.QualitySubgroup


 Sets the value for the FP missing quality measure
 

set_gene(int, double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Cromosoma

Set the value given to a given gene (normal double representation)

set_gene(int, double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Cromosoma

Set the value given to a given gene

set_gene(int, double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Cromosoma

Set the value given to a given gene (normal double representation)

set_gene(int, double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Cromosoma

Set the value given to a given gene (normal double representation)

set_gene(int, double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Cromosoma

Set the value given to a given gene (normal double representation)

set_gene(int, double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Cromosoma

Set the value given to a given gene

set_gene(int, double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Cromosoma

Set the value given to a given gene (normal double representation)

set_gene(int, double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Cromosoma

Set the value given to a given gene (normal double representation)

set_geneA(int, double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Cromosoma

Set the value given to a given gene ("A" double representation)

set_geneA(int, double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Cromosoma

Set the value given to a given gene ("A" double representation)

set_geneA(int, double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Cromosoma

Set the value given to a given gene ("A" double representation)

set_geneA(int, double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Cromosoma

Set the value given to a given gene ("A" double representation)

set_geneR(int, char) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Cromosoma

Set the value given to a given gene ("R" char representation)

set_geneR(int, char) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Cromosoma

Set the value given to a given gene ("R" char representation)

set_geneR(int, char) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Cromosoma

Set the value given to a given gene ("R" char representation)

set_geneR(int, char) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Cromosoma

Set the value given to a given gene ("R" char representation)

set_geneR(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Cromosoma

Set the value given to a given gene ("R" int representation)

set_geneR(int, char) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Cromosoma

Set the value given to a given gene ("R" char representation)

set_max(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Gen

Sets the maximum  variable with the value given.

set_max(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.TipoIntervalo

Sets the maximum variable with the value given.

set_max(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Gen

Sets the maximum  variable with the value given.

set_max(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.TipoIntervalo

Sets the maximum variable with the value given.

set_max(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Gen

Sets the maximum  variable with the value given.

set_max(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.TipoIntervalo

Sets the maximum variable with the value given.

set_max(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Gen

Sets the maximum  variable with the value given.

set_max(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.TipoIntervalo

Sets the maximum variable with the value given.

set_max(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Gen

Sets the maximum  variable with the value given.

set_max(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.TipoIntervalo

Sets the maximum variable with the value given.

set_max(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Gen

Sets the maximum  variable with the value given.

set_max(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.TipoIntervalo

Sets the maximum variable with the value given.

set_max(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Gen

Sets the maximum  variable with the value given.

set_max(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.TipoIntervalo

Sets the maximum variable with the value given.

set_max(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Gen

Sets the maximum  variable with the value given.

set_max(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.TipoIntervalo

Sets the maximum variable with the value given.

set_min(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Gen

Sets the minimum variable with the value given.

set_min(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.TipoIntervalo

Sets the minimum variable with the value given.

set_min(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Gen

Sets the minimum variable with the value given.

set_min(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.TipoIntervalo

Sets the minimum variable with the value given.

set_min(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Gen

Sets the minimum variable with the value given.

set_min(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.TipoIntervalo

Sets the minimum variable with the value given.

set_min(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Gen

Sets the minimum variable with the value given.

set_min(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.TipoIntervalo

Sets the minimum variable with the value given.

set_min(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Gen

Sets the minimum variable with the value given.

set_min(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.TipoIntervalo

Sets the minimum variable with the value given.

set_min(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Gen

Sets the minimum variable with the value given.

set_min(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.TipoIntervalo

Sets the minimum variable with the value given.

set_min(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Gen

Sets the minimum variable with the value given.

set_min(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.TipoIntervalo

Sets the minimum variable with the value given.

set_min(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Gen

Sets the minimum variable with the value given.

set_min(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.TipoIntervalo

Sets the minimum variable with the value given.

set_min_max_values(double[][], int, int) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation


 Setting the mix and max values for data (needed for operators)
 

set_perf(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Cromosoma

Set the performance of a chromosome

set_perf(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Cromosoma

Set the performance of a chromosome

set_perf(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Cromosoma

Set the performance of a chromosome

set_perf(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Cromosoma

Set the performance of a chromosome

set_perf(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Cromosoma

Set the performance of a chromosome

set_perf(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Cromosoma

Set the performance of a chromosome

set_perf(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Cromosoma

Set the performance of a chromosome

set_perf(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Cromosoma

Set the performance of a chromosome

set_q(double) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.QualitySubgroup


 Sets the value for the q quality measure
 

set_tipoBC(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Ecm

Sets the BC type with the value given

set_tp(double) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.QualitySubgroup


 Sets the value for the tp quality measure
 

set_TPm(double) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.QualitySubgroup


 Sets the value for the TP missing quality measure
 

setA(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
 
setAccu(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures


 Sets the value of ACCU
 

setAccuracy(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
 
setAccuracy(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_aggregated
 
setAccuracy(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
 
setAccuracy(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
 
setAccuracy2(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
 
setActAs(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene


 It sets whether a gene is involved in the chromosome being considered.

setActAs(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene


 It sets whether a gene is involved in the chromosome being considered.

setActAs(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene


 It sets whether a gene is involved in the chromosome being considered.

setActAs(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene


 It sets whether a gene is involved in the chromosome being considered.

setAction(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier


 Sets the action passed to the classifier
 

setAction(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation


 Sets the action passed as a parameter.

setActivation(int, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Rule

Sets the activation of the attribute

setActivation(int, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Rule

Sets the activation of the attribute

setAlgorithmType(String) - Method in class keel.GraphInterKeel.experiments.Parameters

Sets the algorithm type

setAll(int[]) - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster

Set all elements into the cluster

setAll_support(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
 
setAll_support(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
 
setAll_support(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule
 
setAll_support(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
 
setAll_support(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
 
setAllAttributes() - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
 
setAllele(double, double) - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Attribute

In case of being  a character attribute, the char value is set as a double in the lowerValue parameter)

setAllele(Attribute) - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Attribute

Sets an Allele (attribute) with the attribute given as parameter.

setAllele(int, Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier


 sets the allele value
 

setAllele(double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep


 Sets the lower and upper values.

setAllele(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep


 Sets the lower and upper values.

setAllele(double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep


 Sets the value of the allele.

setAllele(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep


 Sets the value of the allele.

setAllele(double, double) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute

In case of being  a character attribute, the char value is set as a double in the lowerValue parameter)

setAllele(Attribute) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute

Sets an Allele (attribute) with the attribute given as parameter.

setAllele(int, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier


 Sets the allele value
 

setAllele(int, Classifier) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier


 sets the allele value
 

setAllele(double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep


 Sets the lower and upper values.

setAllele(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep


 Sets the lower and upper values.

setAllele(double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep


 Sets the lower and upper values.

setAllele(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep


 Sets the lower and upper values.

setAllele(int, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation


 Sets the allele value
 

setAllele(int, Representation) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation


 Sets the allele value
 

setAllele(int, char) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation


 Sets the allele value
 

setAllele(double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep


 Sets the value of the allele.

setAllele(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep


 Sets the value of the allele.

setAllFeatures() - Static method in class keel.Algorithms.RST_Learning.KNNClassifier

On the features selector vector, sets the all the features to 1 (selected)

setAllInstances() - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
 
setAllInstances() - Static method in class keel.Algorithms.RST_Learning.KNNClassifier

On the instance selector vector, sets the all the instances to 1 (selected)

setAllInstances() - Static method in class keel.Algorithms.RST_Learning.RSTData
 
setAllowUnclassifiedInstances(boolean) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree

Set the value of AllowUnclassifiedInstances.

setAllowUnclassifiedInstances(boolean) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree

Set the value of AllowUnclassifiedInstances.

setAllowUnclassifiedInstances(boolean) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree

Set the value of AllowUnclassifiedInstances.

setAlpha(double) - Method in class keel.Algorithms.MIL.APR.GFS_Kde_APR.GFS_Kde_APR
 
setAlpha(double) - Method in class keel.Algorithms.MIL.APR.IteratedDiscrimination.IteratedDiscrimination
 
setAlpha(double) - Static method in class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome

Sets the alpha parameter with the value given.

setAlpha(double) - Static method in class keel.Algorithms.RST_Learning.RSTData
 
setAmplitude(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ExpNeuronParametricMutator


 Sets the amplitude coefficient for allowed weights
 

setAmplitude(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.LinearNeuronParametricMutator


 Sets the amplitude coefficient for allowed weights
 

setAmplitude(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator


 Sets the amplitude coefficient for the weights in mutations
 

setAmplitude(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.SigmNeuronParametricMutator


 Sets the amplitude coefficient for allowed weights
 

setAmplitudeInterv(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
 
setantecedent(Integer[]) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
 
setantecedent(Integer[]) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
 
setantecedent(Integer[]) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.rule
 
setantecedent(Integer[]) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
 
setAntecedente(Integer[]) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
 
setAntecedente(Integer[]) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzyrule
 
setAntecedente(Integer[]) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzyrule
 
setAntecedentSupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome
 
setAntecedentSupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule


 It sets the antecedent support of an association rule
 

setAntecedentSupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule


 It sets the antecedent support of an association rule
 

setAnteriores(boolean[][]) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.EUSCHCQstat
 
setAny(int, String) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual


 It sets the condition for the variable in position "atributo" to ANY
 

setArcs(Vector) - Method in class keel.GraphInterKeel.experiments.Graph

Puts a new set of arcs

setArg(Vector<Joint>) - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription

Set args

setArray(int[]) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector

Sets the internal one-dimensional array.

setAsClassifierLeaf() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode

Sets this node as a classifier leaf node.

setAsDefaultRule() - Method in class keel.Algorithms.PSO_Learning.CPSO.Particle
 
setAsDefaultRule() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
 
setAsDefaultRule() - Method in class keel.Algorithms.PSO_Learning.REPSO.Particle
 
setASize(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters


 Sets the classifier action set size estimate
 

setAsLeaf() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode

Sets this node as a leaf node.

setAtribute(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Selector


 Assign the attribute
 

setAtributo(int, double) - Method in class keel.Algorithms.Rule_Learning.Prism.Muestra


 Gives value to an atribute
 

setAtributo(int) - Method in class keel.Algorithms.Rule_Learning.Prism.Selector


 Assign the attribute
 

setAtributo(int, double) - Method in class keel.Algorithms.Rule_Learning.UnoR.Muestra


 Gives value to an atribute
 

setAtributo(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.Selector


 Assign the attribute
 

setAtributo(int, double) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Muestra


 Gives value to an atribute
 

setAtributo(int, double) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Muestra


 Gives value to an atribute
 

setAtributo(int) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Selector


 Assign the attribute
 

setAtributo(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Selector


 Sets the attribute
 

setAttHeader(String) - Method in class keel.Dataset.InstanceSet
 
setAttr(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene
 
setAttr(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
 
setAttr(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
 
setAttr(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Gene
 
setAttr(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Gene
 
setAttr(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene
 
setAttr(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene
 
setAttr(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene
 
setAttr(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene
 
setAttribute(int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.Split

Replaces the position of the attribute for the split with another new position

setAttribute(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Split

Replaces the position of the attribute for the split with another new position

setAttribute(int, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Instance

It sets a value for an attribute

setAttribute(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleRule

Sets the attribute's id and the attribute's value

setAttribute(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.SimpleRule

Sets the attribute's id and the attribute's value

setAttribute(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Condition

It adds the attribute id

setAttribute(int, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Instance

It sets a value for an attribute

setAttribute(int) - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
 
setAttribute(int) - Static method in class keel.Algorithms.RST_Learning.RSTData
 
setAttribute(int, double) - Method in class keel.Algorithms.Rule_Learning.AQ.Instance

It sets a value for an attribute

setAttribute(int) - Method in class keel.Algorithms.Rule_Learning.AQ.Selector

It assigns the attribute

setAttribute(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.SimpleRule

Sets the attribute's id and the attribute's value

setAttribute(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.SimpleRule

Sets the attribute's id and the attribute's value

setAttribute(int, double) - Method in class keel.Algorithms.Rule_Learning.CN2.Instance

It sets a value for an attribute

setAttribute(int) - Method in class keel.Algorithms.Rule_Learning.CN2.Selector

It assigns the attribute

setAttribute(int) - Method in class keel.Algorithms.Rule_Learning.PART.SimpleRule

Sets the attribute's id and the attribute's value

setAttribute(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.SimpleRule

Sets the attribute's id and the attribute's value

setAttribute(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.SimpleRule

Sets the attribute's id and the attribute's value

setAttribute(int, double) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Instance

It sets a value for an attribute

setAttributeI(int, myAttribute) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset

Replaces the information about the ith attribute that the dataset uses with new information about that attribute

setAttributeI(int, myAttribute) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset

Replaces the information about the ith attribute that the dataset uses with new information about that attribute

SetAttributePresence(int, Boolean) - Method in class keel.Algorithms.PSO_Learning.CPSO.Particle
 
SetAttributePresence(int, Boolean) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
 
setAttributes(ArrayList<myAttribute>) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset

Replaces all the information about the attributes that the dataset uses with new information about the attributes

setAttributes(ArrayList<myAttribute>) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset

Replaces all the information about the attributes that the dataset uses with new information about the attributes

setAttributes(Attribute[]) - Static method in class keel.Algorithms.Hyperrectangles.BNGE.Rule

Test which attributes are nominal

setAttributes(Attribute[]) - Static method in class keel.Algorithms.Hyperrectangles.INNER.Rule

Test which attributes are nominal

setAttributes(Attribute[]) - Static method in class keel.Algorithms.Hyperrectangles.RISE.Rule

Test which attributes are nominal

setAttributes(int[]) - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
 
setAttributes(int[]) - Static method in class keel.Algorithms.RST_Learning.RSTData
 
setAttributes(Vector<Integer>) - Method in class keel.Algorithms.Rule_Learning.ART.Rule
 
setAttributesArray(IAttribute[]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.Metadata


 Sets array of all attributes of this specification
 

setAttributesAsNonStatic() - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceSet

setAttributesAsNonStatic

 It stores the static-defined attributes in the class Attributes as
 non static in the object attributes.

setAttributesAsNonStatic() - Method in class keel.Dataset.InstanceSet

setAttributesAsNonStatic

 It stores the static-defined attributes in the class Attributes as
 non static in the object attributes.

setAttributesTypes(InstanceAttributes) - Static method in class keel.Algorithms.Instance_Generation.Basic.Prototype

Update the type of each attribute of the set.

setAttributesTypes(InstanceAttributes) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype

Update the type of each attribute of the set.

setAttributesTypes() - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype

Sets the attributes types.

setAttributeType(int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute

Replaces the data type of the data of the attribute with a new data type

setAttributeType(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute

Replaces the data type of the data of the attribute with a new data type

setAttributeValue(double) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Register

Replaces the value of the corresponding attribute with another new value

setAttributeValue(int) - Method in class keel.Algorithms.Rule_Learning.Riona.Selector


 Assigns the attribute
 

setB(double[], double) - Method in class keel.Algorithms.PSO_Learning.CPSO.Particle
 
setB(double[], double) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
 
setB(double[], double, int) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.Particle
 
setB(double[], double) - Method in class keel.Algorithms.PSO_Learning.REPSO.Particle
 
setB(Difuso[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
 
setB(Difuso[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
 
setB(Difuso[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
 
setB(Difuso[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
 
setB(int, int, Difuso) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
 
setB(Difuso[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
 
setB(Difuso[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
 
setB(Difuso[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
 
setB(Difuso[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
 
setBase(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos

Sets a database with the data in a vector given as argument.

setBaseCodificada(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Codificacion
 
setBc(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion
 
setBd(BaseDatos) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Discretizacion

Sets the database with the one given.

setBDatos(Difuso[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
 
setBDatos(int, int, Difuso) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
 
setBDatos(Difuso[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
 
setBDatos(int, int, Difuso) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
 
setBDatos(Difuso[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
 
setBDatos(int, int, Difuso) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
 
setBDatos(Difuso[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
 
setBDatos(int, int, Difuso) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
 
setBDatos(Difuso[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
 
setBDatos(int, int, Difuso) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
 
setBDatos(Difuso[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
 
setBDatos(int, int, Difuso) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
 
setBDatos(Difuso[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
 
setBDatos(int, int, Difuso) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
 
setBDatos(Difuso[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
 
setBDatos(int, int, Difuso) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
 
setBergman(boolean) - Static method in class keel.GraphInterKeel.statistical.Configuration

Sets the use of Bergman test

setBest(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
 
setBest(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
 
setBest(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
 
setBest(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
 
setBest(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
 
setBest(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
 
setBest(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
 
setBest(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
 
setBEST_CROM(Cromosoma) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
 
setBEST_CROM(Cromosoma) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
 
setBEST_CROM(Cromosoma) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
 
setBEST_CROM(Cromosoma) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
 
setBEST_CROM(Cromosoma) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
 
setBEST_CROM(Cromosoma) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
 
setBestModelResultFile(String) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlusReporterClas


 Sets file name where the best model obtained will be written

setBestModelResultFile(String) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.IRPropPlusReporterRegr


 Sets file name where the best model obtained will be written

setBestModelResultFile(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.listener.NeuralNetReporterClas


 Sets file name where the best model obtained will be written

setBestModelResultFile(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.listener.NeuralNetReporterRegr


 Sets file name where the best model obtained will be written
 

setBestrules(ArrayList<Chromosome>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess

Sets the best rules mined with the array given.

setBeta(double) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.Chromosome

Sets beta value

setBeta(double) - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome

Sets the beta parameter with the value given.

setBiased(boolean) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer


 Sets a boolean indicating if the layer has a bias neuron
 

setBiased(boolean) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron


 Sets a boolean indicating if the layer has a bias neuron
 

setBinaryAttributesNominal(boolean) - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter

Sets if binary attributes are to be treates as nominal ones.

setBit(int, char) - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Gene


 Sets the bits status indicated by index and value
 

setBITS_GEN(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
 
setBITS_GEN(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
 
setBITS_GEN(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
 
setBITS_GEN(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
 
setBITS_GEN(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
 
setBITS_GEN(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
 
setBITS_GEN(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
 
setBITS_GEN(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
 
setBondad(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Corte
 
setBondad(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Union
 
setBonferroni(boolean) - Static method in class keel.GraphInterKeel.statistical.Configuration

Sets the use of Bonferroni-Dunn test

setBounds(double, double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.Gcvfctn

Sets the new bounds

setBounds(double, double) - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute

It sets the bound of the integer or real attribute.

setBounds(double, double) - Method in class keel.Dataset.Attribute

It sets the bound of the integer or real attribute.

setBregla(int[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
 
setBregla(int, int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
 
setBregla(int[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
 
setBregla(int, int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
 
setBregla(int[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
 
setBregla(int, int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
 
setBregla(int[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
 
setBregla(int, int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
 
setBregla(int[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
 
setBregla(int, int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
 
setBregla(int[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
 
setBregla(int, int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
 
setBregla(int[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
 
setBregla(int, int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
 
setBregla(int[][]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
 
setBregla(int, int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
 
setBroken(boolean) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.Link


 Sets a boolean indicating if the link is or not broken
 

setBuildLogisticModels(boolean) - Method in class keel.Algorithms.SVM.SMO.SMO

Set the value of buildLogisticModels.

setC(double) - Method in class keel.Algorithms.SVM.SMO.SMO

Set the value of C.

setC(double) - Method in class keel.Algorithms.SVM.SMO.SMOreg

Set the value of C.

setC(double) - Method in class keel.Algorithms.SVM.SMO.SVMreg

Set the value of C.

setC(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.MembershipFunction


 It sets the center of an isosceles-triangle
 

setC(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.MembershipFunction


 It sets the center of an isosceles-triangle
 

setCa(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene
 
setCabecera(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
 
setCacheSize(int) - Method in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel

Sets the size of the cache to use (a prime number)

setCacheSize(int) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel

Sets the size of the cache to use (a prime number)

setCapacity(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector

Sets the vector's capacity to the given value.

setCapacity(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector

Sets the vector's capacity to the given value.

setCapacity(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector

Sets the vector's capacity to the given value.

setCapacity(int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector

Sets the vector's capacity to the given value.

setCapacity(int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Sets the capacity of the vector

setCapacity(int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector

Sets the capacity of the vector

setCapacity(int) - Method in class keel.Algorithms.SVM.SMO.core.FastVector

Sets the vector's capacity to the given value.

setCaret(int) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelDataset


 Sets care postion for dataset area
 

setCategories(Vector<Category>) - Static method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.WrapperManager
 
setCCnf(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures


 Sets the value of CCNF
 

setcent(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.fuzzy
 
setcent(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.fuzzy
 
setcentd(float) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
 
setcentd(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
 
setcentd(float) - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
 
setcentd(float) - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
 
setcentd(float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
 
setcentd(float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
 
setcentd(float) - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
 
setCenter(double[]) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbf


 Sets the vector of centres of a neuron
 

setcenti(float) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
 
setcenti(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
 
setcenti(float) - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
 
setcenti(float) - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
 
setcenti(float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
 
setcenti(float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
 
setcenti(float) - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
 
setCentre(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbf


 Sets the vector of centres of a neuron
 

setCentre(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbf


 Sets the vector of centres of a neuron
 

setCentre(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbf


 Sets the vector of centres of a neuron
 

setCentre(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf


 Sets the vector of centres of a neuron
 

setCentre(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbf


 Sets the vector of centres of a neuron
 

setCentre(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf


 Sets the vector of centres of a neuron
 

setCentroid(Prototype) - Method in class keel.Algorithms.Instance_Generation.VQ.Cluster

Asssigns the center of the cluster.

setCentroid(double[]) - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster

Set the centroid of the cluster

setCep(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion
 
setCF(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule


 It sets the CF of an association rule
 

setCF(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
 
setCF(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
 
setCF(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule


 It sets the CF of an association rule
 

setCF(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule


 It sets the CF of an association rule
 

setCF(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
 
setCF(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
 
setCF(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
 
setCF(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
 
setCF(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
 
setCF(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
 
setCF(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
 
setCF(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
 
setChain(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGPRegSym


 This method sets the fsChain with the given array value
 

setChart(JFreeChart) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelAttribute


 Set a chart for paint attributes values
 

setChart(JFreeChart) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelCharts2D


 Sets a JFreeChart chart
 

setCheckErrorRate(boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA

Sets whether to check for error rate is in stopping criterion

setChecksTurnedOff(boolean) - Method in class keel.Algorithms.SVM.SMO.SMO

Disables or enables the checks (which could be time-consuming).

setChecksTurnedOff(boolean) - Method in class keel.Algorithms.SVM.SMO.SMOreg

Disables or enables the checks (which could be time-consuming).

setChecksTurnedOff(boolean) - Method in class keel.Algorithms.SVM.SMO.supportVector.Kernel

Disables or enables the checks (which could be time-consuming).

setChildren(Node[]) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node

Sets the children of the node.

setChromosome(Chromosome) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.LimitRoulette
 
setClas(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset

Function which sets the itemset's output class.

setClas(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset

Function which sets the itemset's output class

setClas(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Itemset

Set the class with the value given as argument.

setClas(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Itemset


 Function which sets the itemset's output class
 

setclas(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Regla

It assigns the class of the rule

setClas(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Itemset
 
setClas(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Rule

It assigns a new class for the rule

setClas(int) - Method in class keel.Algorithms.Rule_Learning.ART.Rule
 
setClas(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeDat


 Sets the value of a class
 

setClas(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeDat


 Sets the value of a class
 

setClas(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex


 Asigne the class to the complex
 

setClas(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeDat


 Sets the value of a class
 

setClase(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Regla
 
setClase(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos

Sets the class index with the one given.

setClase(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Corte
 
setClase(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Union
 
setClase(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Sample


 Assigns the class
 

setClase(int) - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo


 Gives the value of the class to the complex
 

setClase(int) - Method in class keel.Algorithms.Rule_Learning.Prism.Muestra


 Assigns the class
 

setClase(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo


 Gives the value of the class to the complex
 

setClase(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.Muestra


 Assigns the class
 

setClase(int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Muestra


 Assigns the class
 

setClase(int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla

Sets the rule class with the value given.

setClase(int) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo

Sets the given value as class.

setClase(int) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Muestra


 Assigns the class
 

setClaseEjemplo(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Resultado
 
setClaseRegla(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Resultado
 
setClass(M5Attribute) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances

Sets the class attribute.

setClass(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Rule

It assigns the class of the rule

setClass(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule

Sets the class for the rule

setClass(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Rule

Sets the class for the rule

setClass(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule

Changes the class of the rule to a new specified class.

setClass(AttributeWeka) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Sets the class attribute.

setClass(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Chromosome


 Set the class of this chromosome
 

setClass(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Instance

It assigns the class

setClass(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome


 Sets the class of this chromosome
 

setClass(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Rule

Sets the consequent of the rule

setClass(MyAttribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

Sets the class attribute.

setClass(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Rule

Sets the consequent of the rule

setClass(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Instance

It assigns the class

setClass(int, int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule

Changes the class of the rule to a new specified class.

setClass(Attribute) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Sets the class attribute.

setClass(double) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype

Assigns a new class (aka new first output) to the prototype.

setClass(int) - Method in class keel.Algorithms.Rule_Learning.AQ.Complex

It assigns a class to the complex

setClass(int) - Method in class keel.Algorithms.Rule_Learning.AQ.Instance

It assigns the class

setClass(int) - Method in class keel.Algorithms.Rule_Learning.CN2.Complex

It assigns a class to the complex

setClass(int) - Method in class keel.Algorithms.Rule_Learning.CN2.Instance

It assigns the class

setClass(int, int) - Method in class keel.Algorithms.Rule_Learning.UnoR.Interval

Sets a class that belong to the interval.

setClass(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype

Assigns a new class (aka new first output) to the prototype.

setClass(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableDat


 Sets the class of the example in position pos
 

setClass(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableDat


 Sets the class of the example in position pos
 

setClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Instance

It assigns the class

setClass(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableDat


 Sets the class of the example in position pos
 

setClass(Attribute) - Method in class keel.Algorithms.SVM.SMO.core.Instances

Sets the class attribute.

setClassAttribute(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex


 Returns the class that defines the comples
 

setClassAttribute(int) - Method in class keel.Algorithms.Rule_Learning.Riona.Complex


 Gives the value of the class to the complex
 

setClasses(int) - Static method in class keel.Algorithms.RST_Learning.KNNClassifier

Sets the number of classes in the data

setClasses(int) - Static method in class keel.Algorithms.RST_Learning.RSTData
 
setClassIndex(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances

Sets the class index of the set.

setClassIndex(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Sets the class index of the set.

setClassIndex(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

Sets the class index of the set.

setClassIndex(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Sets the class index of the set.

setClassIndex(int) - Method in class keel.Algorithms.SVM.SMO.core.Instances

Sets the class index of the set.

setClassLearned(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexResult
 
setClassMissing() - Method in class keel.Algorithms.Decision_Trees.C45.Itemset

Function to set as missing the class value.

setClassMissing() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset

Function to set as missing the class value.

setClassMissing() - Method in class keel.Algorithms.Decision_Trees.ID3.Itemset

Function to set as missing the class value.

setClassMissing() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Sets the class value of an instance to be "missing".

setClassMissing() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Itemset

Function to set as missing the class value.

setClassMissing() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Sets the class value of an instance to be "missing".

setClassMissing() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset

Function to set as missing the class value.

setClassMissing() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset

Function to set as missing the class value.

setClassMissing() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset

Function to set as missing the class value.

setClassMissing() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset

Function to set as missing the class value.

setClassMissing() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset

Function to set as missing the class value.

setClassMissing() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Sets the class value of an instance to be "missing".

setClassMissing() - Method in class keel.Algorithms.Rule_Learning.ART.Itemset

Function to set as missing the class value.

setClassMissing() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Itemset

Function to set as missing the class value.

setClassMissing() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset

Function to set as missing the class value.

setClassMissing() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset

Function to set as missing the class value.

setClassMissing() - Method in class keel.Algorithms.Rule_Learning.PART.Itemset

Function to set as missing the class value.

setClassMissing() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset

Function to set as missing the class value.

setClassMissing() - Method in class keel.Algorithms.SVM.SMO.core.Instance

Sets the class value of an instance to be "missing".

setClassValue(double) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Sets the class value of an instance to the given value (internal
 floating-point format).

setClassValue(String) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Sets the class value of an instance to the given value.

setClassValue(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Sets the class value of an instance to the given value (internal
 floating-point format).

setClassValue(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Sets the class value of an instance to the given value.

setClassValue(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset

Function to set a value.

setClassValue(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset

Function to set a value.

setClassValue(double) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Sets the class value of an instance to the given value (internal
 floating-point format).

setClassValue(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Sets the class value of an instance to the given value.

setClassValue(double) - Method in class keel.Algorithms.SVM.SMO.core.Instance

Sets the class value of an instance to the given value (internal
 floating-point format).

setClassValue(String) - Method in class keel.Algorithms.SVM.SMO.core.Instance

Sets the class value of an instance to the given value.

setClickToExpand(String) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelAttribute


 Cotrol for Expand chart
 

setClusterOf(Instance, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.gCenter


 Updates the cluster membership of the instance to the nearest cluster
 

setCnf(double) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.QualityMeasures


 Sets the value of the confidence
 

setCnf(float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.QualityMeasures


 Method to set the value of the confidence
 

setCnfValue(double) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual


 Sets the value of confidence of the individual
 

setCob(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
 
setCoefficients(double[]) - Method in interface keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IOptimizableFunc


 Establish the final value of a[], that is, the coefficients of
 model

setCoefficients(double[]) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.MSEOptimizablePUNeuralNetClassifier


 Establish the final value of a[], that is, the coefficients of
 model
 B01 B02 ...

setCoefficients(double[]) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.MSEOptimizableSigmNeuralNetClassifier


 Establish the final value of a[], that is, the coefficients of
 model
 B01 B02 ...

setCoefficients(double[]) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.MSEOptimizablePUNeuralNetRegressor


 Establish the final value of a[], that is, the coefficients of
 model
 B01 B02 ...

setCoefficients(double[]) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.MSEOptimizableSigmNeuralNetRegressor


 Establish the final value of a[], that is, the coefficients of
 model
 B01 B02 ...

setColumn(int, double[]) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix

Deprecated.
Sets a column of the matrix to the given column.

setColumnNames(String[]) - Method in class keel.GraphInterKeel.datacf.util.DatasetTable


  Set Column Names
 

setComp(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures


 Sets the value of COMP
 

setComponent(int, FuzzyRule) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.FuzzyClassifier

 
 This method sets a given FuzzyRule in the RuleBase.

setComponent(int, Fuzzy) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyPartition

 
 Rewrites the fuzzy set n of current partition.

setComponent(int, FuzzyRule) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase

 
 Copies a new rule in the RuleBase.

setComprehensibility(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
 
setCompTime(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier


 Sets the compactation time stamp for this classifier.

setCompTime(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters


 Sets the time stamp for this classifier.

setCondicionContinua(int, Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla

Sets the given continuous condition into the position given.

setCondicionNominal(int, Condicion) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Regla

Sets the given nominal condition into the position given.

setCondition(Split) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode

Replaces the relationship between this node and its descendants, this means, changing the condition how
 the two descendants are created.

setCondition(Split) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode

Replaces the relationship between this node and its descendants, this means, changing the condition how
 the two descendants are created.

setCondition(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.Condition
 
setCondition(int, Condition) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual


 It sets to "c" the condition for the variable in position "atributo"
 

setCondition(Condition) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Rule

It adds a new condition to the rule

setConfidence(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule

It sets the rule's confidence.

setConfidence(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule

It sets the rule's confidence

setConfidence(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule


 It sets the confidence of the rule
 

setConfidence(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Rule


 It sets the confidence of the rule
 

setConfidence(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Rule


 It sets the confidence of the rule
 

setConfidence(Itemset) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule
 
setConfidence(double) - Method in class keel.Algorithms.Rule_Learning.ART.Rule
 
setConfidence(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule


 It sets the confidence of an association rule
 

setConfidence(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule

Sets the confidence of an association rule with the value given.

setConfidence(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
 
setConfidence(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule


 It sets the confidence of an association rule
 

setConfidence(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule
 
setConfidence(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
 
setConfidence(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
 
setConfidence(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome


 It sets the confidence of the association rule represented by a chromosome
 

setConfidence(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome


 It sets the confidence of the association rule represented by a chromosome
 

setConfidence(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome


 It sets the confidence of the association rule represented by a chromosome
 

setConfig - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
 
setConsecuente(Float[]) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
 
setConsecuente(int, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
 
setConsecuentes(Difuso[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
 
setConsecuentes(Difuso[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
 
setConsecuentes(Difuso[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
 
setConsecuentes(Difuso[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
 
setConsecuentes(Difuso[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
 
setConsecuentes(Difuso[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
 
setConsecuentes(Difuso[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
 
setConsecuentes(Difuso[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
 
setConsequent(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule

It returns the rule's consequent.

setConsequent(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule

It returns the rule's consequent

setConsequent(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule


 It sets the consequent of the rule
 

setConsequent(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Rule


 It sets the consequent of the rule
 

setConsequent(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Rule


 It sets the consequent of the rule
 

setConsequent(myDataset) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule

It sets the class for this rule

setConsequent(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule
 
setConsequent(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule

Sets the internal representation of the class label to be predicted

setconsequent(Float[]) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
 
setconsequent(int, float) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
 
setconsequent(Integer[]) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.rule
 
setconsequent(int, Integer) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.rule
 
setconsequent(Float[]) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
 
setconsequent(int, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
 
setconsequent(Float[]) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
 
setconsequent(int, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
 
setConsequent(ArrayList<Gene>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
 
setConsequentSupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome
 
setConsequentSupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule


 Sets the consequent support of an association rule with the given value
 

setConsequentSupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule


 It sets the consequent support of an association rule
 

setConsSupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
 
setConsts(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Model.RegSymFuzzyGP


 This method modifies constant part for this individual with parameter passed
 

setContinuous(boolean) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeVar


 Sets the type of the variable
 

setContinuous(boolean) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeVar


 Sets the type of the variable
 

setContinuous(boolean) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeVar


 Sets the type of the variable
 

setContinuous(boolean) - Method in class keel.GraphInterKeel.experiments.UseCase

Sets if the method's variables are continuous

setConv(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule


 It sets the conviction of an association rule
 

setConv(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
 
setConv(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
 
setConv(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule


 It sets the conviction of an association rule
 

setConv(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule


 It sets the conviction of an association rule
 

setConv(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
 
setConv(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
 
setConv(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
 
setConv(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
 
setConv(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
 
setConv(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
 
setConv(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
 
setConv(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
 
setCorrectClassifications(int) - Method in class keel.Algorithms.Statistical_Tests.Shared.StatTest.InformationAboutClass

Sets the number of correct classifications done with the one given.

setCorte(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Corte
 
setCorte(Corte) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Union
 
setCortes(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Discretizacion

Sets the cuts with the ones given.

setCortesCod(Vector) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Codificacion
 
setcost_instance(boolean) - Method in class keel.GraphInterKeel.experiments.Parameters

Set cost instance

setCoste(int) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node

Sets the node cost with the value given.

setCounter(int) - Method in class keel.GraphInterKeel.datacf.editData.EditVariablePanel


 Sets the counter value
 

setCove(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures


 Sets the value of COVE
 

setCoverage(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
 
setCovered(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Instance

It gives a "covered" value

setCovered(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Instance

It gives a "covered" value

setCovered(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Sample


 Assign a new value for the 'n' times that the example has benn matched
 

setCovered(int) - Method in class keel.Algorithms.Rule_Learning.AQ.Instance

It gives a "covered" value

setCovered(int) - Method in class keel.Algorithms.Rule_Learning.CN2.Instance

It gives a "covered" value

setCovered(int, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableDat


 Sets to covered or not the the example in position pos
 

setCovered(boolean) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeDat


 Sets the state of the example
 

setCovered(int, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableDat


 Sets to covered or not the the example in position pos
 

setCovered(boolean) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeDat


 Sets the state of the example
 

setCovered(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Instance

It gives a "covered" value

setCovered(int, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableDat


 Sets to covered or not the the example in position pos
 

setCovered(boolean) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeDat


 Sets the state of the example
 

setCoveredInstances(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Chromosome


 Set the number of covered instances
 

setCr(MyDataset, Mask, Mask, double[]) - Method in class keel.Algorithms.Rule_Learning.Slipper.Rule

Computes the confidence of this rule, according to the equation 4
 of [AAAI99]:
 Cr=1/2ln((W+ + 1/(2n))/(W_ + 1/(2n)))
 W+: sum of the weights of the positive instances that are covered by the current rule
 W_: sum of the weights of the negative instances that are covered by the current rule
 n: |p|+|n|

setCr(double) - Method in class keel.Algorithms.Rule_Learning.Slipper.Rule

Sets the new confidence of the rule.

setCR(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess
 
setCratio(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm


 Sets creation ratio of the algorithm
 

setcrisp(int) - Method in class keel.GraphInterKeel.experiments.Parameters

Set crisp

setCromElem(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Chromosome


 Sets the value of the indicated position of the gene of the chromosome
 

setCromElem(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual


 Sets the value of the indicated value of the gene of the Chromosome
 

setCromElem(int, int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population


 Sets the value of the indicated gene of the Chromosome

setCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.CromCAN


 Sets the value of the indicated gene of the chromosome
 

setCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndCAN


 Sets the value of the indicated gene of the Chromosome
 

setCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndDNF


 Sets the value of the indicated gene of the Chromosome
 

setCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual

Sets a value in a given position in the chromosome.

setCromElem(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population


 Sets the value of the indicated gene of the CromCAN
 

setCromElem(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Chromosome


 Sets the value of the indicated position of the gene of the chromosome
 

setCromElem(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual


 Sets the value of the indicated value of the gene of the Chromosome
 

setCromElem(int, int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population


 Sets the value of the indicated gene of the Chromosome

setCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.CromCAN


 Sets the value of the indicated gene of the chromosome
 

setCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndCAN


 Sets the value of the indicated gene of the Chromosome
 

setCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndDNF


 Sets the value of the indicated gene of the Chromosome
 

setCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual

Sets a value in a given position in the chromosome.

setCromElem(int, int, int, int, String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Population


 Sets the value of the indicated gene of the Chromosome
 

setCromElem(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Chromosome


 Sets the value of the indicated position of the gene of the chromosome
 

setCromElem(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual


 Sets the value of the indicated value of the gene of the Chromosome
 

setCromElem(int, int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population


 Sets the value of the indicated gene of the Chromosome

setCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.CromCAN


 Sets the value of the indicated gene of the chromosome
 

setCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndCAN


 Sets the value of the indicated gene of the Chromosome
 

setCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndDNF


 Sets the value of the indicated gene of the Chromosome
 

setCromElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual


 Sets the value of the indicated gene of the Chromosome
 

setCromElem(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Population


 Sets the value of the indicated gene of the CromCAN
 

setCromElemGene(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.CromDNF


 Sets the value of the indicated gene of the chromosome
 

setCromElemGene(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndCAN


 Sets the value of the indicated gene of the Chromosome
 

setCromElemGene(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.IndDNF


 Sets the value of the indicated gene of the Chromosome
 

setCromElemGene(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual


 Sets the value of the indicated gene of the Chromosome
 

setCromElemGene(int, int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population


 Sets the value of the indicated gene of the CromCAN
 

setCromElemGene(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.CromDNF


 Sets the value of the indicated gene of the chromosome
 

setCromElemGene(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndCAN


 Sets the value of the indicated gene of the Chromosome
 

setCromElemGene(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.IndDNF


 Sets the value of the indicated gene of the Chromosome
 

setCromElemGene(int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual


 Sets the value of the indicated gene of the Chromosome
 

setCromElemGene(int, int, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Population


 Sets the value of the indicated gene of the CromCAN
 

setCromGeneElem(int, int, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.CromDNF


 Sets the value of the indicated gene of the Chromosome
 

setCromGeneElem(int, int, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndCAN


 Sets the value of the indicated gene of the Chromosome
 

setCromGeneElem(int, int, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.IndDNF


 Sets the value of the indicated gene of the Chromosome
 

setCromGeneElem(int, int, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual


 Sets the value of the indicated gene of the Chromosome
 

setCrossPercent(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
 
setCrowdingDistance(double) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual


 Sets the crowdingDistance of the individual
 

setcRule(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Replace

It sets the position of the best rule that correctly classifies the example stored in the structure.

setcRule(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Structure


 It sets the position of the best rule that correctly classifies the example stored in the structure
 

setcRule(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Replace

It sets the position of the best rule that correctly classifies the example stored in the structure

setcRule(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Structure


 It sets the position of the best rule that correctly classifies the example stored in the structure
 

setCSup(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures


 Sets the value of CSUP
 

setCubierta(int) - Method in class keel.Algorithms.Rule_Learning.Prism.Muestra


 Assign a new value for the 'n' times that the example has benn matched
 

setCubierta(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.Muestra


 Assign a new value for the 'n' times that the example has benn matched
 

setCubierta(int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Muestra


 Assign a new value for the 'n' times that the example has benn matched
 

setCubierta(int) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Muestra


 Assign a new value for the 'n' times that the example has benn matched
 

setCutPoint(Dataset) - Method in class keel.Algorithms.Decision_Trees.C45.Cut

Function to set the cut point.

setCutPoint(Dataset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut

Function to set the cut point.

setCutPoint(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut

Function to set the cut point.

setCutPoint(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut

Function to set the cut point.

setCutPoint(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut

Function to set the cut point.

setCutPoint(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut

Function to set the cut point.

setCutPoint(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut

Function to set the cut point.

setCutPoint(MyDataset) - Method in class keel.Algorithms.Rule_Learning.PART.Cut

Function to set the cut point.

setCutPoint(Dataset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut

Function to set the cut point.

setD(double) - Method in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel

Sets the gamma value.

setDat(int, float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeDat


 Initialise a variable of an example
 

setDat(int, float) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeDat


 Initialise a variable of an example
 

setDat(int, float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeDat


 Initialise a variable of an example
 

setData(Vector<ListaAtributos>[]) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node

Sets the dataset that satisfies the node's condition.

setData(Instances) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats

Set the data of the stats, overwriting the old one if any

setData(int, Instance) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.myDataset

It assigns a data.

setData(int, Instance) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.myDataset

It assigns a data.

setData(int, Sample) - Method in class keel.Algorithms.Hyperrectangles.EACH.EachDataSet


 Assign a data item.

setData(double[][], int[]) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.Chromosome

Stores the training data

setData(double[][], boolean[]) - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.ISW

Loads the training data into the classifier

setData(double[][]) - Static method in class keel.Algorithms.RST_Learning.KNNClassifier

Loads the training data into the classifier

setData(double[][]) - Static method in class keel.Algorithms.RST_Learning.RSTData

Loads the training data into the classifier

setData(int, Instance) - Method in class keel.Algorithms.Rule_Learning.AQ.myDataset

It assigns a data.

setData(Vector) - Method in class keel.Algorithms.Rule_Learning.ART.TBAR
 
setData(int, Instance) - Method in class keel.Algorithms.Rule_Learning.CN2.myDataset

It assigns a data.

SetData - Class in keel.Algorithms.Subgroup_Discovery.SDAlgorithm

 Data structure for the examples

SetData() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetData


 Constructor
 

setData(Dataset) - Method in class keel.GraphInterKeel.datacf.editData.EditDataPanel


 Set a Dataset
 

setData(Object[][]) - Method in class keel.GraphInterKeel.datacf.util.DatasetTable


 Sets Data
 

setData(Object[][]) - Method in class keel.GraphInterKeel.datacf.util.VariableTable


 Sets the data array
 

setData(Dataset) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanel


 Sets a Dataset
 

setDataCFView(DataCFFrame) - Method in class keel.GraphInterKeel.datacf.editData.EditPanel


 Sets a view for a dataset
 

setDataCFView(DataCFFrame) - Method in class keel.GraphInterKeel.datacf.importData.ImportPanel


 Sets a view for a dataset
 

setDataI(int, int, double) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset

Replaces the value of the ith instance at the jth attribute in this dataset with the specified value

setDataI(int, int, double) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset

Replaces the value of the ith instance at the jth attribute in this dataset with the specified value

setDataNormalized(boolean) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator


 Sets a boolean value indicating if the DataSets are going to be normalized
 

setDataset(Dataset) - Method in class keel.Algorithms.Decision_Trees.C45.Itemset

Function to set the dataset.

setDataset(DoubleTransposedDataSet) - Method in class keel.Algorithms.Decision_Trees.CART.impurities.Gini

It sets the datasets of patters

setDataset(DoubleTransposedDataSet) - Method in interface keel.Algorithms.Decision_Trees.CART.impurities.IImpurityFunction

It sets the datasets of patters

setDataset(DoubleTransposedDataSet) - Method in class keel.Algorithms.Decision_Trees.CART.impurities.LeastSquaresDeviation

It sets the datasets of patters

setDataset(Dataset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset

Function to set the dataset.

setDataset(Dataset) - Method in class keel.Algorithms.Decision_Trees.ID3.Itemset

Function to set the dataset.

setDataset(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Sets the reference to the dataset.

setDataset(Dataset) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Itemset

Function to set the dataset.

setDataset(Instances) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Sets the reference to the dataset.

setDataset(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset

Function to set the dataset.

setDataset(Dataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset

Function to set the dataset.

setDataset(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset

Function to set the dataset.

setDataset(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset

Function to set the dataset.

setDataset(Dataset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset

Function to set the dataset.

setDataset(Instances) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Sets the reference to the dataset.

setDataset(org.ayrna.jclec.util.dataset.KeelMultiInstanceDataSet) - Method in class keel.Algorithms.MIL.ExceptionDatasets
 
setDataset(Dataset) - Method in class keel.Algorithms.Rule_Learning.ART.Itemset

Function to set the dataset.

setDataset(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Itemset

Function to set the dataset.

setDataset(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset

Function to set the dataset.

setDataset(Dataset) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset

Function to set the dataset.

setDataset(MyDataset) - Method in class keel.Algorithms.Rule_Learning.PART.Itemset

Function to set the dataset.

setDataset(Dataset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset

Function to set the dataset.

setDataset(Instances) - Method in class keel.Algorithms.SVM.SMO.core.Instance

Sets the reference to the dataset.

setDataSet(String) - Method in class keel.GraphInterKeel.datacf.editData.EditDataPanel


 Set the dataset by using its name
 

setDatasetSettings(String, String) - Method in class keel.Algorithms.MIL.AbstractMIAlgorithm
 
setDato(int, Muestra) - Method in class keel.Algorithms.Rule_Learning.Prism.ConjDatos


 Assign a data item
 

setDato(int, Muestra) - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjDatos


 Assign a data item
 

setDato(int, Muestra) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjDatos


 Assign a data item
 

setDato(int, Muestra) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjDatos


 Assign a data item
 

setDato(int, Instance) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetData


 Sets an instance
 

setDatos(Dataset) - Method in class keel.GraphInterKeel.datacf.util.DatasetTable


 Set datos
 

setDebug(boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Classifier

Set debugging mode.

setDebug(boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA

Sets whether debug information is output to the console

setDebug(boolean) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization

Set whether in debug mode

setDebug(boolean) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic

Sets whether debugging output will be printed.

setDebug(boolean) - Method in class keel.Algorithms.SVM.SMO.core.Check

Set debugging mode

setDebug(boolean) - Method in class keel.Algorithms.SVM.SMO.supportVector.Kernel

Enables or disables the output of debug information (if the derived
 kernel supports that)

setDebugStream(PrintStream) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserTokenManager
 
setDebugStream(PrintStream) - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParserTokenManager
 
setDebugStream(PrintStream) - Static method in class keel.Dataset.DataParserTokenManager
 
setDefaultClass(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.RuleBase


 It sets the default class for the rule base
 

setDefaultClass(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Selected

It sets the default class into the "selected" structure.

setDefaultClass(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.RuleBase


 It sets the default class for the rule base
 

setDefaultClass(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Selected

It sets the default class into the "selected" structure

setDefaultRule() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase

Sets the default rule.

setDefaultRule() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
 
setDefaultRule(instanceSet) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_aggregated
 
setdere(float) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
 
setdere(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
 
setdere(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.fuzzy
 
setdere(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.fuzzy
 
setdere(float) - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
 
setdere(float) - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
 
setdere(float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
 
setdere(float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
 
setdere(float) - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
 
setDescriptions() - Method in class keel.GraphInterKeel.experiments.Parameters

return parameter names

setDestination(int) - Method in class keel.GraphInterKeel.experiments.Arc

Sets the destination node

setDestination2(int) - Method in class keel.GraphInterKeel.experiments.Arc

Sets the destination node

setdID(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Replace

It sets in the structure the position in the training dataset of the wanted example.

setdID(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Structure


 It sets in the structure the position in the training dataset of the wanted example
 

setdID(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Replace

It sets in the structure the position in the training dataset of the wanted example

setdID(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Structure


 It sets in the structure the position in the training dataset of the wanted example
 

setDimensions(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex

Sets the number of dimensions with the value given.

setDimensions(int) - Method in class keel.Algorithms.Rule_Learning.Riona.Complex
 
setDirectionAttribute(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute

It sets if the attribute is an input or an output attribute

setDirectionAttribute(int) - Method in class keel.Dataset.Attribute

It sets if the attribute is an input or an output attribute

setDiscretized(boolean) - Method in class keel.GraphInterKeel.experiments.UseCase

Sets if the method's variables are discretized

setDistrib(int[]) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla

Sets the given distribution.

setDistribution(int[]) - Method in class keel.Algorithms.Rule_Learning.AQ.Complex

It assigns a class distribution

setDistribution(int[]) - Method in class keel.Algorithms.Rule_Learning.CN2.Complex

It assigns a class distribution

setDiversity(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic


 Sets the type of diversity of the algorithm
 

setDs(myDataset) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess
 
setE(Ecm) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
 
setE(Ecm) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
 
setE(Ecm) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
 
setE(Ecm) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
 
setE(Ecm) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
 
setE(Ecm) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
 
setE(Ecm) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
 
setE(Ecm) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
 
setEC(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
 
setEc_tra(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Chc
 
setEc_tra(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Chc
 
setEc_tra(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Chc
 
setEc_tra(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Chc
 
setEc_tra(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Chc
 
setEc_tra(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Chc
 
setEc_tra(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Chc
 
setEc_tra(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Chc
 
setEc_tst(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Chc
 
setEc_tst(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Chc
 
setEc_tst(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Chc
 
setEc_tst(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Chc
 
setEc_tst(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Chc
 
setEc_tst(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Chc
 
setEc_tst(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Chc
 
setEc_tst(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Chc
 
setEditDataPanel(EditDataPanel) - Method in class keel.GraphInterKeel.datacf.editData.EditVariablePanel


 Set Edit Data Panel
 

setEditorAt(int, TableCellEditor) - Method in class keel.GraphInterKeel.datacf.util.EachRowEditor


 Set the editor in a row
 

setEditVariablePanel(EditVariablePanel) - Method in class keel.GraphInterKeel.datacf.editData.EditDataPanel


  Set the EditVariablePanel
 

setElement(int, int, double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix

Deprecated.
Sets an element of the matrix to the given value.

setElementAt(Object, int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector

Sets the element at the given index.

setElementAt(Object, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector

Sets the element at the given index.

setElementAt(Object, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector

Sets the element at the given index.

setElementAt(Object, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector

Sets the element at the given index.

setElementAt(Object, int) - Method in class keel.Algorithms.SVM.SMO.core.FastVector

Sets the element at the given index.

setElitism(double) - Static method in class keel.Algorithms.Instance_Selection.CoCoIS.Subpopulation

Sets the Elitism percentage

setElitism(double) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.Subpopulation

Sets the Elitism percentage

setElitismRate(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
setEps(double) - Method in class keel.Algorithms.SVM.SMO.SMOreg

Set the value of eps.

setEpsilon(double) - Method in class keel.Algorithms.MIL.APR.IteratedDiscrimination.IteratedDiscrimination
 
setEpsilon(double) - Method in class keel.Algorithms.SVM.SMO.SMO

Set the value of epsilon.

setEpsilon(double) - Method in class keel.Algorithms.SVM.SMO.SMOreg

Set the value of epsilon.

setEpsilon(double) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMO

Set the value of epsilon.

setEpsilonParameter(double) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer

Set the value of epsilon parameter of the epsilon insensitive loss function.

setError(ErrorInfo) - Method in class keel.Algorithms.Rule_Learning.Swap1.FormatErrorKeeper

Adds one error

setError(ErrorInfo) - Method in class keel.Dataset.FormatErrorKeeper

Adds one error

setErrorDebugMode(boolean) - Static method in class keel.Algorithms.Instance_Generation.utilities.Debug

Set error debug mode to a desired state.

setErrorDebugMode(boolean) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Debug

Set error debug mode to a desired state.

setErroresClase(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion
 
setErrorFunction(IErrorFunction<double[][]>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.SoftmaxClassificationProblemEvaluator


 Sets error function

setErrorFunction(IErrorFunction<double[]>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.problem.regression.RegressionProblemEvaluator


 Sets error function
 

setEvaluated(boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome


 Set this chromosome as evaluated (fitness computed for the current set of genes) or not
 

setEvaluated(boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Cochromosome


 Sets the evaluation condition
 

setEvaluated(int, int, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Population


 Sets the evaluation status of one rule in a specific subpopulation
 

setEvaluated(boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.RuleSet

Returns the evaluation state

setEvaluated(boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.RuleSet

Returns the evaluation state

setEvaluator(<any>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm


 Sets the system evaluator
 

setExample(String) - Method in class keel.GraphInterKeel.experiments.UseCase

Sets the method's example

setExamples(double[][], int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForClassification


 This method initialize the examples and create a new classifier
 

setExamples(double[][], double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForModels


 This method inicialize the examples
 

setExamplesClassObj(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableDat


 Stores and gets in "ej_clase_obj" the number of examples of the target class
 

setExamplesClassObj(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableDat


 Stores and gets in "ej_clase_obj" the number of examples of the target class
 

setExamplesClassObj(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableDat


 Stores and gets in "ej_clase_obj" the number of examples of the target class
 

setExamplesCovered(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableDat


 Sets the number of examples covered by the rules generated
 

setExamplesCovered(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableDat


 Sets the number of examples covered by the rules generated
 

setExceptionsLength(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
 
setExceptionsLength(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
 
setExceptionsLength(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
 
setExe(int) - Method in class keel.GraphInterKeel.experiments.Parameters

modify number of executions

setExitos(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Individual

Sets the number of correctly classified instances for a rule of the chromosome

setExpected(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexResult
 
setExperience(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters


 Sets the experience of the classifier.

setExperience(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters


 Sets the experience of the classifier.

setExperimenttype(int) - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt


 This method set type problem, classification or regression
 

setExponent(double) - Method in class keel.Algorithms.SVM.SMO.supportVector.NormalizedPolyKernel

Sets the exponent value (must be different from 1.0).

setExponent(double) - Method in class keel.Algorithms.SVM.SMO.supportVector.PolyKernel

Sets the exponent value.

setExtensionAndDescription() - Method in class keel.GraphInterKeel.datacf.importData.ImportPanel


 Sets extensions and description of the different import formats
 

setF(Funciones) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
 
setF(Funciones) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
 
setF(Funciones) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
 
setF(Funciones) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
 
setF(Funciones) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
 
setF(Funciones) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
 
setF(Funciones) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
 
setF(Funciones) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
 
setFCnf(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures


 Sets the value of FCNF
 

setFeatures(int[]) - Static method in class keel.Algorithms.RST_Learning.KNNClassifier

Sets the vector of features selected

setFichEnt(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
 
setFicheroTest(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.SMOTE_RSB
 
setFicheroTraining(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.SMOTE_RSB
 
setFichSalRul(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
 
setFichSalTra(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
 
setFichSalTst(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
 
setFileBrowserPanel(FileBrowserPanel) - Method in class keel.GraphInterKeel.datacf.partitionData.PartitionPanel


 Sets the filebrowser of this partition panel
 

setFileName(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet


 Sets the filename used to read the observations and parameters
 

setFileName(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.FileDataset


 Sets the file name
 

setFilePath(File) - Static method in class keel.GraphInterKeel.util.Path

Sets the path from a given file

setFilterName(String) - Method in class keel.GraphInterKeel.datacf.util.KeelFileFilter


 Sets Name of the Filer
 

setFilterName(String) - Method in class keel.GraphInterKeel.experiments.KeelFileFilter

Set the filter name

setFilterName(String) - Method in class keel.GraphInterKeel.statistical.CSVFileFilter

Set the filter name

setFilterType(SelectedTag) - Method in class keel.Algorithms.SVM.SMO.SMO

Sets how the training data will be transformed.

setFilterType(SelectedTag) - Method in class keel.Algorithms.SVM.SMO.SMOreg

Sets how the training data will be transformed.

setFilterType(SelectedTag) - Method in class keel.Algorithms.SVM.SMO.SVMreg

Sets how the training data will be transformed.

setFinales(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
 
setFinner(boolean) - Static method in class keel.GraphInterKeel.statistical.Configuration

Sets the use of Finner test

setFirstOutput(double) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype

Assigns a new value to the first output of the prototype.

setFirstOutput(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype

Assigns a new value to the first output of the prototype.

setFit(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
 
setFit(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
 
setFit(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Chromosome
 
setFit(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Chromosome
 
setFitDif(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm


 Sets significative fitness difference
 

setFitDif(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator


 Sets the difference between two fitnesses that we consider
 enough to say that the fitness has improved
 

setFitness(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.Individuo
 
setFitness(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
 
setFitness(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Chromosome


 Set the fitness of this individual
 

setFitness(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome


 Sets the fitness for this rule
 

setFitness(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
 
setFitness(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Individual


 It sets the fitness of the individual to "fitness"
 

setFitness(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
 
setFitness(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier


 Sets the fitness of the classifier at the value specified
 

setFitness(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters


 Sets the fitness of the classifier.

setFitness(double) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn


 Sets the fitness of a RBF.

setFitness(double) - Method in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.Cromosoma

set the fitness for the chromosome

setFitness(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.Individuo
 
setFitness(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.MatrizR
 
setFitness(double) - Method in class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome

Sets the fitness of this chromosome with the value given.

setFitness(double) - Method in class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome

Sets the fitness of this chromosome with the value given.

setFitness(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures


 Sets the value of fitness
 

setFitness(float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.QualityMeasures


 Sets the value of fitness
 

setFitness(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Chromosome


 It sets the fitness for a chromosome
 

setFitness(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Chromosome


 It sets the fitness for a chromosome
 

setFitness(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Chromosome


 It sets the fitness for a chromosome
 

setFitness(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome


 It sets the fitness for a chromosome
 

setFitness(double[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Chromosome
 
setFitness_Cl(double[][], double[][], int, int) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbfn


 Sets the fitness of a RBFN for classification problems    
 

setFitness_Cl(double[][], double[][], int, int) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation


 Sets the fitness of a Population of RBFNs for classification problems  
 

setFitness_Cl_subPop(double[][], double[][], int, int) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation


 Sets the fitness of a Sub-Population of RBFNs for classification problems 
 

setFitness_rank(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
 
setFixedBounds(boolean) - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute

It sets the fixedBounds value

setFixedBounds(boolean) - Method in class keel.Dataset.Attribute

It sets the fixedBounds value

setFlags() - Method in class keel.Algorithms.Decision_Trees.M5.Interval

Sets the flags array.

setFlags() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Range

Sets the flags array.

setFolds(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA

Sets the number of folds to use

setFp(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
 
setFP(double) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex


 Set the value of FP
 

setFracasos(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Individual

Sets the number of correctly misclassified instances for a rule of the chromosome

setFSup(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures


 Sets the value of FSUP
 

setFuncionEvaluacionXmlFileName(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion
 
setFunction(Function) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule

It sets the right side of the rule.

setfuzzy(boolean) - Method in class keel.GraphInterKeel.experiments.Parameters

Set fuzzy status

setGain(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.Literal


 It sets the gain for a literal
 

setGamma(double) - Method in class keel.Algorithms.SVM.SMO.supportVector.RBFKernel

Sets the gamma value.

setGAparams(double, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.SEM

Set the parameters for this SEM

setGAparams(double, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.SEM

Set the parameters for this SEM

setGen(int, boolean) - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Cromosoma

Set the value of a gene

setGen(int, boolean) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.Chromosome

It sets a value for a given chrom.

setGen(int, boolean) - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma

Set the value of a gene

setGen(int, boolean) - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma

Set the value of a gene

setGen(int, boolean) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma

Set the value of a gene

setGen(int, boolean) - Method in class keel.Algorithms.Instance_Selection.CHC.Cromosoma

Set the value of a gene

setGen(int, boolean) - Method in class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.Cromosoma

Set the value of a gene

setGen(int, boolean) - Method in class keel.Algorithms.Instance_Selection.GGA.Cromosoma

Set the value of a gene

setGen(int, boolean) - Method in class keel.Algorithms.Instance_Selection.IGA.Cromosoma

Set the value of a gene

setGen(int, boolean) - Method in class keel.Algorithms.Instance_Selection.PBIL.Cromosoma

Set the value of a gene

setGen(int, boolean) - Method in class keel.Algorithms.Instance_Selection.SGA.Cromosoma

Set the value of a gene

setGen(int, boolean) - Method in class keel.Algorithms.Instance_Selection.SSMA.Cromosoma

Set the value of a gene

setGen(int, boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.CHC.Cromosoma

Set the value of a gene

setGen(int, boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.Cromosoma

Set the value of a gene

setGen(int, boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.GGA.Cromosoma

Set the value of a gene

setGen(int, boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.IGA.Cromosoma

Set the value of a gene

setGen(int, boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Cromosoma

Set the value of a gene

setGen(int, boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SGA.Cromosoma

Set the value of a gene

setGen(int, boolean) - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma

Set the value of a gene

setGen(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic


 Sets the value of a gene
 

setGene(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Individuo
 
setGene(int, Gene) - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Chromosome


 Set the value of the specified gene
 

setGene(int, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Individual

Sets a value for a specific gene

setGene(Gen[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
 
setGene(Gen[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
 
setGene(Gen[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
 
setGene(Gen[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
 
setGene(Gen[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
 
setGene(Gen[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
 
setGene(Gen[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
 
setGene(Gen[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
 
setGeneElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Gene


 Sets the value of the indicated gene of the chromosome
 

setGeneElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Gene


 Sets the value of the indicated gene of the chromosome
 

setGeneElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Gene


 Sets the value of the indicated gene of the chromosome
 

setGeneElem(int, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Gene


 Sets the value of the indicated gene of the chromosome
 

setGeneElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Gene


 Sets the value of the indicated gene of the chromosome
 

setGeneElem(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Gene


 Sets the value of the indicated gene of the chromosome
 

setGenerality(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters

Sets the generality of the classifier.

setGenerality(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters


 Sets the generality of the classifier.

setGeneration(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm


 Sets the current generation
 

setGenerationLimit(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.SEM

Set the generations limit

setGenerationLimit(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.SEM

Set the generations limit

setGenes(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
 
setGenes(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
 
setGenes(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
 
setGenes(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
 
setGenes(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
 
setGenes(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
 
setGenes(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
 
setGenes(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
 
setGenes(ArrayList<Gene>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Chromosome
 
setGenes(Gene[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome


 It returns the genes of a chromosome
 

setGradoEmp(double[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
 
setGradoEmp(double[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
 
setGradoEmp(double[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
 
setGradoEmp(double[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
 
setGradoEmp(double[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
 
setGradoEmp(double[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
 
setGradoEmp(double[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
 
setGradoEmp(double[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
 
setHausdorffMaxDistance(boolean) - Method in class keel.Algorithms.MIL.Nearest_Neighbour.AbstractNearestNeighbour
 
setHeader(String) - Method in class keel.Algorithms.Decision_Trees.CART.ResultPrinter

It sets the header

setHeader(String) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlusReporterClas


 Sets KEEL file header

setHeader(String) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.IRPropPlusReporterRegr


 Sets KEEL file header

setHeader(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.listener.NeuralNetReporterClas


 Sets KEEL file header

setHeader(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.listener.NeuralNetReporterRegr


 Sets KEEL file header
 

setHeader(String) - Method in class keel.Dataset.InstanceSet
 
setHeuristic(double) - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex


 Assign a heuristic value(Wracc) to the complex
 

setHeuristic(double) - Method in class keel.Algorithms.Rule_Learning.CN2.Complex

Sets the heuristic value (Wracc) of this Complex object.

setheuristic(double) - Method in class keel.Algorithms.Rule_Learning.CN2.Complex

It assigns the heuristic of the complex computed as:

 Covered positives - covered negatives / number of selectors

setHeuristic(double) - Method in class keel.Algorithms.Rule_Learning.Riona.Complex


 Assign a heuristic value (Wracc) to the complex
 

setHeuristica(double) - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo


 Assign a heuristic value(Wracc) to the complex
 

setHeuristica(double) - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo


 Assign a heuristic value (Wracc) to the complex
 

setHeuristica(double) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo


 Assign a heuristic value (Wracc) to the complex
 

setHeuristicaWRAcc(double) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla


 Assign a heuristic value (Wracc) to the rule
 

setHiddenLayerBiased(int, boolean) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies


 Sets a boolean indicating if a hidden layer is biased

setHiddenLayerInitialNofneurons(int, int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies


 Sets initial maximum number of neurons of a hidden layer

setHiddenLayerInitiator(int, String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies


 Sets initiator of neurons of a hidden layer

setHiddenLayerMaxNofneurons(int, int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies


 Sets maximum number of neurons of a hidden layer

setHiddenLayerMinNofneurons(int, int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies


 Sets minimum number of neurons of a hidden layer

setHiddenLayerType(int, String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies


 Sets type of neurons of a hidden layer

setHiddenLayerWeightRange(int, int, Interval) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies


 Sets weight range of a hidden layer

setHochberg(boolean) - Static method in class keel.GraphInterKeel.statistical.Configuration

Sets the use of Hochberg test

setHoja(boolean) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node

Sets the leaf contition.

setHolland(boolean) - Static method in class keel.GraphInterKeel.statistical.Configuration

Sets the use of Holland test

setHolm(boolean) - Static method in class keel.GraphInterKeel.statistical.Configuration

Sets the use of Holm test

setHommel(boolean) - Static method in class keel.GraphInterKeel.statistical.Configuration

Sets the use of Hommel test

setHowWork(String) - Method in class keel.GraphInterKeel.experiments.UseCase

Sets how the method works

setiCondition(int, Condition) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.Rule
 
setiCondition(int, Condition) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule
 
setId(int) - Method in class keel.GraphInterKeel.experiments.Graph

Sets a new id for this graph

setId(int) - Method in class keel.GraphInterKeel.experiments.Node

Sets the new id of the node

setIDAttribute(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Item


 Set the ID of the attribute involved in the item
 

setIdentifier(int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode

Replaces the identifier of the node with another new node

setIdentifier(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Node

Replaces the identifier of the node with another new node

setIdentifier(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Register

Replaces the value of the identifier of the register with another new identifier

setIdentifier(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode

Replaces the identifier of the node with another new node

setIfSeed(boolean) - Method in class keel.GraphInterKeel.experiments.Parameters

Sets the need for seed of the algorithm

setImage(BufferedImage) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelAttribute


 Sets image of attributes
 

setImage(BufferedImage) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelCharts2D


 Sets the image for attributes
 

setIman(boolean) - Static method in class keel.GraphInterKeel.statistical.Configuration

Sets the use of Iman-Davenport test

setImplicator(int) - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
 
setImpurities(double) - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode
 
setImpurityFunction(IImpurityFunction) - Method in class keel.Algorithms.Decision_Trees.CART.CART

It sets the impurity function

setIndex(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute

Sets the index of this attribute.

setIndex(int) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype

Assigns a new index to the prototype.

setIndex(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputNeuron


 Sets the index associated to this neuron
 

setIndex(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
 
setIndex(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
 
setIndex(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
 
setIndex(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
 
setIndex(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
 
setIndex(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
 
setIndex(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
 
setIndex(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
 
setIndex(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
 
setIndex(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
 
setIndex(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
 
setIndex(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
 
setIndex(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
 
setIndex(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
 
setIndex(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
 
setIndex(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
 
setIndex(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype

Assigns a new index to the prototype.

setIndivDensity(int, float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population


 Sets the individual density
 

setIndivDom(boolean) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual


 Sets the value for the domination value
 

setIndivDom(int, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population


 Sets the individual to dominated or not dominated
 

setIndivEvaluated(boolean) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual


 Sets state of the individual
 

setIndivEvaluated(int, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population


 Sets the value for de evaluated attribute of the individual
 

setIndivEvaluated(boolean) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual


 Sets that the individual has been evaluated
 

setIndivEvaluated(int, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population


 Sets the state evaluated of the individual of the population
 

setIndivEvaluated(boolean) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual


 Sets state of the individual
 

setIndivEvaluated(int, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population


 Sets the value for de evaluated attribute of the individual
 

setIndivEvaluated(boolean) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual


 Sets that the individual has been evaluated
 

setIndivEvaluated(int, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Population


 Sets the value for de evaluated attribute of the individual
 

setIndivEvaluated(boolean) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual


 Sets state of the individual
 

setIndivEvaluated(int, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population


 Sets the value for de evaluated attribute of the individual
 

setIndivEvaluated(boolean) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual


 Sets that the individual has been evaluated
 

setIndivEvaluated(int, boolean) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Population


 Sets the state evaluated of the individual of the population
 

setIndivFitness(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual


 Sets the Fitness of the individual
 

setIndivFitness(int, float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population


 Sets the value of fitnes for the the indicated individual
 

setIndivFitness(float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual


 Sets the Fitness of the individual
 

setIndivFitness(int, float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Population


 Sets the value of fitnes for the the indicated individual
 

setIndivNameClass(int, String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population


 Sets the class name of the class of the individual
 

setIndivNameClass(int, String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population


 Sets the class name of the class of the individual
 

setIndivNameClass(int, String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population


 Sets the class name of the class of the individual
 

setIndivNumClass(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population


 Sets the value for the number of the class of the individual
 

setIndivNumClass(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population


 Sets the value for the number of the class of the individual
 

setIndivNumClass(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population


 Sets the value for the number of the class of the individual
 

setIndivNvar(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population


 Sets the number of variables of the indicated individual
 

setIndivNvar(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population


 Sets the number of variables of the indicated individual
 

setIndivNvar(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population


 Sets the number of variables of the indicated individual
 

setIndivOSup(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual


 Sets the original support of the individual
 

setIndivPerf(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual


 Sets the fitness of the individual 
 

setIndivPerf(int, float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population


 Sets the value of fitnes for the the indicated individual
 

setIndivPerf(float) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual


 Sets the fitness of the individual 
 

setIndivPerf(int, float) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population


 Sets the value of fitnes for the the indicated individual
 

setIndivPerf(float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual


 Sets the fitness of the individual 
 

setIndivPerf(int, float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population


 Sets the value of fitnes for the the indicated individual
 

setIndivRawFit(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population


 Sets the individual raw fitness
 

setIndivStrength(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population


 Sets the individual strength
 

setIndivTotalClass(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population


 Sets the number of examples of the DB belonging to the class of the individual
 

setIndivTotalClass(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population


 Sets the number of examples of the DB belonging to the class of the individual
 

setIndivTotalClass(int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population


 Sets the number of examples of the DB belonging to the class of the individual
 

setInf(int[][]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets
 
setInhabitants(List<I>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm


 Sets the population inhabitants
 

setIni() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule


 It sets to 0 the number of right and wrong of the rule
 

setInicial(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
 
setIniOp(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
 
setInitialAlphaInput(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator


 Sets the initial alpha coeficient for the input weigths
 

setInitialAlphaOutput(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator


 Sets the initial alpha coeficient for the output weigths
 

setInitialmaxnofneurons(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer

Sets the initial maximum number of neurons of this layer (without BIAS)

setInitialTime() - Method in class keel.Algorithms.Coevolution.CoevolutionAlgorithm

Sets the time counter

setInitialTime() - Method in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

Sets the time counter

setInitialTime() - Method in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Sets the time counter

setInitialTime() - Method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm

Sets the time counter

setInitiatorNeuronTypes(int, String[]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies


 Sets an array of initiators of neurons of hibrid layers

setInitProb(double) - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome

Sets the initial probability to build the chromosome.

setInitProb(double) - Static method in class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome

Sets the initial probability to build the chromosome.

setInnerBorder(int[]) - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster

Set the inner border of the cluster

setInput(boolean) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute

Changes the logical attribute stating if an attribute is input or not

setInput(boolean) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute

Changes the logical attribute stating if an attribute is input or not

setInput(int, double) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype

Set a input for an attribute

setInput(int, double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype

Set a input for an attribute

setInputFormat(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter

Sets the format of the input instances.

setInputFormat(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter

Sets the format of the input instances.

setInputInterval(Interval) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator


 Sets the input interval of normalized data
 

setInputLayer(InputLayer) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet


 Sets the input layer of this neural net
 

setInputLayer(InputLayer) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuralNet


 Sets the input layer of this neural net
 

setInputNominalValue(int, String) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance

It set the nominal attribute value to the one passed.

setInputNominalValue(InstanceAttributes, int, String) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance

Set a new value of a given input attribute in this instance (nominal)

setInputNominalValue(int, String) - Method in class keel.Dataset.Instance

It set the nominal attribute value to the one passed.

setInputNominalValue(InstanceAttributes, int, String) - Method in class keel.Dataset.Instance

Set a new value of a given input attribute in this instance (nominal)

setInputNumericValue(int, double) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance

It changes the attribute value.

setInputNumericValue(InstanceAttributes, int, double) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance

Set a new value of a given input attribute in this instance (integer or real)

setInputNumericValue(int, double) - Method in class keel.Dataset.Instance

It changes the attribute value.

setInputNumericValue(InstanceAttributes, int, double) - Method in class keel.Dataset.Instance

Set a new value of a given input attribute in this instance (integer or real)

setInstances(Instance[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowing
 
setInstances(Instance[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowingGWS
 
setInstances(Instance[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowingILAS
 
setInstances(Instances) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets

Sets the instances comprising the current neighbourhood.

setInstances(Instances) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Arrays

Sets the instances comprising the current neighbourhood.

setInstances(int[]) - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
 
setInstances(int[]) - Static method in class keel.Algorithms.RST_Learning.KNNClassifier

Sets the vector of instances selected

setInstances(int[]) - Static method in class keel.Algorithms.RST_Learning.RSTData
 
setInstanceTest(InstanceSet) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator

Sets the test instances set given.

setInstanceTrain(InstanceSet) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator

Sets the training instances set given.

setInteger(boolean) - Method in class keel.GraphInterKeel.experiments.UseCase

Sets if the method's variables are integer

setInternalCacheSize(int) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel

sets the size of the internal cache for intermediate results.

setInvert(boolean) - Method in class keel.Algorithms.Decision_Trees.M5.Interval

Sets whether the range sense is inverted, i.e. all except
 the values included by the range string are selected.

setInvert(boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Range

Sets whether the range sense is inverted, i.e. all except
 the values included by the range string are selected.

setInvolvedRule(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypePitts


 This method updates the rule base.

setiRule(int, Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.RuleSet
 
setiRule(int, Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.RuleSet
 
setIS(InstanceSet) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.SEM

Sets the reference data set for this SEM

setIS(InstanceSet) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.SEM

Sets the reference data set for this SEM

setIsEvaluated(boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
 
setIsEvaluated(boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
 
setIsPositiveInterval(boolean) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene


 It sets if a gene treats positive or negative interval
 

setIsPositiveInterval(boolean) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene


 It sets if a gene treats positive or negative interval
 

setIsPositiveInterval(boolean) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene


 It sets if a gene treats positive or negative interval
 

setizd(float) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
 
setizd(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
 
setizd(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.fuzzy
 
setizd(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.fuzzy
 
setizd(float) - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
 
setizd(float) - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
 
setizd(float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
 
setizd(float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
 
setizd(float) - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
 
setJTable(JTable) - Method in class keel.GraphInterKeel.datacf.editData.EditVariablePanel


 Sets a new JTable
 

setJTable(JTable) - Method in class keel.GraphInterKeel.statistical.ExcelAdapter

Public Accessor methods for the Table on which this adapter acts.

setJTable1(JTable) - Method in class keel.GraphInterKeel.datacf.editData.EditDataPanel


 Set JTable of Data
 

setK(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.ENN

Sets the number of NN used.

setK(int) - Static method in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN

Sets the number of prototypes to be used in the knn algorithm.

setK(int) - Static method in class keel.Algorithms.Instance_Selection.CoCoIS.Subpopulation

Sets the K parameter

setK(int) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.Chromosome

Sets K value

setK(int) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.Subpopulation

Sets the K parameter

setK(int) - Static method in class keel.Algorithms.RST_Learning.KNNClassifier

Sets the K value

setK(int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN

Sets the number of prototypes to be used in the knn algorithm.

setKernel(Kernel) - Method in class keel.Algorithms.SVM.SMO.SMO.BinarySMO

sets the kernel to use

setKernel(Kernel) - Method in class keel.Algorithms.SVM.SMO.SMO

sets the kernel to use

setKernel(Kernel) - Method in class keel.Algorithms.SVM.SMO.SMOreg

Sets the kernel to use.

setKernel(Kernel) - Method in class keel.Algorithms.SVM.SMO.SVMreg

sets the kernel to use

setKey(double) - Method in class keel.Algorithms.Rule_Learning.Ripper.Trio

Sets the attribute'value to a new value.

setKey(double) - Method in class keel.Algorithms.Rule_Learning.Slipper.Trio

Sets the attribute'value to a new value.

setKfold(boolean) - Method in class keel.GraphInterKeel.datacf.importData.ImportPanel


 Sets a boolean indicating if "kfold" is going to be the default option
 for the type of partition in the import proccess
 

setKind(int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset

Changes the kind of dataset to a new kind

setKind(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset

Changes the kind of dataset to a new kind

setKValue(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree

Set the value of K.

setKValue(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree

Set the value of K.

setKValue(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree

Set the value of K.

setL(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Gene
 
setL(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Gene
 
setL(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene
 
setLabel(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule

It sets the label for a given position in the antecedent (for a given attribute).

setLabel(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule

It sets the label for a given position in the antecedent (for a given attribute)

setLabel(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.Rule

It sets the label for a given position in the antecedent (for a given attribute)

setLabel(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule

It sets the label for a given position in the antecedent (for a given attribute)

setLabel(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Rule

It sets the label for a given position in the antecedent (for a given attribute)

setLabel(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Rule

It sets the label for a given position in the antecedent (for a given attribute)

setLabel(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Rule

It sets a new label for the rule

setLabel(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule
 
setLabel(double) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype

Assigns a new label (aka first output, class) to the prototype.

setLabel(double) - Method in class keel.Algorithms.Instance_Generation.VQ.Cluster

Asssigns the class of the cluster.

setLabel(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype

Assigns a new label (aka first output, class) to the prototype.

setLabel(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyRegion


 It sets the label associated with a fuzzy region
 

setLabel(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyRegion


 It sets the label associated with a fuzzy region
 

setLabel(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyRegion


 It sets the label associated with a fuzzy region
 

setLabel(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyRegion


 It sets the label associated with a fuzzy region
 

setLabel(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.Item


 It sets the label associated with an item
 

setLabel(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.Item


 It sets the label associated with an item
 

setLambda(double) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel

Sets the lambda constant used in the string kernel

setLaplaceAcc(double) - Method in class keel.Algorithms.Hyperrectangles.RISE.Rule

Sets the Laplace accuracy of the rule

setLeaf(boolean) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode

Changes the logical attribute stating if a node is leaf or not

setLeaf(boolean) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode

Changes the logical attribute stating if a node is leaf or not

setLeft(TreeNode) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode

Replaces the left descendant of the node with another new left descendant

setLeft(TreeNode) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode

Replaces the left descendant of the node with another new left descendant

setLeft(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Intervals
 
setLeft(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Interval


 It sets the left bound of an interval
 

setLeft(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Intervals
 
setLeftSon(TreeNode) - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode
 
setLeftValueRank(String) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelAttribute


 Sets left value Rank
 

setLenghtElite(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic


 Methods to set the lenght of the elite population
 

setLenghtPop(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic


 Methods to set the lenght of the population
 

setLenghtPop(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic


 Methods to set the lenght of the population
 

setLength(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Rule

Allocates new memory for the number of attributes specified (delete
 previous memory).

setLength(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Rule

Allocates new memory for the number of attributes specified (delete
 previous memory).

setLengthAnt(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
 
setLengthAnt(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
 
setLengthPopulation(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic


 Sets the lenght of the population
 

setLi(boolean) - Static method in class keel.GraphInterKeel.statistical.Configuration

Sets the use of Li test

setLift(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule


 It sets the lift of an association rule
 

setLift(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
 
setLift(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
 
setLift(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule


 It sets the lift of an association rule
 

setLift(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule


 It sets the lift of an association rule
 

setLift(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
 
setLift(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
 
setLift(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
 
setLimitHigh(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.LimitRoulette
 
setLimitLow(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.LimitRoulette
 
setLimits(int, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Rule

Sets the new limits of the attribute in the rule

setLimits(int, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Rule

Sets the new limits of the attribute in the rule

setLink(int, Link) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron


 Sets the link with the neuron specified (0 is bias neuron)
 

setLinks(Link[]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron


 Sets the links of the neuron
 

setLocalHierarchicalMeasure(double) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule

Sets the local hierarchical measure field to a specified given value, corresponding to this rule local hierarchical
 measure according to the data set and the other rules considered

setLogger(Vector) - Method in exception keel.Algorithms.Rule_Learning.Swap1.DatasetException

Sets the vector with the errors.

setLogger(Vector) - Method in exception keel.Dataset.DatasetException

Sets the vector with the errors.

setLogTransformation(boolean) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator


 Sets a boolean value indicating if the DataSets are going to be log
 transformated
 

setLost(int, int) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Calculate


 Sets the value of the gen of an example as an lost value 
 lost = max value of the variable + 1
 

setLost(TableVar, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableDat


 Sets the value of the gen of an example as an lost value
 lost = max value of the variable + 1
 

setLost(int, int) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Calculate


 Sets the value of the gen of an example as an lost value 
 lost = max value of the variable + 1
 

setLost(TableVar, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableDat


 Sets the value of the gen of an example as an lost value
 lost = max value of the variable + 1
 

setLost(int, int) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Calculate


 Sets the value of the gen of an example as an lost value 
 lost = max value of the variable + 1
 

setLost(TableVar, int, int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableDat


 Sets the value of the gen of an example as an lost value
 lost = max value of the variable + 1
 

setLowerBound(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Condition

Sets the lower bound

setLowerBound(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene


 It sets the lower bound of the interval stored in a gene
 

setLowerBound(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene


 It sets the lower bound of the interval stored in a gene
 

setLowerBound(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene


 It sets the lower bound of the interval stored in a gene
 

setLSearch(boolean) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic


 Methods to set if local search must be performed
 

setLSup(float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.QualityMeasures


 Method to set the value of the local support
 

setMacroClSum(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population


 Sets the number of macro classifiers in the set.

setMacroClSum(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population


 Sets the number of macro classifiers in the set.

setMark(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule

It sets the rule's mark.

setMark(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule

It sets the rule's mark

setMatrix(int, int, int, int, Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix

Set a submatrix.

setMatrix(int[], int[], Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix

Set a submatrix.

setMatrix(int[], int, int, Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix

Set a submatrix.

setMatrix(int, int, int[], Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix

Set a submatrix.

setMax(double) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute

Replaces the maximum value for the attribute with a new maximum value

setMax(double) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute

Replaces the maximum value for the attribute with a new maximum value

setMax(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeVar


 Sets the maximum value for the variable
 

setMax(float) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeVar


 Sets the maximum value for the variable
 

setMax(float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeVar


 Sets the maximum value for the variable
 

setMax_attr(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene
 
setMax_attr(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene
 
setMAX_SIZE(int) - Static method in class keel.Algorithms.Lazy_Learning.IDIBL.NQueue

Sets maximun size of the queue

setMaxDepth(int) - Method in class keel.Algorithms.Decision_Trees.CART.CART

It sets the maximal depth

setMaxDepth(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree

Set the maximum depth of the tree, 0 for unlimited.

setMaxDepth(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree

Set the maximum depth of the tree, 0 for unlimited.

setMaxDepth(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree

Set the maximum depth of the tree, 0 for unlimited.

setMaximumDelta(double) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus


 Sets the maximum delta value, that is, the maximum increment or
 step size of the corresponding coefficients

setMaximumDistance(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet


 Sets the maximum distance between train data
 

setMaxIteration(int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization

Set the maximal number of iterations in searching (Default 200)

setMaxIterations(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM

Set the maximum nomber of iterations

setMaxIts(int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic

Set the value of MaxIts.

setMaxLinksAdd(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator


 Sets the maximum number of links to add in mutations
 

setMaxLinksDel(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator


 Sets the maximum number of links to remove in mutations
 

setMaxNeuronsAdd(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator


 Sets the maximum number of neurons to add in mutations
 

setMaxNeuronsDel(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator


 Sets the maximum number of neurons to remove in mutations
 

setMaxnofneurons(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputLayer


 Sets the maximum number of neurons of this layer
 

setMaxnofneurons(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer


 Sets the maximum number of neurons of this layer
 

setMaxOfGenerations(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm


 Set the maximum number of iterations for this algorithm
 

setMaxSize(int) - Method in class keel.Algorithms.Rule_Learning.ART.TBAR
 
setMaxSubsequenceLength(int) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel

Sets the maximum length of the subsequence.

setMaxUnitIncrement(int) - Method in class keel.GraphInterKeel.experiments.DinamicDataset
 
setMaxUnitIncrement(int) - Method in class keel.GraphInterKeel.experiments.GraphPanel
 
setMaxUnitIncrement(int) - Method in class keel.GraphInterKeel.experiments.SelectData
 
setMDLTheoryWeight(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats

Set the weight of theory in MDL calcualtion

setMean(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.NormalDistribution

Sets the mean of the distribution.

setMean(double) - Method in class keel.GraphInterKeel.statistical.tests.NormalDistribution

Set the mean of the distribution

setMeasureValue(int, double) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual


 Sets the value of the quality measure in the position pos
 

setMembershipOf(Instance, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.fuzzygCenter


 Computes the memebership degree of a given instance to all the clusters
 

setMicroClSum(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population


 Sets the number of micro classifiers in the set.

setMicroClSum(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population


 Sets the number of micro classifiers in the set.

setMin(double) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute

Replaces the minimum value for the attribute with a new minimum value

setMin(double) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute

Replaces the minimum value for the attribute with a new minimum value

setMin(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeVar


 Sets the minimum value for the variable
 

setMin(float) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeVar


 Sets the minimum value for the variable
 

setMin(float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeVar


 Sets the minimum value for the variable
 

setMin_attr(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene
 
setMin_attr(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene
 
setMinCnf(float) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic


 Sets the minimum confidence
 

setMinConf(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic


 Methods to set the value for the minimum confidence of the rules to be generated
 

setMinConf(float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic


 Methods to set the value for the minimum confidence of the rules to be generated
 

setMinimumDelta(double) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus


 Sets the minimum delta value, that is, the minimum increment or
 step size of the corresponding coefficients

setMinLinksAdd(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator


 Sets the minimum number of links to add in mutations
 

setMinLinksDel(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator


 Sets the minimum number of links to remove in mutations
 

setMinNeuronsAdd(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator


 Sets the minimum number of neurons to add in mutations
 

setMinNeuronsDel(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator


 Sets the minimum number of neurons to remove in mutations
 

setMinNo(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA

Sets the minimum total weight of the instances in a rule

setMinnofneurons(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer


 Sets the minimum number of neurons of this layer
 

setMinNum(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree

Set the value of MinNum.

setMinNum(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree

Set the value of MinNum.

setMinNum(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree

Set the value of MinNum.

setMinSupp(double) - Method in class keel.Algorithms.Rule_Learning.ART.TBAR
 
setMinValues(double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Rule

It stores the minimum values for each attribute

setMissing(int) - Method in class keel.Algorithms.Decision_Trees.C45.Itemset

Function to set a value as missing.

setMissing(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset

Function to set a value as missing.

setMissing(int) - Method in class keel.Algorithms.Decision_Trees.ID3.Itemset

Function to set a value as missing.

setMissing(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Sets a specific value to be "missing".

setMissing(M5Attribute) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Sets a specific value to be "missing".

setMissing(int) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Itemset

Function to set a value as missing.

setMissing(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Sets a specific value to be "missing".

setMissing(AttributeWeka) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Sets a specific value to be "missing".

setMissing(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset

Function to set a value as missing.

setMissing(MyAttribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset

Sets a specific value to be "missing".

setMissing(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset

Function to set a value as missing.

setMissing(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset

Function to set a value as missing.

setMissing(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset

Function to set a value as missing.

setMissing(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset

Function to set a value as missing.

setMissing(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Sets a specific value to be "missing".

setMissing(Attribute) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Sets a specific value to be "missing".

setMissing(int) - Method in class keel.Algorithms.Rule_Learning.ART.Itemset

Function to set a value as missing.

setMissing(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Itemset

Function to set a value as missing.

setMissing(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset

Function to set a value as missing.

setMissing(int) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset

Function to set a value as missing.

setMissing(int) - Method in class keel.Algorithms.Rule_Learning.PART.Itemset

Function to set a value as missing.

setMissing(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset

Function to set a value as missing.

setMissing(int) - Method in class keel.Algorithms.SVM.SMO.core.Instance

Sets a specific value to be "missing".

setMissing(Attribute) - Method in class keel.Algorithms.SVM.SMO.core.Instance

Sets a specific value to be "missing".

setMistakeClass(String) - Method in class keel.Algorithms.Statistical_Tests.Shared.StatTest.InformationAboutClass

Adds the mistaken class given as arguments.

setMistakeClassifications(int) - Method in class keel.Algorithms.Statistical_Tests.Shared.StatTest.InformationAboutClass

Sets the number of incorrect classifications done with the one given.

setml(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Difuso
 
setml(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Difuso
 
setml(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Difuso
 
setml(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Difuso
 
setml(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Difuso
 
setml(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Difuso
 
setml(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Difuso
 
setml(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Difuso
 
setModelResultFile(String) - Method in class keel.Algorithms.Decision_Trees.CART.ResultPrinter

It sets the model result file

setModelTime() - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.Timer

Set model time

setModelTime() - Static method in class keel.Algorithms.RST_Learning.Timer

Set model time

setModelType(SelectedAssociation) - Method in class keel.Algorithms.Decision_Trees.M5.M5

Set the value of Model.

setModified(Classifier[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper
 
setModified(Classifier[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper
 
setModified(boolean) - Method in class keel.GraphInterKeel.experiments.Graph

Sets the modified status of this graph

setMogbest(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm


 Sets maximum number of generations allowed without 
 improving best fitness
 

setMogmean(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm


 Sets maximum number of generations allowed without 
 improving mean fitness
 

setMS(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Itemset


 It sets the cut threshold
 

setMu(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Cochromosome


 Sets a new mu value
 

setMu(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Population


 Sets the new mu value for one subpopulation
 

setMuest(double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Instance

It assigns the values for the instance

setMuest(double[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Instance

It assigns the values for the instance

setMuest(double[]) - Method in class keel.Algorithms.Rule_Learning.AQ.Instance

It assigns the values for the instance

setMuest(double[]) - Method in class keel.Algorithms.Rule_Learning.CN2.Instance

It assigns the values for the instance

setMuest(double[]) - Method in class keel.Algorithms.Rule_Learning.Prism.Muestra


 Assigns the in-puts of the data
 

setMuest(double[]) - Method in class keel.Algorithms.Rule_Learning.UnoR.Muestra


 Assigns the in-puts of the data
 

setMuest(double[]) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Muestra


 Assigns the in-puts of the data
 

setMuest(double[]) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Muestra


 Assigns the in-puts of the data
 

setMuest(double[]) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Instance

It assigns the values for the instance

setMuNext(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic


 Sets the value for indicating the position of the next mutation
 

setMuNext(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic


 Sets the value for indicating the position of the next mutation
 

setMutationProb(double) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.Chromosome

Sets mutation value

setMutationProbability(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
 
setMutationProbability(double) - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome

Sets the mutation probability with the value given.

setMutationProbability(double) - Static method in class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome

Sets the mutation probability with the value given.

setMutationRate(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
setMutator1(<any>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm


 Sets first individual mutator
 

setMutator2(<any>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm


 Sets second individual mutator
 

setN(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
 
setN_e(boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Individual

Sets the chromosome as non-evaluated

setN_etiquetas(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
 
setN_etiquetas(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
 
setN_etiquetas(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
 
setN_etiquetas(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
 
setN_etiquetas(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
 
setN_etiquetas(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
 
setN_etiquetas(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
 
setN_etiquetas(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
 
setN_etiquetas(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
 
setN_etiquetas(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
 
setN_etiquetas(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
 
setN_etiquetas(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
 
setN_etiquetas(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
 
setN_etiquetas(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
 
setN_etiquetas(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
 
setN_etiquetas(int, int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
 
setN_reglas(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
 
setN_reglas(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
 
setN_reglas(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
 
setN_reglas(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
 
setN_reglas(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
 
setN_reglas(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
 
setN_reglas(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
 
setN_reglas(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
 
setN_var_estado(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
 
setN_var_estado(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
 
setN_var_estado(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
 
setN_var_estado(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
 
setN_var_estado(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
 
setN_var_estado(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
 
setN_var_estado(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
 
setN_var_estado(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
 
setN_variables(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
 
setN_variables(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
 
setN_variables(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
 
setN_variables(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
 
setN_variables(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
 
setN_variables(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
 
setN_variables(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
 
setN_variables(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
 
setNAlgorithms(int) - Static method in class keel.GraphInterKeel.statistical.Configuration

Sets the number of algorithms of the test

setName(String) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute

Replaces the name of the attribute with another new name

setName(String) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset

Replaces the name of the dataset with another new name

setName(String) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute

Replaces the name of the attribute with another new name

setName(String) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset

Replaces the name of the dataset with another new name

setName(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

Returns the name of the dataset.

setName(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractAttribute


 Set attribute name
 

setName(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset


 Sets the name of this dataset
 

setName(String) - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute

It sets the attribute name

setName(String) - Method in class keel.Algorithms.Statistical_Tests.Shared.StatTest.InformationAboutClass

Sets the algorithm's name with the one given.

setName(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeVar


 Sets the name of the variable
 

setName(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeVar


 Sets the name of the variable
 

setName(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeVar


 Sets the name of the variable
 

setName(String) - Method in class keel.Dataset.Attribute

It sets the attribute name

setName(String) - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription

Sets the name of the current active layer

setName(String, int) - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription

Sets the name of the indicated layer

setName(String) - Method in class keel.GraphInterKeel.experiments.Graph

Sets the name of this graph

setName(String) - Method in class keel.GraphInterKeel.experiments.UseCase

Set the method's name

setNameClass(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual


 Sets the value of the name of the class of the individual
 

setNameClass(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual


 Sets the value of the name of the class of the individual
 

setNameClass(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual


 Sets the value of the name of the class of the individual
 

setNameClassObj(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar


 Sets the name of the class of the target variable
 

setNameClassObj(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar


 Sets the name of the class of the target variable
 

setNameClassObj(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar


 Sets the name of the class of the target variable
 

setNameObj(int, String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures


 Method to set the name of the objective indicated
 

setnAnts(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome
 
setnAnts(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
 
setnAnts(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
 
setnAnts(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
 
setNClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar


 Sets the number of classes of the target variable
 

setNClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar


 Sets the number of classes of the target variable
 

setNClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar


 Sets the number of classes of the target variable
 

setNClasses(int) - Static method in class keel.Algorithms.Hyperrectangles.INNER.Rule

Sets the number of classes of the problem

setNClasses(int) - Static method in class keel.Algorithms.Hyperrectangles.RISE.Rule

Sets the number of classes

setNClasses(int) - Static method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.Chromosome

Sets the number of classes

setNDatasets(int) - Static method in class keel.GraphInterKeel.statistical.Configuration

Sets the number of data sets of the test

setNegation(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Gene


 Sets the negation bit of this gene
 

setNegativeEta(double) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus


 Sets the negative eta value, that is, the increment
 of the step size at each epoch

setNegEx(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.pnPair


 It sets the negEx value
 

setNemenyi(boolean) - Static method in class keel.GraphInterKeel.statistical.Configuration

Sets the use of Nemenyi test

setNentradas(int) - Method in class keel.GraphInterKeel.datacf.util.Dataset


 Set input variable number
 

setNetConf(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule


 It sets the netconf of an association rule
 

setNetConf(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
 
setNetConf(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
 
setNetConf(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule


 It sets the netconf of an association rule
 

setNetConf(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule


 It sets the netconf of an association rule
 

setNetConf(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
 
setNetConf(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
 
setNetConf(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
 
setNetConf(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
 
setNetConf(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
 
setNetConf(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
 
setNetConf(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
 
setNetConf(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
 
setNeuronTypes(int, String[]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies


 Sets an array of neuron types of a concrete layer
 (this is an hibrid layer)

setNEval(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic


 Methods to set the number of evaluation to perform in an iteration of the GA
 

setNEval(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic


 Sets the number of evaluations of the algorithm
 

setNEval(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual


 Sets the number of evaluation when the individual was created
 

setNEval(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic


 Methods to set the number of evaluation to perform in an iteration of the GA
 

setNew(boolean) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
 
setNew(boolean) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
 
setNew(boolean) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
 
setNewInstancesIndex(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff
 
setNGenerations(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
 
setNInstances(int[]) - Static method in class keel.Algorithms.Hyperrectangles.INNER.Rule

Sets the array of instances of each class

setNInstancesI(int, int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset

Replaces the number of instances of a class that there are in the dataset with a another number of instances for that class

setNInstancesI(int, int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset

Replaces the number of instances of a class that there are in the dataset with a another number of instances for that class

setNLabel(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar


 Sets the number of labels for all the continuous variables
 

setNLabel(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar


 Sets the number of labels for all the continuous variables
 

setNLabel(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar


 Sets the number of labels for all the continuous variables
 

setNLabels(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeVar


 Sets the number of labels used by the continuous variable
 

setNLabels(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeVar


 Sets the number of labels used by the continuous variable
 

setNLabels(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeVar


 Sets the number of labels used by the continuous variable
 

setNObjectives(int, String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic


 Sets the name of an objective
 

setNode(Node) - Method in class keel.GraphInterKeel.experiments.Joint
 
setNodes(Vector) - Method in class keel.GraphInterKeel.experiments.Graph

Sets a new set of nodes

setNoEvaluado() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Individuo
 
setNOfHiddenLayers(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies


 Sets number of hidden layers of the neural nets

setNofinputs(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet


 Sets the number of inputs of the observations stored in the data set
 

setNOfInputs(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies


 Sets number of inputs of the neural nets

setNofobservations(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet


 Sets the number of observations stored in the data set
 

setNofoutputs(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet


 Sets the number of outputs of the observations stored in the data set
 

setNOfOutputs(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies


 Sets number of outputs of the neural nets

setNofvariables(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet


 Sets the number of variables stored in the data set
 

setNombres(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos

Sets the names of the attributes

setNominal(boolean[]) - Static method in class keel.Algorithms.RST_Learning.RSTData
 
setNominal(boolean) - Method in class keel.GraphInterKeel.experiments.UseCase

Sets if the method's variables are nominal

setNominalValue(int, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Gene


 sets the activation for a nominal value, represented by its index in the
 nominal values list of the attribute
 

setNominalValues(String) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelAttribute


 Sets nominal values
 

setNormalization(double) - Static method in class keel.Algorithms.RST_Learning.RSTData
 
setNormalizer(Normalizer) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator


 Sets the normalizer associated to the trainData DataSet
 

setNOutputs(int) - Method in class keel.GraphInterKeel.datacf.util.Dataset


 Set output variable number
 

setNumAliveRules(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
 
setNumAliveRules(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
 
setNumAllConds(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats

Set the number of all conditions that could appear
 in a rule in this RuleStats object, if the number set
 is smaller than 0 (typically -1), then it calcualtes
 based on the data store

setNumAtr(int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset

Replaces the number of attributes in this dataset with a new number of attributes

setNumAtr(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset

Replaces the number of attributes in this dataset with a new number of attributes

setNumAtributos(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
 
setNumber(int) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Cluster


 Set the number of this cluster
 

setNumberCategories(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.CategoricalAttribute


 Set Number of categories
 

setNumberCiters(int) - Method in class keel.Algorithms.MIL.Nearest_Neighbour.CKNN.CKNN
 
setNumberMatches(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier


 Sets the number of matches
 

setNumberMatches(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters


 Sets the number of matches
 

setNumberOfInputs(int) - Static method in class keel.Algorithms.Instance_Generation.utilities.Distance

Assigns the number of inputs of the prototypes.

setNumberOfInputs(int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Distance

Assigns the number of inputs of the prototypes.

setNumberReferences(int) - Method in class keel.Algorithms.MIL.Nearest_Neighbour.AbstractNearestNeighbour
 
setNumberViolatedConstraints(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual


 Sets the numberOfViolatedConstraints of the individual
 

setNumClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual


 Sets the number of the class of the individual
 

setNumClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual


 Sets the number of the class of the individual
 

setNumClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual


 Sets the number of the class of the individual
 

setNumClasses(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList

Sets number of classes field.

setNumClasses(int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset

Replaces the number of classes in this dataset with a new number of classes

setNumClasses(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset

Replaces the number of classes in this dataset with a new number of classes

setNumClassObj(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar


 Sets the number of the class of the target variable
 

setNumClassObj(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar


 Sets the number of the class of the target variable
 

setNumClassObj(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar


 Sets the number of the class of the target variable
 

setNumDatasets(String[]) - Method in class keel.GraphInterKeel.experiments.Experiments

Sets the size of the extenal description objects, and the
 number of data sets

setNumEjemplos(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
 
setNumericRange(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka

Sets the numeric range based on a string.

setNumerosity(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier


 Sets the numerosity of the classifier.

setNumerosity(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters


 Sets the numerosity of the classifier
 

setNumerosity(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier


 Sets the numerosity of the classifier.

setNumerosity(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters


 Sets the numerosity of the classifier
 

setNumFolds(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree

Set the value of NumFolds.

setNumFolds(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree

Set the value of NumFolds.

setNumFolds(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree

Set the value of NumFolds.

setNumFolds(int) - Method in class keel.Algorithms.SVM.SMO.SMO

Set the value of numFolds.

setNumInputs(int) - Method in class keel.GraphInterKeel.experiments.Parameters

sets the number of inputs connections

setNumIns(int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset

Replaces the number of instances in this dataset with a new number of instances

setNumIns(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset

Replaces the number of instances in this dataset with a new number of instances

setNumLow(int) - Method in class keel.Algorithms.Rule_Learning.Riona.Selector


 Set the low number
 

setNumObjectives(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic


 Sets the number of objectives
 

setNumObjectives(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.QualityMeasures


 Sets the num_objetivos of the individual
 

setNumOfFeatures(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
setNumOfGenerations(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
setNumOfRuns(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
setNumOneFrequentItemsets(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Chromosome


 It sets the number of 1-Frequent Itemsets for a chromosome
 

setNumOneFrequentItemsets(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Chromosome


 It sets the number of 1-Frequent Itemsets for a chromosome
 

setNumOneFrequentItemsets(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Chromosome


 It sets the number of 1-Frequent Itemsets for a chromosome
 

setNumOneItemSets(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList

Sets number of one item sets field.

setNumOneItemSets() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree

Sets the number of one item sets field (numOneItemSets to 
    the number of supported one item sets.

setNumOneItemSets() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TotalSupportTree

Sets the number of one item sets field (numOneItemSets to 
    the number of supported one item sets.

setNumOneItemSets() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TotalSupportTree

Sets the number of one item sets field (numOneItemSets to 
    the number of supported one item sets.

setNumOutputs(int) - Method in class keel.GraphInterKeel.experiments.Parameters

Sets the number of extra outputs

setNumParameters(int) - Method in class keel.GraphInterKeel.experiments.Parameters

Sets the number of paramters

setNumRows(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList

Sets number of rows field.

setNumRowsInInputSet() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass

Assigns value to the numRowsInInputSet field.

setNumRowsInTrainingSet() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass

Assigns a value equavalent to the number of rows to the number of
    rows in training set field.

setNumUp(int) - Method in class keel.Algorithms.Rule_Learning.Riona.Selector


 Set the high number
 

setNumValue(int, int) - Static method in class keel.Algorithms.Hyperrectangles.BNGE.Rule

Sets the number of different values for an attribute

setNumValue(int, int) - Static method in class keel.Algorithms.Hyperrectangles.INNER.Rule

Sets the number of different values for an attribute

setNumValue(int, int) - Static method in class keel.Algorithms.Hyperrectangles.RISE.Rule

Sets the number of different values for an attribute

setNumValue(int, int) - Static method in class keel.Algorithms.RST_Learning.RSTData
 
setNumValues() - Static method in class keel.Algorithms.RST_Learning.RSTData
 
setNumValues() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset


 Returns for each attribute the number of attributes for each set of values
 

setNumVar(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual


 Sets the number of variables of the rule (including the consequent)
 

setNumVar(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual


 Sets the number of variables of the rule (including the consequent)
 

setNumVar(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual


 Sets the number of variables of the rule (including the consequent)
 

setNVariables(int) - Method in class keel.GraphInterKeel.datacf.util.Dataset


 Sets input variable number
 

setObj(int, String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic


 Method to set the name of the quality measure in position pos
 

setObj1(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic


 Methods to set the value for the quality measures as objective 1
 

setObj2(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic


 Methods to set the value for the quality measures as objective 2
 

setObj3(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic


 Methods to set the value for the quality measures as objective 3
 

setObjective(String) - Method in class keel.GraphInterKeel.experiments.UseCase

Sets the method's objective

setObjective(int) - Static method in class keel.GraphInterKeel.statistical.Configuration

Sets the objective of the test (maximization or minimization)

setObjectiveValue(int, double) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.QualityMeasures


 Sets the value of the objective pos
 

setObjetives(double[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome


 It sets the objectives for a chromosome
 

setObjetives(double[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome


 It sets the objectives for a chromosome
 

setObjetives(double[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome


 It sets the objectives for a chromosome
 

setObservation(int, double[]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet


 Sets an specified observation
 

setObservationsOf(int, double[]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet


 Sets all the values of a variable in the data set
 

setOmega(double) - Method in class keel.Algorithms.SVM.SMO.supportVector.Puk

Sets the omega value.

setOperacion(Operacion) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.FuncionEvaluacionBean
 
setOperacion(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
 
setOperador(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Condicion

Sets the operator with the value given.

setOperador(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Condicion

Sets the operator with the value given.

setOperador(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Condicion

Sets the operator with the value given.

setOperador(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Condicion

Sets the operator with the value given.

setOperador(int) - Method in class keel.Algorithms.Rule_Learning.Prism.Selector


 Assign the operator
 

setOperador(int) - Method in class keel.Algorithms.Rule_Learning.UnoR.Selector


 Assign the operator
 

setOperador(int) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Selector


 Assign the operator
 

setOperador(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Selector


 Sets the operator
 

setOperator(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleRule

Sets the rule operator

setOperator(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.SimpleRule

Sets the rule operator

setOperator(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Selector


 Assign the operator
 

setoperator(int) - Method in class keel.Algorithms.Rule_Learning.AQ.Selector

It assigns operator

setOperator(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.SimpleRule

Sets the rule operator

setOperator(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.SimpleRule

Sets the rule operator

setoperator(int) - Method in class keel.Algorithms.Rule_Learning.CN2.Selector

It assigns operator

setOperator(int) - Method in class keel.Algorithms.Rule_Learning.PART.SimpleRule

Sets the rule operator

setOperator(int) - Method in class keel.Algorithms.Rule_Learning.Riona.Selector


 Assigns the operator
 

setOperator(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.SimpleRule

Sets the rule operator

setOperator(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.SimpleRule

Sets the rule operator

setOperator(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene


 It sets the type of operator used in a gene
 

setOptimizations(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA

Sets the number of optimization runs

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Decision_Trees.C45.Algorithm

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Decision_Trees.C45.C45

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Decision_Trees.CART.RunCART

Function to read the options from the execution file and assign
 the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.C45

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Decision_Trees.ID3.Algorithm

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Decision_Trees.ID3.ID3

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Static method in class keel.Algorithms.Decision_Trees.M5.M5

Name: setOptions
 Sets the option of the execution of the algorithm, reading them from an input file.

setOptions(String[]) - Method in class keel.Algorithms.Decision_Trees.M5.M5

Parses a given list of options.

setOptions(String[]) - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter

Parses the options for this object.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Decision_Trees.SLIQ.SLIQ

Reads the parameters used by the algorith.

setOptions(String[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Classifier

Parses a given list of options.

setOptions(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
setOptions(StreamTokenizer) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.C45

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.C45CS

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.C45

Function to read the options from the execution file and assign the values to the parameters.

setOptions(String[]) - Method in interface keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.OptionHandler

Sets the OptionHandler's options using the given list.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.C45
 
setOptions(String, String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.C45

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.C45
 
setOptions(String, String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.C45

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.C45
 
setOptions(String, String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.C45

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.C45
 
setOptions(String, String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.C45

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Rule_Learning.ART.Algorithm

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Rule_Learning.ART.ART

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.DataSqueezer

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Rule_Learning.PART.Algorithm

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Rule_Learning.PART.C45

Function to read the options from the execution file and assign the values to the parameters.

setOptions(StreamTokenizer) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45
 
setOptions(String, String) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45

Function to read the options from the execution file and assign the values to the parameters.

setOptions(String[]) - Method in interface keel.Algorithms.Statistical_Classifiers.Logistic.core.OptionHandler

Sets the OptionHandler's options using the given list.

setOptions(String[]) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic

Parses a given list of options.

setOptions(String[]) - Method in class keel.Algorithms.SVM.SMO.core.Check

Parses a given list of options.

setOptions(String[]) - Method in interface keel.Algorithms.SVM.SMO.core.OptionHandler

Sets the OptionHandler's options using the given list.

setOptions(String[]) - Method in class keel.Algorithms.SVM.SMO.supportVector.CachedKernel

Parses a given list of options.

setOptions(String[]) - Method in class keel.Algorithms.SVM.SMO.supportVector.Kernel

Parses a given list of options.

setOptions(String[]) - Method in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel

Parses a given list of options.

setOptions(String[]) - Method in class keel.Algorithms.SVM.SMO.supportVector.PolyKernel

Parses a given list of options.

setOptions(String[]) - Method in class keel.Algorithms.SVM.SMO.supportVector.Puk

Parses a given list of options.

setOptions(String[]) - Method in class keel.Algorithms.SVM.SMO.supportVector.RBFKernel

Parses a given list of options.

setOptions(String[]) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer

Parses a given list of options.

setOptions(String[]) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMO

Parses a given list of options.

setOptions(String[]) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved

Parses a given list of options.

setOptions(String[]) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel

Parses a given list of options.

setOptions(String[]) - Method in class keel.Algorithms.SVM.SMO.SVMreg

Parses a given list of options.

setOrigin(INeuron) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.Link


 Sets the origin neuron of the link
 

setOSup(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures


 Sets the value of original support measure
 

setOutAttribute(int) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanel


 Sets output attribute
 

setOuterBorder(int[]) - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster

Set the outer border of the cluster

setOutput(int, double[]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet


 Sets all the values of an output in the data set
 

setOutput(int[]) - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.ISW

Loads the training output into the classifier

setOutput(int[]) - Static method in class keel.Algorithms.RST_Learning.KNNClassifier

Loads the training output into the classifier

setOutput(int[]) - Static method in class keel.Algorithms.RST_Learning.RSTData

Loads the training output into the classifier

setOutputAttribute(IAttribute) - Method in class keel.Algorithms.Decision_Trees.CART.ResultPrinter

It sets the output attribute

setOutputAttribute(myAttribute) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset

Replaces the information about the output attribute with new information about that attribute

setOutputAttribute(myAttribute) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset

Replaces the information about the output attribute with new information about that attribute

setOutputAttribute(IAttribute) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlusReporterClas


 Sets output attribute metadata

setOutputAttribute(IAttribute) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.IRPropPlusReporterRegr


 Sets output attribute metadata

setOutputAttribute(IAttribute) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.listener.NeuralNetReporterClas


 Sets output attribute metadata

setOutputAttribute(IAttribute) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.listener.NeuralNetReporterRegr


 Sets output attribute metadata
 

setOutputClass(int) - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode
 
setOutputClass(int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode

Replaces the output class of the node with another new output class

setOutputClass(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Register

Replaces the class associated with the register with another class

setOutputClass(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode

Replaces the output class of the node with another new output class

setOutputFile(String) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.ReportTool

Sets the name of the output file to print the report

setOutputFile(String) - Static method in class keel.Algorithms.RST_Learning.ReportTool

Sets the name of the output file to print the report

setOutputFormat(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter

Sets the format of output instances.

setOutputFormat(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter

Sets the format of the output instances.

setOutputI(int, int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset

Replaces the output attribute value at the specified instance in this dataset with the specified value

setOutputI(int, int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset

Replaces the output attribute value at the specified instance in this dataset with the specified value

setOutputInterval(Interval) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator


 Sets the output range of normalized data
 

setOutputLayer(LinkedLayer) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet


 Sets the output layer of this neural net
 

setOutputLayer(LinkedLayer) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.INeuralNet


 Sets the output layer of this neural net
 

setOutputLayerBiased(boolean) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies


 Sets a boolean indicating if the output layer is biased

setOutputLayerInitiator(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies


 Sets initiator of neurons of the output layer

setOutputLayerType(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies


 Sets type of neurons of the output layer

setOutputLayerWeightRange(int, Interval) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies


 Sets weight range of the output layer

setOutputNominalValue(int, String) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance

It set the nominal attribute value to the one passed.

setOutputNominalValue(InstanceAttributes, int, String) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance

Set a new value of a given output attribute in this instance (nominal)

setOutputNominalValue(int, String) - Method in class keel.Dataset.Instance

It set the nominal attribute value to the one passed.

setOutputNominalValue(InstanceAttributes, int, String) - Method in class keel.Dataset.Instance

Set a new value of a given output attribute in this instance (nominal)

setOutputNumericValue(int, double) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance

It changes the attribute value.

setOutputNumericValue(InstanceAttributes, int, double) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance

Set a new value of a given output attribute in this instance (integer or real)

setOutputNumericValue(int, double) - Method in class keel.Dataset.Instance

It changes the attribute value.

setOutputNumericValue(InstanceAttributes, int, double) - Method in class keel.Dataset.Instance

Set a new value of a given output attribute in this instance (integer or real)

setOutputValue(double) - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode
 
setOverallConstraintViolation(double) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual


 Sets the overallConstraintViolation of the individual
 

setP(Poblacion) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Chc
 
setP(Poblacion) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Chc
 
setP(Poblacion) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Chc
 
setP(Poblacion) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Chc
 
setP(Poblacion) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Chc
 
setP(Poblacion) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Chc
 
setP(Poblacion) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Chc
 
setP(Poblacion) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Chc
 
setParam(double[], double, double[]) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbf


 Sets the main parameters of a neuron
 

setParam(double[], double, double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbf

Sets the main parameters of a neuron

setParam(double[], double, double[], int) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbf

Sets the main parameters of a neuron

setParam(double[], double, double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbf

Sets the main parameters of a neuron

setParam(double[], double, double[], int) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbf

Sets the main parameters of a neuron

setParam(double[], double, double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbf

Sets the main parameters of a neuron

setParam(double[], double, double[], int) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbf

Sets the main parameters of a neuron

setParam(double[], double, double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf

Sets the main parameters of a neuron

setParam(double[], double, double[], int) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf

Sets the main parameters of a neuron

setParam(double[], double, double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbf

Sets the main parameters of a neuron

setParam(double[], double, double[], int) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbf

Sets the main parameters of a neuron

setParam(double[], double, double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf

Sets the main parameters of a neuron

setParam(double[], double, double[], int) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf

Sets the main parameters of a neuron

setParent(TreeNode) - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode

It sets the parent of the current node

setParent(Node) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Node

Sets the father of the node with the given node.

setParent(JFrame) - Method in class keel.GraphInterKeel.datacf.DataCFFrame


 Sets parent
 

setParent(JFrame) - Method in class keel.GraphInterKeel.datacf.editData.EditPanel


 Set the JFrame Parent
 

setParent(JFrame) - Method in class keel.GraphInterKeel.datacf.exportData.ExportPanel


 Sets the JFrame parent
 

setParent(JFrame) - Method in class keel.GraphInterKeel.datacf.importData.ImportPanel


 Sets JFrame parent
 

setParent(JFrame) - Method in class keel.GraphInterKeel.datacf.partitionData.PartitionPanel


 Sets the JFrame parent
 

setParent(JFrame) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanel


 Sets the parent JFrame
 

setParent(Frame) - Method in class keel.GraphInterKeel.menu.FrameModules


 Sets parent
 

setParent(JFrame) - Method in class keel.GraphInterKeel.statistical.StatisticalF


 Sets parent
 

setpartitionType(int) - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt


 This methos set type partitions, k-fold or 5x2 

        PK = 0          P5X2 = 1;
 

setPath(String, int) - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription

Sets the path of the indicated layer

setPath(String) - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription

Sets the path of the active layer

setPath(String) - Static method in class keel.GraphInterKeel.statistical.Configuration

Sets the path of the file to store the results of the test

setPath(String) - Static method in class keel.GraphInterKeel.util.Path

Set the path

setPatterns(int[]) - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode
 
setPBit(double) - Static method in class keel.Algorithms.Instance_Selection.CoCoIS.Subpopulation

Sets the bit flip probability

setPBit(double) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.Subpopulation

Sets the bit flip probability

setPDRFType(int) - Method in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel

Sets the Type of Positive Definite Functions used.

setPenalizedFitness(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.Rule

Sets the penalized fitness field to a specified given value, corresponding to this rule penalized
 fitness according to the data set and the other rules considered

setPenalizedFitness(double) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule

Sets the penalized fitness field to a specified given value, corresponding to this rule penalized
 fitness according to the data set and the other rules considered

setPenaltyFactor(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
 
setPercentages(int, double[]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies


 Sets an array of percentages of a concrete layer
 (this is an hibrid layer)

setPercentageSecondMutator(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm


 Sets percentage of census selected to be mutated 
 with second mutator
 

setPerf(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Individual

Sets the performance for the chromosome

setpeso(float) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
 
setpeso(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
 
setpeso(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
 
setpeso(float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
 
setPlus(int, double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Adds a value to an element

setPoblacion(Cromosoma[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
 
setPoblacion(Cromosoma[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
 
setPoblacion(Cromosoma[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
 
setPoblacion(Cromosoma[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
 
setPoblacion(Cromosoma[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
 
setPoblacion(Cromosoma[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
 
setPoblacion(Cromosoma[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
 
setPoblacion(Cromosoma[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
 
setPopBestGuy(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Population


 Sets the position of the best individual of the population
 

setPopBestGuy(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Population


 Sets the position of the best individual of the population
 

setPopBestGuy(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Population


 Sets the position of the best individual of the population
 

setPopsize(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
 
setPOPSIZE(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
 
setPopsize(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
 
setPOPSIZE(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
 
setPopsize(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
 
setPOPSIZE(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
 
setPopsize(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
 
setPOPSIZE(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
 
setPopsize(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
 
setPOPSIZE(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
 
setPopsize(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
 
setPOPSIZE(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
 
setPopsize(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
 
setPOPSIZE(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
 
setPopsize(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
 
setPOPSIZE(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
 
setPopSize(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess
 
setPopulationSize(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
 
setPopulationSize(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
setPopulationSize(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm


 Sets population size of the algorithm
 

setPorcCob(float) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic


 Sets the percentage of biased initialisation in the re-initialisation based on coverage
 

setPosEx(int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.pnPair


 It sets the posEx value
 

setPosFile(long) - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Instance

It assigns the position of the example in the input data file

setPosFile(long) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Instance

It assigns the position of the example in the input data file

setPosFile(long) - Method in class keel.Algorithms.Hyperrectangles.EACH.Sample


 Assigns the position of the example in the in-put file of data
 

setPosFile(long) - Method in class keel.Algorithms.Rule_Learning.AQ.Instance

It assigns the position of the example in the input data file

setPosFile(long) - Method in class keel.Algorithms.Rule_Learning.CN2.Instance

It assigns the position of the example in the input data file

setPosFile(long) - Method in class keel.Algorithms.Rule_Learning.Prism.Muestra


 Assigns the position of the example in the in-put file of data
 

setPosFile(long) - Method in class keel.Algorithms.Rule_Learning.UnoR.Muestra


 Assigns the position of the example in the in-put file of data
 

setPosFile(long) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Muestra


 Assigns the position of the example in the in-put file of data
 

setPosFile(long) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Muestra


 Assigns the position of the example in the in-put file of data
 

setPosFile(long) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Instance

It assigns the position of the example in the input data file

setPosicion(Point) - Method in class keel.GraphInterKeel.experiments.Node

Sets the position of the node in the panel

setPosition(Point) - Method in class keel.GraphInterKeel.experiments.Node

Sets the position of the node in the panel

setPositionRuleMatch(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
 
setPositionRuleMatch(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
 
setPositiveEta(double) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus


 Sets the positive eta value, that is, the increment
 of the step size at each epoch

setPRandom(double) - Static method in class keel.Algorithms.Instance_Selection.CoCoIS.Subpopulation

Sets the random mutation probability

setPRandom(double) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.Subpopulation

Sets the random mutation probability

setPredError(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier


 Sets the prediction error
 

setPredError(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters


 Sets the prediction error
 

setPredicted(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexResult
 
setPrediction(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier


 Sets the prediction value.

setPrediction(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters


 Sets the prediction value.

setPrior(int[]) - Method in class keel.Algorithms.Discretizers.OneR.Opt

Sets the priority of the classes associated to this explanatory value (the less, the more priority)

setPriors(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Sets the class prior probabilities

setPRnn(double) - Static method in class keel.Algorithms.Instance_Selection.CoCoIS.Subpopulation

Sets the RNN mutation probability

setPRnn(double) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.Subpopulation

Sets the RNN mutation probability

setProbCross(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic


 Methods to set the value for the crossover probability
 

setProbCross(float) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic


 Sets the cross probability in the algorithm
 

setProbCross(float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic


 Methods to set the value for the crossover probability
 

setProbMut(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic


 Methods to set the value for the mutation probability
 

setProbMut(float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic


 Methods to set the value for the mutation probability
 

setProbMutacion(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion
 
setProbMutation(float) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic


 Sets the mutation probability
 

setProbMutExtremo(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion
 
setProcessHeader(boolean) - Method in class keel.Algorithms.Preprocess.Converter.Importer

Method for setting a boolean indicating if the header must be processed

setProperties() - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster

Validate the values calculated for the cluster

setProperty(String, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.ProtectedProperties

Overrides a method to prevent the properties from being modified.

setProperty(String, String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ProtectedProperties

Overrides a method to prevent the properties from being modified.

setPrototypeToCompare(Prototype) - Method in class keel.Algorithms.Instance_Generation.utilities.Distance

Assigns base prototype.

setPrototypeToCompare(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Distance

Assigns base prototype.

setProvider(<any>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm


 Sets the individuals provider used in the init task
 

setPruneExamplesFactor(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
 
setPruningFactor(double) - Method in class keel.Algorithms.Decision_Trees.M5.M5

Set the value of PruningFactor.

setPruningFactor(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5

Set the value of PruningFactor.

setPruningMethod(SelectedTag) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel

Sets the method used to for pruning.

setPW(populationWrapper) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.timersManagement
 
setQ(int) - Static method in class keel.Algorithms.Hyperrectangles.RISE.Rule

Sets the Q parameter

setQg(double) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex


 Set the value of Qg
 

setRadius(double) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbf


 Sets the radius of a neuron
 

setRadius(double) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbf


 Sets the radius of a neuron
 

setRadius(double) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbf


 Sets the radius of a neuron
 

setRadius(double) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbf


 Sets the radius of a neuron
 

setRadius(double) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf


 Sets the radius of a neuron
 

setRadius(double) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbf


 Sets the radius of a neuron
 

setRadius(double) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf


 Sets the radius of a neuron
 

setRandGen(IRandGen) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.initiators.PureLayerInitiator


 Sets the random generator of the initiator
 

setRandgen(IRandGen) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ExpNeuronParametricMutator


 Sets the random generator used in mutation
 

setRandgen(IRandGen) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.LinearNeuronParametricMutator


 Sets the random generator used in mutation
 

setRandgen(IRandGen) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.SigmNeuronParametricMutator


 Sets the random generator used in mutation
 

setRandgen(IRandGen) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.ExpNeuronStructuralMutator


 Sets the random generator used in mutation
 

setRandgen(IRandGen) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.LinearNeuronStructuralMutator


 Sets the random generator used in mutation
 

setRandgen(IRandGen) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.SigmNeuronStructuralMutator


 Sets the random generator used in mutation
 

setRandGenFactory(IRandGenFactory) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm


 Sets the randgen factory
 

setRandomSeed(int) - Method in class keel.Algorithms.SVM.SMO.SMO

Set the value of randomSeed.

setRange(float, float) - Method in class keel.Algorithms.Decision_Trees.C45.Attribute

Sets the range of a continuous attribute.

setRanges(String) - Method in class keel.Algorithms.Decision_Trees.M5.Interval

Sets the ranges from a string representation.

setRanges(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Range

Sets the ranges from a string representation.

setRangos(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
 
setRangos(TipoIntervalo[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
 
setRangos(TipoIntervalo[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
 
setRangos(TipoIntervalo[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
 
setRangos(TipoIntervalo[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
 
setRangos(TipoIntervalo[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
 
setRangos(TipoIntervalo[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
 
setRangos(TipoIntervalo[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
 
setRangos(TipoIntervalo[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
 
setRank(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual


 Sets the rank of the individual
 

setRank(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Chromosome
 
setRanking(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Individuo
 
setRealmaxBound(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Gene


 Sets the maximum bound of this gene for the real values (for interval-based relations)
 

setRealminBound(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Gene


 Sets the minimum bound of this gene for the real values (for interval-based relations)
 

setRealValue(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Gene


 Sets the real value of this gene (for one value relations, i.e.

setReconversionArrayRefs(int[][], short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList

Sets the reconversion array reference values.

SETREDAlgorithm - Class in keel.Algorithms.Semi_Supervised_Learning.SETRED

SETRED algorithm calling.

SETREDAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.SETRED.SETREDAlgorithm
 
SETREDGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.SETRED

This class implements the Self-traning wrapper.

SETREDGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.SETRED.SETREDGenerator

Build a new SETREDGenerator Algorithm

SETREDGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.SETRED.SETREDGenerator

Build a new SETREDGenerator Algorithm

setReduccionIni(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
 
setReduccionIni(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
 
setReduccionIni(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
 
setReduccionIni(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
 
setReduccionR(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
 
setReduccionR(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
 
setReducedStepSize(boolean[]) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus


 Sets an array of booleans, indicating which coefficients
 have to be treated as more sensible, using reduced step size
 for these coefficients

setRegOptimizer(RegOptimizer) - Method in class keel.Algorithms.SVM.SMO.SVMreg

sets the learning algorithm

setRegression(boolean) - Method in class keel.Algorithms.Decision_Trees.CART.CART

It sets if we are dealing with a regression problem

setReInitCob(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic


 Sets the value of re-initialisation based on coverage
 

setRelationName(String) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances

Sets the relation's name.

setRelationName(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Sets the relation's name.

setRelationName(String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Sets the relation's name.

setRelationName(String) - Static method in class keel.Algorithms.Rule_Learning.Swap1.Attributes

It sets the relation name.

setRelationName(String) - Method in class keel.Algorithms.Rule_Learning.Swap1.InstanceAttributes

It sets the relation name.

setRelationName(String) - Method in class keel.Algorithms.SVM.SMO.core.Instances

Sets the relation's name.

setRelationName(String) - Static method in class keel.Dataset.Attributes

It sets the relation name.

setRelationName(String) - Method in class keel.Dataset.InstanceAttributes

It sets the relation name.

setRemoved(int, boolean) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset

Sets the flag that indicate if an instance has been removed or not.

setRemoved(int, boolean) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset

Sets the flag that indicate if an instance has been removed or not.

setRemoved(int, boolean) - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset

Sets the flag that indicate if an instance has been removed or not.

setResults(int[], int[], int[], int[], int) - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.ReportTool

Provide information about the classification process to the report tool

setResults(int[], int[], int[], int[], int) - Static method in class keel.Algorithms.RST_Learning.ReportTool

Provide information about the classification process to the report tool

setRidge(double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic

Sets the ridge in the log-likelihood.

setRight(TreeNode) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode

Replaces the right descendant of the node with another new right descendant

setRight(TreeNode) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode

Replaces the right descendant of the node with another new right descendant

setRight(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Intervals
 
setRight(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Interval


 It sets the right bound of an interval
 

setRight(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Intervals
 
setRightSon(TreeNode) - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode
 
setRightValueRank(String) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelAttribute


 Sets right value Rank
 

setRom(boolean) - Static method in class keel.GraphInterKeel.statistical.Configuration

Sets the use of Rom test

setRoot(TreeNode) - Method in class keel.Algorithms.Decision_Trees.CART.tree.DecisionTree

It set the root of the tree

setRoot(Node) - Method in class keel.Algorithms.Rule_Learning.ART.TBAR
 
setRow(int, double[]) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix

Deprecated.
Sets a row of the matrix to the given row.

setRule(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Selected

It sets the rule into the "selected" structure.

setRule(Rule) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Selected

It sets the rule into the "selected" structure

setRuleCF(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome
 
setRuleConfidence(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome


 It sets the confidence of the association rule represented by a chromosome
 

setRuleConfidence(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Chromosome
 
setRuleConv(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome
 
setRuleLift(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome
 
setRuleNetconf(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome
 
setRules(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider
 
SetRules - Class in keel.Algorithms.Subgroup_Discovery.SDAlgorithm

 Defines a set of rules or complex

SetRules() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetRules


 Constructor
 

setRuleset(FastVector) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats

Set the ruleset of the stats, overwriting the old one if any

setRulesRep(String) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic


 Sets the representation of the rules
 

setRulesRep(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic


 Sets the rules representation of the algorithm
 

setRulesRep(String) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic


 Sets the representation of the rules
 

setRuleSupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome


 It sets the support of the association rule represented by a chromosome
 

setRuleSupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule


 It sets the support of an association rule
 

setRuleSupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule


 It sets the support of an association rule
 

setRuleSupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Chromosome
 
setRuleWeight(int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypePitts


 This method updates the weight of a rule.

setRuleYulesQ(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome
 
setS(int) - Static method in class keel.Algorithms.Hyperrectangles.RISE.Rule

Sets the S parameter

setSalidaPDEF(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Base
 
setSalidaPDEF(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Base
 
setSalidaPDEF(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Base
 
setSalidaPDEF(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Base
 
setSalidaPDEF(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Base
 
setSalidaPDEF(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Base
 
setSalidaPDEF(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Base
 
setSalidaPDEF(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Base
 
setSalidasAnteriores(boolean[][]) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.EUSCHCQstat
 
setSample(double[]) - Method in class keel.Algorithms.Hyperrectangles.EACH.Sample


 Assigns the in-puts of the data
 

setSample(int, double) - Method in class keel.Algorithms.Hyperrectangles.EACH.Sample


 Gives value to an atribute
 

setSample(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
 
setSample(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
 
setSample(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
 
setSample(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
 
setSample(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
 
setSample(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
 
setSample(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
 
setSample(int[]) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
 
setScaledFitness(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier
 
setScoringFunction(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
setScrollPane(JScrollPane) - Method in class keel.GraphInterKeel.datacf.editData.EditDataPanel


 Set the JScrollPane
 

setSeed(long) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA

Sets the seed value to use in randomizing the data

setSeed(long) - Static method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
 
setSeed(long) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
 
setSeed(long) - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator

Set the seed of the random generator.

setSeed(long) - Static method in class keel.Algorithms.Instance_Generation.utilities.RandomGenerator

Set the seed of the random method.

setSeed(long) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.MersenneTwister

Initalize the pseudo random number generator.

setSeed(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree

Set the seed for random number generation.

setSeed(long) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator

Set the seed of the random generator.

setSeed(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree

Set the seed for random number generation.

setSeed(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree

Set the seed for random number generation.

setSeed(long) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.RandomGenerator

Set the seed of the random method.

setSeed(int) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer

Sets the seed value for the random number generator

setSeed(long) - Method in class keel.GraphInterKeel.experiments.Graph

Sets the seed of the graph (i.e. the seed of the experiment)

setSeed(long) - Static method in class org.core.Randomize
 
setSeeds(Vector) - Method in class keel.GraphInterKeel.experiments.Parameters

Sets the new seeds

setSelectionAlgorithm(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
setSelective(boolean) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator


 Sets a boolean indicating if the mutator is selective
 

setSet(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.VQ.Cluster

Asssigns the set of prototypes of the cluster.

setShaffer(boolean) - Static method in class keel.GraphInterKeel.statistical.Configuration

Sets the use of Shaffer test

setShowInsertKeel(boolean) - Method in class keel.GraphInterKeel.datacf.importData.ImportPanel


 Sets a boolean indicating the insert to keel option has to be shown
 

setSigma(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.NormalDistribution

Sets the sigma of the distribution.

setSigma(double) - Method in class keel.Algorithms.SVM.SMO.supportVector.Puk

Sets the sigma value.

setSigma(double) - Method in class keel.GraphInterKeel.statistical.tests.NormalDistribution

Set the sigma value of the distribution

setSign(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures


 Sets the value of SIGN
 

setSignificativeWeight(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.initiators.RandomInitiator


 Sets the significative weight value
 

setSignificativeWeigth(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.ExpNeuronStructuralMutator


 Sets the minimum value of new weigths
 

setSignificativeWeigth(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.LinearNeuronStructuralMutator


 Sets the minimum value of new weigths
 

setSignificativeWeigth(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.SigmNeuronStructuralMutator


 Sets the minimum value of new weigths
 

setSignificativeWeigth(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator


 Sets the significative weigth for new links
 

setSilent(boolean) - Method in class keel.Algorithms.SVM.SMO.core.Check

Set slient mode, i.e., no output at all to stdout

setSingleIndex(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.SingleIndex

Sets the index from a string representation.

setSize(int) - Static method in class keel.Algorithms.Hyperrectangles.BNGE.Rule

Sets the size of the rule

setSize(int) - Static method in class keel.Algorithms.Hyperrectangles.INNER.Rule

Sets the size of the problem

setSize(int) - Static method in class keel.Algorithms.Hyperrectangles.RISE.Rule

Sets the size of the rule

setSize(int) - Static method in class keel.Algorithms.Instance_Selection.CoCoIS.Subpopulation

Sets the size of the population

setSize(int) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.Subpopulation

Sets the size of the population

setSize(int) - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome

Sets the size of the chromosome with the value given as parameter.

setSize(int) - Static method in class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome

Sets the size of the chromosome with the value given as parameter.

setSize(int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Sets the size of the vector

setSize(int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector

Sets the size of the vector.

setSource(int) - Method in class keel.GraphInterKeel.experiments.Arc

Sets the node source

setSource2(int) - Method in class keel.GraphInterKeel.experiments.Arc

Sets the node source

setSpecies(<any>) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm


 Sets the system species
 

setStagtol(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM

Sets a new limit to the stagnation tolerance

setStartCMARrulelistToNull() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList

Sets START CMAR RULE LIST to null.

setState(long[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.MTwister
 
setState(long[]) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.MTwister
 
setState(long[]) - Method in class org.core.MTwister
 
setStateAddButton(boolean) - Method in class keel.GraphInterKeel.datacf.editData.EditDataPanel


 Enables or disables Add Button
 

setStateAddButton(boolean) - Method in class keel.GraphInterKeel.datacf.editData.EditVariablePanel


 Set if addButton is enabled or disabled
 

setStateDeleteButton(boolean) - Method in class keel.GraphInterKeel.datacf.editData.EditDataPanel


 Enables or disables Delete Button
 

setStateDeleteButton(boolean) - Method in class keel.GraphInterKeel.datacf.editData.EditVariablePanel


 Enables or Disables Delete Button
 

setStdDebugMode(boolean) - Static method in class keel.Algorithms.Instance_Generation.utilities.Debug

Set standard debug mode to a desired state.

setStdDebugMode(boolean) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Debug

Set standard debug mode to a desired state.

setStrength(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Rule

It assigns a new strength to the rule

setStrictDominance(String) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic


 Sets if the algorithm considers strict dominance
 

setString(FuzzyAlphaCut[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.NodeValue


 This method replace replace the fuzzy alpha cuts 
 

setSubsequenceLength(int) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel

Sets the length of the subsequence.

setSubtype(int) - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription

Sets the subtype of this object

setSubtypelqd(int) - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription

Sets the subtype of this object

setSuitability(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Chromosome


 It sets the suitability for a chromosome
 

setSuitability(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Chromosome


 It sets the suitability for a chromosome
 

setSuitability(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Chromosome


 It sets the suitability for a chromosome
 

setSumFuzzySupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Chromosome


 It sets the sum of fuzzy supports of the 1-Frequent Itemsets covered by a chromosome
 

setSup(int[][]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets
 
setSup(double) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex


 Set the value of Sup
 

setSup(float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.QualityMeasures


 Method to set the value of the support
 

setSupport(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule

It sets the rule's support.

setSupport(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule

It sets the rule's support

setSupport(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule


 It sets the support of the rule
 

setSupport(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Rule


 It sets the support of the rule
 

setSupport(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Rule


 It sets the support of the rule
 

setSupport(double) - Method in class keel.Algorithms.Discretizers.UCPD.Itemset


 It sets the support of this itemset
 

setSupport(Itemset) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule
 
setSupport(int) - Method in class keel.Algorithms.Rule_Learning.ART.Rule
 
setSupport(int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Item

Sets the support value for the item with the one given.

setSupport(int) - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla

Sets the support value of the rule.

setSupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule


 Sets the support of an association rule with the given value.

setSupport(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.Item


 It sets the support of an item
 

setSupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Chromosome
 
setSupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
 
setSupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
 
setSupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome


 It sets the support of the association rule represented by a chromosome
 

setSupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome


 It sets the support of the association rule represented by a chromosome
 

setSupport(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome


 It sets the support of the association rule represented by a chromosome
 

setSupport_Ant(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
 
setSupport_Ant(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule
 
setSupport_cons(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
 
setSupport_cons(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule


 It sets the consequent support of an association rule
 

setSupport_cons(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
 
setSupport_consq(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
 
setSupport_consq(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
 
setSupportAll(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
 
setSupportAll(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
 
setSupportAndConfidence(double, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass

Sets new values for the support and confidence fields.

setSupportAnt(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
 
setSupportAnt(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
 
setSupportCon(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
 
setSupportCon(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
 
setSupportConfidence(double, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Apriori


 Sets the minimum confidence and support thresholds
 

setSupportConfidence(double, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Apriori


 Sets the minimum confidence and support thresholds
 

setSupportConfidence(double, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Apriori


 Sets the minimum confidence and support thresholds
 

setSVMReg(SVMreg) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer

sets the parent SVM

setT(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
 
setTablaDataset(DatasetTable) - Method in class keel.GraphInterKeel.datacf.editData.EditDataPanel


 Set DatasetTable
 

setTabSize(int) - Static method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
 
setTabSize(int) - Static method in class keel.Dataset.SimpleCharStream
 
setTarget(INeuron) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.Link


 Sets the target neuron of the link
 

setTau(double) - Method in class keel.Algorithms.MIL.APR.IteratedDiscrimination.IteratedDiscrimination
 
setTCompOfClassifiers(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population


 Initializes the classifiers' reduction time stamp to the tStamp value
 

setTemperExponent(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator


 Sets the temperature exponent to be used in the mutations
 

setTemperExponent(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator


 Sets the temperature exponent to be used in the mutations
 

setTestData(DoubleTransposedDataSet) - Method in class keel.Algorithms.Decision_Trees.CART.ResultPrinter

It sets the test data

setTestReportFileName(String) - Method in class keel.Algorithms.MIL.AbstractMIAlgorithm
 
setTestResultFile(String) - Method in class keel.Algorithms.Decision_Trees.CART.ResultPrinter

It sets the test result file

setTestResultFile(String) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlusReporterClas


 Sets file name where the testing results of best model 
 obtained will be written

setTestResultFile(String) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.IRPropPlusReporterRegr


 Sets file name where the testing results of best model 
 obtained will be written

setTestResultFile(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.listener.NeuralNetReporterClas


 Sets file name where the testing results of best model 
 obtained will be written

setTestResultFile(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.listener.NeuralNetReporterRegr


 Sets file name where the testing results of best model 
 obtained will be written
 

setTestTime() - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.Timer

Sets training time

setTestTime() - Static method in class keel.Algorithms.RST_Learning.Timer

Sets training time

setTextData(String) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelDataset


 Sets text for dataset area
 

setTHRESHOLD(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
 
setTHRESHOLD(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
 
setTHRESHOLD(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
 
setTHRESHOLD(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
 
setTHRESHOLD(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
 
setTHRESHOLD(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
 
setTHRESHOLD(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
 
setTHRESHOLD(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
 
setThreshold(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess
 
setTHRESHOLDR(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
 
setTHRESHOLDR(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
 
setTime(long) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Rule

It sets the time the rule was added to the rule set.

setTime(long) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Rule

It sets the time the rule was added to the rule set

setTime(long) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Rule


 It sets the time of the rule
 

setTime(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier


 Sets the time stamp for this classifier.

setTime(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters


 Sets the time stamp for this classifier.

setTime(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier


 Sets the time stamp for this classifier.

setTime(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters


 Sets the time stamp for this classifier.

setTimeOfClassifiers(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population


 Initializes the classifiers' time stamp to the tStamp value
 

setTimeOfClassifiers(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population


 Initializes the classifiers' time stamp to the tStamp value
 

setTimes(int, double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Multiplies a value to an element

setTipo(boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Atributo

Sets the attribute type with the one given: numeric (0) or nominal (1).

setTipo(boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Atributo

Sets the attribute type with the one given: numeric (0) or nominal (1).

setTipo(boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Atributo

Sets the attribute type with the one given: numeric (0) or nominal (1).

setTipo(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Atributo

Sets the attribute type with the one given: numeric (0) or nominal (1).

setTipos(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
 
setTMtablaAtributos(AttributeTable) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelAttribute


 Set the Atributtes Table
 

setTNorm(int) - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
 
setTolerance(double) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved

sets the tolerance

setToleranceParameter(double) - Method in class keel.Algorithms.SVM.SMO.SMO

Set the value of tolerance parameter.

setToleranceParameter(double) - Method in class keel.Algorithms.SVM.SMO.SMOreg

Set the value of tolerance parameter.

setTotalClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Individual


 Sets the number of the class of the individual
 

setTotalClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Individual


 Sets the number of the class of the individual
 

setTotalClass(int) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Individual


 Sets the number of the class of the individual
 

setTotalErrors(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Selected

It sets the total of errors made by the rule into the "selected" structure.

setTotalErrors(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Selected

It sets the total of errors made by the rule into the "selected" structure

setTotalexperiments(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Chc
 
setTotalexperiments(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Chc
 
setTotalexperiments(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Chc
 
setTotalexperiments(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Chc
 
setTotalexperiments(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Chc
 
setTotalexperiments(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Chc
 
setTotalexperiments(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Chc
 
setTotalexperiments(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Chc
 
setTotalNumberClass(int) - Method in class keel.Algorithms.Statistical_Tests.Shared.StatTest.InformationAboutClass

Sets the total number of classes of the problem with the one given.

setTotaltrials(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Chc
 
setTotaltrials(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Chc
 
setTotaltrials(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Chc
 
setTotaltrials(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Chc
 
setTotaltrials(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Chc
 
setTotaltrials(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Chc
 
setTotaltrials(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Chc
 
setTotaltrials(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Chc
 
setTP(double) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex


 Set the value of TP
 

setTrainData(DoubleTransposedDataSet) - Method in class keel.Algorithms.Decision_Trees.CART.ResultPrinter

It sets the training data

setTrainingData(DoubleTransposedDataSet) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus

 
 Sets training data

setTrainingSetDocumentsID(List<Integer>) - Static method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.WrapperManager
 
setTrainingTime() - Static method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.Timer

Set training time

setTrainingTime() - Static method in class keel.Algorithms.RST_Learning.Timer

Set training time

setTrainReportFileName(String) - Method in class keel.Algorithms.MIL.AbstractMIAlgorithm
 
setTrainResultFile(String) - Method in class keel.Algorithms.Decision_Trees.CART.ResultPrinter

It sets the training results file

setTrainResultFile(String) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlusReporterClas


 Sets file name where the testing results of best model 
 obtained will be written

setTrainResultFile(String) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.IRPropPlusReporterRegr


 Sets file name where the testing results of best model 
 obtained will be written

setTrainResultFile(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.listener.NeuralNetReporterClas


 Sets file name where the testing results of best model 
 obtained will be written

setTrainResultFile(String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Regr.listener.NeuralNetReporterRegr


 Sets file name where the testing results of best model 
 obtained will be written
 

setTree(GenotypeFuzzyGP) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGP


 This method copies the given parameter into the current object.

setTree(GenotypeFuzzyGPRegSym) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypeFuzzyGPRegSym


 This method copies the given parameter into the current object.

setTrials(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
 
setTrials(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
 
setTrials(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
 
setTrials(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
 
setTrials(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
 
setTrials(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
 
setTrials(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
 
setTrials(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
 
setTrials(int) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic


 Sets the number of trials in the algorithm
 

setType(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Ruleset

Sets the common output (consecuent) of the rules in the ruleset.

setType(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule

It sets the right side of the rule.

setType(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Ruleset

Sets the common output (consecuent) of the rules in the ruleset.

setType(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Condition

It sets the type

setType(int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer


 Sets the type of this layer
 

setType(int) - Method in class keel.Algorithms.Rule_Learning.AQ.Instance

It sets the type of the instance

setType(String) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Rule

It sets the right side of the rule.

setType(String) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Ruleset

Sets the common output (consecuent) of the rules in the ruleset.

setType(String) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule

It sets the right side of the rule.

setType(String) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset

Sets the common output (consecuent) of the rules in the ruleset.

setType(String) - Method in class keel.Algorithms.Rule_Learning.PART.Rule

It sets the right side of the rule.

setType(String) - Method in class keel.Algorithms.Rule_Learning.PART.Ruleset

Sets the common output (consecuent) of the rules in the ruleset.

setType(String) - Method in class keel.Algorithms.Rule_Learning.Ripper.Rule

It sets the right side of the rule.

setType(String) - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset

Sets the common output (consecuent) of the rules in the ruleset.

setType(String) - Method in class keel.Algorithms.Rule_Learning.Slipper.Rule

It sets the right side of the rule.

setType(String) - Method in class keel.Algorithms.Rule_Learning.Slipper.Ruleset

Sets the common output (consecuent) of the rules in the ruleset.

setType(int) - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute

It sets the attribute type.

setType(char) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeVar


 Sets the char with the type used in the variable
 

setType(char) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeVar


 Sets the char with the type used in the variable
 

setType(char) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeVar


 Sets the char with the type used in the variable
 

setType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene
 
setType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
 
setType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
 
setType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Gene
 
setType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Gene
 
setType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene
 
setType(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene
 
setType(int) - Method in class keel.Dataset.Attribute

It sets the attribute type.

setType(int) - Method in class keel.GraphInterKeel.experiments.Graph

Sets the type of the graph (experiment type)

setType(int) - Method in class keel.GraphInterKeel.experiments.Node

Sets the type of the node

setType(String) - Method in class keel.GraphInterKeel.experiments.UseCase

Sets the method's type

setTypelqd(int) - Method in class keel.GraphInterKeel.experiments.Node

Sets the type of the node

setU(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Gene
 
setU(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Gene
 
setU(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene
 
setUnscaledTestData(DoubleTransposedDataSet) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator


 Sets the DataSet associated to the evaluator as unscaled test data
 

setUnscaledTrainData(DoubleTransposedDataSet) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator


 Sets the DataSet associated to the evaluator as unscaled train data
 

setUnus(float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures


 Sets the value of UNUS
 

SetupParameters - Class in keel.Algorithms.Neural_Networks.gann


 Class for capturing the global parameters and data 
 

SetupParameters() - Constructor for class keel.Algorithms.Neural_Networks.gann.SetupParameters


 Empty constructor
 

SetupParameters - Class in keel.Algorithms.Neural_Networks.gmdh


 Class for capturing the global parameters and data
 

SetupParameters() - Constructor for class keel.Algorithms.Neural_Networks.gmdh.SetupParameters


 Empty constructor
 

setUpper(int) - Method in class keel.Algorithms.Decision_Trees.M5.Interval

Sets the value of "last".

setUpper(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Range

Sets the value of "last".

setUpper(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.SingleIndex

Sets the value of "last".

setUpperBound(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Condition

Sets the upper bound

setUpperBound(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene


 It sets the upper bound of the interval stored in a gene
 

setUpperBound(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene


 It sets the upper bound of the interval stored in a gene
 

setUpperBound(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene


 It sets the upper bound of the interval stored in a gene
 

setUse(String, String) - Static method in class keel.Algorithms.Instance_Generation.utilities.Parameters

Sets the use of the algorithm

setUse(String, String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters

Sets the use of the algorithm

setUseful(boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier


 Sets the useful parameter
 

setUseful(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier


 Sets the usefulTimes parameter
 

setUseful(boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters


 Sets the useful parameter
 

setUseful(boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population


 It initialitzes all the useful params of the population.

setUsefulTimes(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters


 Sets the usefulTimes parameter
 

setUseLowerOrder(boolean) - Method in class keel.Algorithms.SVM.SMO.supportVector.PolyKernel

Sets whether to use lower-order terms.

setUseNormalization(boolean) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel

Sets whether to use normalization.

setUserOptions(String[]) - Method in class keel.Algorithms.SVM.SMO.supportVector.KernelEvaluation

sets the option the user supplied for the kernel

setUseRuleStretching(boolean) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA

Sets whether pruning is performed

setUseUnsmoothed(boolean) - Method in class keel.Algorithms.Decision_Trees.M5.M5

Set the value of UseUnsmoothed.

setUseUnsmoothed(boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5

Set the value of UseUnsmoothed.

setUseVariant1(boolean) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved

Sets whether to use variant 1

setVal(float, float, float, float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Fuzzy


 Method to set the values of x0, x1 y x3
 

setVal(float, float, float, float) - Method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Fuzzy


 Method to set the values of x0, x1 y x3
 

setVal(float, float, float, float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Fuzzy


 Method to set the values of x0, x1 y x3
 

setVal3(float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.QualityMeasures


 Method to set the value of the objective 3
 

setValEnum(Vector) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos
 
setValor(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Condicion

Sets the value for the attribute given.

setValor(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Condicion

Sets the value for the attribute given.

setValor(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Condicion

Sets the value for the attribute given.

setValor(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Entero
 
setValor(float) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Atributo

Sets the attribute's value with the one given.

setValor(Atributo) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Condicion

Sets the value for the attribute given.

setValor(float) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Condicion

Sets the value for the value given.

setValor(double) - Method in class keel.Algorithms.Rule_Learning.Prism.Selector


 Assign the value
 

setValor(double) - Method in class keel.Algorithms.Rule_Learning.UnoR.Selector


 Assign the value
 

setValor(double) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Selector


 Assign the value
 

setValor(double) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Selector


 Sets the value for the selector

setValorConstante(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
 
setValores(double[]) - Method in class keel.Algorithms.Rule_Learning.Prism.Selector


 Assign the values
 

setValores(double[]) - Method in class keel.Algorithms.Rule_Learning.UnoR.Selector


 Assign the values
 

setValores(double[]) - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Selector


 Assign the values
 

setValores(double[]) - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Selector


 Sets a set of value for the selector
 

setValorOperacion1(Operacion) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
 
setValorOperacion2(Operacion) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Operacion
 
setValue(int, double) - Method in class keel.Algorithms.Decision_Trees.C45.Itemset

Function to set a value.

setValue(double) - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode
 
setValue(int, double) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset

Function to set a value.

setValue(String, int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute

Replaces the value in a position of a nominal attribute with a new value

setValue(double) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.Split

Replaces the value of the attribute for the split with another new value

setValue(int, double) - Method in class keel.Algorithms.Decision_Trees.ID3.Itemset

Function to set a value.

setValue(int, double) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Sets a specific value in the instance to the given value
 (internal floating-point format).

setValue(int, String) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Sets a value of a nominal or string attribute to the given
 value.

setValue(M5Attribute, double) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Sets a specific value in the instance to the given value
 (internal floating-point format).

setValue(M5Attribute, String) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Sets a value of an nominal or string attribute to the given
 value.

setValue(int, double) - Method in class keel.Algorithms.Decision_Trees.M5.M5SparseInstance

Sets a specific value in the instance to the given value
 (internal floating-point format).

setValue(String, int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute

Replaces the value in a position of a nominal attribute with a new value

setValue(double) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Split

Replaces the value of the attribute for the split with another new value

setValue(int, double) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Itemset

Function to set a value.

setValue(int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Sets a specific value in the instance to the given value
 (internal floating-point format).

setValue(int, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Sets a value of a nominal or string attribute to the given
 value.

setValue(AttributeWeka, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Sets a specific value in the instance to the given value
 (internal floating-point format).

setValue(AttributeWeka, String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Sets a value of an nominal or string attribute to the given
 value.

setValue() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.SingleIndex

Translates a single string selection into it's internal 0-based equivalent

setValue(int, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset

Function to set a value.

setValue(int, String) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset

Sets a value of a nominal or string attribute to the given
 value.

setValue(MyAttribute, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset

Sets a specific value in the instance to the given value
 (internal floating-point format).

setValue(MyAttribute, String) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset

Sets a value of an nominal or string attribute to the given
 value.

setValue(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleRule

Sets the attribute's value

setValue(int, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset

Function to set a value.

setValue(int, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset

Function to set a value.

setValue(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.SimpleRule

Sets the attribute's value

setValue(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Condition

It add the value (or a nan if *)

setValue(int, double) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset

Function to set a value.

setValue(int, double) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset

Function to set a value.

setValue(int, double) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Sets a specific value in the instance to the given value 
 (internal floating-point format).

setValue(int, String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Sets a value of a nominal or string attribute to the given
 value.

setValue(Attribute, double) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Sets a specific value in the instance to the given value
 (internal floating-point format).

setValue(Attribute, String) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Sets a value of an nominal or string attribute to the given
 value.

setValue(int, double) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance

Sets a specific value in the instance to the given value 
 (internal floating-point format).

setValue(int, double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset.Instance


 Sets a double value in the specified attribute index
 

setValue(int, String) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset.Instance


 Sets a string value in the specified attribute index
 

setvalue(double) - Method in class keel.Algorithms.Rule_Learning.AQ.Selector

It assigns the value

setValue(int, double) - Method in class keel.Algorithms.Rule_Learning.ART.Itemset

Function to set a value.

setValue(int, double) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Itemset

Function to set a value.

setValue(double) - Method in class keel.Algorithms.Rule_Learning.C45Rules.SimpleRule

Sets the attribute's value

setValue(int, double) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset

Function to set a value.

setValue(double) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.SimpleRule

Sets the attribute's value

setvalue(double) - Method in class keel.Algorithms.Rule_Learning.CN2.Selector

It assigns the value

setValue(int, double) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset

Function to set a value.

setValue(int, double) - Method in class keel.Algorithms.Rule_Learning.PART.Itemset

Function to set a value.

setValue(double) - Method in class keel.Algorithms.Rule_Learning.PART.SimpleRule

Sets the attribute's value

setValue(double) - Method in class keel.Algorithms.Rule_Learning.Riona.Selector


 Assigns the value
 

setValue(double) - Method in class keel.Algorithms.Rule_Learning.Ripper.SimpleRule

Sets the attribute's value

setValue(double) - Method in class keel.Algorithms.Rule_Learning.Slipper.SimpleRule

Sets the attribute's value

setValue(int, double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset

Function to set a value.

setValue(TechnicalInformation.Field, String) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation

sets the value for the given field, overwrites any previously existing one.

setValue(int, double) - Method in class keel.Algorithms.SVM.SMO.core.Instance

Sets a specific value in the instance to the given value 
 (internal floating-point format).

setValue(int, String) - Method in class keel.Algorithms.SVM.SMO.core.Instance

Sets a value of a nominal or string attribute to the given
 value.

setValue(Attribute, double) - Method in class keel.Algorithms.SVM.SMO.core.Instance

Sets a specific value in the instance to the given value
 (internal floating-point format).

setValue(Attribute, String) - Method in class keel.Algorithms.SVM.SMO.core.Instance

Sets a value of an nominal or string attribute to the given
 value.

setValue(TechnicalInformation.Field, String) - Method in class keel.Algorithms.SVM.SMO.core.TechnicalInformation

sets the value for the given field, overwrites any previously existing one.

setValue(ArrayList<Integer>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
 
setValue(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
 
setValue(ArrayList<Integer>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
 
setValue(int) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
 
setValue(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene


 It sets the value stored in a gene
 

setValue(int, String) - Method in class keel.GraphInterKeel.experiments.Parameters

updates actual value for parameter at index position

setValueAt(Object, int, int) - Method in class keel.GraphInterKeel.datacf.util.AttributeTable


 Sets the value of an element of the table
 

setValueAt(Object, int, int) - Method in class keel.GraphInterKeel.datacf.util.DatasetTable
 
setValueAt(Object, int, int) - Method in class keel.GraphInterKeel.datacf.util.VariableTable


 Sets the value of a cell
 

setValueAt(Object, int, int) - Method in class keel.GraphInterKeel.experiments.ParametersTable

Sets the value of the cell

setValueAt(Object, int, int) - Method in class keel.GraphInterKeel.statistical.statTableModel

Sets the value of a cell

setValueAverage(String) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelAttribute


 Sets value average
 

setvaluees(double[]) - Method in class keel.Algorithms.Rule_Learning.AQ.Selector

It assigns the set of values

setvaluees(double[]) - Method in class keel.Algorithms.Rule_Learning.CN2.Selector

It assigns the set of values

setValueObj(int, float) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.QualityMeasures


 Method to set the value of the objective indicated
 

setValues(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Item

It sets the pair of values to the item.

setValues(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Item

It sets the pair of values to the item.

setValues(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.Literal


 It sets the values for a literal
 

setValues(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Item


 It sets the pair of values to the item
 

setValues(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Item


 It sets the pair of values to the item
 

setValues(int, int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Item


 It sets the pair of values to the item
 

setValues(ArrayList<String>) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute

Replaces the array of possible values for the attribute data type with a new array of values

setValues(ArrayList<String>) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute

Replaces the array of possible values for the attribute data type with a new array of values

setValues(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Item
 
setValues(double[]) - Method in class keel.Algorithms.Hyperrectangles.EACH.Selector


 Assign the values
 

setValues(Vector<Integer>) - Method in class keel.Algorithms.Rule_Learning.ART.Rule
 
setValues(double[]) - Method in class keel.Algorithms.Rule_Learning.Riona.Selector


 Assigns the values
 

setValues(Vector) - Method in class keel.GraphInterKeel.experiments.Parameters

updates value vector

setValueSparse(int, double) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Sets a specific value in the instance to the given value
 (internal floating-point format).

setValueSparse(int, double) - Method in class keel.Algorithms.Decision_Trees.M5.M5SparseInstance

Sets a specific value in the instance to the given value
 (internal floating-point format).

setValueSparse(int, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Sets a specific value in the instance to the given value
 (internal floating-point format).

setValueSparse(int, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset

Sets a specific value in the instance to the given value
 (internal floating-point format).

setValueSparse(int, double) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Sets a specific value in the instance to the given value 
 (internal floating-point format).

setValueSparse(int, double) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance

Sets a specific value in the instance to the given value 
 (internal floating-point format).

setValueSparse(int, double) - Method in class keel.Algorithms.SVM.SMO.core.Instance

Sets a specific value in the instance to the given value 
 (internal floating-point format).

setValueVariance(String) - Method in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelAttribute


 Sets value variance
 

setVariable(int) - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode
 
setVariableTable(VariableTable) - Method in class keel.GraphInterKeel.datacf.editData.EditVariablePanel


 Sets the table including the variables
 

setVariance(double) - Method in class keel.Algorithms.Lazy_Learning.NSC.Cluster

Get variance and stdDev of the cluster

setVectorsNeighbors(int[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
 
setVerbosity(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5

Set the value of Verbosity.

setVerbosity(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5

Set the value of Verbosity.

setVerbosity(Hashtable) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils


 Sets verbosity value from hashtable containing parameters
 

setVerbosity(boolean) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils


 Sets verbosity value from a given value
 

setVerbosity(Hashtable) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils


 Sets verbosity value from hashtable containing parameters
 

setVerbosity(boolean) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils


 Sets verbosity value from a given value
 

setVerbosity(Hashtable) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils


 Sets verbosity value from hashtable containing parameters
 

setVerbosity(boolean) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils


 Sets verbosity value from a given value
 

setVerbosity(Hashtable) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils


 Sets verbosity value from hashtable containing parameters
 

setVerbosity(boolean) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils


 Sets verbosity value from a given value
 

setVerbosity(Hashtable) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils


 Sets verbosity value from hashtable containing parameters
 

setVerbosity(boolean) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils


 Sets verbosity value from a given value
 

setVerbosity(Hashtable) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils


 Sets verbosity value from hashtable containing parameters
 

setVerbosity(boolean) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils


 Sets verbosity value from a given value
 

setVerbosity(Hashtable) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils


 Sets verbosity value from hashtable containing parameters
 

setVerbosity(boolean) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils


 Sets verbosity value from a given value
 

setVolume(double) - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex

Sets the volume with the value given.

setVolume(double) - Method in class keel.Algorithms.Rule_Learning.Riona.Complex
 
setW(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.MembershipFunction


 It sets the spread of an isosceles-triangle
 

setW(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.MembershipFunction


 It sets the spread of an isosceles-triangle
 

setw1(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual

Sets the value of w1 with the given argument.

setw1(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual
 
setW1(float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic


 Methods to set the value for the weight of the objective 1
 

setW2(float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic


 Methods to set the value for the weight of the objective 2
 

setW3(float) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic


 Methods to set the value for the weight of the objective 3
 

setWDifference(double) - Static method in class keel.Algorithms.Instance_Selection.CoCoIS.Subpopulation

Sets the WDifference parameter

setWDifference(double) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.Subpopulation

Sets the WDifference parameter

setWeight(double) - Method in class keel.Algorithms.Decision_Trees.C45.Itemset

Function to set the weight.

setWeight(double) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset

Function to set the weight.

setWeight(double) - Method in class keel.Algorithms.Decision_Trees.ID3.Itemset

Function to set the weight.

setWeight(double) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Sets the weight of an instance.

setWeight(double) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Itemset

Function to set the weight.

setWeight(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka

Sets the new attribute's weight

setWeight(double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Sets the weight of an instance.

setWeight(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset

Function to set the weight.

setWeight(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset

Function to set the weight.

setWeight(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset

Function to set the weight.

setWeight(double) - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex

Sets the weight with the value given.

setWeight(double) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset

Function to set the weight.

setWeight(double) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset

Function to set the weight.

setWeight(double) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Sets the weight of an instance.

setWeight(int, double) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbf


 Sets the ith weight of a neuron
 

setWeight(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset.Instance


 Sets the weigth of this instance
 

setWeight(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.Link


 Sets the weight associated to this link
 

setWeight(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbf

Sets the weights of a neuron

setWeight(int, double) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbf


 Sets the ith weight of a neuron
 

setWeight(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbf

Sets the weights of a neuron

setWeight(int, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbf


 Sets the ith weight of a neuron
 

setWeight(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbf

Sets the weights of a neuron

setWeight(int, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbf


 Sets the ith weight of a neuron
 

setWeight(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf

Sets the weights of a neuron

setWeight(int, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf


 Sets the ith weight of a neuron
 

setWeight(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbf

Sets the weights of a neuron

setWeight(int, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbf


 Sets the ith weight of a neuron
 

setWeight(double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf

Sets the weights of a neuron

setWeight(int, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf


 Sets the ith weight of a neuron
 

setWeight(double) - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.pnPair


 It sets the new weight for the pnPair
 

setWeight(double) - Method in class keel.Algorithms.Rule_Learning.AQ.Complex

It assigns the weight of the complex computed as:

 Covered positives - covered negatives / number of selectors

setWeight(double) - Method in class keel.Algorithms.Rule_Learning.ART.Itemset

Function to set the weight.

setWeight(double) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Itemset

Function to set the weight.

setWeight(double) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset

Function to set the weight.

setWeight(double) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset

Function to set the weight.

setWeight(double) - Method in class keel.Algorithms.Rule_Learning.PART.Itemset

Function to set the weight.

setWeight(double) - Method in class keel.Algorithms.Rule_Learning.Riona.Complex
 
setWeight(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset

Function to set the weight.

setWeight(double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype

Assigns a new weight to the prototype.

setWeight(double) - Method in class keel.Algorithms.SVM.SMO.core.Instance

Sets the weight of an instance.

setWeightAtrib(double[]) - Method in class keel.Algorithms.Hyperrectangles.EACH.HyperrectangleSet
 
setWeightRange(Interval) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron


 Sets the weight range associated to the links
 

setWeights(double[]) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.Rbf


 Sets the weights of a neuron
 

setWeights(double[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ConjGradNN

 
  Copy the weights contained in p to the weights matrix.

setWeights(double[]) - Method in class keel.Algorithms.Shared.ClassicalOptim.ConjGradNN

 
  Copy the weights contained in p to the weights matrix.

setWeightVector(double[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
 
setWError(double) - Static method in class keel.Algorithms.Instance_Selection.CoCoIS.Subpopulation

Sets the WError parameter

setWError(double) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.Subpopulation

Sets the WError parameter

setWhitoutValues(boolean) - Method in class keel.GraphInterKeel.experiments.UseCase

Sets if the method's variables has no values for some examples

setWithImprecise(boolean) - Method in class keel.GraphInterKeel.experiments.UseCase

Sets if the method's variables has imprecise values for some examples

setWk(double[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess
 
setWorst(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Poblacion
 
setWorst(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Poblacion
 
setWorst(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Poblacion
 
setWorst(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Poblacion
 
setWorst(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Poblacion
 
setWorst(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Poblacion
 
setWorst(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Poblacion
 
setWorst(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Poblacion
 
setWracc(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Rule


 It sets the Wracc of the rule
 

setWracc(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule
 
setWReduction(double) - Static method in class keel.Algorithms.Instance_Selection.CoCoIS.Subpopulation

Sets the WReduction parameter

setWReduction(double) - Static method in class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.Subpopulation

Sets the WReduction parameter

setwRule(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Structure


 It sets the position of the first rule that wrongly classifies the example stored in the structure
 

setwRule(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Structure


 It sets the position of the first rule that wrongly classifies the example stored in the structure
 

setX(double[]) - Method in class keel.Algorithms.PSO_Learning.CPSO.Particle
 
setX(double[]) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
 
setX(double[]) - Method in class keel.Algorithms.PSO_Learning.REPSO.Particle
 
setx0(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Difuso
 
setx0(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Difuso
 
setx0(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Difuso
 
setx0(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Difuso
 
setx0(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Difuso
 
setx0(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Difuso
 
setx0(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Difuso
 
setx0(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Difuso
 
setX0(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyRegion


 It sets the X0 value of a fuzzy region
 

setX0(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyRegion


 It sets the X0 value of a fuzzy region
 

setX0(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyRegion


 It sets the X0 value of a fuzzy region
 

setX0(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyRegion


 It sets the X0 value of a fuzzy region
 

setx1(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Difuso
 
setx1(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Difuso
 
setx1(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Difuso
 
setx1(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Difuso
 
setx1(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Difuso
 
setx1(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Difuso
 
setx1(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Difuso
 
setx1(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Difuso
 
setX1(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyRegion


 It sets the X1 value of a fuzzy region
 

setX1(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyRegion


 It sets the X1 value of a fuzzy region
 

setX1(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyRegion


 It sets the X1 value of a fuzzy region
 

setX1(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyRegion


 It sets the X1 value of a fuzzy region
 

setx2(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Difuso
 
setx2(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Difuso
 
setx2(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Difuso
 
setx2(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Difuso
 
setx2(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Difuso
 
setx2(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Difuso
 
setx2(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Difuso
 
setx2(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Difuso
 
setx3(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Difuso
 
setx3(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Difuso
 
setx3(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Difuso
 
setx3(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Difuso
 
setx3(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Difuso
 
setx3(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Difuso
 
setx3(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Difuso
 
setx3(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Difuso
 
setX3(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyRegion


 It sets the X3 value of a fuzzy region
 

setX3(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyRegion


 It sets the X3 value of a fuzzy region
 

setX3(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyRegion


 It sets the X3 value of a fuzzy region
 

setX3(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyRegion


 It sets the X3 value of a fuzzy region
 

setXOver(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
setXOverRate(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
sety(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Replace

It sets in the structure the class of the example.

sety(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Structure


 It sets in the structure the class of the example
 

sety(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Replace

It sets in the structure the class of the example

sety(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Structure


 It sets in the structure the class of the example
 

sety(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Difuso
 
sety(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Difuso
 
sety(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Difuso
 
sety(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Difuso
 
sety(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Difuso
 
sety(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Difuso
 
sety(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Difuso
 
sety(double) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Difuso
 
setY(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyRegion


 It sets the Y value of a fuzzy region
 

setY(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyRegion


 It sets the Y value of a fuzzy region
 

setY(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyRegion


 It sets the Y value of a fuzzy region
 

setY(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyRegion


 It sets the Y value of a fuzzy region
 

setYulesQ(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule


 It sets the yulesQ of an association rule
 

setYulesQ(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule
 
setYulesQ(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
 
setYulesQ(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule


 It sets the yulesQ of an association rule
 

setYulesQ(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule


 It sets the yulesQ of an association rule
 

setYulesQ(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
 
setYulesQ(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
 
setZeroValue(double) - Method in class keel.Algorithms.Hyperrectangles.EACH.Selector


 Assign the value
 

SFEntropyGain() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Returns the total SF, which is the null model entropy minus
 the scheme entropy.

SFGSS(PrototypeSet[], int, int, double) - Method in class keel.Algorithms.Instance_Generation.DROP3SFLSDE.DROP3SFLSDE

SFGSS local Search.

SFGSS(PrototypeSet[], int, int, double) - Method in class keel.Algorithms.Instance_Generation.ICFSFLSDE.ICFSFLSDE

SFGSS local Search.

SFGSS(PrototypeSet, double) - Method in class keel.Algorithms.Instance_Generation.IPLDE.IPLDEGenerator

SFGSS local Search.

SFGSS(PrototypeSet[], int, int, double) - Method in class keel.Algorithms.Instance_Generation.SFLSDE.SFLSDEGenerator

SFGSS local Search.

SFGSS(PrototypeSet[], int, int, double) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE

SFGSS local Search.

SFHC(PrototypeSet[], int, int, double, double) - Method in class keel.Algorithms.Instance_Generation.DROP3SFLSDE.DROP3SFLSDE

SFHC local search

SFHC(PrototypeSet[], int, int, double, double) - Method in class keel.Algorithms.Instance_Generation.ICFSFLSDE.ICFSFLSDE

SFHC local search

SFHC(PrototypeSet, double, double) - Method in class keel.Algorithms.Instance_Generation.IPLDE.IPLDEGenerator

SFHC local search

SFHC(PrototypeSet[], int, int, double, double) - Method in class keel.Algorithms.Instance_Generation.SFLSDE.SFLSDEGenerator

SFHC local search

SFHC(PrototypeSet[], int, int, double, double) - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE

SFHC local search

SFLSDEAlgorithm - Class in keel.Algorithms.Instance_Generation.SFLSDE

SFLSDE algorithm calling.

SFLSDEAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.SFLSDE.SFLSDEAlgorithm
 
SFLSDEGenerator - Class in keel.Algorithms.Instance_Generation.SFLSDE
 
SFLSDEGenerator(PrototypeSet, int, int, int, int, double, double, int) - Constructor for class keel.Algorithms.Instance_Generation.SFLSDE.SFLSDEGenerator

Build a new SFLSDEGenerator Algorithm

SFLSDEGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.SFLSDE.SFLSDEGenerator

Build a new SFLSDEGenerator Algorithm

SFMeanEntropyGain() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Returns the SF per instance, which is the null model entropy
 minus the scheme entropy, per instance.

SFMeanPriorEntropy() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Returns the entropy per instance for the null model

SFMeanSchemeEntropy() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Returns the entropy per instance for the scheme

SFPriorEntropy() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Returns the total entropy for the null model

SFS - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_IEP_FS

 SFS Algorithm

SFS(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_IEP_FS.SFS


 Creates a new instance of SA
 

SFS - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_LIU_FS

 SFS Algorithm

SFS(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_LIU_FS.SFS


 Creates a new instance of SA
 

SFS - Class in keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_MI_FS

 SFS Algorithm

SFS(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.nonevolutionary_algorithms.SFS_MI_FS.SFS


 Creates a new instance of SA
 

SFSchemeEntropy() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Returns the total entropy for the scheme

SGA - Class in keel.Algorithms.Instance_Selection.SGA

File: SGA.java
 
 Steady-State Genetic algorithm for Instance Selection.

SGA(String) - Constructor for class keel.Algorithms.Instance_Selection.SGA.SGA

Default builder.

SGA - Class in keel.Algorithms.Preprocess.Instance_Selection.SGA

File: SGA.java
 
 Steady-State Genetic algorithm for Instance Selection.

SGA(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.SGA.SGA

Default builder.

SGERD - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD

It contains the implementation of the SGERD algorithm

SGERD() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.SGERD

Default constructor

SGERD(parseParameters) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.SGERD

It reads the data from the input files (training, validation and test) and parse all the parameters
 from the parameters array.

SGPAlgorithm - Class in keel.Algorithms.Instance_Generation.SGP

PSO algorithm calling.

SGPAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.SGP.SGPAlgorithm
 
SGPGenerator - Class in keel.Algorithms.Instance_Generation.SGP
 
SGPGenerator(PrototypeSet, int, int, int) - Constructor for class keel.Algorithms.Instance_Generation.SGP.SGPGenerator

Build a new SGPGenerator Algorithm

SGPGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.SGP.SGPGenerator

Build a new SGPGenerator Algorithm

ShapiroWilkC - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest

Classification Shapiro Wilk Stat-test identifier.

ShapiroWilkR - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest

Regression Shapiro Wilk Stat-test identifier.

shift(int, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector

Shifts an element to another position.

shiftRange(int, int, Dataset, int, int) - Method in class keel.Algorithms.Decision_Trees.C45.Classification

Function to shift all itemsets in given range from one value to another.

shiftRange(int, int, Dataset, int, int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Classification

Function to shift all itemsets in given range from one value to another.

shiftRange(int, int, MyDataset, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Classification

Function to shift all itemsets in given range from one value to another.

shiftRange(int, int, Dataset, int, int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Classification

Function to shift all itemsets in given range from one value to another.

shiftRange(int, int, Dataset, int, int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Classification

Function to shift all itemsets in given range from one value to another.

shiftRange(int, int, MyDataset, int, int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Classification

Function to shift all itemsets in given range from one value to another.

shiftRange(int, int, MyDataset, int, int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Classification

Function to shift all itemsets in given range from one value to another.

shiftRange(int, int, MyDataset, int, int) - Method in class keel.Algorithms.Rule_Learning.PART.Classification

Function to shift all itemsets in given range from one value to another.

shiftRange(int, int, Dataset, int, int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Classification

Function to shift all itemsets in given range from one value to another.

shiftToEnd(int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector

Shifts an element to the end of the vector.

shouldSelectCell(EventObject) - Method in class keel.GraphInterKeel.datacf.util.EachRowEditor


 Should a cell be selected
 

show() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
 
show() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyPartition
 
show(FileWriter) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
 
show() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.Interval
 
show() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
 
show() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyPartition
 
show(FileWriter) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
 
show() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.Interval
 
show() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.partition
 
show(FileWriter) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.rule
 
show() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.partition
 
show(FileWriter) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
 
show() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.partition
 
show(FileWriter) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
 
show() - Method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
 
show() - Method in class keel.Algorithms.LQD.preprocess.Expert.interval
 
show() - Method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
 
show() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
 
show() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzypartition
 
show() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.interval
 
show() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
 
show() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzypartition
 
show() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.interval
 
show() - Method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
 
show(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractAttribute


 Show an String which represents a given real value
 

show(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.CategoricalAttribute


 Show an String which represents a given real value
 

show(double) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.data.IAttribute


 Show an String which represents a given real value
 

show(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.IntegerNumericalAttribute


 Show an String which represents a given real value
 

show(double) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.RealNumericalAttribute


 Show an String which represents a given real value
 

show1() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
 
show1() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
 
show1() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.rule
 
show1() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
 
show1() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
 
show1() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzyrule
 
show1() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzyrule
 
show_fitness() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.AlgGenetic
 
show_fitness() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.AlgGenetic
 
show_fitness() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.AlgGenetic
 
show_fitness() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.AlgGenetic
 
show_fitness() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.AlgGenetic
 
show_fitness_total(int) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.AlgGenetic
 
show_fitness_total(int) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.AlgGenetic
 
show_fitness_total(int) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.AlgGenetic
 
show_fitness_total(int, int) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.AlgGenetic
 
show_fitness_total(int, int) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.AlgGenetic
 
show_fitness_total_test(float[][], int) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.AlgGenetic
 
show_fitness_total_test(float[][], int) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.AlgGenetic
 
show_fitness_total_test(float[][], int) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.AlgGenetic
 
show_fitness_total_test(float[][], int) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.AlgGenetic
 
show_fitness_total_test_ltf(float[][], int) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.AlgGenetic
 
show_fitness_total_test_ltf(float[][], int) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.AlgGenetic
 
showAlgButton - Variable in class keel.GraphInterKeel.experiments.Experiments
 
showDatasetStatistics() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessDataset


 prints to standard output statistics about the dataset.

showDatasetStatistics() - Method in class keel.Algorithms.Shared.Parsing.ProcessDataset


 prints to standard output statistics about the dataset.

showDialog() - Method in class keel.GraphInterKeel.experiments.Algorithm

Shows the parameter dialog

showDialog() - Method in class keel.GraphInterKeel.experiments.DataSet

Show data set dialog

showDialog() - Method in class keel.GraphInterKeel.experiments.Jclec

Show dialog

showDialog() - Method in class keel.GraphInterKeel.experiments.Multiplexor

Show dialog

showDialog() - Method in class keel.GraphInterKeel.experiments.Node

Shows the new dialog associated to this node

showDialog() - Method in class keel.GraphInterKeel.experiments.Test

Show associated dialog

showDialog() - Method in class keel.GraphInterKeel.experiments.UserMethod

Show associated dialog

showInsertKeel - Variable in class keel.GraphInterKeel.datacf.importData.ImportPanel

This is used for showing or not the insert to keel option

showPartition - Variable in class keel.GraphInterKeel.datacf.editData.EditPanel
 
showResultsOfAccuracy(int, int, int, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator

Internal function that shows in the screen the parameters of accuracy of the condensation

showResultsOfAccuracy(String, int, PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator

Internal function that shows in the screen the parameters of accuracy of the condensation

Shrink - Class in keel.Algorithms.Instance_Selection.Shrink

File: Shrink.java
 
 The Shrink Instance Selection algorithm.

Shrink(String) - Constructor for class keel.Algorithms.Instance_Selection.Shrink.Shrink

Default constructor.

Shrink - Class in keel.Algorithms.Preprocess.Instance_Selection.Shrink

File: Shrink.java
 
 The Shrink Instance Selection algorithm.

Shrink(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.Shrink.Shrink

Default constructor.

shrinking - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
 
shrinking - Variable in class org.libsvm.svm_parameter
 
SIA - Class in keel.Algorithms.Genetic_Rule_Learning.SIA

Title: Main class of the algorithm
 Description: It contains the esential methods for the SIA algorithm
 Copyright: Copyright (c) 2004
 Company: KEEL

SIA() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.SIA.SIA

Default Builder

SIA(String, String, String, String, String, String, long, int, double, double, double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.SIA.SIA

Builder of the class SIA
 It carries out a local copy of the name of the files for their posterior use

 Then, obtains the data from the input files and stores it in a structure for the program

 Finally, it creates the possible bounds for the attribute values


siblingRef - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PtreeNode

Pointer to sibling structurte.

siEstaCubierta() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Muestra

Sets the example as covered.

siEstaCubierta() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Muestra

Sets the example as covered.

siEstaCubierta() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Muestra

Sets the example as covered.

siEstaCubierta() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Muestra

Sets the example as covered.

siEstaCubierta() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Muestra

Sets the example as covered.

sigmaTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.Puk

Returns the tip text for this property

SigmLayer - Class in keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet

 Represents a neural net layer with all the neurons of SigmNeuron type

SigmLayer() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.SigmLayer


 Empty constructor
 

SigmNeuron - Class in keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet

Represents a sigmoidal neuron of a neural net

SigmNeuron() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.SigmNeuron


 Empty constructor
 

SigmNeuronParametricMutator - Class in keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric

Parametric Mutator of Sigmoidal Neurons.

SigmNeuronParametricMutator() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.SigmNeuronParametricMutator

Empty constructor

SigmNeuronStructuralMutator - Class in keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural

Structural Mutator of Sigmoidal Neurons.

SigmNeuronStructuralMutator() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.SigmNeuronStructuralMutator


 Empty constructor
 

SIGMOID - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
 
SIGMOID - Static variable in class org.libsvm.svm_parameter
 
SIGMOIDAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.ProbabilityManagement
 
SIGMOIDAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.ProbabilityManagement
 
SIGMOIDAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.ProbabilityManagement
 
SIGMOIDAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ProbabilityManagement
 
sign() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Returns the signs of all elements in terms of -1, 0 and +1.

sign - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.ExponentialFormat

Sign flag.

signChange(double[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV


 returns a copy of the parameter vector with changed sign.

signChange(double[][]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV


 returns a copy of the matrix a with changed sign.

signChange(double[][][]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV


 returns a copy of the parameter vector with changed sign.

signChange(double[]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV


 returns a copy of the parameter vector with changed sign.

signChange(double[][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV


 returns a copy of the matrix a with changed sign.

signChange(double[][][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV


 returns a copy of the parameter vector with changed sign.

significativeWeight - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.initiators.RandomInitiator

Minimum absolute value of the new weights

significativeWeigth - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.ExpNeuronStructuralMutator

Minimum value of new weigths

significativeWeigth - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.LinearNeuronStructuralMutator

Minimum value of new weigths

significativeWeigth - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.SigmNeuronStructuralMutator

Minimum value of new weigths

significativeWeigth - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator

Minimum value of new weigths

similar2dec(double, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining

Compares two real numbers and returns true if the two numbers are 
    the same within two decimal places.

similargene(int, ArrayList<Integer>, ArrayList<Integer>, int, int, DenseMatrix, DenseMatrix, ArrayList<Integer>, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.LLSImpute

Computes the most similar (nearest) instances to a given one.

similaridadJurio(double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Rule
 
SimpleCharStream - Class in keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser

An implementation of interface CharStream, where the stream is assumed to
 contain only ASCII characters (without unicode processing).

SimpleCharStream(Reader, int, int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
 
SimpleCharStream(Reader, int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
 
SimpleCharStream(Reader) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
 
SimpleCharStream(InputStream, int, int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
 
SimpleCharStream(InputStream, int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
 
SimpleCharStream(InputStream) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
 
SimpleCharStream - Class in keel.Algorithms.Rule_Learning.Swap1

An implementation of interface CharStream, where the stream is assumed to
 contain only ASCII characters (without unicode processing).

SimpleCharStream(Reader, int, int, int) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
 
SimpleCharStream(Reader, int, int) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
 
SimpleCharStream(Reader) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
 
SimpleCharStream(InputStream, String, int, int, int) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
 
SimpleCharStream(InputStream, int, int, int) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
 
SimpleCharStream(InputStream, String, int, int) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
 
SimpleCharStream(InputStream, int, int) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
 
SimpleCharStream(InputStream, String) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
 
SimpleCharStream(InputStream) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
 
SimpleCharStream - Class in keel.Dataset

An implementation of interface CharStream, where the stream is assumed to
 contain only ASCII characters (without unicode processing).

SimpleCharStream(Reader, int, int, int) - Constructor for class keel.Dataset.SimpleCharStream
 
SimpleCharStream(Reader, int, int) - Constructor for class keel.Dataset.SimpleCharStream
 
SimpleCharStream(Reader) - Constructor for class keel.Dataset.SimpleCharStream
 
SimpleCharStream(InputStream, String, int, int, int) - Constructor for class keel.Dataset.SimpleCharStream
 
SimpleCharStream(InputStream, int, int, int) - Constructor for class keel.Dataset.SimpleCharStream
 
SimpleCharStream(InputStream, String, int, int) - Constructor for class keel.Dataset.SimpleCharStream
 
SimpleCharStream(InputStream, int, int) - Constructor for class keel.Dataset.SimpleCharStream
 
SimpleCharStream(InputStream, String) - Constructor for class keel.Dataset.SimpleCharStream
 
SimpleCharStream(InputStream) - Constructor for class keel.Dataset.SimpleCharStream
 
SimpleRule - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules

Represent one single rule of the form: exemple[a]==v,
 exemple[a]>=v or exemple[a]<=v.

SimpleRule() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleRule

Default constructor (the attribute's id and value are undefined)

SimpleRule(int, double, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleRule

Constructs a SimpleRule with a given attribute and value

SimpleRule - Class in keel.Algorithms.Genetic_Rule_Learning.PART


 Represent one single rule of the form: exemple[a]==v,
 exemple[a]>=v or exemple[a]<=v.

SimpleRule() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.SimpleRule

Default constructor (the attribute's id and value are undefined)

SimpleRule(int, double, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.SimpleRule

Constructs a SimpleRule with a given attribute and value

SimpleRule - Class in keel.Algorithms.Rule_Learning.C45Rules


 Represent one single rule of the form: exemple[a]==v,
 exemple[a]>=v or exemple[a]<=v.

SimpleRule() - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.SimpleRule

Default constructor (the attribute's id and value are undefined)

SimpleRule(int, double, int) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.SimpleRule

Constructs a SimpleRule with a given attribute and value

SimpleRule - Class in keel.Algorithms.Rule_Learning.C45RulesSA


 Represent one single rule of the form: exemple[a]==v,
 exemple[a]>=v or exemple[a]<=v.

SimpleRule() - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.SimpleRule

Default constructor (the attribute's id and value are undefined)

SimpleRule(int, double, int) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.SimpleRule

Constructs a SimpleRule with a given attribute and value

SimpleRule - Class in keel.Algorithms.Rule_Learning.PART

 Represent one single rule of the form: exemple[a]==v,
 exemple[a]>=v or exemple[a]<=v.

SimpleRule() - Constructor for class keel.Algorithms.Rule_Learning.PART.SimpleRule

Default constructor (the attribute's id and value are undefined)

SimpleRule(int, double, int) - Constructor for class keel.Algorithms.Rule_Learning.PART.SimpleRule

Constructs a SimpleRule with a given attribute and value

SimpleRule - Class in keel.Algorithms.Rule_Learning.Ripper

Represent one single rule of the form: exemple[a]==v,
 exemple[a]>=v or exemple[a]<=v.

SimpleRule() - Constructor for class keel.Algorithms.Rule_Learning.Ripper.SimpleRule

Default constructor (the attribute's id and value are undefined)

SimpleRule(int, double, int) - Constructor for class keel.Algorithms.Rule_Learning.Ripper.SimpleRule

Constructs a SimpleRule with a given attribute and value

SimpleRule - Class in keel.Algorithms.Rule_Learning.Slipper

Represent one single rule of the form: exemple[a]==v
 exemple[a]>=v or exemple[a]<=v.

SimpleRule() - Constructor for class keel.Algorithms.Rule_Learning.Slipper.SimpleRule

Default constructor (the attribute's id and value are undefined)

SimpleRule(int, double, int) - Constructor for class keel.Algorithms.Rule_Learning.Slipper.SimpleRule

Constructs a SimpleRule with a given attribute and value

SimpleStatistics - Class in keel.Algorithms.Decision_Trees.M5

A class to store simple statistics

SimpleStatistics() - Constructor for class keel.Algorithms.Decision_Trees.M5.SimpleStatistics
 
SimpleStatistics - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules

A class to store simple statistics

SimpleStatistics() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleStatistics
 
SimulatedAnnealing - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms

 
 SimulatedAnnealing is the simulated annealing evolutionary algorithm and 
 programming (SAP) algorithm as detailed in "Combining GP operators with SA 
 search to evolve fuzzy rule based classifiers", Sanchez, Couso, Corrales
 Information Sciences 136 (2001).

SimulatedAnnealing(GeneticIndividual, double, double, double, double, double, int, Randomize, int, int, int, int, double) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms.SimulatedAnnealing


 Class constructor with the following parameters:
 

SingleIndex - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core

Class representing a single cardinal number.

SingleIndex() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.SingleIndex

Default constructor.

SingleIndex(String) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.SingleIndex

Constructor to set initial index.

singleNodeToString() - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode

Converts the information stored at this node to a string

singleNodeToString() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode

Converts the information stored at this node to a string

SINGULAR - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.Fitness
 
SingularValueDecomposition - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix

Singular Value Decomposition.

SingularValueDecomposition(Matrix) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.SingularValueDecomposition

Construct the singular value decomposition

SInstances - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets
 
SInstances(Reader) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SInstances
 
SInstances(Reader, int) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SInstances
 
SInstances(Instances) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SInstances
 
SInstances(Instances, int) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SInstances
 
SInstances(Instances, int, int) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SInstances
 
SInstances(String, FastVector, int) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SInstances
 
size() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.Itemset

It returns the size of the itemset (the number of items it has).

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset

Returns the total number of instances in the data-set.

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.RuleBase


 It returns the number of rules in the rule base
 

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.Itemset

It returns the size of the itemset (the number of items it has)

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.RuleBase


 It returns the number of rules in the rule base
 

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset

It return the size of the data-set

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset

It return the size of the data-set

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.RuleBase


 It returns the number of rules in the rule base
 

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.Itemset


 It returns the size of the itemset (the number of items it has)
 

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.RuleBase


 It returns the number of rules in the rule base
 

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual


 It returns the number of rules in the rule base
 

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Itemset


 It returns the size of the itemset (the number of items it has)
 

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase


 It returns the number of rules in the rule base
 

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Individual


 It returns the number of rules in the rule base
 

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.Itemset


 It returns the size of the itemset (the number of items it has)
 

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset

It return the size of the data-set

size() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.RuleBase


 It returns the number of rules in the rule base
 

size() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset

It return the size of the data-set

size() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.Rule

Returns the number of selectors in the rule.

size() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.RuleBase

Returns the number of rules in the rule base

size() - Method in class keel.Algorithms.Decision_Trees.DT_GA.BaseR

Return the number of existing rules in the rulebase.

size() - Method in class keel.Algorithms.Decision_Trees.DT_GA.Individuo

Returns the size of the chromosome.

size() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset

Returns the total number of instances in the data-set.

size() - Method in class keel.Algorithms.Decision_Trees.DT_GA.Regla

Returns the size of the rule (number of antecedents).

size() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.Individuo

Returns the size of the chromosome.

size() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector

Returns the vector's current size.

size() - Method in class keel.Algorithms.Decision_Trees.M5.Queue

Gets queue's size.

size() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Discretizers.UCPD.Itemset


 It returns the size (number of different attributes) of this itemset
 

size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.BaseR

It returns the number of rules

size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset

It return the size of the data-set

size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset

It returns the number of examples of the data-set

size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase

It returns the size of the Rule Base

Size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
 
Size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t


 Returns the number of labels in the domain
 

Size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
 
Size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
 
size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset

It returns the number of examples of the data-set

size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.BaseR
 
size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Individuo
 
size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseR
 
size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset

It return the size of the data-set

size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset

It returns the number of examples of the data-set

size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual
 
size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Itemset
 
size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
 
size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.FuzzyClassifier

 
 This method returns the RuleBase size.

size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Genotypes.GenotypePitts


 This method returns the number of rules of the FRBS
 

size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Individuo
 
size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector

Returns the vector's current size.

size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Queue

Gets queue's size.

size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule

the number of antecedents of the rule

size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Rule

The size of the rule.

size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyPartition

 
 Returns the number of fuzzy sets in the partition 
 
 

size() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase

 
 Returns the number of rules.

size() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.myDataset

It returns the number of examples of the data-set

size() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset

It return the size of the data-set

size - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_ADI
 
size - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_GABIL
 
size - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_UBR
 
size() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset

It return the size of the data-set

size() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset

It return the size of the data-set

size() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

It returns the number of exemple of the dataset

size() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue

Gets queue's size.

size() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule

Returns the size (number of simple rules) of the rule

size() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Ruleset

Returns the size (number of rules) of the ruleset.

size - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_ADI
 
size - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_GABIL
 
size() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.BaseR
 
size() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Poblacion
 
size() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset

It returns the number of exemple of the dataset

size() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule

Returns the size (number of simple rules) of the rule

size() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Ruleset

Returns the size (number of rules) of the ruleset.

size() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.myDataset

It returns the number of examples of the data-set

size() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.ruleSet

It returns the size of the rule set

size() - Method in class keel.Algorithms.Hyperrectangles.EACH.Complex


 Returns the size of the complex
 

size() - Method in class keel.Algorithms.Hyperrectangles.EACH.EachDataSet


 Returns the number of examples of the data set
 

size() - Method in class keel.Algorithms.Hyperrectangles.EACH.RuleSet


 Returns the number of rules we are working with
 

size() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset

It return the size of the data-set

size() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Rule

Returns the number of selectors in the rule.

size() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.RuleBase

Returns the number of rules in the rule base

size() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset

It returns the number of examples of the data-set

size() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector

Returns the vector's current size.

size() - Method in class keel.Algorithms.Instance_Generation.GMCA.Cluster

Gets the number of prototypes that forms the cluster.

size() - Method in class keel.Algorithms.Instance_Generation.GMCA.ClusterSet

Gets the number of clusters of the set.

size() - Method in class keel.Algorithms.Instance_Generation.VQ.Cluster

Returns the size of the cluster.

size() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyPartition
 
size() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
 
size() - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.IndMichigan
 
size() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyPartition
 
size() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
 
size() - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.IndMichigan
 
size() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.IndMichigan
 
size() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.partition
 
size() - Method in class keel.Algorithms.LQD.methods.FGFS_Original.rule
 
size() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.IndMichigan
 
size() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.partition
 
size() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
 
size() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.IndMichigan
 
size() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.partition
 
size() - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
 
size() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzypartition
 
size() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzyrule
 
size() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzypartition
 
size() - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzyrule
 
size() - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbfn


 Returns the number of neurons in the net
 

size() - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn


 Returns the number of neurons in the net
 

size() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn


 Returns the number of neurons in the net
 

size() - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn


 Returns the number of neurons in the net
 

size() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn


 Returns the number of neurons in the net
 

size() - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn


 Returns the number of neurons in the net
 

size() - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.IntegerSet


 It returns the size of the set

size() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.BaseR
 
size() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.MatrizR
 
size() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Rule_Learning.AQ.Complex

It returns the number of selectors of the complex

size() - Method in class keel.Algorithms.Rule_Learning.AQ.myDataset

It returns the number of examples of the data-set

size() - Method in class keel.Algorithms.Rule_Learning.AQ.ruleSet

It returns the size of the rule set

size() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset

It returns the number of exemple of the dataset

size() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Rule

Returns the size (number of simple rules) of the rule

size() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Ruleset

Returns the size (number of rules) of the ruleset.

size() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset

It returns the number of exemple of the dataset

size() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule

Returns the size (number of simple rules) of the rule

size() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset

Returns the size (number of rules) of the ruleset.

size() - Method in class keel.Algorithms.Rule_Learning.CN2.Complex

It returns the number of selectors of the complex

size() - Method in class keel.Algorithms.Rule_Learning.CN2.myDataset

It returns the number of examples of the data-set

size() - Method in class keel.Algorithms.Rule_Learning.CN2.ruleSet

It returns the size of the rule set

size() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset

It returns the number of exemple of the dataset

size() - Method in class keel.Algorithms.Rule_Learning.PART.Rule

Returns the size (number of simple rules) of the rule

size() - Method in class keel.Algorithms.Rule_Learning.PART.Ruleset

Returns the size (number of rules) of the ruleset.

size() - Method in class keel.Algorithms.Rule_Learning.Prism.Complejo


 Returns the size of the complex
 

size() - Method in class keel.Algorithms.Rule_Learning.Prism.ConjDatos


 Returns the number of examples of our set of data items
 

size() - Method in class keel.Algorithms.Rule_Learning.Prism.ConjReglas


 Returns the number of rules we are working with
 

size() - Method in class keel.Algorithms.Rule_Learning.Riona.Complex


 Return the size complex
 

size() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Rule_Learning.Ripper.Rule

Returns the size (number of simple rules) of the rule

size() - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset

Returns the size (number of rules) of the ruleset.

size() - Method in class keel.Algorithms.Rule_Learning.Ripper.Score

Returns the size of the vector (the number of values).

size() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Rule_Learning.Slipper.Rule

Returns the size (number of simple rules) of the rule

size() - Method in class keel.Algorithms.Rule_Learning.Slipper.Ruleset

Returns the size (number of rules) of the ruleset.

size() - Method in class keel.Algorithms.Rule_Learning.Slipper.Score

Returns the size of the vector (the number of values).

size() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset

It returns the number of examples

size() - Method in class keel.Algorithms.Rule_Learning.UnoR.Complejo


 Return the size complex
 

size() - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjDatos


 Returns the number of examples of our set of data items
 

size() - Method in class keel.Algorithms.Rule_Learning.UnoR.ConjReglas


 Returns the number of rules we are working with
 

size() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector

Returns the vector's current size.

size() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Gets the size of the vector.

size() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector

Gets the size of the vector.

size() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjDatos


 Returns the number of examples of our set of data items
 

size() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.ConjReglas


 Returns the number of rules we are working with
 

size() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Regla

Returns the rule size (number of attributes/antecedents)

size() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Complejo


 Return the size complex
 

size() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjDatos


 Returns the number of examples of our set of data items
 

size() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.ConjReglas


 Returns the number of rules we are working with
 

size() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Complex


 Return the size of the complex
 

size() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetData


 Return the number of instances
 

size() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.SetRules


 Return the number of rules
 

size() - Method in class keel.Algorithms.SVM.SMO.core.FastVector

Returns the vector's current size.

size() - Method in class keel.Algorithms.SVM.SMO.core.Queue

Gets queue's size.

size() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Itemset


 It returns the number of items contained into an itemset
 

size() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Itemset


 It returns the number of items contained into an itemset
 

size() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Itemset


 It returns the number of items contained into an itemset
 

size() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Itemset


 It returns the number of items contained into an itemset
 

size() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Itemset


 It returns the number of items contained into an itemset
 

size() - Method in class keel.GraphInterKeel.experiments.DinamicParameter
 
SizeBinary(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
 
SizeBinary(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
 
SizeBinary(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
 
sizeD - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_UBR
 
SizeDomain() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
 
SizeDomain(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar


 Returns the number of labels belonging to the domain of the variable "variable"
 

SizeDomain() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t


 Retuns the number of labels in the variable's domain.

SizeDomain(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar


 Retuns the number of labels in the variable in position "variable" of the list domain.

SizeDomain() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
 
SizeDomain(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar


 Returns the number of labels belonging to the domain of the variable "variable"
 

SizeDomain() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
 
SizeDomain(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar


 Returns the number of labels belonging to the domain of the variable "variable"
 

SizeInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
 
SizeInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
 
SizeInteger(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
 
sizePenaltyMinRules - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
 
sizePenaltyMinRules - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
 
SizeReal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.genetcode
 
SizeReal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.genetcode
 
SizeReal(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.genetcode
 
sizeWithoutMissing() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.myDataset

It return the size of the data-set without having account the missing values.

sizeWithoutMissing() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Decision_Trees.Target.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset

        
 It return the size of the data-set without having account the missing values
 

sizeWithoutMissing() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset

        
 It return the size of the data-set without having account the missing values
 

sizeWithoutMissing() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset

        
 It return the size of the data-set without having account the missing values
 

sizeWithoutMissing() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset

        
 It return the size of the data-set without having account the missing values
 

sizeWithoutMissing() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset

        
 It return the size of the data-set without having account the missing values
 

sizeWithoutMissing() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset

        
 It return the size of the data-set without having account the missing values
 

sizeWithoutMissing() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset

        
 It return the size of the data-set without having account the missing values
 

sizeWithoutMissing() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset

        
 It return the size of the data-set without having account the missing values
 

sizeWithoutMissing() - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset

        
 It return the size of the data-set without having account the missing values
 

sizeWithoutMissing() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.myDataset


 It return the size of the data-set without having account the missing values
 

sizeWithoutMissing() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.myDataset

It return the size of the data-set without having account the missing values

sizeWithoutMissing() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.myDataset

It return the size of the data-set without having account the missing values

Skg(int, int[][], int[][]) - Method in class keel.Algorithms.Instance_Generation.SADE.SADEGenerator
 
skipHeader(BufferedReader) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig


  Skips the header of the results file.

skipHeader(BufferedReader) - Method in class keel.Algorithms.Shared.Parsing.ProcessConfig


  Skips the header of the results file.

Slipper - Class in keel.Algorithms.Rule_Learning.Slipper

Implementation of the classification algorithm Slipper, according to the paper [AAAI99].

Slipper(parseParameters) - Constructor for class keel.Algorithms.Rule_Learning.Slipper.Slipper

It reads the data from the input files (training, validation and test) and parse all the parameters
 from the parameters array.

slipper(MyDataset, Mask, Mask, int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Slipper

It implements the algorithm Slipper (2 class version).

slipperMulticlass(MyDataset) - Method in class keel.Algorithms.Rule_Learning.Slipper.Slipper

It implements a multiclass variation of the algorithm Slipper:
 1.

SLIQ - Class in keel.Algorithms.Decision_Trees.SLIQ

Implementation of the SLIQ algorithm.

SLIQ(String) - Constructor for class keel.Algorithms.Decision_Trees.SLIQ.SLIQ

Constructor.

slope - Variable in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
 
sm(double, double) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils

Tests if a is smaller than b.

sm(double, double) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils

Tests if a is smaller than b.

sm(double, double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils

Tests if a is smaller than b.

sm(double, double) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils

Tests if a is smaller than b.

sm(double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils

Tests if a is smaller than b.

sm(double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils

Tests if a is smaller than b.

sm(double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils

Tests if a is smaller than b.

sm(double, double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils

Tests if a is smaller than b.

sm(double, double) - Static method in class keel.Algorithms.SVM.SMO.core.Utils

Tests if a is smaller than b.

SMALL - Static variable in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils

The small deviation allowed in double comparisons

SMALL - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils

The small deviation allowed in double comparisons.

SMALL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils

The small deviation allowed in double comparisons

SMALL - Static variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils

The small deviation allowed in double comparisons

SMALL - Static variable in class keel.Algorithms.Rule_Learning.Slipper.Utilities

The small deviation allowed in double comparisons

SMALL - Static variable in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils

The small deviation allowed in double comparisons.

SMALL - Static variable in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils

The small deviation allowed in double comparisons.

SMALL - Static variable in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils

The small deviation allowed in double comparisons.

SMALL - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils

The small deviation allowed in double comparisons

SMALL - Static variable in class keel.Algorithms.SVM.SMO.core.Utils

The small deviation allowed in double comparisons

smartInit - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper
 
smartInit - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper
 
smartInitMethod - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
SMO - Class in keel.Algorithms.SVM.SMO

Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.

SMO(String) - Constructor for class keel.Algorithms.SVM.SMO.SMO

Creates a new instance of SMO with a file parameter of KEEL format

SMO() - Constructor for class keel.Algorithms.SVM.SMO.SMO


 Default constructor
 

SMO.BinarySMO - Class in keel.Algorithms.SVM.SMO

Class for building a binary support vector machine.

smoothen() - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode

Smoothens all unsmoothed formulae at the tree leaves under this node.

smoothen() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode

Smoothens all unsmoothed formulae at the tree leaves under this node.

smoothenFormula(M5TreeNode) - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode

Recursively smoothens the unsmoothed linear model at this node with the
     unsmoothed linear models at the nodes above this

smoothenFormula(M5TreeNode) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode

Recursively smoothens the unsmoothed linear model at this node with the
     unsmoothed linear models at the nodes above this

smoothenValue(double, double, int, int) - Static method in class keel.Algorithms.Decision_Trees.M5.M5

Returns the smoothed values according to the smoothing formula (np+kq)/(n+k)

smoothenValue(double, double, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5

Returns the smoothed values according to the smoothing formula (np+kq)/(n+k)

SMOreg - Class in keel.Algorithms.SVM.SMO

Implements Alex Smola and Bernhard Scholkopf's sequential minimal optimization algorithm for training a support vector regression model.

SMOreg() - Constructor for class keel.Algorithms.SVM.SMO.SMOreg
 
smOrEq(double, double) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils

Tests if a is smaller or equal to b.

smOrEq(double, double) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils

Tests if a is smaller or equal to b.

smOrEq(double, double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils

Tests if a is smaller or equal to b.

smOrEq(double, double) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils

Tests if a is smaller or equal to b.

smOrEq(double, double) - Static method in class keel.Algorithms.Rule_Learning.Slipper.Utilities

Tests if a is smaller or equal to b.

smOrEq(double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils

Tests if a is smaller or equal to b.

smOrEq(double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils

Tests if a is smaller or equal to b.

smOrEq(double, double) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils

Tests if a is smaller or equal to b.

smOrEq(double, double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils

Tests if a is smaller or equal to b.

smOrEq(double, double) - Static method in class keel.Algorithms.SVM.SMO.core.Utils

Tests if a is smaller or equal to b.

SMOset - Class in keel.Algorithms.SVM.SMO.supportVector

Stores a set of integer of a given size.

SMOset(int) - Constructor for class keel.Algorithms.SVM.SMO.supportVector.SMOset

Creates a new set of the given size.

SMOSSLAlgorithm - Class in keel.Algorithms.Semi_Supervised_Learning.SMOSSL

SMOSSL algorithm calling.

SMOSSLAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.SMOSSL.SMOSSLAlgorithm
 
SMOSSLGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.SMOSSL

This class implements the Self-traning wrapper.

SMOSSLGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.SMOSSL.SMOSSLGenerator

Build a new SMOSSLGenerator Algorithm

SMOSSLGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.SMOSSL.SMOSSLGenerator

Build a new SMOSSLGenerator Algorithm

SMOTE(double[][], double[][], int[][], boolean[][], int[], double[][], double[][], int[][], boolean[][], int[], int, int, double, boolean, int, int, int, int, boolean) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.EUSCHCQstat

SMOTE preprocessing procedure

SMOTE - Class in keel.Algorithms.ImbalancedClassification.Ensembles.SMOTE
 
SMOTE(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.SMOTE.SMOTE
 
SMOTE(InstanceSet, long, int, int, boolean, double, String) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.SMOTE.SMOTE
 
SMOTE - Class in keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H


 File: SMOTE.java
 

 The SMOTE algorithm is an oversampling method used to deal with the imbalanced
 problem.

SMOTE(String) - Constructor for class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.SMOTE


 Constructor of the class.

SMOTE - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE


 File: SMOTE.java
 

 The SMOTE algorithm is an oversampling method used to deal with the imbalanced
 problem.

SMOTE(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE.SMOTE


 Constructor of the class.

SMOTE_ENN - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_ENN


 File: SMOTE_ENN.java
 

 The SMOTE ENN algorithm is an oversampling method used to deal with
 the imbalanced problem.

SMOTE_ENN(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_ENN.SMOTE_ENN


 Constructor of the class.

SMOTE_RSB - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB


 File: SMOTE_RSB.java
 

 The SMOTE_RSB algorithm is an oversampling method used to deal with the imbalanced
 problem.

SMOTE_RSB(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.SMOTE_RSB
 
SMOTE_TomekLinks - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_TomekLinks


 File: SMOTE_TomekLinks.java
 

 The SMOTE Tomek Links algorithm is an oversampling method used to deal with
 the imbalanced problem.

SMOTE_TomekLinks(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_TomekLinks.SMOTE_TomekLinks


 Constructor of the class.

SNN - Class in keel.Algorithms.Instance_Selection.SNN

File: SNN.java
 
 The SNN Instance Selection algorithm.

SNN(String) - Constructor for class keel.Algorithms.Instance_Selection.SNN.SNN

Default constructor.

SNN - Class in keel.Algorithms.Preprocess.Instance_Selection.SNN

File: SNN.java
 
 The SNN Instance Selection algorithm.

SNN(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.SNN.SNN

Default constructor.

SNNRCEAlgorithm - Class in keel.Algorithms.Semi_Supervised_Learning.SNNRCE

SNNRCE algorithm calling.

SNNRCEAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.SNNRCE.SNNRCEAlgorithm
 
SNNRCEGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.SNNRCE

This class implements the Self-traning wrapper.

SNNRCEGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.SNNRCE.SNNRCEGenerator

Build a new SNNRCEGenerator Algorithm

SNNRCEGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.SNNRCE.SNNRCEGenerator

Build a new SNNRCEGenerator Algorithm

socialNetwork - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams

Matrix that stores the complete social network

SoftmaxClassificationProblemEvaluator - Class in keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax

 Softmax Classification problem evaluator

SoftmaxClassificationProblemEvaluator() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.SoftmaxClassificationProblemEvaluator


 Empty constructor
 

softmaxProbabilities(double[]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.neuralnet.NeuralNetClassifier


 Obtain the normalized softmax probabilities of classes of 
 one observation

softmaxProbabilities(double[][]) - Method in class keel.Algorithms.Neural_Networks.NNEP_Clas.neuralnet.NeuralNetClassifier


 Obtain the normalized softmax probabilities of classes 
 of a set of observations, through their inputs values

softmaxProbabilities(double[]) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.ISoftmaxClassifier


 Obtain the normalized softmax probabilities of classes of 
 one observation

softmaxProbabilities(double[][]) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.ISoftmaxClassifier


 Obtain the normalized softmax probabilities of classes 
 of a set of observations, through their inputs values

solucion() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.Algorithm


 It returns the best solution 
 

solve(IOptimizableFunc) - Method in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus


 Apply the iRprop+ over a function that implements the IOptimizableFunc
 interface.

solve(Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.CholeskyDecomposition

Solve A*X = B

solve(Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.LUDecomposition

Solve A*X = B

solve(Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix

Solve A*X = B

solve(Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.QRDecomposition

Least squares solution of A*X = B

solve(double[]) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix

Deprecated.
Solve A*X = B using backward substitution.

solveTranspose(Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix

Solve X*A = B, which is also A'*X' = B'

solveTriangle(double[][], double[], boolean, boolean[]) - Method in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
 
solveTriangle(Matrix, double[], boolean, boolean[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization

Solve the linear equation of TX=B where T is a triangle matrix
 It can be solved using back/forward substitution, with O(N^2) 
 complexity

some_active(Vector, ExternalObjectDescription, Vector, int, Point) - Method in class keel.GraphInterKeel.experiments.DinamicDataset

Updates status of active data sets

sonn - Class in keel.Algorithms.Neural_Networks.gmdh


 Class for the algorithm sonn
 

sonn(SetupParameters, Data) - Constructor for class keel.Algorithms.Neural_Networks.gmdh.sonn


 Constructor
 

sons - Variable in class keel.Algorithms.Decision_Trees.C45.Tree

Sons of the node.

sons - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Tree

Sons of the node.

sons - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree

Sons of the node.

sons - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Tree

Sons of the node.

sons - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree

Sons of the node.

sons - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Tree

Sons of the node.

sons - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree

Sons of the node.

sons - Variable in class keel.Algorithms.Rule_Learning.PART.Tree

Sons of the node.

sons - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree

Sons of the node.

sort() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.RuleBase


 Function to sort the rule base
 

sort() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.RuleBase


 Function to sort the rule base
 

sort() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.RuleBase


 Function to sort the rule base
 

sort() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.RuleBase


 Function to sort the rule base
 

sort() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.RuleBase


 Function to sort the rule base
 

sort() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.RuleBase


 Function to sort the rule base
 

sort(int) - Method in class keel.Algorithms.Decision_Trees.C45.Dataset

Function to sort the dataset based on an attribute.

sort(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset

Function to sort the dataset based on an attribute.

sort(int) - Method in class keel.Algorithms.Decision_Trees.ID3.Dataset

Function to sort the dataset based on an attribute.

sort(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances

Sorts the instances based on an attribute.

sort(M5Attribute) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances

Sorts the instances based on an attribute.

sort(int[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils

Sorts a given array of integers in ascending order and returns an
 array of integers with the positions of the elements of the original
 array in the sorted array.

sort(double[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils

Sorts a given array of doubles in ascending order and returns an
 array of integers with the positions of the elements of the
 original array in the sorted array.

sort(int) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Dataset

Function to sort the dataset based on an attribute.

sort(double[], int, int) - Static method in class keel.Algorithms.Discretizers.HellingerBD.Quicksort


 It sorts the values array based on opt
 

sort(double[], int, int) - Static method in class keel.Algorithms.Discretizers.UCPD.Quicksort


 It sorts the values array basing on opt
 

sort() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.RuleBase

It sorts the rule base according to the fitness of the rules

Sort() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
 
Sort(int, int[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation


 Sorts the individuals according to their fitness values
 

Sort() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary
 
Sort() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger
 
Sort() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.vectordouble


 Sorts the values in the vector (in ascending order)
 

Sort() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation


 Sorts the individuals according to their fitness values
 

Sort() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
 
Sort() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationinteger
 
Sort() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation


 Sorts the individuals according to their fitness values
 

Sort() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
 
Sort() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationinteger
 
sort() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.RuleBase
 
sort(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Sorts the instances based on an attribute.

sort(AttributeWeka) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Sorts the instances based on an attribute.

sort(int[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils

Sorts a given array of integers in ascending order and returns an
 array of integers with the positions of the elements of the original
 array in the sorted array.

sort(double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils

Sorts a given array of doubles in ascending order and returns an
 array of integers with the positions of the elements of the
 original array in the sorted array.

sort(double[], int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Quicksort


 Sorts the values array basing on opt
 

sort(int[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils

Sorts a given array of integers in ascending order and returns an
 array of integers with the positions of the elements of the original
 array in the sorted array.

sort(double[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils

Sorts a given array of doubles in ascending order and returns an
 array of integers with the positions of the elements of the
 original array in the sorted array.

sort(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

Function to sort the dataset based on an attribute.

sort(MyAttribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

Function to sort the dataset based on an attribute.

sort(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset

Function to sort the dataset based on an attribute.

sort(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset

Function to sort the dataset based on an attribute.

sort(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset

Function to sort the dataset based on an attribute.

sort(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset

Function to sort the dataset based on an attribute.

sort(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Sorts the instances based on an attribute.

sort(Attribute) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Sorts the instances based on an attribute.

sort(int[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils

Sorts a given array of integers in ascending order and returns an 
 array of integers with the positions of the elements of the original 
 array in the sorted array.

sort(double[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils

Sorts a given array of doubles in ascending order and returns an
 array of integers with the positions of the elements of the
 original array in the sorted array.

sort(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet

Sort the prototype set in ascending distance to current prototype.

sort(double[], int, int) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Quicksort


 It sorts the values array based on opt
 

sort(double[], int, int) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Quicksort


 Sorts the values array basing on opt
 

sort(double[], int, int) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Quicksort


 Sorts the values array basing on opt
 

sort(int) - Method in class keel.Algorithms.Rule_Learning.ART.Dataset

Function to sort the dataset based on an attribute.

sort(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset

Function to sort the dataset based on an attribute.

sort(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset

Function to sort the dataset based on an attribute.

sort(int) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset

Function to sort the dataset based on an attribute.

sort(int) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset

Function to sort the dataset based on an attribute.

sort() - Method in class keel.Algorithms.Rule_Learning.Ripper.Score

Sorts the trios stored with the mergesort algorithm.

sort() - Method in class keel.Algorithms.Rule_Learning.Slipper.Score

Sorts the trios stored with the mergesort algorithm.

sort(int[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils

Sorts a given array of integers in ascending order and returns an 
 array of integers with the positions of the elements of the original 
 array in the sorted array.

sort(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils

Sorts a given array of doubles in ascending order and returns an
 array of integers with the positions of the elements of the
 original array in the sorted array.

sort(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset

Function to sort the dataset based on an attribute.

sort(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Sort the prototype set in ascending distance to current prototype.

sort(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Sorts the instances based on an attribute.

sort(int[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils

Sorts a given array of integers in ascending order and returns an 
 array of integers with the positions of the elements of the original 
 array in the sorted array.

sort(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils

Sorts a given array of doubles in ascending order and returns an
 array of integers with the positions of the elements of the
 original array in the sorted array.

sort(int[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils

Sorts a given array of integers in ascending order and returns an 
 array of integers with the positions of the elements of the original 
 array in the sorted array.

sort(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils

Sorts a given array of doubles in ascending order and returns an
 array of integers with the positions of the elements of the
 original array in the sorted array.

sort() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Sorts the array in place

sort() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector

Sorts the elements in place

sort(int[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils

Sorts a given array of integers in ascending order and returns an 
 array of integers with the positions of the elements of the original 
 array in the sorted array.

sort(double[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils

Sorts a given array of doubles in ascending order and returns an
 array of integers with the positions of the elements of the
 original array in the sorted array.

sort(int) - Method in class keel.Algorithms.SVM.SMO.core.Instances

Sorts the instances based on an attribute.

sort(Attribute) - Method in class keel.Algorithms.SVM.SMO.core.Instances

Sorts the instances based on an attribute.

sort(int[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils

Sorts a given array of integers in ascending order and returns an 
 array of integers with the positions of the elements of the original 
 array in the sorted array.

sort(double[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils

Sorts a given array of doubles in ascending order and returns an
 array of integers with the positions of the elements of the
 original array in the sorted array.

Sort - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh

Title: Class Sort

 Description: Here you have methods to sort populations

 Company: KEEL

Sort() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Sort
 
Sort - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII

Title: Class Sort

 Description: Here you have methods to sort populations

 Company: KEEL

Sort() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Sort
 
Sort_4L(example_set, int, int[], int[][], double[], double[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation


 Sorts the individuals according to their fitness values
 

sort_population() - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation


 Sorts population
 

sortByAmplitude(ArrayList<Chromosome>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AlatasetalProcess

Sorts the population given by their amplitude values.

sortByAverageClassValues() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

Computes the average class values for each attribute and value,
 and sort them by it.

sortDatasetC() - Method in class keel.GraphInterKeel.experiments.DinamicDataset

Sort the data sets list loaded from disc, so they
 appear in alphabetic order

sortDatasetC() - Method in class keel.GraphInterKeel.experiments.SelectData

This function sorts the inserted data set lists,
 so they will appear sorted in the GUI

sortDatasetC_LQD() - Method in class keel.GraphInterKeel.experiments.DinamicDataset

Sort the data sets list loaded from disc, so they
 appear in alphabetic order

sortDatasetC_LQD() - Method in class keel.GraphInterKeel.experiments.SelectData

This function sorts the inserted data set lists,
 so they will appear sorted in the GUI

sortDatasetLQD_C() - Method in class keel.GraphInterKeel.experiments.DinamicDataset

Sort the data sets list loaded from disc, so they
 appear in alphabetic order

sortDatasetLQD_C() - Method in class keel.GraphInterKeel.experiments.SelectData

This function sorts the inserted data set lists,
 so they will appear sorted in the GUI

sortDatasets() - Method in class keel.GraphInterKeel.experiments.DinamicDataset

Sort the data sets list loaded from disc, so they
 appear in alphabetic order

sortDatasets() - Method in class keel.GraphInterKeel.experiments.SelectData

This function sorts the inserted data set lists,
 so they will appear sorted in the GUI

sortGenes(ArrayList<Gene>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
 
sortGenes(ArrayList<Gene>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule
 
sortGenes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Chromosome
 
sortGenes(ArrayList<Gene>) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
 
sortGenes() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Chromosome
 
sortItemSet(short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining

Sorts an unordered item set.

sortItemSet(short[]) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining

Sorts an unordered item set.

sortItemSet(short[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining

Sorts an unordered item set.

sortMembershipFunctions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Gene


 It orders the membership functions involved in a gene
 

sortMembershipFunctions() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Gene


 It orders the membership functions involved in a gene
 

sortPopulation(int, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population


 Sorts the population with a quicksort algorithm.

sortPopulation(int, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population


 Sorts the population with a quicksort algorithm.

sortValues(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.Basic.Discretizer

Sorts the instances values of the attribute given.

sortValues(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.Chi2_Discretizer.Discretizer

Sorts the instances values of the attribute given.

sortValues(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.ExtendedChi2_Discretizer.Discretizer

Sorts the instances values of the attribute given.

sortValues(Quicksort.OrderStructure[], int, int) - Static method in class keel.Algorithms.Discretizers.HellingerBD.Quicksort


 It sorts the values between the begin and the end indexes
 

sortValues(IDD.rank[], int, int, int) - Method in class keel.Algorithms.Discretizers.IDD.IDD


 Sorts an array
 

sortValues(int, int[], int, int) - Method in class keel.Algorithms.Discretizers.ModifiedChi2_Discretizer.Discretizer

Sorts the instances values of the attribute given.

sortValues(int, int[], int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.Basic.Discretizer
 
sortValues(Quicksort.OrderStructure[], int, int) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Quicksort


 It sorts the values between the begin and the end indexes
 

sortValues(int, int[], int, int) - Method in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Discretizer
 
sortWithIndex() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Sorts the array in place with index returned

sortWithIndex(int, int, IntVector) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Sorts the array in place with index changed

sparseIndices() - Method in class keel.Algorithms.SVM.SMO.SMO

Returns the indices in sparse format.

SparseInstance - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets

Class for storing an instance as a sparse vector.

SparseInstance() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance

Dafault constructor.

SparseInstance(Instance) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance

Constructor that generates a sparse instance from the given
 instance.

SparseInstance(SparseInstance) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance

Constructor that copies the info from the given instance.

SparseInstance(double, double[]) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance

Constructor that generates a sparse instance from the given
 parameters.

SparseInstance(double, double[], int[], int) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance

Constructor that inititalizes instance variable with given
 values.

SparseInstance(int) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance

Constructor of an instance that sets weight to one, all values to
 be missing, and the reference to the dataset to null.

sparseWeights() - Method in class keel.Algorithms.SVM.SMO.SMO

Returns the weights in sparse format.

specialConstructor - Variable in exception keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParseException

This variable determines which constructor was used to create
 this object and thereby affects the semantics of the
 "getMessage" method (see below).

specialConstructor - Variable in exception keel.Algorithms.Rule_Learning.Swap1.ParseException

This variable determines which constructor was used to create
 this object and thereby affects the semantics of the
 "getMessage" method (see below).

specialConstructor - Variable in exception keel.Dataset.ParseException

This variable determines which constructor was used to create
 this object and thereby affects the semantics of the
 "getMessage" method (see below).

specializeProbability - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
specialToken - Variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Token

This field is used to access special tokens that occur prior to this
 token, but after the immediately preceding regular (non-special) token.

specialToken - Variable in class keel.Algorithms.Rule_Learning.Swap1.Token

This field is used to access special tokens that occur prior to this
 token, but after the immediately preceding regular (non-special) token.

specialToken - Variable in class keel.Dataset.Token

This field is used to access special tokens that occur prior to this
 token, but after the immediately preceding regular (non-special) token.

species - Static variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.KEELIRPropPlusWrapperClas

Individual species

species - Static variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Regr.KEELIRPropPlusWrapperRegr

Individual species

species - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.algorithm.NeuralNetAlgorithm

Individual species

species - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.initiators.PureLayerInitiator

Associated species

species - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.NeuralNetMutator

Individuals species

species - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetCreator

Associated species

SPECIFY - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
Specify - Class in keel.Algorithms.Genetic_Rule_Learning.XCS

The class implement the specify operator proposed by Lanzi

SPIDER - Class in keel.Algorithms.ImbalancedClassification.Ensembles.SPIDER
 
SPIDER(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.SPIDER.SPIDER
 
SPIDER(InstanceSet, int, String, String) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.SPIDER.SPIDER
 
SPIDER - Class in keel.Algorithms.ImbalancedClassification.Resampling.SPIDER


 File: SPIDER.java
 

 The SPIDER algorithm is an instance selection method used to deal with
 the imbalanced problem.

SPIDER(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SPIDER.SPIDER


 Constructor of the class.

SPIDER2 - Class in keel.Algorithms.ImbalancedClassification.Resampling.SPIDER2


 File: SPIDER2.java
 

 The SPIDER algorithm is an instance selection method used to deal with
 the imbalanced problem.

SPIDER2(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SPIDER2.SPIDER2


 Constructor of the class.

splineDegreeOne(double[], double[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.Interpolation

finds the piecewice linear function whose graph interpolates the points (ti,xi)

Split - Class in keel.Algorithms.Decision_Trees.FunctionalTrees

Data structure that is used during the construction of the decision tree.

split(Split, int) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode

Splits a node into two nodes from a split following the identifier of a given number into an arraylist
 of nodes.

split(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode

Splits the node recursively, unless there are few instances or
     instances have similar values of the class attribute

split(Split, int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Node

Splits a node into two nodes from a split following the identifier of a given number into an arraylist
 of nodes.

Split - Class in keel.Algorithms.Decision_Trees.PUBLIC

File: Split.java
 
 Data structure that is used during the construction of the decision tree.

split(ArrayList<Double>, int, int, int, int[]) - Method in class keel.Algorithms.Discretizers.MODL.MODL

Search for the best cutpoint in a given interval.

split(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode

Splits the node recursively, unless there are few itemsets or
     itemsets have similar values of the class attribute

split(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

It split phisically the itemsets into two subdatasets,
 according to the coverage of a rule.

split(Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset

It split phisically the itemsets into two subdatasets,
 according to the coverage of a rule.

split(double, Randomize) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Mask

Splits, at random, this mask in two new masks

split(double, Randomize) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Mask

Splits, at random, this mask in two new masks

split(Rule) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset

It split phisically the itemsets into two subdatasets,
 according to the coverage of a rule.

split(double, Randomize) - Method in class keel.Algorithms.Rule_Learning.Ripper.Mask

Splits, at random, this mask in two new masks

split(double, Randomize) - Method in class keel.Algorithms.Rule_Learning.Slipper.Mask

Splits, at random, this mask in two new masks

splitData(Instances, double, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Antd
 
splitData(Instances, double, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.NominalAntd

Implements the splitData function.

splitData(Instances, double, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.NumericAntd

Implements the splitData function.

splitData(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.RandomTree

Splits instances into subsets based on the given split.

splitData(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.RandomTree

Splits instances into subsets based on the given split.

splitData(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.RandomTree

Splits instances into subsets based on the given split.

SplitInfo - Class in keel.Algorithms.Decision_Trees.M5

Stores split information.

SplitInfo(int, int, int) - Constructor for class keel.Algorithms.Decision_Trees.M5.SplitInfo

Constructs an object which contains the split information

SplitInfo - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules

Stores split information.

SplitInfo(int, int, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SplitInfo

Constructs an object which contains the split information

splitOptions(String) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils

Split up a string containing options into an array of strings,
 one for each option.

splitOptions(String) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils

Split up a string containing options into an array of strings,
 one for each option.

splitOptions(String) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils

Split up a string containing options into an array of strings,
 one for each option.

splitOptions(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils

Split up a string containing options into an array of strings,
 one for each option.

splitOptions(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils

Split up a string containing options into an array of strings,
 one for each option.

splitOptions(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils

Split up a string containing options into an array of strings,
 one for each option.

splitOptions(String) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils

Split up a string containing options into an array of strings,
 one for each option.

splitOptions(String) - Static method in class keel.Algorithms.SVM.SMO.core.Utils

Split up a string containing options into an array of strings,
 one for each option.

splitPoint - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.NumericAntd

The split point for this numeric antecedent

splitRule(int) - Method in class keel.Algorithms.Decision_Trees.Target.Tree

Splits the rule of the given node.

splitSet(int, double, double) - Method in class keel.Algorithms.Decision_Trees.Target.Nodo

Splits the node with the given attribute randomly with the given probabilities.

splitSet(int) - Method in class keel.Algorithms.Decision_Trees.Target.Tree

Splits the given node.

splittedRule() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL.splittedRule
 
SPrint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
 
SPrint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.fuzzy_t
 
SPrint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.ruleset
 
SPrint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t


 Prints as a String the name of the label number i in the domain
 

SPrint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.fuzzy_t


 Prints in a String the name of the label
 

SPrint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
 
SPrint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.fuzzy_t
 
SPrint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.ruleset
 
SPrint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
 
SPrint() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.fuzzy_t
 
SPrint(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.ruleset
 
SPrintDomain(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
 
SPrintDomain(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
 
SPrintDomain(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t


 Returns a string with the name of the label "value" of the variable.

SPrintDomain(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar


 Returns a string with the name of the label "value" of the variable in position "variable" in the list.

SPrintDomain(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
 
SPrintDomain(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
 
SPrintDomain(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
 
SPrintDomain(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
 
sprintf(Object[]) - Method in class keel.Algorithms.Neural_Networks.gmdh.PrintfFormat

Format an array of objects.

sprintf() - Method in class keel.Algorithms.Neural_Networks.gmdh.PrintfFormat

Format nothing.

sprintf(int) - Method in class keel.Algorithms.Neural_Networks.gmdh.PrintfFormat

Format an int.

sprintf(long) - Method in class keel.Algorithms.Neural_Networks.gmdh.PrintfFormat

Format an long.

sprintf(double) - Method in class keel.Algorithms.Neural_Networks.gmdh.PrintfFormat

Format a double.

sprintf(String) - Method in class keel.Algorithms.Neural_Networks.gmdh.PrintfFormat

Format a String.

sprintf(Object) - Method in class keel.Algorithms.Neural_Networks.gmdh.PrintfFormat

Format an Object.

SPrintVar() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
 
SPrintVar() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t


 Returns a string with the name of the variable.

SPrintVar(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar


 Returns a string with the name of the variable in position "variable" in the list
 

SPrintVar() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
 
SPrintVar() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
 
sqr() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut

 
  Returns the square of the present FuzzyInterval.

sqrSum(int, M5Instances) - Static method in class keel.Algorithms.Decision_Trees.M5.M5

Returns the squared sum of the instances values of an attribute

sqrSum(int, MyDataset) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5

Returns the squared sum of the itemsets values of an attribute

sqrt() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut

 
  Returns the square root of the present FuzzyInterval.

sqrt() - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype

Performs sqrt operation to all the inputs of the prototype.

sqrt(DenseVector) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM

Square root of the elements of the vector

sqrt() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype

Performs sqrt operation to all the inputs of the prototype.

sqrt() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Returns the square-root of all the elements in the vector

sqrt() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix

returns the square root of the matrix, i.e., X from the equation
 X*X = A.

SQRT_EPSILON - Static variable in class keel.Algorithms.Preprocess.Missing_Values.BPCA.MachineAccuracy

SQRT Machine accuracy constant

SQRT_EPSILON - Static variable in class keel.Algorithms.Preprocess.Missing_Values.EM.util.MachineAccuracy

SQRT Machine accuracy constant

SQRT_SQRT_EPSILON - Static variable in class keel.Algorithms.Preprocess.Missing_Values.BPCA.MachineAccuracy

SQRT SQRT Machine accuracy constant

SQRT_SQRT_EPSILON - Static variable in class keel.Algorithms.Preprocess.Missing_Values.EM.util.MachineAccuracy

SQRT SQRT Machine accuracy constant

SQRTH - Static variable in class keel.Algorithms.Lazy_Learning.Statistics

squared root of H

SQRTH - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics
 
SQTPI - Static variable in class keel.Algorithms.Lazy_Learning.Statistics

squared root of PI

SQTPI - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics
 
square() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Returns the squared vector

square(double) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths

Returns the square of a value

squaredEuclideanDistance(Prototype, Prototype) - Static method in class keel.Algorithms.Instance_Generation.utilities.Distance

Compute the squared euclidean distance between two prototypes.

squaredEuclideanDistance(Prototype, Prototype) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Distance

Compute the squared euclidean distance between two prototypes.

SquaredSinc - Static variable in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel

Type of Positive Definite Functions supported (Squared sinc)

squaredSinc(double) - Method in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel

Computes the result of the Squared Sinc PDRF

SquaresErrorNN - Class in keel.Algorithms.Preprocess.NoiseFilters.ANR


 Derived class from FUN that implements the error for a perceptron trained with conjugated gradient.

SquaresErrorNN(ConjGradNN) - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.ANR.SquaresErrorNN


 Constructor of an error calculator for neural network based on the conjugated gradient.

SquaresErrorNN - Class in keel.Algorithms.Shared.ClassicalOptim


 Derived class from FUN that implements the error for a perceptron trained with conjugated gradient.

SquaresErrorNN(ConjGradNN) - Constructor for class keel.Algorithms.Shared.ClassicalOptim.SquaresErrorNN


 Constructor of an error calculator for neural network based on the conjugated gradient.

SquaresErrorQUAD - Class in keel.Algorithms.Shared.ClassicalOptim


 Derived class from FUN that implements the error for a perceptron trained with quadratic conjugated gradient.

SquaresErrorQUAD(ConjGradQUAD, double[][], double[][]) - Constructor for class keel.Algorithms.Shared.ClassicalOptim.SquaresErrorQUAD


 Constructor of an error calculator for neural network based on the quadratic conjugated gradient.

SSFileEnvironment - Class in keel.Algorithms.Genetic_Rule_Learning.UCS

This is the base class for all the single step problems environments
 that read the examples from a file.

SSFileEnvironment(String, boolean) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.SSFileEnvironment

It's the constructor of the class.

SSFileEnvironment - Class in keel.Algorithms.Genetic_Rule_Learning.XCS

This is the base class for all the single step problems environments
 that read the examples from a file.

SSFileEnvironment(String, boolean) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.SSFileEnvironment


 It's the constructor of the class.

SSGABinaryIncon - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_BinCod.filter

MAIN CLASS FOR STATIONARY STATE GA FOR FEATURES SELECTION
  USING INCONSISTENCY RATIO AS EVALUATION MEASURE

SSGABinaryIncon(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_BinCod.filter.SSGABinaryIncon

Creates a new instance of SSGABinaryIncon

SSGABinaryLVO - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_BinCod.wrapper

MAIN CLASS FOR STATIONARY STATE GA FOR FEATURES SELECTION
  USING LVO AS WRAPPER ALGORITHM

SSGABinaryLVO(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_BinCod.wrapper.SSGABinaryLVO

Creates a new instance of SSGABinaryLVO

SSGAIntegerIncon - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_IntCod.filter

MAIN CLASS FOR STATIONARY STATE GA FOR FEATURES SELECTION
  USING INCONSISTENCY RATIO AS EVALUATION MEASURE

SSGAIntegerIncon(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_IntCod.filter.SSGAIntegerIncon

Creates a new instance of SSGAIntegerIncon

SSGAIntegerLVO - Class in keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_IntCod.wrapper


 SSGAIntegerLVO.java
 Main class for stationary state GA with integer encoding for feature selection using LVO as wrapper algorithm
 Inputs: seed, nearest neighbours for KNN Classifier, population size, number of evaluations, number of selected features
 Encoding: Integer
 Selection: NO SELECTION.

SSGAIntegerLVO(String) - Constructor for class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.GA_SS_IntCod.wrapper.SSGAIntegerLVO

 
 Creates a new instance of SSGAIntegerLVO
 

SSL - Static variable in class keel.GraphInterKeel.experiments.Experiments
 
SSL_mouseEntered(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules

Enter in SSLbutton

SSL_mouseExited(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules

Exit from ssl button

SSL_mouseReleased(MouseEvent) - Method in class keel.GraphInterKeel.menu.FrameModules

Entering in ssl module

SSMA - Class in keel.Algorithms.Instance_Selection.SSMA

File: SGA.java
 
 Steady-State Menetic algorithm for Instance Selection.

SSMA(String) - Constructor for class keel.Algorithms.Instance_Selection.SSMA.SSMA

Default builder.

SSMA - Class in keel.Algorithms.Preprocess.Instance_Selection.SSMA

File: SGA.java
 
 Steady-State Menetic algorithm for Instance Selection.

SSMA(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.SSMA.SSMA

Default builder.

SSMALVQ3 - Class in keel.Algorithms.Instance_Generation.SSMALVQ3
 
SSMALVQ3(String) - Constructor for class keel.Algorithms.Instance_Generation.SSMALVQ3.SSMALVQ3
 
SSMAPSO - Class in keel.Algorithms.Instance_Generation.SSMAPSO
 
SSMAPSO(String) - Constructor for class keel.Algorithms.Instance_Generation.SSMAPSO.SSMAPSO

Builder.

SSMASFLSDE - Class in keel.Algorithms.Instance_Generation.SSMASFLSDE
 
SSMASFLSDE(String) - Constructor for class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE
 
SSMASFLSDE(String, InstanceSet) - Constructor for class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE
 
stableSort(double[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils

Sorts a given array of doubles in ascending order and returns an
 array of integers with the positions of the elements of the original
 array in the sorted array.

stableSort(double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils

Sorts a given array of doubles in ascending order and returns an
 array of integers with the positions of the elements of the original
 array in the sorted array.

stableSort(double[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils

Sorts a given array of doubles in ascending order and returns an
 array of integers with the positions of the elements of the original
 array in the sorted array.

stableSort(double[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils

Sorts a given array of doubles in ascending order and returns an 
 array of integers with the positions of the elements of the original 
 array in the sorted array.

stableSort(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils

Sorts a given array of doubles in ascending order and returns an 
 array of integers with the positions of the elements of the original 
 array in the sorted array.

stableSort(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils

Sorts a given array of doubles in ascending order and returns an 
 array of integers with the positions of the elements of the original 
 array in the sorted array.

stableSort(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils

Sorts a given array of doubles in ascending order and returns an 
 array of integers with the positions of the elements of the original 
 array in the sorted array.

stableSort(double[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils

Sorts a given array of doubles in ascending order and returns an 
 array of integers with the positions of the elements of the original 
 array in the sorted array.

stableSort(double[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils

Sorts a given array of doubles in ascending order and returns an 
 array of integers with the positions of the elements of the original 
 array in the sorted array.

STANDARD - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividual

Operator flag (STANDARD).

STANDARDACCURACY - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig

Standard accuracy identifier.

standardize(double, int) - Method in class keel.Algorithms.SVM.SMO.SMO

Standardize the provided value, converting it to a new double value from
 a normal distribution with mean = 0 and std. deviation = 1

Start(String, String) - Method in class keel.Algorithms.Preprocess.Converter.CsvToKeel

Method used to transform the data from the csv file given as parameter to 
 KEEL format file which will be stored in the second file given.

Start(String) - Method in class keel.Algorithms.Preprocess.Converter.DbToKeel

Method used to transform the data from the SQL database table to 
 KEEL format file which will be stored in the second file given.

Start(String, String) - Method in class keel.Algorithms.Preprocess.Converter.DifToKeel

Method used to transform the data from the DIF file given as parameter to 
 KEEL format file which will be stored in the second file given.

Start(String, String) - Method in class keel.Algorithms.Preprocess.Converter.ExcelToKeel

Method used to transform the data from the excel file given as parameter to 
 KEEL format file which will be stored in the second file given.

Start(String) - Method in class keel.Algorithms.Preprocess.Converter.Exporter

This method reads the data stored in KEEL format file and initializes all
 the structures needed to export the data to other formats.

Start(String, String) - Method in class keel.Algorithms.Preprocess.Converter.HtmlToKeel

Method used to transform the data from the html file given as parameter to 
 KEEL format file which will be stored in the second file given.

Start(String, String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToCsv

Method used to transform the data from the KEEL file given as parameter to 
 CSV format file which will be stored in the second file given.

Start(String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToDb

Method used to transform the data from the KEEL file given as parameter to 
 a new SQL database table.

Start(String, String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToDif

Method used to transform the data from the KEEL file given as parameter to 
 DIF format file which will be stored in the second file given.

Start(String, String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToExcel

Method used to transform the data from the KEEL file given as parameter to 
 Excel format file which will be stored in the second file given.

Start(String, String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToHtml

Method used to transform the data from the KEEL file given as parameter to 
 HTML format file which will be stored in the second file given.

Start(String, String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToPrn

Method used to transform the data from the KEEL file given as parameter to 
 PRN format file which will be stored in the second file given.

Start(String, String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToPropertyList

Method used to transform the data from the KEEL file given as parameter to 
 PropertyList format file which will be stored in the second file given.

Start(String, String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToTxt

Method used to transform the data from the KEEL file given as parameter to 
 TXT format file which will be stored in the second file given.

Start(String, String, String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToUci

Method used to transform the data from the KEEL file given as parameter to 
 UCI format files which will be stored in the second file given.

Start(String, String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToWeka

Method used to transform the data from the KEEL file given as parameter to 
 Weka format file which will be stored in the second file given.

Start(String, String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToXml

Method used to transform the data from the KEEL file given as parameter to 
 XML format file which will be stored in the second file given.

Start(String, String) - Method in class keel.Algorithms.Preprocess.Converter.PrnToKeel

Method used to transform the data from the PRN file given as parameter to 
 KEEL format file which will be stored in the second file given.

Start(String, String) - Method in class keel.Algorithms.Preprocess.Converter.PropertyListToKeel

Method used to transform the data from the PropertyList file given as parameter to 
 KEEL format file which will be stored in the second file given.

Start(String, String) - Method in class keel.Algorithms.Preprocess.Converter.TxtToKeel

Method used to transform the data from the TXT file given as parameter to 
 KEEL format file which will be stored in the second file given.

Start(String, String, String) - Method in class keel.Algorithms.Preprocess.Converter.UciToKeel

Method used to transform the data from the UCI file given as parameter to 
 KEEL format file which will be stored in the second file given.

Start(String, String) - Method in class keel.Algorithms.Preprocess.Converter.WekaToKeel

Method used to transform the data from the Weka file given as parameter to 
 KEEL format file which will be stored in the second file given.

Start(String, String) - Method in class keel.Algorithms.Preprocess.Converter.XmlToKeel

Method used to transform the data from the XML file given as parameter to 
 KEEL format file which will be stored in the second file given.

start() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.StopWatch


 Starts the stop watch, saves the start time and clears old times.

start() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.StopWatch

Starts the stop watch, saves the start time and clears old times.

start() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.StopWatch

Starts the stop watch, saves the start time and clears old times.

start() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.StopWatch

Starts the stop watch, saves the start time and clears old times.

start() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.StopWatch

Starts the stop watch, saves the start time and clears old times.

start() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.StopWatch

Starts the stop watch, saves the start time and clears old times.

start() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.StopWatch

Starts the stop watch, saves the start time and clears old times.

start() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.StopWatch

Starts the stop watch, saves the start time and clears old times.

start() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.StopWatch

Starts the stop watch, saves the start time and clears old times.

start() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.StopWatch

Starts the stop watch, saves the start time and clears old times.

start() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.StopWatch

Starts the stop watch, saves the start time and clears old times.

start() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.StopWatch

Starts the stop watch, saves the start time and clears old times.

start() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.StopWatch

Starts the stop watch, saves the start time and clears old times.

start() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.StopWatch

Starts the stop watch, saves the start time and clears old times.

start() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.StopWatch

Starts the stop watch, saves the start time and clears old times.

start() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.StopWatch

Starts the stop watch, saves the start time and clears old times.

startChronCrossover() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Enables crossover's chronometer

startChronCrossover() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Enables crossover's chronometer

startChronEvaluation() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Enables evaluation's chronometer

startChronEvaluation() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Enables evaluation's chronometer

startChronGAOperators() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Enables GA operators' chronometer

startChronGAOperators() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Enables GA operators' chronometer

startChronMutation() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Enables mutation's chronometer

startChronMutation() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Enables mutation's chronometer

startChronReplacement() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Enables selection's chronometer

startChronReplacement() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Enables selection's chronometer

startChronSelection() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Enables selection's chronometer

startChronSelection() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Enables selection's chronometer

startChronStatistics() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Enables statistics' chronometer

startChronStatistics() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Enables statistics' chronometer

startCMARclassification() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFP_CMAR

Starts CMAR classifier generation proces.

startCMARclassificationWithOutput() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFP_CMAR

Starts CMAR classifier generation proces (version with full output).

startCMARrulelist - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList

The reference to start of the CMAR rule list.

startElement(String, String, String, Attributes) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.FuncionEvaluacionBeanHandler
 
startElement(String, String, String, Attributes) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.OperacionHandler
 
startMining() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.FPtree

Top level "FP-growth method" to mine the FP tree.

startMining(FPtree.FPgrowthItemPrefixSubtreeNode, short, short[]) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.FPtree

Commence process of mining FP tree with respect to a single element in
    the header table.

startMining() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.FPtree

Top level "FP-growth method" to mine the FP tree.

startMining(FPtree.FPgrowthItemPrefixSubtreeNode, short, short[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.FPtree

Commence process of mining FP tree with respect to a single element in
    the header table.

startPtreeTable - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PartialSupportTree

Array of arrays data structures for P-tree table (used as a
    computational efficiency measure).

startReductXCS() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.XCS


 Applies the reduction choosen by the user.

startRulelist - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList

The reference to start of the rule list.

startRulelist - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining

The reference to start of the rule list.

startRulelist - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining

The reference to start of the rule list.

startTestXCS() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.XCS


 Runs one test XCS experiment.

startTime - Variable in class keel.Algorithms.Decision_Trees.C45.Algorithm

The instant of starting the algorithm.

startTime - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm

The instant of starting the algorithm.

startTime - Variable in class keel.Algorithms.Decision_Trees.ID3.Algorithm

The instant of starting the algorithm.

startTime - Variable in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm

Starting time of the execution.

startTime - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm

The instant of starting the algorithm.

startTime - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm

The instant of starting the algorithm.

startTime - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm

The instant of starting the algorithm.

startTime - Variable in class keel.Algorithms.Rule_Learning.ART.Algorithm

The instant of starting the algorithm.

startTime - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Algorithm

The instant of starting the algorithm.

startTime - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Algorithm

The instant of starting the algorithm.

startTime - Variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm

The instant of starting the algorithm.

startTime - Variable in class keel.Algorithms.Rule_Learning.PART.Algorithm

The instant of starting the algorithm.

startTrainUCS() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.UCS

Runs one UCS train experiment.

startTrainXCS() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.XCS


 Runs one XCS train experiment.

startTtreeRef - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree

The reference to start of t-tree.

startTtreeRef - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TotalSupportTree

The reference to start of t-tree.

startTtreeRef - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TotalSupportTree

The reference to start of t-tree.

startup() - Method in class keel.GraphInterKeel.datacf.DataCFApp


 At startup create and show the main frame of the application.

StatCellEditor - Class in keel.GraphInterKeel.statistical

File: statCellEditor.java.

StatCellEditor() - Constructor for class keel.GraphInterKeel.statistical.StatCellEditor
 
StatFunc - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat

Statistical Functions

StatFunc() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.StatFunc
 
staticFlag - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
 
staticFlag - Static variable in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
 
staticFlag - Static variable in class keel.Dataset.SimpleCharStream
 
Statistic - Class in keel.Algorithms.Genetic_Rule_Learning.UCS

This class is used to show and configurate all the possible statistics.

Statistic() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.Statistic

Creates an Statistic object.

Statistic - Class in keel.Algorithms.Genetic_Rule_Learning.XCS

This class is used to show and configurate all the possible statistics.

Statistic(String, String) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.Statistic


 Creates an Statistic object.

StatisticalF - Class in keel.GraphInterKeel.statistical
 
StatisticalF() - Constructor for class keel.GraphInterKeel.statistical.StatisticalF

Builder

statisticalTest(int, boolean, ProcessConfig) - Method in class keel.Algorithms.Statistical_Tests.Shared.ParseFileList


 Parse a list of files and perform certain the statistical test identified by 'selector' over them
 

statisticFileOutName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 It is the name of the file where the statistics will be written in.

STATISTICFILEOUTNAME - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
statisticFileOutName1 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

It is the name of the file where the statistics will be written in ("outFile1.txt").

statisticFileOutName2 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

It is the name of the file where the statistics will be written in ("outFile2.txt").

statisticFileOutName3 - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

It is the name of the file where the statistics will be written in ("outFile3.txt").

Statistics - Class in keel.Algorithms.Complexity_Metrics

This is the main class of the Statistics computation
 
 

Statistics - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist

Computes and stores several statistics about the learning process

Statistics() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.Statistics
 
Statistics - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS

Computes and stores several statistics about the learning process

Statistics() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Statistics
 
STATISTICS - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
Statistics - Class in keel.Algorithms.Lazy_Learning

Class implementing some distributions, tests, etc.

Statistics() - Constructor for class keel.Algorithms.Lazy_Learning.Statistics
 
Statistics - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core

Class implementing some distributions, tests, etc.

Statistics() - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.Statistics
 
StatisticsStore - Class in keel.Algorithms.Decision_Trees.M5

Stores some statistics.

StatisticsStore(int, int, int, M5Instances) - Constructor for class keel.Algorithms.Decision_Trees.M5.StatisticsStore

Constructs an object which stores some statistics of the instances such
      as sum, squared sum, variance, standard deviation

StatisticsStore - Class in keel.Algorithms.Genetic_Rule_Learning.M5Rules

Stores some statistics.

StatisticsStore(int, int, int, MyDataset) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.M5Rules.StatisticsStore

Constructs an object which stores some statistics of the itemsets such
      as sum, squared sum, variance, standard deviation

statisticsToFile() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Statistics
 
statisticsToFile() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Statistics
 
statisticWindowSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

It the size of the window for the incremental statistics.

statisticWindowSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 It the size of the window for the incremental statistics.

STATISTICWINDOWSIZE - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
Stats - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets

A class to store simple statistics

Stats() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Stats
 
statTableModel - Class in keel.GraphInterKeel.statistical

File: statTableModel.java.

statTableModel() - Constructor for class keel.GraphInterKeel.statistical.statTableModel
 
StatTest - Class in keel.Algorithms.Statistical_Tests.Shared

 In this class all the statistical tests and output modules are defined

StatTest(int, double[][][][], double[][][][], double, String, String, Vector, String[]) - Constructor for class keel.Algorithms.Statistical_Tests.Shared.StatTest


 This method calls the selected statistical test or output module.

StatTest.InformationAboutClass - Class in keel.Algorithms.Statistical_Tests.Shared


 Class to store general information of the algorithms.

status - Variable in class keel.GraphInterKeel.experiments.Experiments
 
statusBarItem - Variable in class keel.GraphInterKeel.experiments.Experiments
 
std_dev - Variable in class keel.Algorithms.SVM.SMO.SMO

Variable with the std deviation of each attribute.

stdDev(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset

It returns the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.myDataset

It returns the standard deviation of an specific attribute

stdDev - Static variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo

Standard deviation.

stdDev(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset

It return the standard deviation of an specific attribute

stdDev(int, M5Instances) - Static method in class keel.Algorithms.Decision_Trees.M5.M5

Returns the standard deviation value of the instances values of an attribute

stdDev - Variable in class keel.Algorithms.Decision_Trees.M5.SimpleStatistics

The std deviation of values at the last calculateDerived() call

stdDev(int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierIshibuchi99.myDataset

        
 It return the standard deviation of an specific attribute
 

stdDev(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.myDataset

        
 It return the standard deviation of an specific attribute
 

stdDev(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierShi_Eberhart_Chen.myDataset

        
 It return the standard deviation of an specific attribute
 

stdDev(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.myDataset

        
 It return the standard deviation of an specific attribute
 

stdDev(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.myDataset

        
 It return the standard deviation of an specific attribute
 

stdDev(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.myDataset

        
 It return the standard deviation of an specific attribute
 

stdDev(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Dataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.myDataset

        
 It return the standard deviation of an specific attribute
 

stdDev(int, MyDataset) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5

Returns the standard deviation value of the itemsets values of an attribute

stdDev - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleStatistics

The std deviation of values at the last calculateDerived() call

stdDev(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset

It return the standard deviation of an specific attribute

stdDev - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
 
stdDev(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset

It return the standard deviation of an specific attribute

stdDev - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
 
stdDev(int) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset

It return the standard deviation of an specific attribute

stdDev - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Stats

The std deviation of values at the last calculateDerived() call

stdDev - Static variable in class keel.Algorithms.Preprocess.Basic.Metodo

Standard deviation.

stdDev(int) - Method in class keel.Algorithms.PSO_Learning.CPSO.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.PSO_Learning.REPSO.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.RE_SL_Methods.P_FCS1.myDataset

        
 It return the standard deviation of an specific attribute
 

stdDev(int) - Method in class keel.Algorithms.RE_SL_Methods.SEFC.myDataset

        
 It return the standard deviation of an specific attribute
 

stdDev(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Rule_Learning.LEM1.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Rule_Learning.LEM2.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Rule_Learning.Ritio.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Rule_Learning.Rules6.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Rule_Learning.SRI.myDataset

It return the standard deviation of an specific attribute

stdDev(int) - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset

It return the standard deviation of an specific attribute

SteadyStepReproduction(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic


 Reproductdion schema - Modified steady step
 
 Not used

SteadyStepReproductionCAN(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic


 Uses a Steady Step method
 

SteadyStepReproductionDNF(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Genetic


 Uses a Steady Step method
 

step(int, String) - Method in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.INFFC_2STEPS
 
stim(Prototype, Prototype) - Method in class keel.Algorithms.Instance_Generation.PSCSA.PSCSAGenerator

Stim function.

stimString(String[][], int, String[][], int) - Method in class keel.Algorithms.Instance_Generation.PSCSA.PSCSAGenerator

Stim for binary codification

stimString2(String[], String[]) - Method in class keel.Algorithms.Instance_Generation.PSCSA.PSCSAGenerator

Stim for binary codification

stirling(int) - Static method in class keel.Algorithms.Discretizers.MODL.MODL

Stirling formula for aproximating Log(n!)

stop() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.StopWatch


 Stops the watch and saves the end time.

stop() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.StopWatch

Stops the watch and saves the end time.

stop() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.StopWatch

Stops the watch and saves the end time.

stop() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.StopWatch

Stops the watch and saves the end time.

stop() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.StopWatch

Stops the watch and saves the end time.

stop() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.StopWatch

Stops the watch and saves the end time.

stop() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.StopWatch

Stops the watch and saves the end time.

stop() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.StopWatch

Stops the watch and saves the end time.

stop() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.StopWatch

Stops the watch and saves the end time.

stop() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.StopWatch

Stops the watch and saves the end time.

stop() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.StopWatch

Stops the watch and saves the end time.

stop() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.StopWatch

Stops the watch and saves the end time.

stop() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.StopWatch

Stops the watch and saves the end time.

stop() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.StopWatch

Stops the watch and saves the end time.

stop() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.StopWatch

Stops the watch and saves the end time.

stop() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.StopWatch

Stops the watch and saves the end time.

stopCellEditing() - Method in class keel.GraphInterKeel.datacf.util.EachRowEditor


 Stops the cell editor
 

stopChronCrossover() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Stops crossover's chronometer

stopChronCrossover() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Stops crossover's chronometer

stopChronEvaluation() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Stops evaluation's chronometer

stopChronEvaluation() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Stops evaluation's chronometer

stopChronGAOperators() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Stops GA operators' chronometer

stopChronGAOperators() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Stops GA operators' chronometer

stopChronMutation() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Stops crossover's chronometer

stopChronMutation() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Stops crossover's chronometer

stopChronReplacement() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Stops selection's chronometer

stopChronReplacement() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Stops selection's chronometer

stopChronSelection() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Stops selection's chronometer

stopChronSelection() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Stops selection's chronometer

stopChronStatistics() - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Stops statistics' chronometer

stopChronStatistics() - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Stops statistics' chronometer

stopCriteria(TreeNode) - Method in class keel.Algorithms.Decision_Trees.CART.CART

It checks if the stop criteria has been reached

stopProcess() - Method in class keel.RunKeelTxtDocente.EducationalRunKeelTxt


 This method is used for stop the experiment.

StopWatch - Class in keel.Algorithms.Subgroup_Discovery.SDMap.SDMap

 Class for timing applications.

StopWatch() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.StopWatch


 Basic constructor for getting an instance of a StopWatch.

StopWatch - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal

Class for timing applications.

StopWatch() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.StopWatch

Basic constructor for getting an instance of a StopWatch.

StopWatch - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori

Class for timing applications.

StopWatch() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.StopWatch

Basic constructor for getting an instance of a StopWatch.

StopWatch - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori

Class for timing applications.

StopWatch() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.StopWatch

Basic constructor for getting an instance of a StopWatch.

StopWatch - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC

Class for timing applications.

StopWatch() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.StopWatch

Basic constructor for getting an instance of a StopWatch.

StopWatch - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal

Class for timing applications.

StopWatch() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.StopWatch

Basic constructor for getting an instance of a StopWatch.

StopWatch - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori

Class for timing applications.

StopWatch() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.StopWatch

Basic constructor for getting an instance of a StopWatch.

StopWatch - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA

Class for timing applications.

StopWatch() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.StopWatch

Basic constructor for getting an instance of a StopWatch.

StopWatch - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA

Class for timing applications.

StopWatch() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.StopWatch

Basic constructor for getting an instance of a StopWatch.

StopWatch - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat

Class for timing applications.

StopWatch() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.StopWatch

Basic constructor for getting an instance of a StopWatch.

StopWatch - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth

Class for timing applications.

StopWatch() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.StopWatch

Basic constructor for getting an instance of a StopWatch.

StopWatch - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR

Class for timing applications.

StopWatch() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.StopWatch

Basic constructor for getting an instance of a StopWatch.

StopWatch - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR

Class for timing applications.

StopWatch() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.StopWatch

Basic constructor for getting an instance of a StopWatch.

StopWatch - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh

Class for timing applications.

StopWatch() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.StopWatch

Basic constructor for getting an instance of a StopWatch.

StopWatch - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR

Class for timing applications.

StopWatch() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.StopWatch

Basic constructor for getting an instance of a StopWatch.

StopWatch - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII

Class for timing applications.

StopWatch() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.StopWatch

Basic constructor for getting an instance of a StopWatch.

store_in_memory(Hyperrectangle) - Method in class keel.Algorithms.Hyperrectangles.EACH.HyperrectangleSet


 Stores in memory the new hyperrectangle 


Store_Variable(String, double[], double[], double[], double[], String[], Double_t, Double_t, boolean, int, int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
 
Store_Variable(String, double[], double[], double[], double[], String[], Double_t, Double_t, boolean, int, int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
 
STPMX - Variable in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
 
stratify(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances

Stratifies a set of instances according to its class values
 if the class attribute is nominal (so that afterwards a
 stratified cross-validation can be performed).

stratify(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Stratifies a set of instances according to its class values
 if the class attribute is nominal (so that afterwards a
 stratified cross-validation can be performed).

stratify(Instances, int, Randomize) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats

Stratify the given data into the given number of bags based on the class
 values.

stratify(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

Stratifies a set of itemsets according to its class values
 if the class attribute is nominal (so that afterwards a
 stratified cross-validation can be performed).

stratify(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Stratifies a set of instances according to its class values if the class
 attribute is nominal (so that afterwards a stratified cross-validation
 can be performed).

stratify(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Stratifies a set of instances according to its class values 
 if the class attribute is nominal (so that afterwards a 
 stratified cross-validation can be performed).

stratify(int) - Method in class keel.Algorithms.SVM.SMO.core.Instances

Stratifies a set of instances according to its class values 
 if the class attribute is nominal (so that afterwards a 
 stratified cross-validation can be performed).

stratStep(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Help function needed for stratification of set.

stratStep(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Help function needed for stratification of set.

stratStep(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Help function needed for stratification of set.

stratStep(int) - Method in class keel.Algorithms.SVM.SMO.core.Instances

Help function needed for stratification of set.

STRING - Static variable in class keel.Algorithms.Decision_Trees.M5.M5Attribute

Constant set for attributes with string values.

STRING - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka

Constant set for attributes with string values.

STRING - Static variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute

Constant set for attributes with string values.

StringCompare() - Constructor for class keel.Algorithms.SVM.SMO.core.ClassDiscovery.StringCompare
 
stringFreeStructure() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances

Create a copy of the structure, but "cleanse" string types (i.e.

stringFreeStructure() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Create a copy of the structure if the data has string or
 relational attributes, "cleanses" string types (i.e. doesn't
 contain references to the strings seen in the past) and all
 relational attributes.

stringFreeStructure() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Create a copy of the structure, but "cleanse" string types (i.e. doesn't
 contain references to the strings seen in the past).

StringKernel - Class in keel.Algorithms.SVM.SMO.supportVector

Implementation of the subsequence kernel (SSK) as described in [1] and of the subsequence kernel with lambda pruning (SSK-LP) as described in [2].

StringKernel() - Constructor for class keel.Algorithms.SVM.SMO.supportVector.StringKernel

default constructor

StringKernel(Instances, int, int, double, boolean) - Constructor for class keel.Algorithms.SVM.SMO.supportVector.StringKernel

creates a new StringKernel object.

StringLocator - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core

This class locates and records the indices of String attributes,
 recursively in case of Relational attributes.

StringLocator(Instances) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.StringLocator

initializes the StringLocator with the given data

StringLocator(Instances, int, int) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.StringLocator

Initializes the StringLocator with the given data.

StringLocator(Instances, int[]) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.StringLocator

Initializes the AttributeLocator with the given data.

stringValue(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Returns the value of a nominal (or string) attribute
 for the instance.

stringValue(M5Attribute) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Returns the value of a nominal (or string) attribute
 for the instance.

stringValue(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Returns the value of a nominal, string, date, or relational attribute
 for the instance as a string.

stringValue(AttributeWeka) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Returns the value of a nominal, string, date, or relational attribute
 for the instance as a string.

stringValue(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Returns the value of a nominal, string, date, or relational attribute
 for the instance as a string.

stringValue(Attribute) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Returns the value of a nominal, string, date, or relational attribute
 for the instance as a string.

stringValue(int) - Method in class keel.Algorithms.SVM.SMO.core.Instance

Returns the value of a nominal, string, date, or relational attribute
 for the instance as a string.

stringValue(Attribute) - Method in class keel.Algorithms.SVM.SMO.core.Instance

Returns the value of a nominal, string, date, or relational attribute
 for the instance as a string.

stringWithoutHeader() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Returns the instances in the dataset as a string in ARFF format.

stringWithoutHeader() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Returns the instances in the dataset as a string in ARFF format.

stringWithoutHeader() - Method in class keel.Algorithms.SVM.SMO.core.Instances

Returns the instances in the dataset as a string in ARFF format.

StructuralMutator<I extends NeuralNetIndividual> - Class in keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural

Structural mutator for neural nets
 
 IMPORTANT NOTE: Structural mutator works directly with  he individuals instead
                 of returning a mutated copy of them.

StructuralMutator() - Constructor for class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator


 Empty Constructor
 

Structure - Class in keel.Algorithms.Associative_Classification.ClassifierCBA

This class contains the representation of the structure .

Structure() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.Structure


 Default Constructor
 

Structure(int, int, int, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA.Structure


 Parameters Constructor
 

Structure - Class in keel.Algorithms.Associative_Classification.ClassifierCBA2

This class contains the representation of the structure 

Structure() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.Structure


 Default Constructor
 

Structure(int, int, int, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCBA2.Structure


 Parameters Constructor
 

StructureEstimationCriterion(node[], Data, SetupParameters) - Method in class keel.Algorithms.Neural_Networks.gmdh.node


 Calculates the Structure Estimation Criterion
 

student(double, int) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.StatFunc

Student t Distribution Function.

studentPercentage(double, int) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.StatFunc

Percentage point of the student t distribution.

studentTConfidenceInterval(int, double, double) - Static method in class keel.Algorithms.Decision_Trees.M5.Distributions

Computes absolute size of half of a student-t confidence interval
 for given degrees of freedom, probability, and observed value.

su(int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion

Up

su(int, int, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Evolucion

Up

sub(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype

Performs substract operation between two prototypes.

sub(Function, Function) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.Function

the difference of two functions

sub(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype

Performs substract operation between two prototypes.

SUBGROUPDISCOVERY - Static variable in class keel.GraphInterKeel.experiments.Experiments
 
subMul(Prototype, double) - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype

Performs substract and product operation between two prototypes.

subMul(Prototype, double) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype

Performs substract and product operation between two prototypes.

Subpopulation - Class in keel.Algorithms.Genetic_Rule_Learning.CORE


 This class represents a subpopulation of rules belonging to a same feature (class).

Subpopulation() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.CORE.Subpopulation


 Default constructor.

Subpopulation - Class in keel.Algorithms.Instance_Selection.CoCoIS

File: Subpopulation.java
 
 This class manage the subpopulations of selectors of the CoCoIS model

Subpopulation(int, double[][], int[]) - Constructor for class keel.Algorithms.Instance_Selection.CoCoIS.Subpopulation

Builder.

Subpopulation - Class in keel.Algorithms.Preprocess.Instance_Selection.CoCoIS

File: Subpopulation.java
 
 This class manage the subpopulations of selectors of the CoCoIS model

Subpopulation(int, double[][], int[]) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.CoCoIS.Subpopulation

Builder.

Subpopulation_Binary(int, int, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation


 Returns the binary subpopulation of the individual "individuo"
 

Subpopulation_Binary(int, int, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation


 Returns the binary subpopulation of the individual "individuo"
 

Subpopulation_Binary(int, int, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation


 Returns the binary subpopulation of the individual "individuo"
 

Subpopulation_Integer(int, int, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation


 Returns the integer subpopulation of the individual "individuo"
 

Subpopulation_Integer(int, int, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation


 Returns the integer subpopulation of the individual "individuo"
 

Subpopulation_Integer(int, int, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation


 Returns the integer subpopulation of the individual "individuo"
 

Subpopulation_Real(int, int, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation


 Returns the real subpopulation of the individual "individuo"
 

Subpopulation_Real(int, int, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation


 Returns the real subpopulation of the individual "individuo"
 

Subpopulation_Real(int, int, Int_t) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation


 Returns the real subpopulation of the individual "individuo"
 

subsequenceLengthTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel

Returns the tip text for this property

subsetDL(double, double, double) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats

Subset description length: 

 S(t,k,p) = -k*log2(p)-(n-k)log2(1-p)

 Details see Quilan: "MDL and categorical theories (Continued)",ML95

subsetDL(double, double, double) - Static method in class keel.Algorithms.Rule_Learning.C45Rules.Rule

Subset description length: 

 S(t,k,p) = -k*log2(p)-(n-k)log2(1-p)

 Details see Quilan: "MDL and categorical theories (Continued)",ML95

subsetDL(double, double, double) - Static method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule

Subset description length: 

 S(t,k,p) = -k*log2(p)-(n-k)log2(1-p)

 Details see Quilan: "MDL and categorical theories (Continued)",ML95

subsetDL(double, double, double) - Static method in class keel.Algorithms.Rule_Learning.Ripper.Rule

Subset description length: 

 S(t,k,p) = -k*log2(p)-(n-k)log2(1-p)

 Details see Quilan: "MDL and categorical theories (Continued)",ML95

substract(Mask, int, double, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

It substracts the instances covered by a simple rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, SimpleRule) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

It substracts the instances covered by a simple rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

It substracts the instances covered by a rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, Ruleset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

It substracts the instances covered by a set of rule from this dataset;
 i.e., it deactivates the instances covered by that ruleset.

substract(Mask, Ruleset, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

It substracts the instances covered by a set of rules from this dataset;
 i.e., it deactivates the instances covered by that ruleset.

substract(Mask, int, double, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset

It substracts the instances covered by a simple rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, SimpleRule) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset

It substracts the instances covered by a simple rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, Rule) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset

It substracts the instances covered by a rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, Ruleset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset

It substracts the instances covered by a set of rule from this dataset;
 i.e., it deactivates the instances covered by that ruleset.

substract(Mask, Ruleset, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset

It substracts the instances covered by a set of rules from this dataset;
 i.e., it deactivates the instances covered by that ruleset.

substract(Mask, int, double, int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset

It substracts the instances covered by a simple rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, SimpleRule) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset

It substracts the instances covered by a simple rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, Rule) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset

It substracts the instances covered by a rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, Ruleset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset

It substracts the instances covered by a set of rule from this dataset;
 i.e., it deactivates the instances covered by that ruleset.

substract(Mask, Ruleset, int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset

It substracts the instances covered by a set of rules from this dataset;
 i.e., it deactivates the instances covered by that ruleset.

substract(Mask, int, double, int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset

It substracts the instances covered by a simple rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, SimpleRule) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset

It substracts the instances covered by a simple rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, Rule) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset

It substracts the instances covered by a rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, Ruleset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset

It substracts the instances covered by a set of rule from this dataset;
 i.e., it deactivates the instances covered by that ruleset.

substract(Mask, Ruleset, int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset

It substracts the instances covered by a set of rules from this dataset;
 i.e., it deactivates the instances covered by that ruleset.

substract(Mask, int, double, int) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset

It substracts the instances covered by a simple rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, SimpleRule) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset

It substracts the instances covered by a simple rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, Rule) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset

It substracts the instances covered by a rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, Ruleset) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset

It substracts the instances covered by a set of rule from this dataset;
 i.e., it deactivates the instances covered by that ruleset.

substract(Mask, Ruleset, int) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset

It substracts the instances covered by a set of rules from this dataset;
 i.e., it deactivates the instances covered by that ruleset.

substract(Mask, int, double, int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset

It substracts the instances covered by a simple rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, SimpleRule) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset

It substracts the instances covered by a simple rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, Rule) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset

It substracts the instances covered by a rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, Ruleset) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset

It substracts the instances covered by a set of rule from this dataset;
 i.e., it deactivates the instances covered by that ruleset.

substract(Mask, Ruleset, int) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset

It substracts the instances covered by a set of rules from this dataset;
 i.e., it deactivates the instances covered by that ruleset.

substract(Mask, int, double, int) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset

It substracts the instances covered by a simple rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, SimpleRule) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset

It substracts the instances covered by a simple rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, Rule) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset

It substracts the instances covered by a rule from this dataset;
 i.e., it deactivates the instances covered by that rule.

substract(Mask, Ruleset) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset

It substracts the instances covered by a set of rule from this dataset;
 i.e., it deactivates the instances covered by that ruleset.

substract(Mask, Ruleset, int) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset

It substracts the instances covered by a set of rules from this dataset;
 i.e., it deactivates the instances covered by that ruleset.

subsumes(Attribute) - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Attribute

It checks if that attribute of the representation of the classifier can subsume the attribute passed as a
        parameter

subsumes(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep


 Returns true if the current representation allele subsumes the representation allele passed as a parameter
 

subsumes(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep


 Returns true if the allele is subsumed by the ternary representation given as a parameter.

subsumes(Attribute) - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute

It checks if that attribute of the representation of the classifier can subsume the attribute passed as a
        parameter

subsumes(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep


 Returns true if the current representation allele subsumes the representation allele passed as a parameter
 

subsumes(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep


 Returns true if the current representation allele subsumes the representation allele passed as a parameter
 

subsumes(Representation) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Representation


 Returns true if the current representation subsumes the representation passed as a parameter
 

subsumes(Attribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep


 Returns true if the allele is subsumed by the ternary representation given as a parameter.

subsumido(Selector) - Method in class keel.Algorithms.Rule_Learning.AQ.Selector

It checks if the values of a selector are subsumed in another

subsumido(Selector) - Method in class keel.Algorithms.Rule_Learning.CN2.Selector

It checks if the values of a selector are subsumed in another

subtFont - Variable in class keel.GraphInterKeel.experiments.Credits
 
subtract(double) - Method in class keel.Algorithms.Decision_Trees.M5.SimpleStatistics

Removes a value to the observed values (no checking is done
 that the value being removed was actually added).

subtract(double, double) - Method in class keel.Algorithms.Decision_Trees.M5.SimpleStatistics

Subtracts a value that has been seen n times from the observed values

subtract(FuzzyAlphaCut) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut

 
  Returns the subtract of the present FuzzyInterval and the parameter x.

subtract(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleStatistics

Removes a value to the observed values (no checking is done
 that the value being removed was actually added).

subtract(double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleStatistics

Subtracts a value that has been seen n times from the observed values

subtract(double) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Stats

Removes a value to the observed values (no checking is done
 that the value being removed was actually added).

subtract(double, double) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Stats

Subtracts a value that has been seen n times from the observed values

subtract(double[], double[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV


 returns the subtraction of vector a and b.

subtract(double[][], double[][]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV


 returns the subtraction of matrix a and b.

subtract(double[][][], double[][][]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV


 returns the subtraction of matrix a and b.

subtract(double[], double[]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV


 returns the subtraction of vector a and b.

subtract(double[][], double[][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV


 returns the subtraction of matrix a and b.

subtract(double[][][], double[][][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV


 returns the subtraction of matrix a and b.

subvector(int, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Returns a subvector.

subvector(IntVector) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Returns a subvector.

subvector(int, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector

Returns a subvector.

subvector(IntVector) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector

Returns a subvector as indexed by an IntVector.

successRatio - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricSRMutator

Ratio of successful mutations

sum(int, M5Instances) - Static method in class keel.Algorithms.Decision_Trees.M5.M5

Returns the sum of the instances values of an attribute

sum(double[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils

Computes the sum of the elements of an array of doubles.

sum(int[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils

Computes the sum of the elements of an array of integers.

sum - Variable in class keel.Algorithms.Decision_Trees.M5.SimpleStatistics

The sum of values seen

sum(double[], double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV


 This static method implements the sum of vectors
 

sum(double[][], double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV


 This static method implements the sum of matrix
 

sum(double[][][], double[][][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.OPV


 This static method implements the sum of cubic matrix
 

sum(double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils

Computes the sum of the elements of an array of doubles.

sum(int[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils

Computes the sum of the elements of an array of integers.

sum(FuzzyAlphaCut) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut

 
  Returns the sum of the present FuzzyInterval and the parameter x.

sum - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase

Sum configuration flag.

sum(int, MyDataset) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5

Returns the sum of the itemsets values of an attribute

sum(double[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils

Computes the sum of the elements of an array of doubles.

sum(int[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils

Computes the sum of the elements of an array of integers.

sum - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleStatistics

The sum of values seen

sum - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Stats

The sum of values seen

sum(double[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils

Computes the sum of the elements of an array of doubles.

sum(int[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils

Computes the sum of the elements of an array of integers.

sum(DenseVector) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM

Sum of the elements of the vector

sum(double[]) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.Gcvfctn

Computes the sum of the elements of the vector

sum(DenseVector) - Method in class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.LLSImpute

Compute the sum of all members of vector v

sum(double[], double[]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV


 returns the sum of vector a and b.

sum(double[][], double[][]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV


 returns the sum of matrix a and b.

sum(double[][][], double[][][]) - Static method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.OPV


 returns the sum of matrix a and b.

sum(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils

Computes the sum of the elements of an array of doubles.

sum(int[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils

Computes the sum of the elements of an array of integers.

sum(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils

Computes the sum of the elements of an array of doubles.

sum(int[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils

Computes the sum of the elements of an array of integers.

sum(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils

Computes the sum of the elements of an array of doubles.

sum(int[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils

Computes the sum of the elements of an array of integers.

sum(double[], double[]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV


 returns the sum of vector a and b.

sum(double[][], double[][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV


 returns the sum of matrix a and b.

sum(double[][][], double[][][]) - Static method in class keel.Algorithms.Shared.ClassicalOptim.OPV


 returns the sum of matrix a and b.

sum() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Returns the sum of all elements in the vector.

sum(double[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils

Computes the sum of the elements of an array of doubles.

sum(int[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils

Computes the sum of the elements of an array of integers.

sum(double[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils

Computes the sum of the elements of an array of doubles.

sum(int[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils

Computes the sum of the elements of an array of integers.

sum2() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Returns the squared sum of all elements in the vector.

sum2(DoubleVector) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Returns ||u-v||^2

SUMA - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Selec.Fuzzy_Ish
 
suma(double, double) - Static method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Discretizacion

Sums two number.

suma(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
 
suma(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
 
suma(Interval) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.Interval
 
suma(Interval) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.Interval
 
suma(Interval) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.Interval
 
suma(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
 
suma(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
 
suma(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
 
suma(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
 
suma(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
 
suma_ltf(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzy
 
suma_ltf(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzy
 
suma_ltf(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Expert.fuzzy
 
suma_ltf(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fuzzy
 
suma_ltf(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzy
 
suma_ltf(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzy
 
suma_ltf(fuzzy, fuzzy) - Static method in class keel.Algorithms.LQD.tests.IntermediateBoost.fuzzy
 
sumar(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet

Sums two prototype sets, element by element.

sumar(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

SUMAR dos conjuntos de prototipos , uno a uno.

sumarValor(float) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Atributo

Sums the value given to the one stored.

sumarValor(float) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Condicion

Sums the value given to the one stored.

sumatoria() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.myDataset

Computes the summation of the output values as real.

sumatoria() - Method in class keel.Algorithms.RE_SL_Postprocess.Genetic_NFRM.myDataset

Computes the sum of the outputs of every instances.

sumatorio(Vector<Float>, Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.AlgGenetic
 
sumatorio(Vector<Float>, Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.AlgGenetic
 
sumatorio(float, float, int) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzyrule
 
sumatorio(float, float, int) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzyrule
 
sumbyRows(DenseMatrix) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM

Performs the sum by the rows of the matrix

sumbyRows(DenseMatrix) - Method in class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.LLSImpute

From a given matrix mat, it performs the summatory by rows of such matrix

sumDifferentClasses(int, int) - Method in class keel.Algorithms.Preprocess.Feature_Selection.Datos

Sums the prior probabilities of the classes that are different to the one of the example given.

sumInterval - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
 
sumInterval - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
 
sumInterval - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
 
sumInterval - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
 
summary() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.Histogram

Returns a String summary with all the statistical variables

summary - Variable in class keel.GraphInterKeel.experiments.Experiments
 
summaryC - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest

First classification algorithm summary of data identifier.

summaryI - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest

Summary of data, 1 algorithm imbalanced

summaryR - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest

First regression algorithm summary of data identifier.

summation(Vector<Float>, Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.AlgGenetic
 
summation(Vector<Float>, Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.AlgGenetic
 
summation(Vector<Float>, Vector<Float>) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.AlgGenetic
 
sumMinorityClasses() - Method in class keel.Algorithms.Genetic_Rule_Learning.OCEC.myDataset

Returns the summation of the number of instances that belong to the minority classes

sumOfWeights() - Method in class keel.Algorithms.Decision_Trees.C45.Dataset

Function to compute the sum of all the weights of the itemsets.

sumOfWeights() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Dataset

Function to compute the sum of all the weights of the itemsets.

sumOfWeights() - Method in class keel.Algorithms.Decision_Trees.ID3.Dataset

Function to compute the sum of all the weights of the itemsets.

sumOfWeights() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances

Computes the sum of all the instances' weights.

sumOfWeights() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Dataset

Function to compute the sum of all the weights of the itemsets.

sumOfWeights() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Computes the sum of all the instances' weights.

sumOfWeights() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

Function to compute the sum of all the weights of the itemsets.

sumOfWeights() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Dataset

Function to compute the sum of all the weights of the itemsets.

sumOfWeights() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset

Function to compute the sum of all the weights of the itemsets.

sumOfWeights() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Dataset

Function to compute the sum of all the weights of the itemsets.

sumOfWeights() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Dataset

Function to compute the sum of all the weights of the itemsets.

sumOfWeights() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Computes the sum of all the instances' weights.

sumOfWeights(int, Vector<pnPair>) - Method in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.SaturationFilter


 Constructor of the class
 

sumOfWeights() - Method in class keel.Algorithms.Rule_Learning.ART.Dataset

Function to compute the sum of all the weights of the itemsets.

sumOfWeights() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset

Function to compute the sum of all the weights of the itemsets.

sumOfWeights() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset

Function to compute the sum of all the weights of the itemsets.

sumOfWeights() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Dataset

Function to compute the sum of all the weights of the itemsets.

sumOfWeights() - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset

Function to compute the sum of all the weights of the itemsets.

sumOfWeights() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Dataset

Function to compute the sum of all the weights of the itemsets.

sumOfWeights() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet
 
sumOfWeights() - Method in class keel.Algorithms.SVM.SMO.core.Instances

Computes the sum of all the instances' weights.

sumPairFreq(String, String) - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqListPair


 Searches for the all the pairs which have its elements equals to the provided ones, 
 and return their cummulative frequency
 

sumSq - Variable in class keel.Algorithms.Decision_Trees.M5.SimpleStatistics

The sum of values squared seen

sumSq - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleStatistics

The sum of values squared seen

sumSq - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Stats

The sum of values squared seen

Sup_Range() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.domain_t
 
Sup_Range() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
 
Sup_Range(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
 
Sup_Range() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.domain_t


 Retuns the upper value for all the labels in the domain.

Sup_Range() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t


 Returns the upper value of the definition interval of variable's domain.

Sup_Range(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar


 Returns the upper value of the definition interval of variable in position "var" of the list
 

Sup_Range() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.domain_t
 
Sup_Range() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
 
Sup_Range(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
 
Sup_Range() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.domain_t
 
Sup_Range() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
 
Sup_Range(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
 
SupBound(int[][], int[][]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets
 
superior - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets

Aprox Superior

superSetInputFormat(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter

Store the input formats

superSetInputFormat(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter

Sets the format of the input instances.

support - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining

Command line argument for % support (default = 1%).

support - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PtreeNode

Partial support for the rows.

support - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.PtreeNodeTop

Partial support for the rows.

support - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TtreeNode

The support associate wuth the itemset represented by the node.

support() - Method in class keel.Algorithms.Discretizers.MVD.Interval

Provides the number of instances covered by this interval

support() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.Fuzzy

 
  Creates and returns a FuzzyInterval with unique point of the support set.

support() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyAlphaCut

 
  Creates and returns a FuzzyInterval with the extremes of the support set.

support() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyInterval

 
  Creates and returns a FuzzyInterval with the extremes of the support set.

support() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyNumberTRIANG

 
  Creates and returns a FuzzyInterval with the extremes of the support set.

support() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyNumberTRLEFT

 
  Creates and returns a FuzzyInterval with the extremes of the support set.

support() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzySingleton

 
  Creates and returns a FuzzyInterval with unique point of the support set.

support - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining

% support.

support - Variable in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TtreeNode

The support associate with the itemset represented by the node.

support - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining

% support.

support - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TtreeNode

The support associate with the itemset represented by the node.

support - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
 
support - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
 
support - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
 
support - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
 
support - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
 
support - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
 
supportBound - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.NumericAntd

The edge point for the fuzzy set of this numeric antecedent

SVBPS - Class in keel.Algorithms.Instance_Selection.SVBPS

File: SVBPS.java
 
 The SVBPS Instance Selection algorithm.

SVBPS(String) - Constructor for class keel.Algorithms.Instance_Selection.SVBPS.SVBPS

Default constructor.

SVBPS - Class in keel.Algorithms.Preprocess.Instance_Selection.SVBPS

File: SVBPS.java
 
 The SVBPS Instance Selection algorithm.

SVBPS(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.SVBPS.SVBPS

Default constructor.

svd() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix

Singular Value Decomposition

SVDimpute - Class in keel.Algorithms.Preprocess.Missing_Values.SVDimpute

This class implements the Single Value Decomposition Imputation

SVDimpute(String) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.SVDimpute.SVDimpute


 Creates a new object of SVDI based on the parameter file provided
 

svm - Class in keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost
 
svm() - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm
 
SVM - Static variable in class keel.Algorithms.Instance_Generation.HYB.HYBGenerator

SVM title text

svm - Class in org.libsvm
 
svm() - Constructor for class org.libsvm.svm
 
svm_check_parameter(svm_problem, svm_parameter) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm
 
svm_check_parameter(svm_problem, svm_parameter) - Static method in class org.libsvm.svm
 
svm_check_probability_model(svm_model) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm
 
svm_check_probability_model(svm_model) - Static method in class org.libsvm.svm
 
svm_cross_validation(svm_problem, svm_parameter, int, double[]) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm
 
svm_cross_validation(svm_problem, svm_parameter, int, double[]) - Static method in class org.libsvm.svm
 
svm_get_labels(svm_model, int[]) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm
 
svm_get_labels(svm_model, int[]) - Static method in class org.libsvm.svm
 
svm_get_nr_class(svm_model) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm
 
svm_get_nr_class(svm_model) - Static method in class org.libsvm.svm
 
svm_get_svm_type(svm_model) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm
 
svm_get_svm_type(svm_model) - Static method in class org.libsvm.svm
 
svm_get_svr_probability(svm_model) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm
 
svm_get_svr_probability(svm_model) - Static method in class org.libsvm.svm
 
svm_load_model(String) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm
 
svm_load_model(String) - Static method in class org.libsvm.svm
 
svm_model - Class in keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost
 
svm_model() - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_model
 
svm_model - Class in org.libsvm
 
svm_model() - Constructor for class org.libsvm.svm_model
 
svm_node - Class in keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost
 
svm_node() - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_node
 
svm_node - Class in org.libsvm
 
svm_node() - Constructor for class org.libsvm.svm_node
 
svm_parameter - Class in keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost
 
svm_parameter() - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
 
svm_parameter - Class in org.libsvm
 
svm_parameter() - Constructor for class org.libsvm.svm_parameter
 
svm_predict(svm_model, svm_node[]) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm
 
svm_predict(svm_model, svm_node[]) - Static method in class org.libsvm.svm
 
svm_predict_probability(svm_model, svm_node[], double[]) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm
 
svm_predict_probability(svm_model, svm_node[], double[]) - Static method in class org.libsvm.svm
 
svm_predict_values(svm_model, svm_node[], double[]) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm
 
svm_predict_values(svm_model, svm_node[], double[]) - Static method in class org.libsvm.svm
 
svm_problem - Class in keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost
 
svm_problem() - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_problem
 
svm_problem - Class in org.libsvm
 
svm_problem() - Constructor for class org.libsvm.svm_problem
 
svm_save_model(String, svm_model) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm
 
svm_save_model(String, svm_model) - Static method in class org.libsvm.svm
 
svm_train(svm_problem, svm_parameter) - Static method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm
 
svm_train(svm_problem, svm_parameter) - Static method in class org.libsvm.svm
 
svm_type - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
 
svm_type - Variable in class org.libsvm.svm_parameter
 
svmClassifier - Class in keel.Algorithms.SVM.C_SVM


 This class is a wrapper to the LibSVM C-SVM classifier, in order to operate with KEEL data sets and parameters.

svmClassifier(String) - Constructor for class keel.Algorithms.SVM.C_SVM.svmClassifier

Creates a new instance of svmClassifier

svmClassifier - Class in keel.Algorithms.SVM.NU_SVM


 This class is a wrapper to the LibSVM Nu-SVM classifier, in order to operate with KEEL data sets and parameters.

svmClassifier(String) - Constructor for class keel.Algorithms.SVM.NU_SVM.svmClassifier

Creates a new instance of svmClassifier

svmClassifierCost - Class in keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost


 This class is a wrapper to the LibSVM C-SVM classifier, in order to operate with KEEL data sets and parameters.

svmClassifierCost(String) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svmClassifierCost

Creates a new instance of svmClassifier

svmImpute - Class in keel.Algorithms.Preprocess.Missing_Values.svmImpute


 This class imputes the missing values by means of the SVM regression.

svmImpute(String) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.svmImpute.svmImpute

Creates a new instance of svmImpute

SVMOutput(int, Instance) - Method in class keel.Algorithms.SVM.SMO.SMO.BinarySMO

Computes SVM output for given instance.

SVMOutput(int) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer

SVMOutput of an instance in the training set, m_data
 This uses the cache, unlike SVMOutput(Instance)

SVMOutput(Instance) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer
 
SVMreg - Class in keel.Algorithms.SVM.SMO

SVMreg implements the support vector machine for regression.

SVMreg() - Constructor for class keel.Algorithms.SVM.SMO.SVMreg
 
svmRegression - Class in keel.Algorithms.SVM.EPSILON_SVR


 This class is a wrapper to the LibSVM eps-SVR regression, in order to operate with KEEL data sets and parameters.

svmRegression(String) - Constructor for class keel.Algorithms.SVM.EPSILON_SVR.svmRegression

Creates a new instance of svmRegression

svmRegression - Class in keel.Algorithms.SVM.NU_SVR


 This class is a wrapper to the LibSVM eps-SVR regression, in order to operate with KEEL data sets and parameters.

svmRegression(String) - Constructor for class keel.Algorithms.SVM.NU_SVR.svmRegression

Creates a new instance of svmRegression

SVMSEL - Class in keel.Algorithms.Instance_Generation.HYB

SVM process of selection of prototype set.

SVMSEL(PrototypeSet, String, double, double, int, double, double, double, int) - Constructor for class keel.Algorithms.Instance_Generation.HYB.SVMSEL

Performs an selection of the training data set using SVM.

SVMSEL(PrototypeSet, String) - Constructor for class keel.Algorithms.Instance_Generation.HYB.SVMSEL

Performs an selection of the training data set using SVM.

swap(int, int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector

Swaps two elements in the vector.

Swap(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
 
Swap(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary
 
Swap(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger
 
Swap(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal
 
Swap(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
 
Swap(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
 
Swap(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationinteger
 
Swap(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal
 
Swap(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
 
Swap(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
 
Swap(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationinteger
 
Swap(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationreal
 
swap(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector

Swaps two elements in the vector.

swap(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Swaps two instances in the set.

swap(Chromosome, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome


 Performs the one-point crossover with other chromosome
 

swap(int, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector

Swaps two elements in the vector.

swap(int, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Swaps two instances in the set.

swap(int, int) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet

Swaps two elements of the prototype set.

swap(int, int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Swaps two elements of the prototype set.

swap(int, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector

Swaps two elements in the vector.

swap(int, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Swaps the values stored at i and j

swap(int, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector

Swaps the values stored at i and j

swap(int, int) - Method in class keel.Algorithms.SVM.SMO.core.FastVector

Swaps two elements in the vector.

swap(int, int) - Method in class keel.Algorithms.SVM.SMO.core.Instances

Swaps two instances in the set.

swap1 - Class in keel.Algorithms.Rule_Learning.Swap1
 
swap1(String, String) - Constructor for class keel.Algorithms.Rule_Learning.Swap1.swap1

Parameter Constructor.

Swap_bool(boolean[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
 
Swap_bool(boolean[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary
 
Swap_bool(boolean[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger
 
Swap_bool(boolean[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal
 
Swap_bool(boolean[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
 
Swap_bool(boolean[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
 
Swap_bool(boolean[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationinteger
 
Swap_bool(boolean[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal
 
Swap_bool(boolean[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
 
Swap_bool(boolean[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
 
Swap_bool(boolean[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationinteger
 
Swap_bool(boolean[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationreal
 
Swap_double(double[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
 
Swap_double(double[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary
 
Swap_double(double[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger
 
Swap_double(double[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal
 
Swap_double(double[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
 
Swap_double(double[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
 
Swap_double(double[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationinteger
 
Swap_double(double[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal
 
Swap_double(double[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
 
Swap_double(double[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
 
Swap_double(double[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationinteger
 
Swap_double(double[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationreal
 
Swap_int(int[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
 
Swap_int(int[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary
 
Swap_int(int[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger
 
Swap_int(int[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal
 
Swap_int(int[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
 
Swap_int(int[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary
 
Swap_int(int[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationinteger
 
Swap_int(int[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal
 
Swap_int(int[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
 
Swap_int(int[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary
 
Swap_int(int[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationinteger
 
Swap_int(int[], int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationreal
 
swapOnePoint(Chromosome) - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Chromosome


 Crossover between chromosomes, using the swap from one point approach
 

SwitchTo(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserTokenManager
 
SwitchTo(int) - Static method in class keel.Algorithms.Rule_Learning.Swap1.DataParserTokenManager
 
SwitchTo(int) - Static method in class keel.Dataset.DataParserTokenManager
 
symbolicRegressionFuzzyGAP(int, boolean, ProcessConfig, Randomize) - Static method in class keel.Algorithms.Symbolic_Regression.Shared.ParseFileRegSym

 
 This method learns a symbolic model for training input data given in pc using the Genetic Algorithm Programming (GAP) paradigm.

symbolicRegressionFuzzySAP(int, boolean, ProcessConfig, Randomize) - Static method in class keel.Algorithms.Symbolic_Regression.Shared.ParseFileRegSym

 
 This method learns a symbolic model for training input data given in pc using the Simulated Anneling Programming (SAP) paradigm.

symmetricalUncertainty(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ContingencyTables

Calculates the symmetrical uncertainty for base 2.

symmetricalUncertainty(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.ContingencyTables

Calculates the symmetrical uncertainty for base 2.

symmetricalUncertainty(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.ContingencyTables

Calculates the symmetrical uncertainty for base 2.

SymmetricTriangle - Static variable in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel

Type of Positive Definite Functions supported (Symmetric Triangle)

symmetricTriangle(double) - Method in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel

Computes the result of the Symmetric Triangle PDRF

synopsis() - Method in class keel.Algorithms.Decision_Trees.M5.Information

Returns the option's synopsis.

synopsis() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Option

Returns the option's synopsis.

synopsis() - Method in class keel.Algorithms.SVM.SMO.core.Option

Returns the option's synopsis.

SystemCommandExecutor - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams
 
SystemCommandExecutor(List<String>) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.SystemCommandExecutor
 




T

T - Variable in class keel.Algorithms.Instance_Generation.VQ.AVQGenerator

Partition of the training data set used as training.

T - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
 
T_Consequent - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL


 Each consequent type has this form
 
 
 

T_Consequent() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.T_Consequent
 
T_FRM - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL

        
 Each FRM type has this form
 
 
 

T_FRM() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.T_FRM
 
T_Min() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseR
 
tabla - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Sel

Dataset.

tabla - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Tun

Dataset.

tablaDataset - Variable in class keel.GraphInterKeel.datacf.editData.EditDataPanel
 
TableDat - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF

Class defined to store the information of the complete dataset

TableDat() - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableDat


 Creates a new instance of TableDat
 

TableDat - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD

Class defined to store the information of the complete dataset

TableDat() - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableDat


 Creates a new instance of TableDat
 

TableDat - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA

Class defined to store the information of the complete dataset

TableDat() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableDat


 Creates a new instance of TableDat
 

tableHeader - Variable in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelAttribute

Table header

tableType1 - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig

tableType1 (YES or NO)

tableType1 - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig

Table type flag 1.

tableType2 - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig

tableType2 (YES or NO)

tableType2 - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig

Table type flag 2.

tableType3 - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig

tableType3 (YES or NO)

tableType3 - Static variable in class keel.Algorithms.Shared.Parsing.ProcessConfig

Table type flag 3.

TableVar - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF

Class defined to store the information of the variable of the dataset

TableVar() - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TableVar


 Creates a new instance of TableVar
 

TableVar - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD

 Class defined to store the information of the variable of the dataset

TableVar() - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TableVar


 Creates a new instance of TableVar
 

TableVar - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA

Class defined to store the information of the variable of the dataset

TableVar() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TableVar


 Creates a new instance of TableVar
 

tableVector - Variable in class keel.GraphInterKeel.experiments.DataSet
 
tableVector - Variable in class keel.GraphInterKeel.experiments.Joint
 
tabSize - Static variable in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
 
tabSize - Static variable in class keel.Dataset.SimpleCharStream
 
tabularC - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest

Summary of classification data for  multiple algorithms identifier.

tabularI - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest

Summary of data, multiple algorithms imbalanced

tabularR - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest

Summary of regression data for  multiple algorithms identifier.

Tag - Class in keel.Algorithms.SVM.SMO.core

A Tag simply associates a numeric ID with a String description.

Tag(int, String) - Constructor for class keel.Algorithms.SVM.SMO.core.Tag

Creates a new Tag instance.

Tag(int, String, String) - Constructor for class keel.Algorithms.SVM.SMO.core.Tag

Creates a new Tag instance.

TAGS_FILTER - Static variable in class keel.Algorithms.SVM.SMO.SMO

The filter to apply to the training data

TAGS_FILTER - Static variable in class keel.Algorithms.SVM.SMO.SMOreg

The filter to apply to the training data

TAGS_FILTER - Static variable in class keel.Algorithms.SVM.SMO.SVMreg

The filter to apply to the training data

TAGS_MODEL_TYPES - Static variable in class keel.Algorithms.Decision_Trees.M5.M5

Tags for the model types.

TAGS_PRUNING - Static variable in class keel.Algorithms.SVM.SMO.supportVector.StringKernel

Pruning methods

takeStep(int, int, double) - Method in class keel.Algorithms.SVM.SMO.SMO.BinarySMO

Method solving for the Lagrange multipliers for
 two instances.

takeStep(int, int) - Method in class keel.Algorithms.SVM.SMO.SMOreg

Method solving for the Lagrange multipliers for
 two instances.

takeStep(int, int, double, double, double) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMO

takeStep method from pseudocode.

takeStep(int, int, double, double, double) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved

takeStep method from Shevade et al.s paper.

tam_population - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.Parameters

Population size.

tam_population - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.Parameters
 
tam_population - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.Parameters
 
tamanio() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.ConjuntoDatos

Returns the number of examples in the dataset.

tamanio() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.ConjuntoDatos

Returns the number of examples in the dataset.

tamanio() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.ConjuntoDatos

Returns the number of examples in the dataset.

tamanio() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.ConjuntoDatos

Returns the number of examples in the dataset.

tamanio() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.ConjuntoDatos

Returns the number of examples in the dataset.

tamano - Variable in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Individual

Size of the individual.

tamano - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Individual

Size of the individual.

tamano - Variable in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Individual

Size of the individual.

tamCromosoma - Variable in class keel.Algorithms.Preprocess.Feature_Selection.evolutionary_algorithms.Cromosoma

chromosome length

Target - Class in keel.Algorithms.Decision_Trees.Target

Title: Target

 Description: It contains the implementation of the algorithm Target


 Company: KEEL 

Target() - Constructor for class keel.Algorithms.Decision_Trees.Target.Target

Default constructor

Target(parseParameters) - Constructor for class keel.Algorithms.Decision_Trees.Target.Target

It reads the data from the input files (training, validation and test) and parse all the parameters
 from the parameters array.

target - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.Link

Target neuron

tauVal(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ContingencyTables

Computes Goodman and Kruskal's tau-value for a contingency table.

tauVal(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.ContingencyTables

Computes Goodman and Kruskal's tau-value for a contingency table.

tauVal(double[][]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.ContingencyTables

Computes Goodman and Kruskal's tau-value for a contingency table.

TBAR - Class in keel.Algorithms.Rule_Learning.ART

A Java implementation of the TBAR algorithm

TBAR(int, double, Vector, List<Attribute>) - Constructor for class keel.Algorithms.Rule_Learning.ART.TBAR

Parameter constructor.

tC - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest

Classification t Stat-test identifier.

TCNN - Class in keel.Algorithms.Instance_Selection.TCNN

File: TCNN.java
 
 The TCNN Instance Selection algorithm.

TCNN(String) - Constructor for class keel.Algorithms.Instance_Selection.TCNN.TCNN

Default constructor.

TCNN - Class in keel.Algorithms.Preprocess.Instance_Selection.TCNN

File: TCNN.java
 
 The TCNN Instance Selection algorithm.

TCNN(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.TCNN.TCNN

Default constructor.

tconorma(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
 
tconorma(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
 
tconorma(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.rule
 
tconorma(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
 
tconorma(float, float) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
 
tconorma(float, float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzyrule
 
tconorma(float, float) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzyrule
 
TEACHING - Static variable in class keel.GraphInterKeel.experiments.Experiments
 
teaching_mouseEntered(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame

Enter in teaching button

teaching_mouseExited(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame

Exit from teaching button

teaching_mouseReleased(MouseEvent) - Method in class keel.GraphInterKeel.menu.Frame

Entering in Educational module

TechnicalInformation - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core

Used for paper references in the Javadoc and for BibTex generation.

TechnicalInformation(TechnicalInformation.Type) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation

Initializes the information with the given type

TechnicalInformation(TechnicalInformation.Type, String) - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation

Initializes the information with the given type

TechnicalInformation - Class in keel.Algorithms.SVM.SMO.core

Used for paper references in the Javadoc and for BibTex generation.

TechnicalInformation(TechnicalInformation.Type) - Constructor for class keel.Algorithms.SVM.SMO.core.TechnicalInformation

Initializes the information with the given type

TechnicalInformation(TechnicalInformation.Type, String) - Constructor for class keel.Algorithms.SVM.SMO.core.TechnicalInformation

Initializes the information with the given type

TechnicalInformation.Field - Enum in keel.Algorithms.Statistical_Classifiers.Logistic.core

the possible fields

TechnicalInformation.Field - Enum in keel.Algorithms.SVM.SMO.core

the possible fields

TechnicalInformation.Type - Enum in keel.Algorithms.Statistical_Classifiers.Logistic.core

the different types of information

TechnicalInformation.Type - Enum in keel.Algorithms.SVM.SMO.core

the different types of information

TechnicalInformationHandler - Interface in keel.Algorithms.Statistical_Classifiers.Logistic.core

For classes that are based on some kind of publications.

TechnicalInformationHandler - Interface in keel.Algorithms.SVM.SMO.core

For classes that are based on some kind of publications.

temp - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm

Temporal instance.

temp - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm

Temporal instance.

temp - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

Temporal instance.

temp - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Temporal instance.

temp - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm

Temporal instance.

temp - Variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN

Temporal instance.

temp - Variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN

Temporal instance.

temp - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm

Temporal instance.

temperExponent - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricMutator

Temperature exponent for the mutations

temperExponent - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator

Temperature exponent for the mutations

Tend - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters
 
Tend - Variable in class keel.Algorithms.Neural_Networks.gmdh.SetupParameters
 
tenthDataSets - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass

3-data array to hold 10th sets of input data.

terminate() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.EARMGAProcess
 
terminate() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENARProcess

Checks the stop condition for the algorithm.

terminateProgram() - Static method in class keel.Algorithms.Instance_Generation.utilities.Debug

Exits the current program

terminateProgram() - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Debug

Exits the current program

TERMS - Static variable in class keel.Algorithms.Neural_Networks.gmdh.node

Number of terms

TernaryMutation - Interface in keel.Algorithms.Genetic_Rule_Learning.UCS

 Ternary mutation.

TernaryMutation - Interface in keel.Algorithms.Genetic_Rule_Learning.XCS

Ternary Mutation.

ternaryRep - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

Represents the type of the representation.

TernaryRep - Class in keel.Algorithms.Genetic_Rule_Learning.UCS

It contains a char value, that represents the value of the alelle.

TernaryRep() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep


 It is the default constructor of the class.

TernaryRep(double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep


 It's the  constructor of the class value from the environmental value.

TernaryRep(char) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep


 It is the constructor of the class.

TernaryRep(Attribute) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep


 It is the constructor of the class.

ternaryRep - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 Represents the type of the representation.

TernaryRep - Class in keel.Algorithms.Genetic_Rule_Learning.XCS

It contains a char value, that represents the value of the alelle.

TernaryRep() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep


 It is the default constructor of the class.

TernaryRep(double) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep


 It's the  constructor of the class value from the environmental value.

TernaryRep(char) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep


 It is the constructor of the class.

TernaryRep(Attribute) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep


 It is the constructor of the class.

test - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm

Test dataset

test - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo

Test dataset.

test(String[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5Instances

Method for testing this class.

test - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm

Test dataset.

TEST - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
test(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Gene


 Test if the passed value (index of the nominal in the 
 attribute) is covered by this gene
 

test(double) - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Gene


 Test a value from an instance (of the data set), to see if it is covered.

TEST - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
test - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

Test dataset

test - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Data

Testing data

test - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
 
test - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
 
test(String[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Method for testing this class.

TEST - Static variable in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm

Type of file: test data set.

test - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Test dataset.

TEST - Variable in class keel.Algorithms.Neural_Networks.ensemble.Ensemble

Training flag.

test - Variable in class keel.Algorithms.Neural_Networks.gann.Data

Testing data

test - Variable in class keel.Algorithms.Neural_Networks.gmdh.Data

Testing data

test - Variable in class keel.Algorithms.Neural_Networks.net.Data

Testing data

test - Variable in class keel.Algorithms.Preprocess.Basic.Metodo

Test dataset

test - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm

Test dataset

test - Static variable in class keel.Algorithms.PSO_Learning.CPSO.CPSO

Training dataset.

test - Static variable in class keel.Algorithms.PSO_Learning.LDWPSO.LDWPSO

Training dataset.

test - Static variable in class keel.Algorithms.PSO_Learning.REPSO.REPSO

Training dataset.

test - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm

Test dataset

test() - Method in class keel.Algorithms.Rule_Learning.Swap1.swap1

Test process.

TEST - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm

Type of file: test data set.

Test - Class in keel.GraphInterKeel.experiments
 
Test() - Constructor for class keel.GraphInterKeel.experiments.Test

Builder

Test(ExternalObjectDescription, Point, GraphPanel) - Constructor for class keel.GraphInterKeel.experiments.Test

Builder

Test(ExternalObjectDescription, Point, GraphPanel, Vector, int, int) - Constructor for class keel.GraphInterKeel.experiments.Test
 
test_data - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters

Check if test,validation or cross validation data is going to be used

test_data - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters

Check if test,validation or cross validation data is going to be used

test_data - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters

Check if test,validation or cross validation data is going to be used

test_data - Variable in class keel.Algorithms.Neural_Networks.net.Parameters

Check if test,validation or cross validation data is going to be used

Test_Example(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
 
Test_Example(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set


 Returns the number of examples belonging to the partition "particion"
 

Test_Example(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set
 
Test_Example(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set
 
test_file - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters

File names

test_file - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters

File names

test_file - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters

File names

test_file - Variable in class keel.Algorithms.Neural_Networks.net.Parameters

File names

test_output - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters

File names

test_output - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters

File names

test_output - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters

File names

test_output - Variable in class keel.Algorithms.Neural_Networks.net.Parameters

File names

testClasification(double[][], int, int[], int, int) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbfn

        
 Evaluates the net for clasification problem
 

testClasification(double[][], int, int[], int, int) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn

        
 Evaluates the net for clasification problem
 

testClasification(double[][], int, int[], int, int) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn

        
 Evaluates the net for clasification problem
 

testClasification(double[][], int, int[], int, int) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn

        
 Evaluates the net for clasification problem
 

testClasification(double[][], int, int[], int, int) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn

        
 Evaluates the net for clasification problem
 

testClasification(double[][], int, int[], int, int) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn

        
 Evaluates the net for clasification problem
 

testClassification(String, Configuration) - Static method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
testClassifier(Classifier, String, String, String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PopulationWrapper
 
testClassifier(Classifier, String, String, String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PopulationWrapper
 
testCombinations(short[]) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree

Commences the process of testing whether the N-1 sized sub-sets of a 
    newly created T-tree node are supported elsewhere in the Ttree --- (a 
    process refered to as "X-Checking").

testCorrect - Variable in class keel.Algorithms.Decision_Trees.C45.Algorithm

Correctly classified in test.

testCorrect - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm

Correctly classified in test.

testCorrect - Variable in class keel.Algorithms.Decision_Trees.ID3.Algorithm

Correctly classified in test.

testCorrect - Variable in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm

Number of correctly classified example from test dataset.

testCorrect - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm

Correctly classified in test.

testCorrect - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm

Correctly classified in test.

testCorrect - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm

Correctly classified in test.

testCorrect - Variable in class keel.Algorithms.Rule_Learning.ART.Algorithm

Correctly classified in test.

testCorrect - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Algorithm

Correctly classified in test.

testCorrect - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Algorithm

Correctly classified in test.

testCorrect - Variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm

Correctly classified in test.

testCorrect - Variable in class keel.Algorithms.Rule_Learning.PART.Algorithm

Correctly classified in test.

testCV(int, int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances

Creates the test set for one fold of a cross-validation on
 the dataset.

testCV(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Creates the test set for one fold of a cross-validation on
 the dataset.

testCV(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

Creates the test set for one fold of a cross-validation on
 the dataset.

testCV(int, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Creates the test set for one fold of a cross-validation on the dataset.

testCV(int, int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Creates the test set for one fold of a cross-validation on 
 the dataset.

testCV(int, int) - Method in class keel.Algorithms.SVM.SMO.core.Instances

Creates the test set for one fold of a cross-validation on 
 the dataset.

testData - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm

Test input data.

testData - Variable in class keel.Algorithms.Decision_Trees.CART.RunCART

Test dataset read.

testData - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm

Test input data.

testData - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
 
testData - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

Test input data.

testData - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Test input data.

testData - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm

Test input data.

testData - Variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN

Test input data.

testData - Variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN

Test input data.

testData - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm

Test input data.

testDataArray - Variable in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass

2-D array to hold the test data  Note that classifiaction
    involves producing a set of Classification Rules (CRs) from a training
    set and then testing the effectiveness of the CRs on a test set.

testDataSet(myDataset, DataBase) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AprioriTFPclass

Populates test and training datasets.

testDataset - Variable in class keel.Algorithms.Decision_Trees.C45.Algorithm

The test dataset.

testDataset - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm

The test dataset.

testDataset - Variable in class keel.Algorithms.Decision_Trees.ID3.Algorithm

The test dataset.

testDataset - Variable in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm

Test dataset.

testDataset - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm

The test dataset.

testDataset - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm

The test dataset.

testDataset - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm

The test dataset.

testDataset - Static variable in class keel.Algorithms.MIL.AbstractMIAlgorithm
 
testDataset() - Method in class keel.Algorithms.MIL.ExceptionDatasets
 
testDataset - Variable in class keel.Algorithms.Rule_Learning.ART.Algorithm

The test dataset.

testDataset - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Algorithm

The test dataset.

testDataset - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Algorithm

The test dataset.

testDataset - Variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm

The test dataset.

testDataset - Variable in class keel.Algorithms.Rule_Learning.PART.Algorithm

The test dataset.

testDataset - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45

The test dataset.

testDataSet - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator

Test prototype set.

testea(Dataset, boolean) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Hider

Test rules with a test file in the 'DataSet' object

TestEnsembleInClassification(EnsembleParameters, double[][], int) - Method in class keel.Algorithms.Neural_Networks.ensemble.Ensemble


 Test ensemble in classification
 

TestEnsembleInRegression(EnsembleParameters, double[][], int) - Method in class keel.Algorithms.Neural_Networks.ensemble.Ensemble


 Test ensemble in regression
 

testExtension(Rule) - Method in class keel.Algorithms.Hyperrectangles.INNER.Rule

Performs an extension of a rule to another

testFile - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm

Test file name

testFile - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm

Test file name.

testFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

It the BD of test examples

testFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 It the BD of test examples
 

TESTFILE - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
testFile - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

Test file name

testFile - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Test file name.

testFile - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm

Test file name

testFile - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm

Test file name

testFileName - Static variable in class keel.Algorithms.Decision_Trees.C45.Algorithm

The name of the file that contains the information to make the test.

testFileName - Static variable in class keel.Algorithms.Decision_Trees.CART.RunCART

The name of the file that contains the information to make the test.

testFileName - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm

The name of the file that contains the information to make the test.

testFileName - Static variable in class keel.Algorithms.Decision_Trees.ID3.Algorithm

The name of the file that contains the information to make the test.

testFileName - Static variable in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm

Test dataset file's name.

testFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm

The name of the file that contains the information to make the test.

testFileName - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm

The name of the file that contains the information to make the test.

testFileName - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm

The name of the file that contains the information to make the test.

testFileName - Static variable in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm

Test data set file name.

testFileName - Static variable in class keel.Algorithms.Rule_Learning.ART.Algorithm

The name of the file that contains the information to make the test.

testFileName - Static variable in class keel.Algorithms.Rule_Learning.C45Rules.Algorithm

The name of the file that contains the information to make the test.

testFileName - Static variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Algorithm

The name of the file that contains the information to make the test.

testFileName - Static variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm

The name of the file that contains the information to make the test.

testFileName - Static variable in class keel.Algorithms.Rule_Learning.PART.Algorithm

The name of the file that contains the information to make the test.

testFileName - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm

Test data set file name.

testingModel - Variable in class keel.GraphInterKeel.datacf.exportData.ExportPanel

The model for storing the testing files used in the partition mode

testingModel - Variable in class keel.GraphInterKeel.datacf.importData.ImportPanel

The model for storing the testing files used in the partition mode

testInputFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
testInputFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
 
testInputFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
 
testInputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters

Original Test data filename.

testInputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Parameters

Original Test data filename.

testInputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters

Original Test data filename.

testInputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters

Original Test data filename.

testInputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Parameters

Original Test data filename.

testInputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters

Original Test data filename.

testInputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Parameters

Original Test data filename.

testInputFile - Static variable in class keel.Algorithms.Rule_Learning.Swap1.Parameters
 
testInputFile - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerCSVM

Test dataset filename.

testInputFile - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerSMO

Test dataset filename.

testInputFile - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.ParametersSMO

Test input filename.

testInstances - Variable in class keel.Algorithms.MIL.AbstractMIAlgorithm
 
testModeling(double[][], int, double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbfn


 Evaluates the net for modeling problem
 

testModeling(double[][], int, double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn


 Evaluates the net for modeling problem
 

testModeling(double[][], int, double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn


 Evaluates the net for modeling problem
 

testModeling(double[][], int, double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn


 Evaluates the net for modeling problem
 

testModeling(double[][], int, double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn


 Evaluates the net for modeling problem
 

testModeling(double[][], int, double[]) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn


 Evaluates the net for modeling problem
 

TestNetworkInClassification(Parameters, double[][], int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network


 Test network in classification
 

TestNetworkInClassification(SetupParameters, double[][], int) - Method in class keel.Algorithms.Neural_Networks.gann.Network


 Test network in classification
 

TestNetworkInClassification(Parameters, double[][], int) - Method in class keel.Algorithms.Neural_Networks.gmdh.Network


 Test network in classification
 

TestNetworkInClassification(Parameters, double[][], int) - Method in class keel.Algorithms.Neural_Networks.net.Network


 Test network in classification
 

TestNetworkInRegression(Parameters, double[][], int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network


 Test network in regression
 

TestNetworkInRegression(SetupParameters, double[][], int) - Method in class keel.Algorithms.Neural_Networks.gann.Network


 Test network in regression.

TestNetworkInRegression(Parameters, double[][], int) - Method in class keel.Algorithms.Neural_Networks.gmdh.Network


 Test network in regression.

TestNetworkInRegression(Parameters, double[][], int) - Method in class keel.Algorithms.Neural_Networks.net.Network


 Test network in regression
 

TestNominalOutOfRange - Static variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo
 
TestNominalOutOfRange - Static variable in class keel.Dataset.ErrorInfo
 
TestNumberOutOfRange - Static variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo
 
TestNumberOutOfRange - Static variable in class keel.Dataset.ErrorInfo
 
testOutput - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm

Test output data.

testOutput - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm

Test output data.

testOutput - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

Test output data.

testOutput - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Test output data.

testOutput - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm

Test output data.

testOutput - Variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN

Test output data.

testOutput - Variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN

Test output data.

testOutput - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm

Test output data.

testOutputFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
testOutputFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
 
testOutputFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
 
testOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters

Modified Training data filename.

testOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Parameters

Modified Training data filename.

testOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters

Modified Training data filename.

testOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters

Modified Training data filename.

testOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Parameters

Modified Training data filename.

testOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters

Modified Training data filename.

testOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Parameters

Modified Training data filename.

testOutputFile - Static variable in class keel.Algorithms.Rule_Learning.Swap1.Parameters
 
testOutputFileName - Static variable in class keel.Algorithms.Decision_Trees.C45.Algorithm

The name of the test output file.

testOutputFileName - Static variable in class keel.Algorithms.Decision_Trees.CART.RunCART

The name of the test output file.

testOutputFileName - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm

The name of the test output file.

testOutputFileName - Static variable in class keel.Algorithms.Decision_Trees.ID3.Algorithm

The name of the test output file.

testOutputFileName - Static variable in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm

Name of the test output file.

testOutputFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm

The name of the test output file.

testOutputFileName - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm

The name of the test output file.

testOutputFileName - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm

The name of the test output file.

testOutputFileName - Static variable in class keel.Algorithms.Rule_Learning.ART.Algorithm

The name of the test output file.

testOutputFileName - Static variable in class keel.Algorithms.Rule_Learning.C45Rules.Algorithm

The name of the test output file.

testOutputFileName - Static variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Algorithm

The name of the test output file.

testOutputFileName - Static variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm

The name of the test output file.

testOutputFileName - Static variable in class keel.Algorithms.Rule_Learning.PART.Algorithm

The name of the test output file.

testPrediction - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm

Predicted classes for the test dataset.

testPrediction - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm

Test predicted classes

testReportFileName - Variable in class keel.Algorithms.MIL.AbstractMIAlgorithm
 
testroyston(double[]) - Static method in class keel.Algorithms.Statistical_Tests.Shared.StatTest


 This method computes the statistic and the p-value of Shapiro Wilk test using Royston algorithm.

testRuleUsingChiSquaredTesting(double, double, double, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.RuleList

Tests a classification rule with the given parameters to determine  
    the interestingness/surprisingness of the rule.

testSelectionTree - Variable in class keel.GraphInterKeel.experiments.Experiments
 
TestSONNInClassification(SetupParameters, double[][], int) - Method in class keel.Algorithms.Neural_Networks.gmdh.sonn


 Obtains fitness for a classification problem
 

TestSONNInRegression(SetupParameters, double[][], int) - Method in class keel.Algorithms.Neural_Networks.gmdh.sonn

Test the SONN algorithm in a regression problem

TestsResults - Class in keel.RunKeelGraph

File: TestResults.java

 Class to help in the processing of the execution of a experiment.

TestsResults(Frame, String, boolean) - Constructor for class keel.RunKeelGraph.TestsResults

Builder

TestsResults() - Constructor for class keel.RunKeelGraph.TestsResults

Default builder

testsw(double[][][], double, PrintStream) - Static method in class keel.Algorithms.Statistical_Tests.Shared.StatTest


 Computes the p-value of  Shapiro Wilk statistical test for a set of samples obtained from
 two algorithms and an arbitrary number of datasets 
 

testTime - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm

Test prediction time.

testTime - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

Test prediction time.

testTime - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Test prediction time.

testWindow - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

Indicates the number of train executions that has to be made
 to do a test execution.

testWindow - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 Indicates the number of train executions that has to be made
 to do a test execution.

TESTWINDOW - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
testWithFail(Dataset) - Static method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.DatasetChecker
 
texto_actionPerformed(ActionEvent) - Method in class keel.GraphInterKeel.help.HelpOptions

Manages changes in text

textRules - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams

Matrix that stores the text of the rules.

TFreeMutation - Class in keel.Algorithms.Genetic_Rule_Learning.UCS

This class implements the free mutation.

TFreeMutation() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.TFreeMutation
 
TFreeMutation - Class in keel.Algorithms.Genetic_Rule_Learning.XCS

This class implements the free mutation.

TFreeMutation() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.TFreeMutation
 
themes - Variable in class keel.GraphInterKeel.datacf.help.HelpFrame
 
theoryDL(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RuleStats

The description length of the theory for a given rule.

theoryDL(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Rule

The description length of the theory for a given rule.

theoryDL(MyDataset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule

The description length of the theory for a given rule.

theoryDL(MyDataset) - Method in class keel.Algorithms.Rule_Learning.Ripper.Rule

The description length of the theory for a given rule.

theoryLength - Variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Classifier
 
theoryLength - Variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Classifier
 
theoryWeight - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
 
theoryWeight - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
 
thereInstancesOfClass(int) - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset


 Checks if in the instances set left instances of a determined class
 

theta_del - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

Is the deletion threshold.

theta_del - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 Is the deletion threshold.

THETA_DEL - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
theta_GA - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

It represents the time from last GA aplication for aplicate again the GA
 in the action set.

theta_GA - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 It represents the time from last GA aplication for aplicate again the GA
 in the action set.

THETA_GA - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
theta_mna - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 Number of classifiers that has to be covered when creating the
 prediction array.

THETA_MNA - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
theta_reduct - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 Is the reduction threshold.

THETA_REDUCT - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
theta_sub - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

Is the subsumption threshold.

theta_sub - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 Is the subsumption threshold.

THETA_SUB - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
ThreadedStreamHandler - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams
 
threeDecPlaces(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining

Converts given real number to real number rounded up to three decimal
    places.

threshold - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters

Problem coefficients

threshold - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters

Problem coefficients

threshold - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters

Problem coefficients

threshold - Variable in class keel.Algorithms.Neural_Networks.net.Parameters

Problem coefficients

threshold - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters

Threshold value.

Thrift - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift

Title: Algorithm

 Description: It contains the implementation of the algorithm


 Company: KEEL 

Thrift() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Thrift

Default constructor

Thrift(parseParameters) - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.Thrift

It reads the data from the input files (training, validation and test) and parse all the parameters
 from the parameters array.

tiempoTotal() - Method in class keel.Algorithms.PSO_Learning.CPSO.Crono

Returns the total time recorded.

tiempoTotal() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.Crono

Returns the total time recorded.

tiempoTotal() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.Crono

Returns the total time recorded.

tiempoTotal() - Method in class keel.Algorithms.PSO_Learning.REPSO.Crono

Returns the total time recorded.

tieneValor(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Condicion

Checks if the pair attribute id and operator given are the one set on this condition.

tieneValor(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Condicion

Checks if the pair attribute id and operator given are the one set on this condition.

tieneValor(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Condicion

Checks if the pair attribute id and operator given are the one set on this condition.

tieneValor(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.Condicion

Checks if the pair attribute id and operator given are the one set on this condition.

tieneValorAtributo(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.Regla

Checks if the given attribute is already in the rule with a value
 and the given operator.

tieneValorAtributo(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.Regla

Checks if the given attribute is already in the rule with a value
 and the give operator.

tieneValorAtributo(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.Regla

Checks if the given attribute is already in the rule with a value.

tieneValorAtributo(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.Regla

Checks if the given attribute is already in the rule with a value
 and the given operator.

TimeControl - Class in keel.Algorithms.Genetic_Rule_Learning.UCS

This class makes the UCS time control.

TimeControl() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.TimeControl

Default Constructor.

TimeControl - Class in keel.Algorithms.Genetic_Rule_Learning.XCS

This class makes the XCS time control
 

TimeControl() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.TimeControl

Default constructor.

timeConverter(long, long) - Static method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.WrapperManager
 
Timer - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning

File: Timer.java
 
 Auxiliar class to support timing reports in model+training+test algorithms

Timer() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.Timer
 
Timer - Class in keel.Algorithms.RST_Learning

File: Timer.java
 
 Auxiliar class to support timing reports in model+training+test algorithms

Timer() - Constructor for class keel.Algorithms.RST_Learning.Timer
 
timerEvolutionStats - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
 
timerEvolutionStats() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.timerEvolutionStats
 
timerMDL - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
 
timerMDL() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.timerMDL
 
timers - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
Timers - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist

Manages timers: flags and parameters that are triggered at
 certain iterations

Timers() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.Timers
 
Timers - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS

Manages timers: flags and parameters that are triggered at
 certain iterations

Timers() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Timers
 
timersManagement - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
 
timersManagement() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.timersManagement
 
times(double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Multiplies a scalar

times(DoubleVector) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Multiplies another DoubleVector element by element

times(double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix

Multiply a matrix by a scalar, C = s*A

times(Matrix) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix

Linear algebraic matrix multiplication, A * B

times - Variable in class keel.GraphInterKeel.experiments.Joint
 
timesEquals(double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Multiply a vector by a scalar in place, u = s * u

timesEquals(DoubleVector) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Multiplies another DoubleVector element by element in place

timesEquals(double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix

Multiply a matrix by a scalar in place, A = s*A

timingProcess - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
 
timingProcess() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.timingProcess
 
tipify_inputs - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters

Check if is going to be tipified

tipify_inputs - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters

Check if is going to be tipified

tipify_inputs - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters

Check if is going to be tipified

tipify_inputs - Variable in class keel.Algorithms.Neural_Networks.net.Parameters

Check if is going to be tipified

TipifyInputData(Parameters) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Data


 Tipify all data inputs
 

TipifyInputData(SetupParameters) - Method in class keel.Algorithms.Neural_Networks.gann.Data


 Tipify all data inputs
 

TipifyInputData(Parameters) - Method in class keel.Algorithms.Neural_Networks.gmdh.Data


 Tipify all data inputs
 

TipifyInputData(Parameters) - Method in class keel.Algorithms.Neural_Networks.net.Data


 Tipify all data inputs
 

tipo_modificadores - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Sel

Type of fuzzy rules identifier.

tipo_modificadores - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Tun

Type of fuzzy rules identifier.

tipo_reglas - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Sel

Type of fuzzy rules identifier.

tipo_reglas - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.RuleBase_Tun

Type of fuzzy rules identifier.

tipoBC() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Ecm

Returns the BC type.

TipoIntervalo - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs

Class that defines the TipoIntervalo

TipoIntervalo() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.TipoIntervalo
 
TipoIntervalo - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs

Class that defines the TipoIntervalo

TipoIntervalo() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.TipoIntervalo
 
TipoIntervalo - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs

Class that defines the TipoIntervalo (Interval)

TipoIntervalo() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.TipoIntervalo
 
TipoIntervalo - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs

Class that defines the TipoIntervalo

TipoIntervalo() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.TipoIntervalo
 
TipoIntervalo - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs

Class that defines the TipoIntervalo

TipoIntervalo() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.TipoIntervalo
 
TipoIntervalo - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs

Class that defines the TipoIntervalo

TipoIntervalo() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.TipoIntervalo
 
TipoIntervalo - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs

Class that defines the TipoIntervalo

TipoIntervalo() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.TipoIntervalo
 
TipoIntervalo - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs

Class that defines the TipoIntervalo

TipoIntervalo() - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.TipoIntervalo
 
TIPOMAX - Static variable in class keel.Algorithms.Genetic_Rule_Learning.LogenPro.Condition

Tag (MAX TYPE).

tiposVar() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset

Returns the type of each input attribute (NOMINAL = 0 OR NUMERIC = 1)

tiposVar() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset

Returns the type of each input attribute (NOMINAL = 0 OR NUMERIC = 1)

tiposVar() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset

Returns the type of each input attribute (NOMINAL = 0 OR NUMERIC = 1)

tiposVar() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset

Returns the type of each input attribute (NOMINAL = 0 OR NUMERIC = 1)

tiposVar() - Method in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Dataset

It returns the types of each input (NOMINAL[0] or NUMERIC[1])

tiposVar() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset

Returns the type of each input attribute (NOMINAL = 0 OR NUMERIC = 1)

tiposVar() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset

It returns the types of each input (NOMINAL[0] or NUMERICAL[1])

tiposVar() - Method in class keel.Algorithms.Rule_Learning.AQ.Dataset

It returns the types of each input (NOMINAL[0] or NUMERICAL[1])

tiposVar() - Method in class keel.Algorithms.Rule_Learning.CN2.Dataset

It returns the types of each input (NOMINAL[0] or NUMERICAL[1])

tiposVar() - Method in class keel.Algorithms.Rule_Learning.Prism.Dataset


 Return the types of each in-put(NOMINAL[0] o NUMERIC[1])
 

tiposVar() - Method in class keel.Algorithms.Rule_Learning.UnoR.Dataset


 Return the types of each in-put(NOMINAL[0] o NUMERIC[1])
 

tiposVar() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.Dataset

It returns the types of each input (NOMINAL[0] or NUMERIC[1])

tiposVar() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.Dataset

It returns the types of each input (NOMINAL[0] or NUMERIC[1])

tiposVar() - Method in class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Dataset

It returns the types of each input (NOMINAL[0] or NUMERICAL[1])

titleFont - Variable in class keel.GraphInterKeel.experiments.Credits
 
titleLabel - Variable in class keel.GraphInterKeel.experiments.Credits
 
tMDL - Variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.timersManagement
 
tmGlobalCrossover - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
 
tmGlobalCrossover - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
 
tmGlobalEvaluation - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
 
tmGlobalEvaluation - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
 
tmGlobalGAOperators - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
 
tmGlobalGAOperators - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
 
tmGlobalMutation - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
 
tmGlobalMutation - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
 
tmGlobalReplacement - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
 
tmGlobalReplacement - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
 
tmGlobalSelection - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
 
tmGlobalSelection - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
 
tmGlobalStatistics - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
 
tmGlobalStatistics - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
 
tmInitialGlobalCrossover - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
 
tmInitialGlobalCrossover - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
 
tmInitialGlobalEvaluation - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Parameters controlling evaluation time

tmInitialGlobalEvaluation - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Parameters controlling evaluation time

tmInitialGlobalGAOperators - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Parameters controlling genetic operators' time

tmInitialGlobalGAOperators - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Parameters controlling genetic operators' time

tmInitialGlobalMutation - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
 
tmInitialGlobalMutation - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
 
tmInitialGlobalReplacement - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Parameters controlling reemplacement time

tmInitialGlobalReplacement - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Parameters controlling reemplacement time

tmInitialGlobalSelection - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Parameters controlling selection time

tmInitialGlobalSelection - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Parameters controlling selection time

tmInitialGlobalStatistics - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer

Parameters controlling statistics time

tmInitialGlobalStatistics - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer

Parameters controlling statistics time

tmIterationCrossover - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
 
tmIterationCrossover - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
 
tmIterationEvaluation - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
 
tmIterationEvaluation - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
 
tmIterationGAOperators - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
 
tmIterationGAOperators - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
 
tmIterationMutation - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
 
tmIterationMutation - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
 
tmIterationReplacement - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
 
tmIterationReplacement - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
 
tmIterationSelection - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
 
tmIterationSelection - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
 
tmIterationStatistics - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Chronometer
 
tmIterationStatistics - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Chronometer
 
tmpTestingImportedFile - Variable in class keel.GraphInterKeel.datacf.importData.ImportPanel

Temporal File used for previsualizing the results of the import proccess (in testing)

tmpTrainingExportedFile - Variable in class keel.GraphInterKeel.datacf.exportData.ExportPanel

Temporal File used for previsualizing the results of the export proccess (in training)

tmpTrainingImportedFile - Variable in class keel.GraphInterKeel.datacf.importData.ImportPanel

Temporal File used for previsualizing the results of the import proccess (in training)

TMtablaAtributos - Variable in class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelAttribute

Attribute table

TN - Variable in class keel.Algorithms.PSO_Learning.CPSO.Particle
 
TN - Variable in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
 
TN - Variable in class keel.Algorithms.PSO_Learning.REPSO.Particle
 
TNichedMutation - Class in keel.Algorithms.Genetic_Rule_Learning.UCS

This class applies the niched mutation.

TNichedMutation() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.TNichedMutation
 
TNichedMutation - Class in keel.Algorithms.Genetic_Rule_Learning.XCS

This class applies the niched mutation.

TNichedMutation() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.TNichedMutation
 
tnorm(double, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase

 
 Returns the T-Norm of two membership values.

tnorma(double, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyRule
 
tnorma(float, float, int) - Method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fuzzyRule
 
tnorma(float, float, int) - Method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fuzzyRule
 
tnorma(float, float, int) - Method in class keel.Algorithms.LQD.methods.FGFS_Original.rule
 
tnorma(float, float, int) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.rule
 
tnorma(float, float, int) - Method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.rule
 
tnorma(float, float, int) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling.fuzzyrule
 
tnorma(float, float, int) - Method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fuzzyrule
 
TNormLukasiewicz(double, double) - Static method in class keel.Algorithms.RST_Learning.Operators

Lukasiewicz T-Norm

TNormMin(double, double) - Static method in class keel.Algorithms.RST_Learning.Operators

Returns the smaller of two double values.

TNormProd(double, double) - Static method in class keel.Algorithms.RST_Learning.Operators

Returns the product of two double values.

To - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters
 
To - Variable in class keel.Algorithms.Neural_Networks.gmdh.SetupParameters
 
to8GrayString() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet

Transform the prototypeSet (this) in a matrix of binary string Gray Code

to8GrayString() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Transform the prototypeSet (this) in a matrix of binary string Gray Code

to_fuzzy(String) - Static method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fun_aux

Convert the number in fuzzy

to_fuzzy(String) - Static method in class keel.Algorithms.LQD.methods.FGFS_Minimum_Risk.fun_aux

Convert the number in fuzzy

to_fuzzy(String) - Static method in class keel.Algorithms.LQD.methods.FGFS_Original.fun_aux

Convert the number in fuzzy

to_fuzzy(String) - Static method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight.fun_aux

Convert the number in fuzzy

to_fuzzy(String) - Static method in class keel.Algorithms.LQD.methods.FGFS_Rule_Weight_Penalty.fun_aux

Convert the number in fuzzy

to_fuzzy(String) - Static method in class keel.Algorithms.LQD.preprocess.Expert.fun_aux

Convert the number in fuzzy

to_fuzzy(String) - Static method in class keel.Algorithms.LQD.preprocess.Fuzzy_SMOTE.fun_aux

Convert the number in fuzzy

to_fuzzy(String) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling.fun_aux

Convert the number in fuzzy

to_fuzzy(String) - Static method in class keel.Algorithms.LQD.preprocess.Prelabelling_Expert.fun_aux

Convert the number in fuzzy

toArray() - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector

Returns all the elements of this vector as an array

toArray() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector

Returns all the elements of this vector as an array

toArray() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector

Returns all the elements of this vector as an array

toArray() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector

Returns all the elements of this vector as an array

toArray() - Method in class keel.Algorithms.SVM.SMO.core.FastVector

Returns all the elements of this vector as an array

toBibTex() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation

Returns a BibTex string representing this technical information.

toBibTex() - Method in class keel.Algorithms.SVM.SMO.core.TechnicalInformation

Returns a BibTex string representing this technical information.

toBinaryString() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet

Transform the prototypeSet (this) in a matrix of binary string 8-bit codification.

toBinaryString() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Transform the prototypeSet (this) in a matrix of binary string 8-bit codification.

toBitString() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule
 
toBitString(myDataset, int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule

It return a bit string correspoding to a certain example

toByteArray(Object, boolean) - Static method in class keel.Algorithms.Decision_Trees.M5.SerializedObject

Serializes the supplied object to a byte array.

toClassDetailsString() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Returns a string with the deatails of this class.

toClassDetailsString(String) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Generates a breakdown of the accuracy for each class,
 incorporating various information-retrieval statistics, such as
 true/false positive rate, precision/recall/F-Measure.

toCumulativeMarginDistributionString() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Output the cumulative margin distribution as a string suitable
 for input for gnuplot or similar package.

todoBien() - Method in class keel.Algorithms.Rule_Learning.Prism.Prism


 Checks if there're sets or not.

todoBien() - Method in class keel.Algorithms.Subgroup_Discovery.aprioriSD.aprioriSD

Checks if all the attributes are nominal/discrete.

todoBien() - Method in class keel.Algorithms.Subgroup_Discovery.CN2SD.CN2SD
 
toDoubleArray() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Returns the values of each attribute as an array of doubles.

toDoubleArray() - Method in class keel.Algorithms.Decision_Trees.M5.M5SparseInstance

Returns the values of each attribute as an array of doubles.

toDoubleArray() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Returns the values of each attribute as an array of doubles.

toDoubleArray() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Returns the values of each attribute as an array of doubles.

toDoubleArray() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance

Returns the values of each attribute as an array of doubles.

toDoubleArray() - Method in class keel.Algorithms.SVM.SMO.core.Instance

Returns the values of each attribute as an array of doubles.

toHashMap() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet

Converts the dataset into a hashmap (Prototype p, index of p in the set)

toHashMap() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Converts the dataset into a hashmap (Prototype p, index of p in the set)

toHashSet() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet

Converts the dataset into a hashset Â¿Para quÃ© cojones se usa?

toHashSet() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Converts the dataset into a hashset Â¿Para quÃ© cojones se usa?

toInstanceSet() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Transform the prototype set to a instance set object.

toInteger() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Entero
 
token - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
 
Token - Class in keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser

Describes the input token stream.

Token() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Token
 
token - Static variable in class keel.Algorithms.Rule_Learning.Swap1.DataParser
 
Token - Class in keel.Algorithms.Rule_Learning.Swap1

Describes the input token stream.

Token() - Constructor for class keel.Algorithms.Rule_Learning.Swap1.Token
 
token - Static variable in class keel.Dataset.DataParser
 
Token - Class in keel.Dataset

Describes the input token stream.

Token() - Constructor for class keel.Dataset.Token
 
token_competition() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Core


 Performs the token competition.

token_source - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Parser
 
token_source - Static variable in class keel.Algorithms.Rule_Learning.Swap1.DataParser
 
token_source - Static variable in class keel.Dataset.DataParser
 
tokenCaptured() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome


 Increases the amount of captured tokens by 1
 

tokenImage - Variable in exception keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParseException

This is a reference to the "tokenImage" array of the generated
 parser within which the parse error occurred.

tokenImage - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
tokenImage - Static variable in interface keel.Algorithms.Rule_Learning.Swap1.DataParserConstants

Token images array.

tokenImage - Variable in exception keel.Algorithms.Rule_Learning.Swap1.ParseException

This is a reference to the "tokenImage" array of the generated
 parser within which the parse error occurred.

tokenImage - Static variable in interface keel.Dataset.DataParserConstants
 
tokenImage - Variable in exception keel.Dataset.ParseException

This is a reference to the "tokenImage" array of the generated
 parser within which the parse error occurred.

tokenLost() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Chromosome


 Decreases the amount of captured tokens by 1
 

TokenMgrError - Error in keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser

Ordinals for various reasons why an Error of this type can be thrown.

TokenMgrError() - Constructor for error keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.TokenMgrError
 
TokenMgrError(String, int) - Constructor for error keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.TokenMgrError
 
TokenMgrError(boolean, int, int, int, String, char, int) - Constructor for error keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.TokenMgrError
 
TokenMgrError - Error in keel.Algorithms.Rule_Learning.Swap1
 
TokenMgrError() - Constructor for error keel.Algorithms.Rule_Learning.Swap1.TokenMgrError
 
TokenMgrError(String, int) - Constructor for error keel.Algorithms.Rule_Learning.Swap1.TokenMgrError
 
TokenMgrError(boolean, int, int, int, String, char, int) - Constructor for error keel.Algorithms.Rule_Learning.Swap1.TokenMgrError
 
TokenMgrError - Error in keel.Dataset
 
TokenMgrError() - Constructor for error keel.Dataset.TokenMgrError
 
TokenMgrError(String, int) - Constructor for error keel.Dataset.TokenMgrError
 
TokenMgrError(boolean, int, int, int, String, char, int) - Constructor for error keel.Dataset.TokenMgrError
 
toleranceParameterTipText() - Method in class keel.Algorithms.SVM.SMO.SMO

Returns the tip text for this property

toleranceParameterTipText() - Method in class keel.Algorithms.SVM.SMO.SMOreg

Returns the tip text for this property

toleranceTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved

Returns the tip text for this property

TOLX - Variable in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
 
toMatlab() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix

converts the Matrix into a single line Matlab string: matrix is enclosed 
 by parentheses, rows are separated by semicolon and single cells by
 blanks, e.g., [1 2; 3 4].

toMatlab() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix

Deprecated.
converts the Matrix into a single line Matlab string: matrix is enclosed 
 by parentheses, rows are separated by semicolon and single cells by
 blanks, e.g., [1 2; 3 4].

toMatrixString() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Calls toMatrixString() with a default title.

toMatrixString(String) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Outputs the performance statistics as a classification confusion
 matrix.

TomekLinks - Class in keel.Algorithms.ImbalancedClassification.Resampling.TomekLinks


 File: TomekLinks.java
 

 The TomekLinks algorithm is an undersampling method that can be used to deal with the imbalanced
 problem.

TomekLinks(String) - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.TomekLinks.TomekLinks


 Constructor of the class.

toOptionList(Tag[]) - Static method in class keel.Algorithms.SVM.SMO.core.Tag

returns a list that can be used in the listOption methods to list all
 the available ID strings, e.g.: <0|1|2> or <what|ever>

toOptionSynopsis(Tag[]) - Static method in class keel.Algorithms.SVM.SMO.core.Tag

returns a string that can be used in the listOption methods to list all
 the available options, i.e., "\t\tID = Text\n" for each option

top - Variable in class keel.GraphInterKeel.datacf.help.HelpOptions

Top of the tree

toPrototypeSet(String[][], double[]) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet

Transform  a matrix of binary string 8-bit codification in a double PrototypeSEt

toPrototypeSet(String[][], double[]) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Transform  a matrix of binary string 8-bit codification in a double PrototypeSEt

toString() - Method in class keel.Algorithms.Decision_Trees.C45.C45

Function to print the tree.

toString() - Method in class keel.Algorithms.Decision_Trees.C45.Itemset

Function to print the itemset.

toString() - Method in class keel.Algorithms.Decision_Trees.C45.Tree

Function to print the tree.

toString() - Method in class keel.Algorithms.Decision_Trees.CART.tree.DecisionTree


 
 Returns tree as a String in preorder format.

toString() - Method in class keel.Algorithms.Decision_Trees.CART.tree.TreeNode


 Returns a String representation of the node and its descendents.

toString() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.C45

Function to print the tree.

toString() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset

Function to print the itemset.

toString() - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Tree

Function to print the tree.

toString() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myAttribute

Overriden function that converts the class to a string

toString() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.myDataset

Overriden function that converts the class to a string

toString() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.Split

Overriden function that converts the class to a string

toString() - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode

Overriden function that converts the class to a string

toString() - Method in class keel.Algorithms.Decision_Trees.ID3.Itemset

Function to print the itemset.

toString(M5Instances, int) - Method in class keel.Algorithms.Decision_Trees.M5.Function

Converts a function to a string

toString() - Method in class keel.Algorithms.Decision_Trees.M5.Impurity

Converts an Impurity object to a string

toString(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.InformationHandler

Prints information stored in an 'InformationHandler' object, basically containing
     command line options

toString() - Method in class keel.Algorithms.Decision_Trees.M5.Interval

Constructs a representation of the current range.

toString() - Method in class keel.Algorithms.Decision_Trees.M5.M5

Converts the output of the training process into a string

toString() - Method in class keel.Algorithms.Decision_Trees.M5.M5Attribute

Returns a description of this attribute in ARFF format.

toString() - Method in class keel.Algorithms.Decision_Trees.M5.M5AttrStats

Returns a human readable representation of this AttributeStats instance.

toString() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Returns the description of one instance.

toString(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Returns the description of one value of the instance as a
 string.

toString(M5Attribute) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Returns the description of one value of the instance as a
 string.

toString() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances

Returns the dataset as a string.

toString() - Method in class keel.Algorithms.Decision_Trees.M5.M5Kernel

Display a representation of this estimator

toString(int, int, int, int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Matrix

Converts a matrix to a string

toString() - Method in class keel.Algorithms.Decision_Trees.M5.M5SparseInstance

Returns the description of one instance in sparse format.

toString(double, double, String, String) - Method in class keel.Algorithms.Decision_Trees.M5.Measures

Converts the performance measures to a string

toString() - Method in class keel.Algorithms.Decision_Trees.M5.Queue

Produces textual description of queue.

toString() - Method in class keel.Algorithms.Decision_Trees.M5.Results

Converts the evaluation results of a model to a string

toString() - Method in class keel.Algorithms.Decision_Trees.M5.SerializedObject

Returns a text representation of the state of this object.

toString() - Method in class keel.Algorithms.Decision_Trees.M5.SimpleStatistics

Returns a string summarising the stats so far.

toString(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.SplitInfo

Converts the spliting information to string

toString() - Method in class keel.Algorithms.Decision_Trees.M5.StatisticsStore

Converts the stats to a string

toString() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myAttribute

Overriden function that converts the class to a string

toString() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.myDataset

Overriden function that converts the class to a string

toString() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Node

Overriden function that converts the class to a string

toString() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Register

Overriden function that converts the class to a string

toString() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Split

Overriden function that converts the class to a string

toString() - Method in class keel.Algorithms.Decision_Trees.PUBLIC.TreeNode

Overriden function that converts the class to a string

toString() - Method in class keel.Algorithms.Decision_Trees.SLIQ.Itemset

Function to print the itemset.

toString() - Method in class keel.Algorithms.Discretizers.MVD.Interval

Returns the object in form of string

toString() - Method in exception keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs.ErrorDimension
 
toString() - Method in exception keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs.ErrorSingular
 
toString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Antd
 
toString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeLocator

returns a string representation of this object

toString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka

Returns a description of this attribute in ARFF format.

toString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Returns the description of one instance.

toString(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Returns the description of one value of the instance as a
 string.

toString(AttributeWeka) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Returns the description of one value of the instance as a
 string.

toString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Returns the dataset as a string in ARFF format.

toString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.NominalAntd

Prints this antecedent

toString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.NumericAntd

Prints this antecedent

toString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Queue

Produces textual description of queue.

toString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Range

Constructs a representation of the current range.

toString(AttributeWeka) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.RipperRule

Prints this rule

toString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.SingleIndex

Constructs a representation of the current range.

toString() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA

Prints the all the rules of the rule learner.

toString(myDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.Condition
 
toString(myDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.Rule
 
toString(myDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.RuleSet
 
toString(myDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Condition
 
toString(myDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule
 
toString(myDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.RuleSet
 
toString(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.BaseDatos

ToString Function.

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Corte

Returns the object Corte (Cut) as a String

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Discretizacion
 
toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Entero
 
toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Resultado
 
toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.Hider.Union
 
toString(MyDataset, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Function

Converts a function to a string

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Function
 
toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Impurity

Converts an Impurity object to a string

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset

Function to print the itemset.

toString(MyAttribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset

Function to print of one value of the itemset.

toString(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset

Returns the description of one value of the instance as a
 string.

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5

Converts the output of the training process into a string

toString(int, int, int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5Matrix

Converts a matrix to a string

toString(double, double, String, String) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Measures

Converts the performance measures to a string

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

Returns a string representation of the entries of this MyDataset.

toString(Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

Returns a string representation of the active entries of this MyDataset.

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Queue

Produces textual description of queue.

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Results

Converts the evaluation results of a model to a string

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Rule

Returns a string representation of this Rule, containing the String representation of each SimpleRule.

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Ruleset

Returns a string representation of this Ruleset, containing the String representation of each Rule.

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleRule

Returns a string representation of this SimpleRule

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SimpleStatistics

Returns a string summarising the stats so far.

toString(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.SplitInfo

Converts the spliting information to string

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.StatisticsStore

Converts the stats to a string

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset

Function to print the itemset.

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexResult
 
toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.C45

Function to print the tree.

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset

Function to print the itemset.

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset

Returns a string representation of the entries of this MyDataset.

toString(Mask) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset

Returns a string representation of the active entries of this MyDataset.

toString(IncrementalMask) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyDataset

Returns a string representation of the active entries of this MyDataset.

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Rule

Returns a string representation of this Rule, containing the String representation of each SimpleRule.

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Ruleset

Returns a string representation of this Ruleset, containing the String representation of each Rule.

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.SimpleRule

Returns a string representation of this SimpleRule

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree

Function to print the tree.

toString() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Token

Returns the image.

toString() - Method in class keel.Algorithms.Hyperrectangles.BNGE.Rule

To String method

toString() - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Cromosoma

To String Method

toString() - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Hyper
 
toString() - Method in class keel.Algorithms.Hyperrectangles.INNER.Rule

To String method

toString() - Method in class keel.Algorithms.Hyperrectangles.RISE.Rule

To String method

toString() - Method in class keel.Algorithms.ImbalancedClassification.Auxiliar.AUC.PosProb

Aggregates the class of the associated instance together with its associated probability

toString() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.C45CS

Function to print the tree.

toString() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset

Function to print the itemset.

toString() - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Tree

Function to print the tree.

toString() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Referencia
 
toString() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.C45

Function to print the tree.

toString() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset

Function to print the itemset.

toString() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree

Function to print the tree.

toString() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Referencia

To String Method

toString() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Instance_Selection.EUSCHCQstat.Chromosome

Prints the chrosome into a string value

toString() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute

Returns a description of this attribute in ARFF format.

toString() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.AttributeStats

Returns a human readable representation of this AttributeStats instance.

toString() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Returns the description of one instance.

toString(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Returns the description of one value of the instance as a 
 string.

toString(Attribute) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Returns the description of one value of the instance as a 
 string.

toString() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Returns the dataset as a string in ARFF format.

toString() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance

Returns the description of one instance in sparse format.

toString() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Stats

Returns a string summarising the stats so far.

toString() - Method in class keel.Algorithms.Instance_Generation.Basic.Prototype

Converts the prototype to a String object

toString() - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet

Converts data set into a String.

toString() - Method in class keel.Algorithms.Instance_Generation.SSMALVQ3.Cromosoma

To String Method

toString() - Method in class keel.Algorithms.Instance_Generation.SSMAPSO.Cromosoma

To String Method

toString() - Method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.Cromosoma

To String Method

toString() - Method in class keel.Algorithms.Instance_Generation.utilities.Pair

Show par as String.

toString() - Method in class keel.Algorithms.Instance_Selection.CCIS.Pareja
 
toString() - Method in class keel.Algorithms.Instance_Selection.CHC.Cromosoma

To String Method

toString() - Method in class keel.Algorithms.Instance_Selection.CPruner.Trio
 
toString() - Method in class keel.Algorithms.Instance_Selection.GA_MSE_CC_FSM.Cromosoma
 
toString() - Method in class keel.Algorithms.Instance_Selection.GGA.Cromosoma

To String Method

toString() - Method in class keel.Algorithms.Instance_Selection.IGA.Cromosoma

To String Method

toString() - Method in class keel.Algorithms.Instance_Selection.MNV.ReferenciaMNV
 
toString() - Method in class keel.Algorithms.Instance_Selection.PBIL.Cromosoma

To String Method

toString() - Method in class keel.Algorithms.Instance_Selection.SGA.Cromosoma

To String Method

toString() - Method in class keel.Algorithms.Instance_Selection.SSMA.Cromosoma

To String Method

toString() - Method in class keel.Algorithms.Instance_Selection.ZhangTS.Cromosoma

To String Method

toString() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.data.DoubleTransposedDataSet


 Returns a string representation of the DataSet
 

toString() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet


 Returns a string representation of the neural net
 

toString() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.ExpNeuron


 Returns a string representation of the ExpNeuron
 

toString() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputNeuron


 Returns a string representation of the InputNeuron
 

toString() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinearNeuron


 Returns a string representation of the LinearNeuron
 

toString() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.SigmNeuron


 Returns a string representation of the SigmNeuron
 

toString() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividual


 Converts a individual to string

toString() - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.util.random.RanNnep


 Returns a String representation of the random generator
 

toString() - Method in class keel.Algorithms.Preprocess.Basic.Referencia

To String Method

toString() - Method in class keel.Algorithms.Preprocess.Feature_Selection.Shared.Chromosome

To string method

toString() - Method in class keel.Algorithms.Preprocess.Instance_Selection.CCIS.Pareja
 
toString() - Method in class keel.Algorithms.Preprocess.Instance_Selection.CHC.Cromosoma

To String Method

toString() - Method in class keel.Algorithms.Preprocess.Instance_Selection.CPruner.Trio
 
toString() - Method in class keel.Algorithms.Preprocess.Instance_Selection.GA_MSE_CC_FSM.Cromosoma

To String Method

toString() - Method in class keel.Algorithms.Preprocess.Instance_Selection.GGA.Cromosoma

To String Method

toString() - Method in class keel.Algorithms.Preprocess.Instance_Selection.IGA.Cromosoma

To String Method

toString() - Method in class keel.Algorithms.Preprocess.Instance_Selection.MNV.ReferenciaMNV
 
toString() - Method in class keel.Algorithms.Preprocess.Instance_Selection.PBIL.Cromosoma

To String Method

toString() - Method in class keel.Algorithms.Preprocess.Instance_Selection.SGA.Cromosoma

To String Method

toString() - Method in class keel.Algorithms.Preprocess.Instance_Selection.SSMA.Cromosoma

To String Method

toString() - Method in class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.Cromosoma

To String Method

toString(double[]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.Interpolation

converts a double[] array to a String object

toString(double[][]) - Static method in class keel.Algorithms.Preprocess.Missing_Values.EM.Interpolation

converts a double[][] array to a String object

toString() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.ConfidenceInterval
 
toString() - Method in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.C45

Function to print the tree.

toString() - Method in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.C45

Function to print the tree.

toString() - Method in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.C45

Function to print the tree.

toString() - Method in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.C45

Function to print the tree.

toString() - Method in class keel.Algorithms.RST_Learning.EFS_RPS.Chromosome
 
toString() - Method in class keel.Algorithms.RST_Learning.EIS_RFS.Chromosome
 
toString() - Method in class keel.Algorithms.Rule_Learning.ART.Itemset

Function to print the itemset.

toString() - Method in class keel.Algorithms.Rule_Learning.C45Rules.C45

Function to print the tree.

toString() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Itemset

Function to print the itemset.

toString() - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset

Returns a string representation of the entries of this MyDataset.

toString(Mask) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset

Returns a string representation of the active entries of this MyDataset.

toString(IncrementalMask) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyDataset

Returns a string representation of the active entries of this MyDataset.

toString() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Rule

Returns a string representation of this Rule, containing the String representation of each SimpleRule.

toString() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Ruleset

Returns a string representation of this Ruleset, containing the String representation of each Rule.

toString() - Method in class keel.Algorithms.Rule_Learning.C45Rules.SimpleRule

Returns a string representation of this SimpleRule

toString() - Method in class keel.Algorithms.Rule_Learning.C45Rules.Tree

Function to print the tree.

toString() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.C45

Function to print the tree.

toString() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset

Function to print the itemset.

toString() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset

Returns a string representation of the entries of this MyDataset.

toString(Mask) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset

Returns a string representation of the active entries of this MyDataset.

toString(IncrementalMask) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyDataset

Returns a string representation of the active entries of this MyDataset.

toString() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Rule

Returns a string representation of this Rule, containing the String representation of each SimpleRule.

toString() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Ruleset

Returns a string representation of this Ruleset, containing the String representation of each Rule.

toString() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.SimpleRule

Returns a string representation of this SimpleRule

toString() - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree

Function to print the tree.

toString() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset

Function to print the itemset.

toString() - Method in class keel.Algorithms.Rule_Learning.PART.C45

Function to print the tree.

toString() - Method in class keel.Algorithms.Rule_Learning.PART.Itemset

Function to print the itemset.

toString() - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset

Returns a string representation of the entries of this MyDataset.

toString(Mask) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset

Returns a string representation of the active entries of this MyDataset.

toString(IncrementalMask) - Method in class keel.Algorithms.Rule_Learning.PART.MyDataset

Returns a string representation of the active entries of this MyDataset.

toString() - Method in class keel.Algorithms.Rule_Learning.PART.Rule

Returns a string representation of this Rule, containing the String representation of each SimpleRule.

toString() - Method in class keel.Algorithms.Rule_Learning.PART.Ruleset

Returns a string representation of this Ruleset, containing the String representation of each Rule.

toString() - Method in class keel.Algorithms.Rule_Learning.PART.SimpleRule

Returns a string representation of this SimpleRule

toString() - Method in class keel.Algorithms.Rule_Learning.PART.Tree

Function to print the tree.

toString() - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset

Returns a string representation of the entries of this MyDataset.

toString(Mask) - Method in class keel.Algorithms.Rule_Learning.Ripper.MyDataset

Returns a string representation of the active entries of this MyDataset.

toString() - Method in class keel.Algorithms.Rule_Learning.Ripper.Rule

Returns a string representation of this Rule, containing the String representation of each SimpleRule.

toString() - Method in class keel.Algorithms.Rule_Learning.Ripper.Ruleset

Returns a string representation of this Ruleset, containing the String representation of each Rule.

toString() - Method in class keel.Algorithms.Rule_Learning.Ripper.Score

Returns a string representation of this Score, containing the String representation of each Trio.

toString(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Score

Returns a string representation of this Score, containing the String representation of each Trio, taking into account the given attribute's id.

toString() - Method in class keel.Algorithms.Rule_Learning.Ripper.SimpleRule

Returns a string representation of this SimpleRule

toString() - Method in class keel.Algorithms.Rule_Learning.Ripper.Trio

Returns a string that represent a Trio.

toString(int) - Method in class keel.Algorithms.Rule_Learning.Ripper.Trio

Returns a string that represent a Trio, taking into account the given attribute's id.

toString() - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset

Returns a string representation of the entries of this MyDataset.

toString(Mask) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset

Returns a string representation of the active entries of this MyDataset.

toString(Mask, double[]) - Method in class keel.Algorithms.Rule_Learning.Slipper.MyDataset

Returns a string representation of the active entries of this MyDataset
 wiht its associated weights.

toString() - Method in class keel.Algorithms.Rule_Learning.Slipper.Rule

Returns a string representation of this Rule, containing the String representation of each SimpleRule.

toString() - Method in class keel.Algorithms.Rule_Learning.Slipper.Ruleset

Returns a string representation of this Ruleset, containing the String representation of each Rule.

toString() - Method in class keel.Algorithms.Rule_Learning.Slipper.Score

Returns a string representation of this Score, containing the String representation of each Trio.

toString(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Score

Returns a string representation of this Score, containing the String representation of each Trio, taking into account the given attribute's id.

toString() - Method in class keel.Algorithms.Rule_Learning.Slipper.SimpleRule

Returns a string representation of this SimpleRule

toString() - Method in class keel.Algorithms.Rule_Learning.Slipper.Trio

Returns a string that represent a Trio.

toString(int) - Method in class keel.Algorithms.Rule_Learning.Slipper.Trio

Returns a string that represent a Trio, taking into account the given attribute's id.

toString() - Method in class keel.Algorithms.Rule_Learning.Swap1.Attribute

It returns a String with the attribute information in keel format

toString() - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance

It does return an string with the instance information.

toString(InstanceAttributes) - Method in class keel.Algorithms.Rule_Learning.Swap1.Instance

Prints the instance in KEEL format, according to the given Attributes definition

toString() - Method in class keel.Algorithms.Rule_Learning.Swap1.Token

Returns the image.

toString() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45

Function to print the tree.

toString() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset

Function to print the itemset.

toString() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree

Function to print the tree.

toString() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype

Converts the prototype to a String object

toString() - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Converts data set into a String.

toString() - Method in class keel.Algorithms.Semi_Supervised_Learning.utilities.Pair

Show par as String.

toString() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Convert the DoubleVecor to a string

toString(int, boolean) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Convert the DoubleVecor to a string

toString() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector

Converts the IntVecor to a string

toString(int, boolean) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.IntVector

Convert the IntVecor to a string

toString() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.LinearRegression

returns the coefficients in a string representation

toString() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix

Converts a matrix to a string.

toString() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix

Deprecated.
Converts a matrix to a string

toString() - Method in enum keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation.Field

returns the display string of the Type

toString() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation

Returns a plain-text string representing this technical information.

toString() - Method in enum keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation.Type

returns the display string of the Type

toString() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic

Gets a string describing the classifier.

toString() - Method in exception keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.ErrorDimension
 
toString() - Method in exception keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.ErrorSingular
 
toString() - Method in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Relation

To string method

toString() - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.SDMap.AssociationRule


 It returns a raw string representation of an association rule
 

toString() - Method in class keel.Algorithms.SVM.SMO.core.Instance

Returns the description of one instance.

toString(int) - Method in class keel.Algorithms.SVM.SMO.core.Instance

Returns the description of one value of the instance as a 
 string.

toString() - Method in class keel.Algorithms.SVM.SMO.core.Instances

Returns the dataset as a string in ARFF format.

toString() - Method in class keel.Algorithms.SVM.SMO.core.Queue

Produces textual description of queue.

toString() - Method in class keel.Algorithms.SVM.SMO.core.SelectedTag

returns the selected tag in string representation

toString() - Method in class keel.Algorithms.SVM.SMO.core.Tag

returns the IDStr

toString() - Method in enum keel.Algorithms.SVM.SMO.core.TechnicalInformation.Field

returns the display string of the Type

toString() - Method in class keel.Algorithms.SVM.SMO.core.TechnicalInformation

Returns a plain-text string representing this technical information.

toString() - Method in enum keel.Algorithms.SVM.SMO.core.TechnicalInformation.Type

returns the display string of the Type

toString() - Method in class keel.Algorithms.SVM.SMO.supportVector.KernelEvaluation

returns the current result

toString() - Method in class keel.Algorithms.SVM.SMO.supportVector.NormalizedPolyKernel

returns a string representation for the Kernel

toString() - Method in class keel.Algorithms.SVM.SMO.supportVector.PDRFKernel

returns a string representation for the Kernel

toString() - Method in class keel.Algorithms.SVM.SMO.supportVector.PolyKernel

returns a string representation for the Kernel

toString() - Method in class keel.Algorithms.SVM.SMO.supportVector.Puk

returns a string representation for the Kernel

toString() - Method in class keel.Algorithms.SVM.SMO.supportVector.RBFKernel

returns a string representation for the Kernel

toString() - Method in class keel.Algorithms.SVM.SMO.SVMreg

Prints out the classifier.

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.AssociationRule


 It returns a raw string representation of an association rule
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Chromosome


 It returns a raw string representation of a chromosome
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyAttribute


 It returns a raw string representation of a fuzzy attribute
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.FuzzyRegion


 It returns a raw string representation of a fuzzy region
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Gene


 It returns a raw string representation of a gene
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Item


 It returns a raw string representation of an item
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Alcalaetal.Itemset


 It returns a raw string representation of an itemset
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.AssociationRule


 It returns a raw string representation of an association rule
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.FuzzyRegion


 It returns a raw string representation of a fuzzy region
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Item


 It returns a raw string representation of an item
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori.Itemset


 It returns a raw string representation of an itemset
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.AssociationRule


 It returns a raw string representation of an association rule
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Chromosome


 It returns a raw string representation of a chromosome
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyAttribute


 It returns a raw string representation of a fuzzy attribute
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.FuzzyRegion


 It returns a raw string representation of a fuzzy region
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Gene


 It returns a raw string representation of a gene
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Item


 It returns a raw string representation of an item
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.Itemset


 It returns a raw string representation of an itemset
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyApriori.MembershipFunction


 It returns a raw string representation of a membership function
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.AssociationRule


 It returns a raw string representation of an association rule
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Chromosome


 It returns a raw string representation of a chromosome
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyAttribute


 It returns a raw string representation of a fuzzy attribute
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.FuzzyRegion


 It returns a raw string representation of a fuzzy region
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Gene


 It returns a raw string representation of a gene
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Item


 It returns a raw string representation of an item
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.Itemset


 It returns a raw string representation of an itemset
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.GeneticFuzzyAprioriDC.MembershipFunction


 It returns a raw string representation of a membership function
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.AssociationRule


 It returns a raw string representation of an association rule
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Chromosome


 It returns a string representation of a chromosome
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Gene


 It returns a string representation of a gene
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.AssociationRule


 It returns a raw string representation of an association rule
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.AssociationRule


 It returns a raw string representation of an association rule
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
 
toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Gene
 
toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.AssociationRule
 
toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Chromosome
 
toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.Gene
 
toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.AssociationRule


 It returns a raw string representation of an association rule
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.AssociationRule


 It returns a raw string representation of an association rule
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.AssociationRule
 
toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Chromosome
 
toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.Gene
 
toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.AssociationRule
 
toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Chromosome
 
toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.Gene
 
toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.AssociationRule
 
toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Chromosome
 
toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.Gene
 
toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule


 It returns a raw string representation of an association rule
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome


 It returns a string representation of a chromosome
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene


 It returns a string representation of a gene
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule


 It returns a raw string representation of an association rule
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome


 It returns a string representation of a chromosome
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene


 It returns a string representation of a gene
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule


 It returns a raw string representation of an association rule
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome


 It returns a string representation of a chromosome
 

toString() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene


 It returns a string representation of a gene
 

toString() - Method in class keel.Dataset.Attribute

It returns a String with the attribute information in keel format

toString() - Method in class keel.Dataset.Instance

It does return an string with the instance information.

toString(InstanceAttributes) - Method in class keel.Dataset.Instance

Prints the instance in KEEL format, according to the given Attributes definition

toString() - Method in class keel.Dataset.Token

Returns the image.

toString() - Method in class keel.GraphInterKeel.datacf.help.HelpSheet


 Overriding toString method to obtain a description of the class
 

toString() - Method in class keel.GraphInterKeel.experiments.ExternalObjectDescription

To string method

toString() - Method in class keel.GraphInterKeel.experiments.UseCase

Writes the Use Case to an String

toString() - Method in class keel.GraphInterKeel.help.HelpSheet

To string method

toString() - Method in class keel.GraphInterKeel.statistical.tests.Relation

To string method

toStringOVO() - Method in class keel.Algorithms.Decision_Trees.C45.Tree

Function to print the tree (OVO code).

toSummaryString() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Calls toSummaryString() with no title and no complexity stats

toSummaryString(boolean) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Calls toSummaryString() with a default title.

toSummaryString(String, boolean) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Outputs the performance statistics in summary form.

toSummaryString() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances

Generates a string summarizing the set of instances.

toSummaryString() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Generates a string summarizing the set of instances.

toSummaryString() - Method in class keel.Algorithms.SVM.SMO.supportVector.KernelEvaluation

returns a summary string of the evaluation with a no title

toSummaryString(String) - Method in class keel.Algorithms.SVM.SMO.supportVector.KernelEvaluation

returns a summary string of the evaluation with a default title

totalClass - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PerformanceAgent
 
totalClass - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PerformanceAgent
 
totalCost() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Gets the total cost, that is, the cost of each prediction times the
 weight of the instance, summed over all instances.

totalCount - Variable in class keel.Algorithms.Decision_Trees.M5.M5AttrStats

The total number of values (i.e. number of instances)

totalCount - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.AttributeStats

The total number of values (i.e. number of instances)

totalElements - Variable in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.FreqList

Total elements in the list.

totalElements - Variable in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.FreqList

Total elements in the list.

totalElements - Variable in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.FreqList

Total elements in the list.

totalElements - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqList

Total elements in the list.

totalElements - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqListPair

Total elements in the list.

totalElements - Variable in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.FreqList

Total elements in the list.

totalElements - Variable in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.FreqList

Total elements in the list.

totalElements - Variable in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.FreqList

Total elements in the list.

totalElements - Variable in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.FreqList

Total elements in the list.

totalElements - Variable in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.FreqList

Total elements in the list.

totalElements - Variable in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.FreqList

Total elements in the list.

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.FreqList


 The total number of elements stored, i.e. the sum of all the frequencies
 

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.FreqList


 The total number of elements stored, i.e. the sum of all the frequencies
 

totalElems - Variable in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.VAList

Total number of elements in the list.

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.valueAssociations


 Gives the total number of elements in the list (i.e. the sum of the frequencies).

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.FreqList


 The total number of elements stored, i.e. the sum of all the frequencies
 

totalElems - Variable in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.VAList

Total number of elements in the list.

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.valueAssociations


 Gives the total number of elements in the list (i.e. the sum of the frequencies).

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqList


 The total number of elements stored, i.e. the sum of all the frequencies
 

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.FreqListPair


 The total number of elements stored, i.e. the sum of all the frequencies
 

totalElems - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.VAList

Total number of elements in the list.

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.valueAssociations


 Gives the total number of elements in the list (i.e. the sum of the frequencies).

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.FreqList


 The total number of elements stored, i.e. the sum of all the frequencies
 

totalElems - Variable in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.VAList

Total number of elements in the list.

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.valueAssociations


 Gives the total number of elements in the list (i.e. the sum of the frequencies).

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.FreqList


 The total number of elements stored, i.e. the sum of all the frequencies
 

totalElems - Variable in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.VAList

Total number of elements in the list.

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.valueAssociations


 Gives the total number of elements in the list (i.e. the sum of the frequencies).

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.FreqList


 The total number of elements stored, i.e. the sum of all the frequencies
 

totalElems - Variable in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.VAList

Total number of elements in the list.

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.valueAssociations


 Gives the total number of elements in the list (i.e. the sum of the frequencies).

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.FreqList


 The total number of elements stored, i.e. the sum of all the frequencies
 

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.FreqList


 The total number of elements stored, i.e. the sum of all the frequencies
 

totalElems - Variable in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.VAList

Total number of elements in the list.

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.valueAssociations


 Gives the total number of elements in the list (i.e. the sum of the frequencies).

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.FreqList


 The total number of elements stored, i.e. the sum of all the frequencies
 

totalElems - Variable in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.VAList

Total number of elements in the list.

totalElems() - Method in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.valueAssociations


 Gives the total number of elements in the list (i.e. the sum of the frequencies).

totalFiringDegrees - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams

Vector that contains the total firing degrees of rules.

totalFractions - Variable in class keel.GraphInterKeel.datacf.partitionData.HoldOutOptionsJDialog

Number of fractions

totalNominales(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset

It returns the number of nominal values for a given variable

totalNominales(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset

It returns the number of nominal values for a given variable

totalNominales(int) - Method in class keel.Algorithms.Decision_Trees.Target.myDataset

It returns the number of nominal values for a given variable

totalNominales(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset

It returns the number of nominal values for a given variable

totalNominals(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset

It returns the number of nominal values for a given variable

totalReplacement() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.geneticAlgorithm
 
TotalSupportTree - Class in keel.Algorithms.Associative_Classification.ClassifierCMAR

Methods concerned with the generation, processing and manipulation of 
T-tree data storage structures used to hold the total support counts for large 
itemsets.

TotalSupportTree(double, double, int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCMAR.TotalSupportTree

Processes command line arguments.

TotalSupportTree - Class in keel.Algorithms.Subgroup_Discovery.SDMap.FPTree

 Methods concerned with the generation, processing and manipulation of T-tree data
 storage structures used to hold the total support counts for large itemsets

TotalSupportTree(myDataset, double, double) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TotalSupportTree

Constructor to process dataset and parameters.

TotalSupportTree - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD
TotalSupportTree(myDataset, double, double) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TotalSupportTree

Constructor to process dataset and parameters.

totalValores(int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset

Function to get the number of different feasible values for a given attribute

TotalVariables() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
 
TotalVariables() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar


 Retuns the number of variables in the list
 

TotalVariables() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
 
TotalVariables() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
 
TOURNAMENT - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
Tournament(double[], int, int, SetupParameters) - Static method in class keel.Algorithms.Neural_Networks.gann.Selector


 Tournament selection method
 

tournament_selection() - Method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Core

Applies a tournament selection, with tournament size of 2

tournament_selection() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Ilga

Applies a tournament selection, with tournament size of 2

tournament_selection() - Method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.SEM

Applies a tournament selection, with tournament size of 2

tournament_selection() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Oiga

Applies a tournament selection, with tournament size of 2

tournament_selection() - Method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.SEM

Applies a tournament selection, with tournament size of 2

TOURNAMENT_WOR - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
TournamentSelection() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.geneticAlgorithm
 
tournamentSelection() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population

It implements tournament selection.

TournamentSelection - Class in keel.Algorithms.Genetic_Rule_Learning.UCS

This class implements the tournament selection method.

TournamentSelection() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.TournamentSelection

Creates a TournamentSelection object

TournamentSelection - Class in keel.Algorithms.Genetic_Rule_Learning.XCS

This class implements the tournament selection method.

TournamentSelection() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.TournamentSelection


 Creates a TournamentSelection object
 

TournamentSelectionWOR() - Method in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.geneticAlgorithm
 
tournamentSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
tournamentSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
 
tournamentSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
 
tournamentSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

It represents the percentage of population that has to be selected 
 to make tournament.

tournamentSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 It represents the percentage of population that has to be selected 
 to make tournament.

TOURNAMENTSIZE - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
toVector() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyPartition

 
  Characteristic points from fuzzy partition are copied to a vector of numbers.

TP - Variable in class keel.Algorithms.PSO_Learning.CPSO.Particle
 
TP - Variable in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
 
TP - Variable in class keel.Algorithms.PSO_Learning.REPSO.Particle
 
TPA() - Method in class keel.Algorithms.Instance_Generation.ICPL.ICPLGenerator

TPA.

tr(double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
 
tr(double[][]) - Static method in class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
 
tR - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest

Regression t Stat-test identifier.

tra_val - Variable in class keel.GraphInterKeel.experiments.Parameters
 
trace(DenseMatrix) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM

Sum of the diagonal elements

trace() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix

Matrix trace.

trailing - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.ExponentialFormat

Trailing flag.

trailing - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FloatingPointFormat

Trailing flag.

train - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm

Training dataset

train - Variable in class keel.Algorithms.Decision_Trees.C45.Tree

The dataset.

train - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Tree

The dataset.

train - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm

Training dataset.

TRAIN - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
train - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Tree

The dataset.

train - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

Training dataset

train - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Tree

The dataset.

train - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Data

Training data

train - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree

The dataset.

train - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Training dataset.

TRAIN - Variable in class keel.Algorithms.Neural_Networks.ensemble.Ensemble

Training flag.

train - Variable in class keel.Algorithms.Neural_Networks.gann.Data

Training data

train - Variable in class keel.Algorithms.Neural_Networks.gmdh.Data

Training data

train - Variable in class keel.Algorithms.Neural_Networks.net.Data

Training data

train - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm

Training dataset

train - Static variable in class keel.Algorithms.PSO_Learning.CPSO.CPSO

Training dataset.

train - Static variable in class keel.Algorithms.PSO_Learning.LDWPSO.LDWPSO

Training dataset.

train - Static variable in class keel.Algorithms.PSO_Learning.REPSO.REPSO

Training dataset.

train - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm

Training dataset

train - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Tree

The dataset.

train - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Tree

The dataset.

train - Variable in class keel.Algorithms.Rule_Learning.PART.Tree

The dataset.

train() - Method in class keel.Algorithms.Rule_Learning.Swap1.swap1

Training process.

train - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree

The dataset.

train - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm

Training instances set.

train2InputFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
train2InputFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
 
train2InputFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
 
train2InputFile - Static variable in class keel.Algorithms.Rule_Learning.Swap1.Parameters
 
train_file - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters

File names

train_file - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters

File names

train_file - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters

File names

train_file - Variable in class keel.Algorithms.Neural_Networks.net.Parameters

File names

train_output - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters

File names

train_output - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters

File names

train_output - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters

File names

train_output - Variable in class keel.Algorithms.Neural_Networks.net.Parameters

File names

trainConfMatrix - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

Confusion matrix for training

trainConfMatrix - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Training Confusion matrix.

trainCV(int, int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances

Creates the training set for one fold of a cross-validation
 on the dataset.

trainCV(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Creates the training set for one fold of a cross-validation
 on the dataset.

trainCV(int, int, Randomize) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Creates the training set for one fold of a cross-validation
 on the dataset.

trainCV(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

Creates the training set for one fold of a cross-validation
 on the dataset.

trainCV(int, int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Creates the training set for one fold of a cross-validation on the
 dataset.

trainCV(int, int, Random) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Creates the training set for one fold of a cross-validation on the
 dataset.

trainCV(int, int) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Creates the training set for one fold of a cross-validation 
 on the dataset.

trainCV(int, int) - Method in class keel.Algorithms.SVM.SMO.core.Instances

Creates the training set for one fold of a cross-validation 
 on the dataset.

trainCV(int, int, Random) - Method in class keel.Algorithms.SVM.SMO.core.Instances

Creates the training set for one fold of a cross-validation 
 on the dataset.

trainData - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm

Training input data.

trainData - Variable in class keel.Algorithms.Decision_Trees.CART.RunCART

Training dataset read.

trainData - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm

Training input data.

trainData - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
 
trainData - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

Training input data.

trainData - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Training input data.

trainData - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm

Training input data.

trainData - Variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN

Training input data.

trainData - Variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN

Training input data.

trainData - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm

Training input data.

trainDataset - Variable in class keel.Algorithms.Decision_Trees.C45.Algorithm

The train dataset.

trainDataset - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm

The train dataset.

trainDataset - Variable in class keel.Algorithms.Decision_Trees.ID3.Algorithm

The train dataset.

trainDataset - Variable in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm

Training dataset.

trainDataset - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm

The train dataset.

trainDataset - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm

The train dataset.

trainDataset - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm

The train dataset.

trainDataset - Static variable in class keel.Algorithms.MIL.AbstractMIAlgorithm
 
trainDataset - Variable in class keel.Algorithms.Rule_Learning.ART.Algorithm

The train dataset.

trainDataset - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Algorithm

The train dataset.

trainDataset - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Algorithm

The train dataset.

trainDataset - Variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm

The train dataset.

trainDataset - Variable in class keel.Algorithms.Rule_Learning.PART.Algorithm

The train dataset.

trainDataset - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.C45

The train dataset.

TrainEnsemble(EnsembleParameters, Data) - Method in class keel.Algorithms.Neural_Networks.ensemble.Ensemble


 Train Ensemble
 

TrainEnsembleAda(EnsembleParameters, Data) - Method in class keel.Algorithms.Neural_Networks.ensemble.Ensemble


 Train ensemble using Ada
 

TrainEnsembleArcing(EnsembleParameters, Data) - Method in class keel.Algorithms.Neural_Networks.ensemble.Ensemble


 Train ensemble using Arcing
 

TrainEnsembleBagging(EnsembleParameters, Data) - Method in class keel.Algorithms.Neural_Networks.ensemble.Ensemble


 Train ensemble using Bagging
 

TrainEnsembleNoSampling(EnsembleParameters, Data) - Method in class keel.Algorithms.Neural_Networks.ensemble.Ensemble


 Train ensemble without sampling
 

trainFile - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm

Train file name

trainFile - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm

Train file name.

trainFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

It the BD of train examples

trainFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 It the BD of train examples
 

TRAINFILE - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
trainFile - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

Train file name

trainFile - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Train file name.

trainFile - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm

Train file name

trainFile - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm

Train file name

trainFileName - Static variable in class keel.Algorithms.Decision_Trees.C45.Algorithm

The name of the file that contains the information to make the training.

trainFileName - Static variable in class keel.Algorithms.Decision_Trees.CART.RunCART

The name of the file that contains the information to make the training.

trainFileName - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm

The name of the file that contains the information to make the training.

trainFileName - Static variable in class keel.Algorithms.Decision_Trees.ID3.Algorithm

The name of the file that contains the information to make the training.

trainFileName - Static variable in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm

Training dataset file's name.

trainFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm

The name of the file that contains the information to make the training.

trainFileName - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm

The name of the file that contains the information to make the training.

trainFileName - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm

The name of the file that contains the information to make the training.

trainFileName - Static variable in class keel.Algorithms.Rule_Learning.ART.Algorithm

The name of the file that contains the information to make the training.

trainFileName - Static variable in class keel.Algorithms.Rule_Learning.C45Rules.Algorithm

The name of the file that contains the information to make the training.

trainFileName - Static variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Algorithm

The name of the file that contains the information to make the training.

trainFileName - Static variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm

The name of the file that contains the information to make the training.

trainFileName - Static variable in class keel.Algorithms.Rule_Learning.PART.Algorithm

The name of the file that contains the information to make the training.

training - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.Metodo

Training dataset.

training - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Basic.Metodo
 
training - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.Metodo
 
TRAINING - Static variable in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm

Type of file: training data set.

training - Variable in class keel.Algorithms.Preprocess.Basic.Metodo

Training dataset

TRAINING - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm

Type of file: training data set.

Training_Example(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
 
Training_Example(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set


 Returns the number of examples belonging to the partition "particion"
 

Training_Example(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set
 
Training_Example(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set
 
trainingDataSet - Variable in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerator

Original data set to be condensed

trainingDataSet - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator

Training prototype set.

trainingFileName - Static variable in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm

Training data set file name.

trainingFileName - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm

Training data set file name.

trainingModel - Variable in class keel.GraphInterKeel.datacf.exportData.ExportPanel

The model for storing the training files used in the partition mode

trainingModel - Variable in class keel.GraphInterKeel.datacf.importData.ImportPanel

The model for storing the training files used in the partition mode

trainingResults(double[], double[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig


 Writes the training result file with expected and obtained data for modelling problems.

trainingResults(int[], int[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig


 Writes the training result file with expected and obtained data for classification problems.

trainingResults(double[][], int[]) - Method in class keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig


 Writes the training result file with pattern and obtained data for clustering problems.

trainingResults(double[], double[]) - Method in class keel.Algorithms.Shared.Parsing.ProcessConfig


 Writes the training result file with expected and obtained data for modelling problems.

trainingResults(int[], int[]) - Method in class keel.Algorithms.Shared.Parsing.ProcessConfig


 Writes the training result file with expected and obtained data for classification problems.

trainingResults(double[][], int[]) - Method in class keel.Algorithms.Shared.Parsing.ProcessConfig


 Writes the training result file with pattern and obtained data for clustering problems.

trainingTime - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm

Training prediction time.

trainingTime - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

Training prediction time.

trainingTime - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Training prediction time.

trainInputFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
trainInputFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
 
trainInputFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
 
trainInputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters

Original Training data filename.

trainInputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Parameters

Original Training data filename.

trainInputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters

Original Training data filename.

trainInputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters

Original Training data filename.

trainInputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Parameters

Original Training data filename.

trainInputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters

Original Training data filename.

trainInputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Parameters

Original Training data filename.

trainInputFile - Static variable in class keel.Algorithms.Rule_Learning.Swap1.Parameters
 
trainInputFile - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerCSVM

Training dataset filename.

trainInputFile - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.HandlerSMO

Training dataset filename.

trainInputFile - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.ParametersSMO

Training input filename.

trainInstances - Variable in class keel.Algorithms.MIL.AbstractMIAlgorithm
 
trainInstancesCopy - Variable in class keel.Algorithms.MIL.AbstractMIAlgorithm
 
trainLMS(double[][], double[][], int, int, double) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation


 Trains a Population of RBFNs
 

trainLMS(double[][], double[][], int, int, double) - Method in class keel.Algorithms.Neural_Networks.RBFN.Rbfn


 Uses LMS to train the net.

trainLMS(double[][], double[][], int, int, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_CL.Rbfn


 Uses LMS to train the net.

trainLMS(double[][], double[][], int, int, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental.Rbfn


 Uses LMS to train the net.

trainLMS(double[][], double[][], int, int, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbfn


 Uses LMS to train the net.

trainLMS(double[][], double[][], int, int, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental.Rbfn


 Uses LMS to train the net.

trainLMS(double[][], double[][], int, int, double) - Method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbfn


 Uses LMS to train the net.

trainLMS_subPop(double[][], double[][], int, int, double) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation


 Trains a Population of RBFNs
 

TrainNetwork(Parameters, double[][], int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network


 Train network without cross validation
 

TrainNetwork(EnsembleParameters, double[][], int) - Method in class keel.Algorithms.Neural_Networks.ensemble.EnsembleNetwork


 Back-propagation algorithm without cross validation
 

TrainNetwork(SetupParameters, double[][], int) - Method in class keel.Algorithms.Neural_Networks.gann.Network


 Train network without cross validation
 

TrainNetwork(Parameters, double[][], int) - Method in class keel.Algorithms.Neural_Networks.gmdh.Network


 Train network without cross validation
 

TrainNetwork(Parameters, double[][], int) - Method in class keel.Algorithms.Neural_Networks.net.Network


 Train network without cross validation
 

TrainNetworkWithCrossvalidation(Parameters, Data) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network


 Train Network using cross validation
 

TrainNetworkWithCrossvalidation(EnsembleParameters, Data) - Method in class keel.Algorithms.Neural_Networks.ensemble.EnsembleNetwork


 Back-propagation algorithm using cross validation
 

TrainNetworkWithCrossvalidation(SetupParameters, Data) - Method in class keel.Algorithms.Neural_Networks.gann.Network


 Train Network using cross validation
 

TrainNetworkWithCrossvalidation(Parameters, Data) - Method in class keel.Algorithms.Neural_Networks.gmdh.Network


 Train the Network using cross validation
 

TrainNetworkWithCrossvalidation(Parameters, Data) - Method in class keel.Algorithms.Neural_Networks.net.Network


 Train Network using cross validation
 

trainNominal - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm

Training nominal input data.

TrainNominalOutOfRange - Static variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo
 
TrainNominalOutOfRange - Static variable in class keel.Dataset.ErrorInfo
 
trainNulls - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm

Training missing input data.

TrainNumberOutOfRange - Static variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo
 
TrainNumberOutOfRange - Static variable in class keel.Dataset.ErrorInfo
 
trainOutput - Variable in class keel.Algorithms.Coevolution.CoevolutionAlgorithm

Training output data.

trainOutput - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm

Training output data.

trainOutput - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

Training output data.

trainOutput - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Training output data.

trainOutput - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm

Training output data.

trainOutput - Variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN

Training output data.

trainOutput - Variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN

Training output data.

trainOutput - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm

Training output data.

trainOutputFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
trainOutputFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
 
trainOutputFile - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
 
trainOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.Parameters

Modified Training data filename.

trainOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.CVCommitteesFilter.Parameters

Modified Training data filename.

trainOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.Parameters

Modified Training data filename.

trainOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters

Modified Training data filename.

trainOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.IterativePartitioningFilter.Parameters

Modified Training data filename.

trainOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.PANDA.Parameters

Modified Training data filename.

trainOutputFile - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.SaturationFilter.Parameters

Modified Training data filename.

trainOutputFile - Static variable in class keel.Algorithms.Rule_Learning.Swap1.Parameters
 
trainOutputFileName - Static variable in class keel.Algorithms.Decision_Trees.C45.Algorithm

The name of the train output file.

trainOutputFileName - Static variable in class keel.Algorithms.Decision_Trees.CART.RunCART

The name of the train output file.

trainOutputFileName - Static variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Algorithm

The name of the train output file.

trainOutputFileName - Static variable in class keel.Algorithms.Decision_Trees.ID3.Algorithm

The name of the train output file.

trainOutputFileName - Static variable in class keel.Algorithms.Decision_Trees.SLIQ.Algorithm

Name of the training output file.

trainOutputFileName - Static variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Algorithm

The name of the train output file.

trainOutputFileName - Static variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Algorithm

The name of the train output file.

trainOutputFileName - Static variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Algorithm

The name of the train output file.

trainOutputFileName - Static variable in class keel.Algorithms.Rule_Learning.ART.Algorithm

The name of the train output file.

trainOutputFileName - Static variable in class keel.Algorithms.Rule_Learning.C45Rules.Algorithm

The name of the train output file.

trainOutputFileName - Static variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Algorithm

The name of the train output file.

trainOutputFileName - Static variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Algorithm

The name of the train output file.

trainOutputFileName - Static variable in class keel.Algorithms.Rule_Learning.PART.Algorithm

The name of the train output file.

trainPrediction - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm

Predicted classes for the training dataset.

trainPrediction - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

Predictions for training

trainPrediction - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Predited classes of the training instances.

trainPrediction - Variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN

Training predicted classes.

trainPrediction - Variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN

Training predicted classes.

trainPrediction - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm

Training predicted classes

trainReal - Variable in class keel.Algorithms.Preprocess.Feature_Selection.Shared.FSAlgorithm

Training real input data.

trainRealClass - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

real classes values for training

trainRealClass - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Real classes of the training instances.

trainRealClass - Variable in class keel.Algorithms.Preprocess.NoiseFilters.ClassificationFilter.KNN

Real training classes.

trainRealClass - Variable in class keel.Algorithms.Preprocess.NoiseFilters.EnsembleFilter.KNN

Real training classes.

trainReportFileName - Variable in class keel.Algorithms.MIL.AbstractMIAlgorithm
 
trainSize - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm

Training size.

trainSize - Variable in class keel.Algorithms.RST_Learning.RSTAlgorithm

Number of instances on training dataset.

trainTestC - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest

Summary of data, train & test, one algorithm identifier (classification).

trainTestR - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest

Summary of data, train & test, one algorithm identifier (regression).

trainUnclassified - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

number of instances unclassified for training

trainUnclassified - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Number of unclassified training instances.

transductiveDataSet - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator

Transductive prototype set.

transfer - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network

Transfer function of each layer (LOG | HTAN | LINEAR)

transfer - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters

Transfer function ( LOG | HTAN | LINEAR )

transfer - Variable in class keel.Algorithms.Neural_Networks.gann.Network

Transfer function of each layer (LOG | HTAN | LINEAR)

transfer - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters

Transfer function ( LOG | HTAN | LINEAR )

transfer - Variable in class keel.Algorithms.Neural_Networks.gmdh.Network

Transfer function of each layer (LOG | HTAN | LINEAR)

transfer - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters

Transfer function ( LOG | HTAN | LINEAR )

transfer - Variable in class keel.Algorithms.Neural_Networks.net.Network

Transfer function of each layer (LOG | HTAN | LINEAR)

transfer - Variable in class keel.Algorithms.Neural_Networks.net.Parameters

Transfer function ( LOG | HTAN | LINEAR )

transform(DataBase) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.myDataset

Transform the data-set given as argument to correspond the fuzzy labels.

transform() - Method in class keel.Algorithms.Preprocess.Transformations.Nominal2Binary.Nominal2Binary


 Process the training and test files provided in the parameters file to the constructor.

transpose(int, int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Matrix

Returns the transpose of a matrix [0:n-1][0:m-1]

transpose(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5Matrix

Returns the transpose of a matrix [0:n-1][0:m-1]

transpose() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix

Matrix transpose.

transpose() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix

Deprecated.
Returns the transpose of a matrix.

trapezoidal(String) - Static method in class keel.Algorithms.LQD.methods.FGFS_costInstances.fun_aux

Convert the number in fuzzy in trapezoidal way.

trapezoidal(String) - Static method in class keel.Algorithms.LQD.tests.IntermediateBoost.fun_aux

Convert the number in fuzzy with trapezoidal scheme.

Tree - Class in keel.Algorithms.Decision_Trees.C45

Class to handle the classifier tree
 
 

Tree(SelectCut, boolean, float) - Constructor for class keel.Algorithms.Decision_Trees.C45.Tree

Constructor.

Tree - Class in keel.Algorithms.Decision_Trees.DT_GA.C45

Class to handle the classifier tree

Tree(SelectCut, boolean, float) - Constructor for class keel.Algorithms.Decision_Trees.DT_GA.C45.Tree

Constructor.

Tree - Class in keel.Algorithms.Decision_Trees.DT_oblicuo

Title: Tree.

Tree() - Constructor for class keel.Algorithms.Decision_Trees.DT_oblicuo.Tree

Default Constructor.

Tree(Tree, myDataset, int, int[], int) - Constructor for class keel.Algorithms.Decision_Trees.DT_oblicuo.Tree

Paramenter constructor.

Tree - Class in keel.Algorithms.Decision_Trees.Target

Title: Tree 

 Description: It contains the implementation of the tree structure 


 Company: KEEL 

Tree() - Constructor for class keel.Algorithms.Decision_Trees.Target.Tree

Default Constructor.

Tree(Tree, myDataset, double, boolean, double, double) - Constructor for class keel.Algorithms.Decision_Trees.Target.Tree

Paramenter constructor.

Tree - Class in keel.Algorithms.Genetic_Rule_Learning.PART


 Class to handle the classifier tree
 
 
 

Tree() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.Tree

Constructor.

Tree(SelectCut, boolean, float) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.Tree

Constructor.

Tree - Class in keel.Algorithms.ImbalancedClassification.CSMethods.C45CS

Class to handle the classifier tree

Tree(SelectCut, boolean, float) - Constructor for class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Tree

Constructor.

Tree - Class in keel.Algorithms.ImbalancedClassification.Ensembles.C45

Class to handle the classifier tree

Tree(SelectCut, boolean, float) - Constructor for class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Tree

Constructor.

Tree - Class in keel.Algorithms.Rule_Learning.C45Rules

 Class to handle the classifier tree

Tree(SelectCut, boolean, float) - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.Tree

Constructor.

Tree - Class in keel.Algorithms.Rule_Learning.C45RulesSA

 Class to handle the classifier tree

Tree(SelectCut, boolean, float) - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.Tree

Constructor.

Tree - Class in keel.Algorithms.Rule_Learning.PART

 Class to handle the classifier tree

Tree() - Constructor for class keel.Algorithms.Rule_Learning.PART.Tree

Constructor.

Tree(SelectCut, boolean, float) - Constructor for class keel.Algorithms.Rule_Learning.PART.Tree

Constructor.

Tree - Class in keel.Algorithms.Semi_Supervised_Learning.Basic.C45

Class to handle the classifier tree

Tree(SelectCut, boolean, float) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Tree

Constructor.

tree - Variable in class keel.GraphInterKeel.datacf.help.HelpOptions

Help options tree

TreeNode - Class in keel.Algorithms.Decision_Trees.CART.tree

Class that implements a tree used by the CART algorithm

TreeNode(TreeNode) - Constructor for class keel.Algorithms.Decision_Trees.CART.tree.TreeNode

Default Constructor.

TreeNode(TreeNode, int[]) - Constructor for class keel.Algorithms.Decision_Trees.CART.tree.TreeNode

Constructor.

TreeNode - Class in keel.Algorithms.Decision_Trees.FunctionalTrees

Data structure that is used in the construction of the decision tree.

TreeNode - Class in keel.Algorithms.Decision_Trees.PUBLIC

File: TreeNode.java
 
 Data structure that is used in the construction of the decision tree.

treeSwap(int, int, Tree) - Method in class keel.Algorithms.Decision_Trees.Target.Tree

Swaps the two trees given with the given tree.

treeToString(int, double) - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode

Converts the tree under this node to a string

treeToString(int, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode

Converts the tree under this node to a string

Trials - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
 
trimToSize() - Method in class keel.Algorithms.Decision_Trees.M5.M5Vector

Sets the vector's capacity to its size.

trimToSize() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.FastVector

Sets the vector's capacity to its size.

trimToSize() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.FastVector

Sets the vector's capacity to its size.

trimToSize() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.FastVector

Sets the vector's capacity to its size.

trimToSize() - Method in class keel.Algorithms.SVM.SMO.core.FastVector

Sets the vector's capacity to its size.

Trio - Class in keel.Algorithms.Instance_Selection.CPruner

Trio implementation.

Trio() - Constructor for class keel.Algorithms.Instance_Selection.CPruner.Trio

Default constructor.

Trio(int, int, double) - Constructor for class keel.Algorithms.Instance_Selection.CPruner.Trio

Parameter constructor.

Trio - Class in keel.Algorithms.Preprocess.Instance_Selection.CPruner

Trio implementation.

Trio() - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.CPruner.Trio

Default constructor.

Trio(int, int, double) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.CPruner.Trio

Parameter constructor.

Trio - Class in keel.Algorithms.Rule_Learning.Ripper

Auxiliar class.

Trio() - Constructor for class keel.Algorithms.Rule_Learning.Ripper.Trio

Default constructor.

Trio(double) - Constructor for class keel.Algorithms.Rule_Learning.Ripper.Trio

Constructs a Trio with the given value and 0 instaces for both positives and negatives.

Trio - Class in keel.Algorithms.Rule_Learning.Slipper


 Auxiliar class.


Trio() - Constructor for class keel.Algorithms.Rule_Learning.Slipper.Trio

Default constructor.

Trio(double) - Constructor for class keel.Algorithms.Rule_Learning.Slipper.Trio

Constructs a Trio with the given value and 0 instaces for both positives and negatives.

Trio(double, double, int) - Constructor for class keel.Algorithms.Rule_Learning.Slipper.Trio

Constructs a Trio with the given value and 0 instaces for both positives and negatives.

Triplet - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN

File: Triplet.java
 
 A Triplet class for the JFKNN algorithm.

Triplet(int, int) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN.Triplet

Parameter constructor.

Triplet(double[][]) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN.Triplet

Parameter constructor.

Triplet(Triplet) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN.Triplet

Copy Constructor.

TriTrainingAlgorithm - Class in keel.Algorithms.Semi_Supervised_Learning.TriTraining

TriTraining algorithm calling.

TriTrainingAlgorithm() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.TriTraining.TriTrainingAlgorithm
 
TriTrainingGenerator - Class in keel.Algorithms.Semi_Supervised_Learning.TriTraining

This class implements the Tri-training.

TriTrainingGenerator(PrototypeSet, int, int, int, int, double, double, double, double, double) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.TriTraining.TriTrainingGenerator

Build a new TriTrainingGenerator Algorithm

TriTrainingGenerator(PrototypeSet, PrototypeSet, PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Semi_Supervised_Learning.TriTraining.TriTrainingGenerator

Build a new TriTrainingGenerator Algorithm

TRIVIAL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.OCEC.Organizacion
 
TrivialAlgorithm - Class in keel.Algorithms.Instance_Generation.Trivial

Main class

TrivialAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.Trivial.TrivialAlgorithm
 
TRKNN - Class in keel.Algorithms.Instance_Selection.TRKNN

File: TRKNN.java
 
 The TRKNN Instance Selection algorithm.

TRKNN(String) - Constructor for class keel.Algorithms.Instance_Selection.TRKNN.TRKNN

Default constructor.

TRKNN - Class in keel.Algorithms.Preprocess.Instance_Selection.TRKNN

File: TRKNN.java
 
 The TRKNN Instance Selection algorithm.

TRKNN(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.TRKNN.TRKNN

Default constructor.

TRS - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm

Type of file: trs data set.

trueHShaffer(int) - Static method in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Multiple

Computes the trueHShaffer distribution from a given parameter.

trueHShaffer(int) - Static method in class keel.GraphInterKeel.statistical.tests.Multiple

Computes the trueHShaffer distribution from a given parameter.

trueNegativeRate(int) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Calculate the true negative rate with respect to a particular class.

truePositiveRate(int) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Calculate the true positive rate with respect to a particular class.

TruncaElite() - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic


 TruncaElite
 
 Stores in elite part of the non-dominated individuals of temporal
    This is because temporal contains more non-dominated that fits in elite

tst_val - Variable in class keel.GraphInterKeel.experiments.Parameters
 
Ttabla - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs

Class that defines the Ttabla

Ttabla(int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Ttabla

Constructor with one parameter

Ttabla(double[], double, double, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Ttabla

Constructor that defines all elements of the class

Ttabla - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs

Class that defines the Ttabla

Ttabla(int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Ttabla

Constructor with one parameter

Ttabla(double[], double, double, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Ttabla

Constructor that defines all elements of the class

Ttabla - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs

Class that defines the Ttabla

Ttabla(int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Ttabla

Constructor with one parameter

Ttabla(double[], double, double, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Ttabla

Constructor that defines all elements of the class

Ttabla - Class in keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs

Class that defines the Ttabla

Ttabla(int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Ttabla

Constructor with one parameter

Ttabla(double[], double, double, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Ttabla

Constructor that defines all elements of the class

Ttabla - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs

Class that defines the Ttabla

Ttabla(int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Ttabla

Constructor with one parameter

Ttabla(double[], double, double, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Ttabla

Constructor that defines all elements of the class

Ttabla - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs

Class that defines the Ttabla

Ttabla(int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Ttabla

Constructor with one parameter

Ttabla(double[], double, double, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Ttabla

Constructor that defines all elements of the class

Ttabla - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs

Class that defines the Ttabla

Ttabla(int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Ttabla

Constructor with one parameter

Ttabla(double[], double, double, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Ttabla

Constructor that defines all elements of the class

Ttabla - Class in keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs

Class that defines the Ttabla

Ttabla(int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Ttabla

Constructor with one parameter

Ttabla(double[], double, double, int) - Constructor for class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Ttabla

Constructor that defines all elements of the class

TTable - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate

This class is defined to contain a bidimensional array with the instances
 of the dataset, the class of the instance and if it is covered by any rule

TTable() - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.TTable


 Creates a new instance of TTable
 

TTable - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate

This class is defined to contain a bidimensional array with the instances
 of the dataset, the class of the instance and if it is covered by any rule

TTable() - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.TTable


 Creates a new instance of TTable
 

TTable - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate

 This class is defined to contain a bidimensional array with the instances
 of the dataset, the class of the instance and if it is covered by any rule

TTable() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.TTable


 Creates a new instance of TTable
 

TTLS - Static variable in class keel.Algorithms.Preprocess.Missing_Values.EM.EM

TTLS flag.

TtreeNode - Class in keel.Algorithms.Associative_Classification.ClassifierCMAR

Methods concerned with Ttree node structure.

TtreeNode() - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCMAR.TtreeNode

Default constructor

TtreeNode(int) - Constructor for class keel.Algorithms.Associative_Classification.ClassifierCMAR.TtreeNode

One argument constructor.

TtreeNode - Class in keel.Algorithms.Subgroup_Discovery.SDMap.FPTree

 Methods concerned with Ttree node structure.

TtreeNode() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TtreeNode

Default constructor

TtreeNode(int) - Constructor for class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.TtreeNode

One argument constructor.

TtreeNode - Class in keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD
TtreeNode() - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TtreeNode

Default constructor

TtreeNode(int) - Constructor for class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.TtreeNode

One argument constructor.

Tuneados - Class in keel.GraphInterKeel.datacf
 
Tuneados() - Constructor for class keel.GraphInterKeel.datacf.Tuneados
 
tuneInterval(myDataset, int[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Gene
 
tuneInterval(myDataset, int[]) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Gene
 
turnChecksOff() - Method in class keel.Algorithms.SVM.SMO.SMO

Turns off checks for missing values, etc.

turnChecksOff() - Method in class keel.Algorithms.SVM.SMO.SMOreg

Turns off checks for missing values, etc.

turnChecksOn() - Method in class keel.Algorithms.SVM.SMO.SMO

Turns on checks for missing values, etc.

turnChecksOn() - Method in class keel.Algorithms.SVM.SMO.SMOreg

Turns on checks for missing values, etc.

turningConjunctionIntoDisjunction(myDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Rule
 
twoDecPlaces(double) - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.AssocRuleMining

Converts given real number to real number rounded up to two decimal
    places.

twoDecPlaces(double) - Method in class keel.Algorithms.Subgroup_Discovery.SDMap.FPTree.AssocRuleMining

Converts given real number to real number rounded up to two decimal 
    places.

twoDecPlaces(double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.FPgrowth.LUCS_KDD.AssocRuleMining

Converts given real number to real number rounded up to two decimal 
    places.

TwoPointCrossover - Class in keel.Algorithms.Genetic_Rule_Learning.UCS

This class implements the two-point crossover operator.

TwoPointCrossover() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.TwoPointCrossover

Creates an object of the class.

TwoPointCrossover - Class in keel.Algorithms.Genetic_Rule_Learning.XCS

 Implements the two point crossover.

TwoPointCrossover() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.TwoPointCrossover


 Creates an object of the class.

TwoPointsCrossover() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal


 Two-points crossover operator
 

TwoPointsCrossover_operator(int, int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary


 Given the individuals "indiv1" and "indiv2", it selects two points
 exchanging their central zones.

TwoPointsCrossover_operator(int, int, int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary


 Given the individuals "indiv1" and "indiv2", it selects two points
 exchanging their central zones.

TwoPointsCrossover_Stationary(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary


 Given the individuals "indiv1" and "indiv2", it selects two points
 exchanging their central zones.

TwoPointsCrossover_Stationary(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger


 Given the individuals "indiv1" and "indiv2", it selects two points
 exchanging their central zones.

TwoPointsCrossover_Stationary(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal


 Given the individuals "indiv1" and "indiv2", it selects two points
 exchanging their central zones.

TxtToKeel - Class in keel.Algorithms.Preprocess.Converter


  TxtToKeel 
 
 This class extends from the Importer class.

TxtToKeel(String) - Constructor for class keel.Algorithms.Preprocess.Converter.TxtToKeel

TxtToKeel class Constructor.

type() - Method in class keel.Algorithms.Decision_Trees.M5.M5Attribute

Returns the attribute's type as an integer.

type - Variable in class keel.Algorithms.Discretizers.MODL.Neighbour

type: Split, MergeSplit or MergeMergeSplit

type() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.Node


 This method return the type of node
 

type() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka

Returns the attribute's type as an integer.

type() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute

Returns the attribute's type as an integer.

type - Static variable in class keel.Algorithms.Instance_Generation.Basic.Prototype

Type of each attribute of the set.

type - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies

Type of each LinkedLayer of the neural nets

type - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer

Type of layer (HIDDEN_LAYER or OUTPUT_LAYER)

type - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype

Type of each attribute of the set.

type - Variable in class keel.GraphInterKeel.experiments.Node
 
type_lqd() - Method in class keel.GraphInterKeel.experiments.GraphPanel

Nodes of type LQD

type_lqd - Variable in class keel.GraphInterKeel.experiments.Joint
 
type_lqd - Variable in class keel.GraphInterKeel.experiments.Node
 
TypeAlreadyFixed - Static variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo
 
TypeAlreadyFixed - Static variable in class keel.Dataset.ErrorInfo
 
TypeDat - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF

 Class defined to store the data characteristics

TypeDat() - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeDat


 Creates a new instance of TypeDat
 

TypeDat - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD

Class defined to store the data characteristics

TypeDat() - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeDat


 Creates a new instance of TypeDat
 

TypeDat - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA

 Class defined to store the data characteristics

TypeDat() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeDat


 Creates a new instance of TypeDat
 

typeOfAttributes - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

Represents the type of each attribute of a classifier.

typeOfAttributes - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 Represents the type of each attribute of a classifier.

typeOfCrossover - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

Represents the type of crossover.

typeOfCrossover - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 Represents the type of crossover.

TYPEOFCROSSOVER - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
typeOfError - Variable in class keel.Algorithms.Rule_Learning.Swap1.ErrorInfo

It stores the type of the error

typeOfError - Variable in class keel.Dataset.ErrorInfo

It stores the type of the error

typeOfInitialReduction - Variable in class keel.Algorithms.Instance_Generation.HYB.HYBGenerator

Type of initial reduction.

typeOfMutation - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

Represents the type of mutation.

typeOfMutation - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 Represents the type of mutation.

TYPEOFMUTATION - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
typeOfProblem - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

It represents the type of problem to be executed.

typeOfProblem - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 It represents the type of problem to be executed.

TYPEOFPROBLEM - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
typeOfReduction - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 Indicates the type of reduction to be made.

TYPEOFREDUCTION - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
typeOfSelection - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

Represents the type of selection.

typeOfSelection - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 Represents the type of selection.

TYPEOFSELECTION - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
types - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_ADI
 
types - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.Globals_UBR
 
types - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Globals_ADI
 
typesOfReduction - Static variable in class keel.Algorithms.Instance_Generation.HYB.HYBGenerator

Titles of types of initial reduction

typesVariable() - Method in class keel.Algorithms.Hyperrectangles.EACH.Dataset


 Return the types of each in-put(NOMINAL[0] o NUMERIC[1])
 

TypeVar - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate

Class defined to store the attributes characteristics

TypeVar() - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.TypeVar


 Creates a new instance of TypeVar
 

TypeVar - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF

 Class defined to store the attributes characteristics

TypeVar() - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.TypeVar


 Creates a new instance of TypeVar
 

TypeVar - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate

 Class defined to store the attributes characteristics

TypeVar() - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.TypeVar


 Creates a new instance of TypeVar
 

TypeVar - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD

 Class defined to store the attributes characteristics

TypeVar() - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.TypeVar


 Creates a new instance of TypeVar
 

TypeVar - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate

 Class defined to store the attributes characteristics

TypeVar() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.TypeVar


 Creates a new instance of TypeVar
 

TypeVar - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA

 Class defined to store the attributes characteristics

TypeVar() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.TypeVar


 Creates a new instance of TypeVar
 

typicality(int) - Method in class keel.Algorithms.Instance_Generation.ICPL.ICPLGenerator
 




U

UcFirst(String) - Method in class keel.Algorithms.Preprocess.Converter.Importer

Sets as capital letter the first one of the line given as parameter.

UcFirst(String) - Method in class keel.Algorithms.Preprocess.Converter.KeelToDb

Sets as capital letter the first one of the line given as parameter.

UciToKeel - Class in keel.Algorithms.Preprocess.Converter


  UciToKeel 
 

 This class extends from the Importer class.

UciToKeel(String) - Constructor for class keel.Algorithms.Preprocess.Converter.UciToKeel

UciToKeel class Constructor.

UCPD - Class in keel.Algorithms.Discretizers.UCPD


 This class implements the UCPD algorithm
 

UCPD(InstanceSet) - Constructor for class keel.Algorithms.Discretizers.UCPD.UCPD


 Constructor of the class
 

UCS - Class in keel.Algorithms.Genetic_Rule_Learning.UCS

This class controls the UCS run.

UCS(String) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.UCS


 Initializes an UCS object.

UCSControl - Class in keel.Algorithms.Genetic_Rule_Learning.UCS


 UCSControl.

UCSControl() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.UCSControl
 
UCSControl() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.UCSControl



 UCSControl
 

 This is the main class of the package.

UCSRun - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

It represents the number of explores iterations that have to be 
 made to do a exploit

ult_cambio_eval - Variable in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Population

Last change in the population

uminus() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix

Unary minus

UnassignedClassException - Exception in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core

Exception that is raised when trying to use some data that has no
 class assigned to it, but a class is needed to perform the operation.

UnassignedClassException() - Constructor for exception keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.UnassignedClassException

Creates a new UnassignedClassException with no message.

UnassignedClassException(String) - Constructor for exception keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.UnassignedClassException

Creates a new UnassignedClassException.

UnassignedClassException - Exception in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets

Exception that is raised when trying to use some data that has no
 class assigned to it, but a class is needed to perform the operation.

UnassignedClassException() - Constructor for exception keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.UnassignedClassException

Creates a new UnassignedClassException with no message.

UnassignedClassException(String) - Constructor for exception keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.UnassignedClassException

Creates a new UnassignedClassException.

UnassignedClassException - Exception in keel.Algorithms.SVM.SMO.core

Exception that is raised when trying to use some data that has no
 class assigned to it, but a class is needed to perform the operation.

UnassignedClassException() - Constructor for exception keel.Algorithms.SVM.SMO.core.UnassignedClassException

Creates a new UnassignedClassException with no message.

UnassignedClassException(String) - Constructor for exception keel.Algorithms.SVM.SMO.core.UnassignedClassException

Creates a new UnassignedClassException.

UnassignedDatasetException - Exception in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core

Exception that is raised when trying to use something that has no
 reference to a dataset, when one is required.

UnassignedDatasetException() - Constructor for exception keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.UnassignedDatasetException

Creates a new UnassignedDatasetException with no message.

UnassignedDatasetException(String) - Constructor for exception keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.UnassignedDatasetException

Creates a new UnassignedDatasetException.

UnassignedDatasetException - Exception in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets

Exception that is raised when trying to use something that has no
 reference to a dataset, when one is required.

UnassignedDatasetException() - Constructor for exception keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.UnassignedDatasetException

Creates a new UnassignedDatasetException with no message.

UnassignedDatasetException(String) - Constructor for exception keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.UnassignedDatasetException

Creates a new UnassignedDatasetException.

UnassignedDatasetException - Exception in keel.Algorithms.SVM.SMO.core

Exception that is raised when trying to use something that has no
 reference to a dataset, when one is required.

UnassignedDatasetException() - Constructor for exception keel.Algorithms.SVM.SMO.core.UnassignedDatasetException

Creates a new UnassignedDatasetException with no message.

UnassignedDatasetException(String) - Constructor for exception keel.Algorithms.SVM.SMO.core.UnassignedDatasetException

Creates a new UnassignedDatasetException.

unbackQuoteChars(String) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils

The inverse operation of backQuoteChars().

unbackQuoteChars(String) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils

The inverse operation of backQuoteChars().

unbackQuoteChars(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils

The inverse operation of backQuoteChars().

unbackQuoteChars(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils

The inverse operation of backQuoteChars().

unbackQuoteChars(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils

The inverse operation of backQuoteChars().

unbackQuoteChars(String) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils

The inverse operation of backQuoteChars().

unbackQuoteChars(String) - Static method in class keel.Algorithms.SVM.SMO.core.Utils

The inverse operation of backQuoteChars().

unclassified() - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Gets the number of instances not classified (that is, for
 which no prediction was made by the classifier).

unclassified - Variable in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

number of instances unclassified for test

unclassified - Variable in class keel.Algorithms.Lazy_Learning.LazyAlgorithm

Number of unclassified test instances.

uncoveredExamples - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams

Uncovered examples.

undefinedDistribution - Static variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Maths

Distribution type: undefined

undoRandomizeAttribute() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Does an undo of a previous call to randomizeAttribute, so that the
 original values of the attribute are restored.

UNEQUAL - Static variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Gene
 
UniformBiasedMutation(TableVar) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Genetic


 Applies the mutation operator.

UniformCrossover - Class in keel.Algorithms.Genetic_Rule_Learning.UCS

Implements crossover according to the uniform crossover operator

UniformCrossover() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.UCS.UniformCrossover

Creates an object of the class.

UniformCrossover - Class in keel.Algorithms.Genetic_Rule_Learning.XCS

Introduce a new crossover model (uniform crossover), implements Crossover.

UniformCrossover() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.UniformCrossover


 Creates an object of the class.

uniformFrequencyCutpoints(int, double[][], int) - Method in class keel.Algorithms.Discretizers.UCPD.UCPD


 It calculates the cutpoints with uniform frequency
 

UniformFrequencyDiscretizer - Class in keel.Algorithms.Discretizers.UniformFrequency_Discretizer


 This class implements the Uniform Frequency discretizer.

UniformFrequencyDiscretizer(int) - Constructor for class keel.Algorithms.Discretizers.UniformFrequency_Discretizer.UniformFrequencyDiscretizer

 
 Constructor of the class, initializes the numInt attribute
 

UniformFrequencyDiscretizer - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.UniformFrequency_Discretizer
 
UniformFrequencyDiscretizer(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.UniformFrequency_Discretizer.UniformFrequencyDiscretizer
 
UniformFrequencyDiscretizer - Class in keel.Algorithms.Preprocess.NoiseFilters.PANDA


 This class implements the Uniform Frequency discretizer.

UniformFrequencyDiscretizer(int) - Constructor for class keel.Algorithms.Preprocess.NoiseFilters.PANDA.UniformFrequencyDiscretizer

 
 Constructor of the class, initializes the numInt attribute
 

UniformMutation() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation


 Uniform mutation operator
 

UniformMutation() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationbinary


 Uniform mutation operator
 

UniformMutation() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.populationreal


 Uniform mutation operator
 

UniformMutation() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation


 Uniform mutation operator
 

UniformMutation() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.populationbinary


 Uniform mutation operator
 

UniformMutation_Stationary() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation


 Stationary uniform mutation operator
 

UniformMutation_Stationary() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationbinary


 Stationary uniform mutation operator
 

UniformMutation_Stationary() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationinteger


 Stationary uniform mutation operator
 

UniformMutation_Stationary() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.populationreal


 Stationary uniform mutation operator
 

UniformWidthDiscretizer - Class in keel.Algorithms.Discretizers.UniformWidth_Discretizer


 This class implements the Uniform Width discretizer.

UniformWidthDiscretizer(int) - Constructor for class keel.Algorithms.Discretizers.UniformWidth_Discretizer.UniformWidthDiscretizer


 Constructor of the class, initializes the numCP attribute
 

UniformWidthDiscretizer - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.UniformWidth_Discretizer
 
UniformWidthDiscretizer(int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.UniformWidth_Discretizer.UniformWidthDiscretizer
 
Union - Class in keel.Algorithms.Genetic_Rule_Learning.Hider
 
Union() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Union

Empty constructor

Union(Corte, double, int) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.Hider.Union

Constructor

Union(int[][]) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets
 
union(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet

Join two prototype sets

union(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Join two prototype sets

unionVectores(Vector<Integer>, Vector<Integer>) - Static method in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Multiple

Joins two vectors

unionVectores(Vector<Integer>, Vector<Integer>) - Static method in class keel.GraphInterKeel.statistical.tests.Multiple

Joins two vectors

uniqueAdd(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet

Adds prototype only if it is not already in the set.

uniqueAdd(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Adds prototype only if it is not already in the set.

uniqueCount - Variable in class keel.Algorithms.Decision_Trees.M5.M5AttrStats

The number of values that only appear once

uniqueCount - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.AttributeStats

The number of values that only appear once

unitNeuronsWeights(ExpNeuron, ExpNeuron, LinkedLayer, LinkedLayer, int, int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.ExpNeuronStructuralMutator


 Units the weights of two specific neurons, and stores
 the result in the first neuron
 

unitNeuronsWeights(N, N, LinkedLayer, LinkedLayer, int, int) - Method in interface keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.INeuronStructuralMutator


 Units the weights of two specific neurons, and stores
 the result in the first neuron
 

unitNeuronsWeights(LinearNeuron, LinearNeuron, LinkedLayer, LinkedLayer, int, int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.LinearNeuronStructuralMutator


 Units the weights of two specific neurons, and stores
 the result in the first neuron
 

unitNeuronsWeights(SigmNeuron, SigmNeuron, LinkedLayer, LinkedLayer, int, int) - Method in class keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.SigmNeuronStructuralMutator


 Units the weights of two specific neurons, and stores
 the result in the first neuron
 

UnivariateFunction - Interface in keel.Algorithms.Preprocess.Missing_Values.EM.util

Interface for a function of one variable.

UnivariateMinimum - Class in keel.Algorithms.Preprocess.Missing_Values.EM.util

minimization of a real-valued function of one variable
 without using derivatives.

UnivariateMinimum() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EM.util.UnivariateMinimum
 
UNKNOW_INDEX - Static variable in class keel.Algorithms.Instance_Generation.Basic.Prototype

Informs that the prototype has not got a definided index.

UNKNOW_INDEX - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype

Informs that the prototype has not got a definided index.

unknownValue - Static variable in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config

It represents the unkown value in a training instance

UnMark(int[], int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set


 Marks the examples in "v" as uncovered and set their coverage degree to -1
 

UnMark(int[], int, double[]) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set


 Marks the examples in "v" as uncovered and set their coverage degree to the negative value for the one in "grado"
 

UnMarkAll() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set


 Set all positions (referred to examples) of vector "covered" to false (not covered)
 Set all positions (referred to examples) of vector "gcovered" (degree of coverage) to 0
 Set all positions (referred to examples) of vectors "g_positive_covered" and "g_negative_covered" (degree of positive/negative coverage) to 0
 Set all positions (referred to examples) of vectors "peso_positive" and "peso_negative" (positive/negative weights) to 0
 

UnMarkAll() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.example_set


 Marks all the examples as uncovered and set their coverage degree to 0
 

UnMarkAll() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.example_set


 Set all positions (referred to examples) of vector "covered" to false (not covered)
 Set all positions (referred to examples) of vector "gcovered" (degree of coverage) to 0
 

UnMarkAll() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.example_set


 Set all positions (referred to examples) of vector "covered" to false (not covered)
 Set all positions (referred to examples) of vector "gcovered" (degree of coverage) to 0
 

unnormalizedKernel(char[], char[]) - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel

evaluates the unnormalized kernel between s and t.

UnoR - Class in keel.Algorithms.Rule_Learning.UnoR

Contents the principal methods of the UnoR algorithm

UnoR() - Constructor for class keel.Algorithms.Rule_Learning.UnoR.UnoR

Default constructor.

UnoR(String, String, String, String, String, long, int) - Constructor for class keel.Algorithms.Rule_Learning.UnoR.UnoR


 Constructor with all the attributes to initialize
 

unpivoting(IntVector, int) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Returns a vector from the pivoting indices.

unquote(String) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils

unquotes are previously quoted string (but only if necessary), i.e., it
 removes the single quotes around it.

unquote(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils

unquotes are previously quoted string (but only if necessary), i.e., it
 removes the single quotes around it.

unquote(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils

unquotes are previously quoted string (but only if necessary), i.e., it
 removes the single quotes around it.

unquote(String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils

unquotes are previously quoted string (but only if necessary), i.e., it
 removes the single quotes around it.

unquote(String) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils

unquotes are previously quoted string (but only if necessary), i.e., it
 removes the single quotes around it.

unquote(String) - Static method in class keel.Algorithms.SVM.SMO.core.Utils

unquotes are previously quoted string (but only if necessary), i.e., it
 removes the single quotes around it.

unscaledMax - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator

Auxiliary arrays

unscaledMin - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator

Auxiliary arrays

unscaledTestData - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator

Unscaled test DataSet with data to evaluate the individuals

unscaledTrainData - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.problem.ProblemEvaluator

Unscaled train DataSet with data to evaluate the individuals

unsetAttribute(int) - Static method in class keel.Algorithms.RST_Learning.EFS_RPS.ISW
 
unsetAttribute(int) - Static method in class keel.Algorithms.RST_Learning.RSTData
 
unsorted() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.DoubleVector

Returns true if vector not sorted

UnsupportedAttributeTypeException - Exception in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core

Exception that is raised by an object that is unable to process some of the
 attribute types it has been passed.

UnsupportedAttributeTypeException() - Constructor for exception keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.UnsupportedAttributeTypeException

Creates a new UnsupportedAttributeTypeException with no message.

UnsupportedAttributeTypeException(String) - Constructor for exception keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.UnsupportedAttributeTypeException

Creates a new UnsupportedAttributeTypeException.

unthresholdedOutput(Instance) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.PDFCS.FuzzyRuleSet

This method computes the input output mapping of this rule set

UnZipFiles(String, String) - Static method in class keel.GraphInterKeel.experiments.FileUtils

Unzip a .zip file

update(Instance) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.ApproximateSets

Updates the Edit2RS to cater for the new added instance.

update(Instance) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Arrays

Updates the Edit2RS to cater for the new added instance.

update(MyDataset, Rule, Mask, Mask, double[], double) - Method in class keel.Algorithms.Rule_Learning.Slipper.Slipper

It reweights the instances, making use of the confidence of the last rule.

update(double) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FlexibleDecimalFormat

Updates the format of the FlexibleDecimalFormat by parsering a number given.

update() - Method in class keel.GraphInterKeel.experiments.DinamicDataset

Update data sets

updateAlpha(int) - Method in class keel.Algorithms.Instance_Generation.VQ.VQGenerator

Update alpha in stage t to the stage t+1

updateBoundaries(int, double) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved

updates boundaries bLow and bHi and corresponding indexes

UpdateCache(EnsembleParameters, Data) - Method in class keel.Algorithms.Neural_Networks.ensemble.Ensemble


 Update cache data
 

updateCholeskyFactor(double[][], double[], double[], double, boolean[]) - Method in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
 
updateCholeskyFactor(Matrix, double[], double[], double, boolean[]) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Optimization

One rank update of the Cholesky factorization of B matrix in BFGS updates,
 i.e.

updateFitness(double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier


 Updates the fitness of a classifier
 

updateFitness(double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters


 Updates the fitness of a classifier
 

updateFitness(double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier


 Updates the fitness of a classifier
 

updateFitness(double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters


 Updates the fitness of a classifier
 

updateFuzzyLabels(double[][]) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.DataBase

Updates the fuzzy labels stored in the current database according to a lateral tuning given

updateFuzzyLabels(double[][], myDataset, double) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.Rule

Updates the current rule according to a specified lateral tuning of the associated fuzzy labels

updateIndexSetFor(int, double) - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved

updates the index sets I0a, IOb, I1, I2 and I3 for vector i

UpdateLineColumn(char) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.SimpleCharStream
 
UpdateLineColumn(char) - Static method in class keel.Algorithms.Rule_Learning.Swap1.SimpleCharStream
 
UpdateLineColumn(char) - Static method in class keel.Dataset.SimpleCharStream
 
updateMatches(int[], int, int[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ClassifierGABIL
 
UpdateMemory(String[][], double[], int, String[], double, String[], double, String[], double) - Method in class keel.Algorithms.Instance_Generation.PSCSA.PSCSAGenerator

Update of immne Memory

updateMPA(int, matchProfileAgent) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.ruleOrderAgent
 
updateParameters(double, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Classifier


 Updates the parameters of a classifier.

updateParameters(double, double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Parameters


 Updates the parameters of a classifier (reinforcement component)
 

updateParameters(double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Classifier


 Updates the parameters of a classifier (reinforcement component)
 

updateParameters(double, double) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Parameters


 Updates the parameters of a classifier (reinforcement component)
 

updateParametersSet(double, double[], int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Population


 Updates  UCS' parameters.

updateParametersSet(double, double, double[], int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Population


 Updates the XCS parameters of the set.

updatePosition(MouseEvent) - Method in class keel.GraphInterKeel.experiments.GraphPanel

Updates the position of the node in the panel

updatePriors(M5Instance) - Method in class keel.Algorithms.Decision_Trees.M5.EvaluateModel

Updates the class prior probabilities (when incrementally
 training)

updateProbability(int, int[][], int[][]) - Method in class keel.Algorithms.Instance_Generation.SADE.SADEGenerator
 
updateReductionTime(long) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TimeControl

Updates the reduction time.

updateRow(Vector, int, Attribute) - Method in class keel.GraphInterKeel.datacf.util.AttributeTable


 Updates a row in the table using a vector with its new values
 

updateState() - Method in class keel.GraphInterKeel.experiments.Node

It does update the state of the node, regarding the variables
 that define the types of values accepted as input and output

updateTestTime(long) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TimeControl

Update the test time

updateTestTime(long) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TimeControl

Update the test time

updateTotalTime(long) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TimeControl

Update the total time

updateTotalTime(long) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TimeControl

Update the total time.

updateTrainTime(long) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TimeControl

Updates the training time

updateTrainTime(long) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TimeControl

Updates the training time

updateV(Particle) - Method in class keel.Algorithms.PSO_Learning.CPSO.Particle
 
updateV(Particle, double) - Method in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
 
updateV(Particle) - Method in class keel.Algorithms.PSO_Learning.PSOLDA.Particle
 
updateV(Particle, double) - Method in class keel.Algorithms.PSO_Learning.REPSO.Particle
 
updateX() - Method in class keel.Algorithms.PSO_Learning.CPSO.Particle
 
updateX() - Method in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
 
updateX() - Method in class keel.Algorithms.PSO_Learning.PSOLDA.Particle
 
updateX() - Method in class keel.Algorithms.PSO_Learning.REPSO.Particle
 
upper_aproximation() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff

Compute the upper aproximation

upper_aproximation - Variable in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal
 
upper_aproximation() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal

Compute the upper aproximation

upper_aproximation_Set(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsCuttoff

Compute the upper aproximation of a set

upper_aproximation_Set(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.RoughSetsOriginal

Compute the upper aproximation of a set

upperbound - Variable in class keel.Algorithms.Discretizers.MVD.Interval

Interval upper bound.

upperNumericBoundIsOpen() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka

Returns whether the upper numeric bound of the attribute is open.

upperrealBoundIsOpen() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute

Returns whether the upper real bound of the attribute is open.

urlAdress - Variable in class keel.GraphInterKeel.datacf.help.HelpSheet

Current URL Adress

USDDiscretizer - Class in keel.Algorithms.Discretizers.USD_Discretizer


 This class implements the USD discretizer.

USDDiscretizer() - Constructor for class keel.Algorithms.Discretizers.USD_Discretizer.USDDiscretizer
 
USDDiscretizer - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.USD_Discretizer
 
USDDiscretizer() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Discretizers.USD_Discretizer.USDDiscretizer
 
use - Static variable in class keel.Algorithms.Instance_Generation.utilities.Parameters

Contains the message of correct use to the user of the program.

use - Static variable in class keel.Algorithms.Semi_Supervised_Learning.utilities.Parameters

Contains the message of correct use to the user of the program.

use_C45 - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters

If the C4.5 is used.

use_KNN - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters

If the KNN is used.

use_LOG - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters

If the LOG is used.

use_SVM - Static variable in class keel.Algorithms.Preprocess.NoiseFilters.INFFC.Parameters

If the SVM is used.

useAttb(Gene) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.Chromosome
 
UseCase - Class in keel.GraphInterKeel.experiments
 
UseCase() - Constructor for class keel.GraphInterKeel.experiments.UseCase
 
useCaseTextArea - Variable in class keel.GraphInterKeel.experiments.Experiments
 
used - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.OptimLocal.FUN

Mark the used rules

useDiscrete - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
 
useFilter(M5Instances, NominalToBinaryFilter) - Static method in class keel.Algorithms.Decision_Trees.M5.NominalToBinaryFilter

Filters an entire set of instances through a filter and returns
 the new set.

useFilter(M5Instances, ReplaceMissingValuesFilter) - Static method in class keel.Algorithms.Decision_Trees.M5.ReplaceMissingValuesFilter

Uses the Missing value filter given as parameter to the given dat.

usefulAttribute(Attribute) - Method in class keel.Algorithms.Preprocess.Transformations.CleanAttributes.CleanAttributes

Returns true if the attribute given is useful, false otherwise.

useLowerOrderTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.PolyKernel

Returns the tip text for this property

useMDL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
useMDL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
 
useMDL - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
 
useNormalizationTipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.StringKernel

Returns the tip text for this property

useNumberOfPrototypes - Variable in class keel.Algorithms.Instance_Generation.PNN.PNNGenerator

Informs if the algorithm must generate a specified number of prototypes.

user - Variable in class keel.GraphInterKeel.experiments.DatasetXML
 
UserMethod - Class in keel.GraphInterKeel.experiments
 
UserMethod(ExternalObjectDescription, Point, GraphPanel) - Constructor for class keel.GraphInterKeel.experiments.UserMethod

Builder

UserMethod(ExternalObjectDescription, Point, GraphPanel, Parameters, int) - Constructor for class keel.GraphInterKeel.experiments.UserMethod

Builder

useRuleStretchingTipText() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.FURIA

Returns the tip text for this property

useVariant1TipText() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved

Returns the tip text for this property

Util - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning

File: Util.java
 
 Auxiliar class with useful function for working with fuzzy instance learning based methods

Util() - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.Util
 
Util - Class in keel.Algorithms.RST_Learning

File: Util.java
 
 Auxiliar class with useful function for working with instance based methods

Util() - Constructor for class keel.Algorithms.RST_Learning.Util
 
Utilidades - Class in keel.Algorithms.Semi_Supervised_Learning.Basic

Utilities class.

Utilidades() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.Basic.Utilidades
 
Utilities - Class in keel.Algorithms.Genetic_Rule_Learning.PART


 Collection of auxiliary methods.

Utilities() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.PART.Utilities
 
Utilities - Class in keel.Algorithms.Rule_Learning.C45Rules

Collection of auxiliar methods.

Utilities() - Constructor for class keel.Algorithms.Rule_Learning.C45Rules.Utilities
 
Utilities - Class in keel.Algorithms.Rule_Learning.C45RulesSA

 Collection of auxiliar methods.

Utilities() - Constructor for class keel.Algorithms.Rule_Learning.C45RulesSA.Utilities
 
Utilities - Class in keel.Algorithms.Rule_Learning.PART

 Collection of auxiliar methods.

Utilities() - Constructor for class keel.Algorithms.Rule_Learning.PART.Utilities
 
Utilities - Class in keel.Algorithms.Rule_Learning.Ripper

 Collection of auxiliar methods.

Utilities() - Constructor for class keel.Algorithms.Rule_Learning.Ripper.Utilities
 
Utilities - Class in keel.Algorithms.Rule_Learning.Slipper

 Collection of auxiliar methods.

Utilities() - Constructor for class keel.Algorithms.Rule_Learning.Slipper.Utilities
 
utilRules - Static variable in class keel.Algorithms.Genetic_Rule_Learning.GAssist.PerformanceAgent
 
utilRules - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.PerformanceAgent
 
Utils - Class in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core

Class implementing some simple utility methods.

Utils() - Constructor for class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils
 
Utils - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist

Small routines doing trivial stuff here

Utils() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.Utils
 
Utils - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS

Small routines doing trivial stuff here

Utils() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Utils
 
Utils - Class in keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets

Class implementing some simple utility methods.

Utils() - Constructor for class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils
 
Utils - Class in keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest

Class implementing some simple utility methods.

Utils() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils
 
Utils - Class in keel.Algorithms.Semi_Supervised_Learning.CLCC

Class implementing some simple utility methods.

Utils() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils
 
Utils - Class in keel.Algorithms.Semi_Supervised_Learning.CoForest

Class implementing some simple utility methods.

Utils() - Constructor for class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils
 
Utils - Class in keel.Algorithms.Statistical_Classifiers.Logistic.core

Class implementing some simple utility methods.

Utils() - Constructor for class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils
 
Utils - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate

 Assorted methods to manage several topics

Utils() - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Utils
 
Utils - Class in keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF

 Assorted methods to manage several topics

Utils() - Constructor for class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Utils
 
Utils - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate

 Assorted methods to manage several topics

Utils() - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Utils
 
Utils - Class in keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD

 Assorted methods to manage several topics

Utils() - Constructor for class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Utils
 
Utils - Class in keel.Algorithms.Subgroup_Discovery.SDAlgorithm

 Assorted methods to manage several topics

Utils() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDAlgorithm.Utils
 
Utils - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate

 Assorted methods to manage several topics

Utils() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Utils
 
Utils - Class in keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA

Assorted methods to manage several topics

Utils() - Constructor for class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.Utils
 
Utils - Class in keel.Algorithms.SVM.SMO.core

Class implementing some simple utility methods.

Utils() - Constructor for class keel.Algorithms.SVM.SMO.core.Utils
 




V

V(int) - Method in class keel.Algorithms.Decision_Trees.PUBLIC.Node

Calculates V for the data in this node with k class, in other words, this calculates the minimum 
 cost of specifying the split value at the parent of a node containing the data in this node

V - Variable in class keel.Algorithms.Instance_Generation.VQ.AVQGenerator

Partition of the training data set used as validation set.

V - Variable in class keel.Algorithms.Preprocess.Missing_Values.EM.EV

eigenvectors

V - Variable in class keel.Algorithms.PSO_Learning.CPSO.Particle
 
V - Variable in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
 
V - Variable in class keel.Algorithms.PSO_Learning.PSOLDA.Particle
 
V - Variable in class keel.Algorithms.PSO_Learning.REPSO.Particle
 
vaciar() - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.Condition
 
vacio() - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset

Checks if there is a class without instances.

VAL - Variable in class keel.Algorithms.Neural_Networks.ensemble.Ensemble

Training flag.

val - Static variable in class keel.Algorithms.PSO_Learning.CPSO.CPSO

Training dataset.

val - Static variable in class keel.Algorithms.PSO_Learning.LDWPSO.LDWPSO

Training dataset.

val - Static variable in class keel.Algorithms.PSO_Learning.REPSO.REPSO

Training dataset.

val_data - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters

Check if test,validation or cross validation data is going to be used

val_data - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters

Check if test,validation or cross validation data is going to be used

val_data - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters

Check if test,validation or cross validation data is going to be used

val_data - Variable in class keel.Algorithms.Neural_Networks.net.Parameters

Check if test,validation or cross validation data is going to be used

val_file - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters

File names

val_file - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters

File names

val_file - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters

File names

val_file - Variable in class keel.Algorithms.Neural_Networks.net.Parameters

File names

val_output - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters

File names

val_output - Variable in class keel.Algorithms.Neural_Networks.net.Parameters

File names

valid_split(Split) - Method in class keel.Algorithms.Decision_Trees.FunctionalTrees.TreeNode

Checks if a split is valid for a TreeNode, this means, that it will generate valid child nodes

validacionCruzada(boolean[]) - Method in class keel.Algorithms.Preprocess.Feature_Selection.Datos

calculates the precision (errors/total_instances) in the classification of all instances in the TEST DATASET
        using the given features and THE TRAINING DATASET TO PREDICT.

validation(M5Instances) - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode

Computes performance measures for both unsmoothed and smoothed models

validation(MyDataset) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode

Computes performance measures for both unsmoothed and smoothed models

validation - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Data

Validation data

VALIDATION - Static variable in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm

Type of file: test data set.

validation - Variable in class keel.Algorithms.Neural_Networks.gann.Data

Validation data

validation - Variable in class keel.Algorithms.Neural_Networks.gmdh.Data

Validation data

validation - Variable in class keel.Algorithms.Neural_Networks.net.Data

Validation data

validation(Vector<String>, String, String, int, JTextField) - Method in class keel.GraphInterKeel.experiments.Container_Selected
 
VAList - Class in keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues


 This class stores a list of Value-attribute elements.

VAList() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.VAList


 Creates a new instance of VAList
 

VAList - Class in keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue


 This class stores a list of Value-attribute elements.

VAList() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.VAList


 Creates a new instance of VAList
 

VAList - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering


 This class stores a list of Value-attribute elements.

VAList() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.VAList


 Creates a new instance of VAList
 

VAList - Class in keel.Algorithms.Preprocess.Missing_Values.fkmeans


 This class stores a list of Value-attribute elements.

VAList() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.fkmeans.VAList


 Creates a new instance of VAList
 

VAList - Class in keel.Algorithms.Preprocess.Missing_Values.kmeansImpute


 This class stores a list of Value-attribute elements.

VAList() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.VAList


 Creates a new instance of VAList
 

VAList - Class in keel.Algorithms.Preprocess.Missing_Values.knnImpute


 This class stores a list of Value-attribute elements.

VAList() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.knnImpute.VAList


 Creates a new instance of VAList
 

VAList - Class in keel.Algorithms.Preprocess.Missing_Values.svmImpute


 This class stores a list of Value-attribute elements.

VAList() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.svmImpute.VAList


 Creates a new instance of VAList
 

VAList - Class in keel.Algorithms.Preprocess.Missing_Values.wknnImpute


 This class stores a list of Value-attribute elements.

VAList() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.VAList


 Creates a new instance of VAList
 

valor(int, int) - Method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset

Returns the value of a given attribute in a given position (id).

valor - Variable in class keel.Algorithms.Decision_Trees.SLIQ.ListaAtributos

Attribute value

valor - Variable in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.MultiplePair

second element

valor - Variable in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.Pair

second element

valor - Variable in class keel.GraphInterKeel.statistical.tests.MultiplePair

second element

valor - Variable in class keel.GraphInterKeel.statistical.tests.Pair

second element

valorAtributo(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.RMini.myDataset

It returns de ith value of the nominal given attribute

valores_propios(double[][], double[][], double[][]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.MatrixCalcs
 
valores_propios(double[][], double[][], double[][]) - Static method in class keel.Algorithms.Statistical_Classifiers.Shared.MatrixCalcs.MatrixCalcs
 
valoresMin() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner.myDataset

It returns an array with the minimum values of the attributes

valoresMin() - Method in class keel.Algorithms.Genetic_Rule_Learning.Advanced_Ant_Miner_Plus.myDataset

It returns an array with the minimum values of the attributes

valoresMin() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner.myDataset

It returns an array with the minimum values of the attributes

valoresMin() - Method in class keel.Algorithms.Genetic_Rule_Learning.Ant_Miner_Plus.myDataset

It returns an array with the minimum values of the attributes

valoresMin() - Method in class keel.Algorithms.Genetic_Rule_Learning.PSO_ACO.myDataset

It returns an array with the minimum values of the attributes

valoresMin() - Method in class keel.Algorithms.Genetic_Rule_Learning.SIA.Dataset

It returns an array with the lower ranges of the attributes

valorNominal(int, double) - Static method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset

Returns a nominal representation of a attribute's real value given as argument.

valorNominal(int, double) - Static method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset

Returns a nominal representation of a attribute's real value given as argument.

valorNominal(int, double) - Static method in class keel.Algorithms.Decision_Trees.Target.myDataset

Returns a nominal representation of a attribute's real value given as argument.

valorNominal(int, double) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset

Returns a nominal representation of a attribute's real value given as argument.

valorReal(int, String) - Static method in class keel.Algorithms.Decision_Trees.DT_GA.myDataset

Returns a real representation of a attribute's nominal value given as argument.

valorReal(int, String) - Static method in class keel.Algorithms.Decision_Trees.DT_oblicuo.myDataset

Returns a real representation of a attribute's nominal value given as argument.

valorReal(int, String) - Static method in class keel.Algorithms.Decision_Trees.Target.myDataset

Returns a real representation of a attribute's nominal value given as argument.

valorReal(int, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.myDataset

Returns a real representation of a attribute's nominal value given as argument.

value(int) - Method in class keel.Algorithms.Decision_Trees.C45.Attribute

Returns the value with the given index.

value(int) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Attribute

Returns the value with the given index.

value(int) - Method in class keel.Algorithms.Decision_Trees.ID3.Attribute

Returns the value with the given index.

value(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Attribute

Returns a value of a nominal or string attribute.

value(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Returns an instance's attribute value in internal format.

value(M5Attribute) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Returns an instance's attribute value in internal format.

value(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5SparseInstance

Returns an instance's attribute value in internal format.

value(int) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Attribute

Returns the value with the given index.

value - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.Double_t

Double value.

value - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.Int_t

Integer value.

value - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.Double_t

Double value.

value - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.Int_t

Integer value.

value - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.Double_t

Double value.

value - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.Int_t

Integer value.

value - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Antd

The attribute value of the antecedent.

value(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka

Returns a value of a nominal or string attribute.

value(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Returns an instance's attribute value in internal format.

value(AttributeWeka) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Returns an instance's attribute value in internal format.

value(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute

Returns the value with the given index.

value(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA_Attribute

Returns the value with the given index.

value(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyAttribute

Returns the value with the given index.

value - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Pair

Pair's value.

value(int) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Attribute

Returns the value with the given index.

value - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_node
 
value(int) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Attribute

Returns the value with the given index.

value(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute

Returns a value of a nominal or string attribute.

value(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Returns an instance's attribute value in internal format.

value(Attribute) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Returns an instance's attribute value in internal format.

value(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.SparseInstance

Returns an instance's attribute value in internal format.

Value - Variable in class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.ValueFreq

String value.

value - Variable in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.valueAssociations

Associated value.

Value - Variable in class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.ValueFreq

String value.

value - Variable in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.valueAssociations

Associated value.

Value - Variable in class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.ValueFreq

String value.

value - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.Stat.ConfidenceInterval

Value inside the interval.

value - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.valueAssociations

Associated value.

Value - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValueFreq

String value.

value - Variable in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.valueAssociations

Associated value.

Value - Variable in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.ValueFreq

String value.

value - Variable in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.valueAssociations

Associated value.

Value - Variable in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.ValueFreq

String value.

value - Variable in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.valueAssociations

Associated value.

Value - Variable in class keel.Algorithms.Preprocess.Missing_Values.knnImpute.ValueFreq

String value.

value - Variable in class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.IndexValuePair

Value stored.

Value - Variable in class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.ValueFreq

String value.

value - Variable in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.valueAssociations

Associated value.

Value - Variable in class keel.Algorithms.Preprocess.Missing_Values.svmImpute.ValueFreq

String value.

value - Variable in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.valueAssociations

Associated value.

Value - Variable in class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.ValueFreq

String value.

value(int) - Method in class keel.Algorithms.Rule_Learning.ART.Attribute

Returns the value with the given index.

value(int) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyAttribute

Returns the value with the given index.

value(int) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyAttribute

Returns the value with the given index.

value(int) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Attribute

Returns the value with the given index.

value(int) - Method in class keel.Algorithms.Rule_Learning.PART.MyAttribute

Returns the value with the given index.

value - Variable in class keel.Algorithms.Rule_Learning.PART.Pair

value of the pair.

value - Variable in class keel.Algorithms.Rule_Learning.Ripper.Pair

value of the pair.

value - Variable in class keel.Algorithms.Rule_Learning.Slipper.Pair

Pair's value.

value(int) - Method in class keel.Algorithms.SVM.SMO.core.Instance

Returns an instance's attribute value in internal format.

value(Attribute) - Method in class keel.Algorithms.SVM.SMO.core.Instance

Returns an instance's attribute value in internal format.

value(ActionEvent, JTextField, int, Joint, String) - Method in class keel.GraphInterKeel.experiments.Container_Selected
 
value - Variable in class keel.GraphInterKeel.experiments.Parameters
 
value - Variable in class org.libsvm.svm_node
 
Value1 - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValuesFreq

First String value.

Value2 - Variable in class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValuesFreq

Second String value.

value_show(ActionEvent, JTextField, int, Joint, String) - Method in class keel.GraphInterKeel.experiments.Container_Selected
 
value_show_ant(ActionEvent, JTextField, int, Joint, String) - Method in class keel.GraphInterKeel.experiments.Container_Selected
 
valueAssociations - Class in keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues


 This class stores a frequency list of classes for a given value, i.e. for a same value in a attribute, 
 it stores the number of times a determined class is associated with the value.

valueAssociations(double) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.valueAssociations

 
 Creates a new instance of valueAssociations
 

valueAssociations - Class in keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue


 This class stores a frequency list of classes for a given value, i.e. for a same value in a attribute, 
 it stores the number of times a determined class is associated with the value.

valueAssociations(double) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.valueAssociations

 
 Creates a new instance of valueAssociations
 

valueAssociations - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering


 This class stores a frequency list of classes for a given value, i.e. for a same value in a attribute, 
 it stores the number of times a determined class is associated with the value.

valueAssociations(double) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.valueAssociations

 
 Creates a new instance of valueAssociations
 

valueAssociations - Class in keel.Algorithms.Preprocess.Missing_Values.fkmeans


 This class stores a frequency list of classes for a given value, i.e. for a same value in a attribute, 
 it stores the number of times a determined class is associated with the value.

valueAssociations(double) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.fkmeans.valueAssociations

 
 Creates a new instance of valueAssociations
 

valueAssociations - Class in keel.Algorithms.Preprocess.Missing_Values.kmeansImpute


 This class stores a frequency list of classes for a given value, i.e. for a same value in a attribute, 
 it stores the number of times a determined class is associated with the value.

valueAssociations(double) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.valueAssociations

 
 Creates a new instance of valueAssociations
 

valueAssociations - Class in keel.Algorithms.Preprocess.Missing_Values.knnImpute


 This class stores a frequency list of classes for a given value, i.e. for a same value in a attribute, 
 it stores the number of times a determined class is associated with the value.

valueAssociations(double) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.knnImpute.valueAssociations

 
 Creates a new instance of valueAssociations
 

valueAssociations - Class in keel.Algorithms.Preprocess.Missing_Values.svmImpute


 This class stores a frequency list of classes for a given value, i.e. for a same value in a attribute, 
 it stores the number of times a determined class is associated with the value.

valueAssociations(double) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.svmImpute.valueAssociations

 
 Creates a new instance of valueAssociations
 

valueAssociations - Class in keel.Algorithms.Preprocess.Missing_Values.wknnImpute


 This class stores a frequency list of classes for a given value, i.e. for a same value in a attribute, 
 it stores the number of times a determined class is associated with the value.

valueAssociations(double) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.valueAssociations

 
 Creates a new instance of valueAssociations
 

valueAt(double) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.Function

the value of the function at the point x

valueAt(int, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.fkmeans.fuzzygCenter


 Get the value of an attribute of the indicated centroid
 

valueAt(int, int) - Method in class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.gCenter


 Get the value of an attribute of the indicated centroid
 

valueAttribute(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset

Returns the given value of the given attribute.

valueAttribute(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset

Returns the given value of the given attribute.

valueExample(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.DMEL.myDataset

Returns the value of the given attribute of a given example.

valueExample(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.GIL.myDataset

Returns the value of the given attribute of a given example.

ValueFitness(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
 
ValueFitness(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
 
ValueFitness(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
 
ValueFitness(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
 
ValueFitness(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
 
ValueFitness(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
 
ValueFreq - Class in keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues


 this class store a value (String) and its frequency (int)
 
 

ValueFreq() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.ValueFreq

 
 Creates a new instance of Pair Value-frequency
 

ValueFreq(String, int) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.AllPossibleValues.ValueFreq


        Creates a new pair with established value and frequency
 

ValueFreq - Class in keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues


 this class store a value (String) and its frequency (int)
 
 

ValueFreq() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.ValueFreq

 
 Creates a new instance of Pair Value-frequency
 

ValueFreq(String, int) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.ConceptAllPossibleValues.ValueFreq


        Creates a new pair with established value and frequency
 

ValueFreq - Class in keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue


 this class store a value (String) and its frequency (int)
 
 

ValueFreq() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.ValueFreq

 
 Creates a new instance of Pair Value-frequency
 

ValueFreq(String, int) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.ConceptMostCommonValue.ValueFreq


        Creates a new pair with established value and frequency
 

ValueFreq - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering


 this class store a value (String) and its frequency (int)
 
  

ValueFreq() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValueFreq

 
 Creates a new instance of Pair Value-frequency
 

ValueFreq(String, int) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValueFreq


        Creates a new pair with established value and frequency
 

ValueFreq - Class in keel.Algorithms.Preprocess.Missing_Values.fkmeans


 this class store a value (String) and its frequency (int)
 
 

ValueFreq() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.fkmeans.ValueFreq

 
 Creates a new instance of Pair Value-frequency
 

ValueFreq(String, int) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.fkmeans.ValueFreq


        Creates a new pair with established value and frequency
 

ValueFreq - Class in keel.Algorithms.Preprocess.Missing_Values.kmeansImpute


 this class store a value (String) and its frequency (int)
 
 

ValueFreq() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.ValueFreq

 
 Creates a new instance of Pair Value-frequency
 

ValueFreq(String, int) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.kmeansImpute.ValueFreq


        Creates a new pair with established value and frequency
 

ValueFreq - Class in keel.Algorithms.Preprocess.Missing_Values.knnImpute


 this class store a value (String) and its frequency (int)
 
 

ValueFreq() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.knnImpute.ValueFreq

 
 Creates a new instance of Pair Value-frequency
 

ValueFreq(String, int) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.knnImpute.ValueFreq


        Creates a new pair with established value and frequency
 

ValueFreq - Class in keel.Algorithms.Preprocess.Missing_Values.MostCommonValue


 this class store a value (String) and its frequency (int)
 
 

ValueFreq() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.ValueFreq

 
 Creates a new instance of Pair Value-frequency
 

ValueFreq(String, int) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.MostCommonValue.ValueFreq


        Creates a new pair with established value and frequency
 

ValueFreq - Class in keel.Algorithms.Preprocess.Missing_Values.svmImpute


 this class store a value (String) and its frequency (int)
 
 

ValueFreq() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.svmImpute.ValueFreq

 
 Creates a new instance of Pair Value-frequency
 

ValueFreq(String, int) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.svmImpute.ValueFreq


        Creates a new pair with established value and frequency
 

ValueFreq - Class in keel.Algorithms.Preprocess.Missing_Values.wknnImpute


 this class store a value (String) and its frequency (int)
 
 

ValueFreq() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.ValueFreq

 
 Creates a new instance of Pair Value-frequency
 

ValueFreq(String, int) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.ValueFreq


        Creates a new pair with established value and frequency
 

valueIndex(String) - Method in class keel.Algorithms.Decision_Trees.C45.Attribute

Function to get the index of a value in the list of values.

valueIndex(String) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Attribute

Function to get the index of a value in the list of values.

valueIndex(String) - Method in class keel.Algorithms.Decision_Trees.ID3.Attribute

Function to get the index of a value in the list of values.

valueIndex(String) - Method in class keel.Algorithms.Decision_Trees.SLIQ.Attribute

Function to get the index of a value in the list of values.

valueIndex(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyAttribute

Function to get the index of a value in the list of values.

valueIndex(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA_Attribute

Function to get the index of a value in the list of values.

valueIndex(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.MyAttribute

Function to get the index of a value in the list of values.

valueIndex(String) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Attribute

Function to get the index of a value in the list of values.

valueIndex(String) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Attribute

Function to get the index of a value in the list of values.

valueIndex(String) - Method in class keel.Algorithms.Rule_Learning.ART.Attribute

Function to get the index of a value in the list of values.

valueIndex(String) - Method in class keel.Algorithms.Rule_Learning.C45Rules.MyAttribute

Function to get the index of a value in the list of values.

valueIndex(String) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.MyAttribute

Function to get the index of a value in the list of values.

valueIndex(String) - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.Attribute

Function to get the index of a value in the list of values.

valueIndex(String) - Method in class keel.Algorithms.Rule_Learning.PART.MyAttribute

Function to get the index of a value in the list of values.

ValueIndividual(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.multipopulation
 
ValueIndividual(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.multipopulation
 
ValueIndividual(int, int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.multipopulation
 
valueNode() - Method in class keel.Algorithms.Decision_Trees.M5.M5TreeNode

Takes a constant value as the function at the node

valueNode() - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5TreeNode

Takes a constant value as the function at the node

valueOf(String) - Static method in enum keel.Algorithms.Neural_Networks.NNEP_Common.data.AttributeType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation.Field

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation.Type

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum keel.Algorithms.SVM.SMO.core.TechnicalInformation.Field

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum keel.Algorithms.SVM.SMO.core.TechnicalInformation.Type

Returns the enum constant of this type with the specified name.

values - Variable in class keel.Algorithms.Decision_Trees.C45.Itemset

Values of the itemset.

values - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset

Values of the itemset.

values - Variable in class keel.Algorithms.Decision_Trees.ID3.Itemset

Values of the itemset.

values - Variable in class keel.Algorithms.Decision_Trees.SLIQ.Itemset

Values of the itemset.

values - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset

Values of the itemset.

values - Variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset

Values of the itemset.

values - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset

Values of the itemset.

values - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset

Values of the itemset.

values - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset

Values of the itemset.

values - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset.Instance

Attribute values

values() - Static method in enum keel.Algorithms.Neural_Networks.NNEP_Common.data.AttributeType

Returns an array containing the constants of this enum type, in
the order they are declared.

values - Variable in class keel.Algorithms.Rule_Learning.ART.Itemset

Values of the itemset.

values - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Itemset

Values of the itemset.

values - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset

Values of the itemset.

values - Variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset

Values of the itemset.

values - Variable in class keel.Algorithms.Rule_Learning.PART.Itemset

Values of the itemset.

values - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset

Values of the itemset.

values() - Static method in enum keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation.Field

Returns an array containing the constants of this enum type, in
the order they are declared.

values() - Static method in enum keel.Algorithms.Statistical_Classifiers.Logistic.core.TechnicalInformation.Type

Returns an array containing the constants of this enum type, in
the order they are declared.

values() - Static method in enum keel.Algorithms.SVM.SMO.core.TechnicalInformation.Field

Returns an array containing the constants of this enum type, in
the order they are declared.

values() - Static method in enum keel.Algorithms.SVM.SMO.core.TechnicalInformation.Type

Returns an array containing the constants of this enum type, in
the order they are declared.

valuesAt(double[]) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.Function

the array of values of this function at an array of values

valuesAt(double[][]) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.Function

the array of values of this function at a 2 dimensional array of values

ValuesFreq - Class in keel.Algorithms.Preprocess.Missing_Values.EventCovering


 this class store a pair of values (String) and their frequency (int)
 
  

ValuesFreq() - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValuesFreq

 
 Creates a new instance of Pair Value-frequency
 

ValuesFreq(String, String, int) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.EventCovering.ValuesFreq


        Creates a new pair with established values and frequency
 

valueSparse(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Returns an instance's attribute value in internal format.

valueSparse(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Returns an instance's attribute value in internal format.

valueSparse(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Returns an instance's attribute value in internal format.

valueSparse(int) - Method in class keel.Algorithms.SVM.SMO.core.Instance

Returns an instance's attribute value in internal format.

Variable() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.variable_t
 
Variable(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.vectorvar
 
Variable() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.variable_t


 Returns a copy of the variable
 

Variable(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE.VectorVar


 Creates a new variable_t object containing the variable in position "var" of the list
 

Variable() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.variable_t
 
Variable(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.vectorvar
 
Variable() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.variable_t
 
Variable(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.vectorvar
 
variable_t - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV
 
variable_t - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE
variable_t - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2
 
variable_t - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0
 
variableNames(long) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.RuleBase

 
 Returns a String with the antecedents (Vi) of rule r.

Variables_per_rule() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.ruleset


 Returns the average number of variables per rule.

Variables_per_rule() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.ruleset


 Returns the average number of variables per rule.

Variables_per_rule() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.ruleset


 Returns the average number of variables per rule.

Variables_Used() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.ruleset


 Returns the rate of variables used in rules related to all variables involved.

Variables_Used() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.ruleset


 Returns the rate of variables used in rules related to all variables involved.

Variables_Used() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.ruleset


 Returns the rate of variables used in rules related to all variables involved.

variableTable - Variable in class keel.GraphInterKeel.datacf.editData.EditVariablePanel
 
VariableTable - Class in keel.GraphInterKeel.datacf.util

Implements a table for storing variables for a dataset

VariableTable(Dataset, JPanel) - Constructor for class keel.GraphInterKeel.datacf.util.VariableTable


 Constructor
 

variableType() - Method in class keel.Algorithms.Rule_Learning.Riona.Dataset


 Returns the types of each in-put (NOMINAL[0] or NUMERICO[1])
 

variance(int, M5Instances) - Static method in class keel.Algorithms.Decision_Trees.M5.M5

Returns the variance value of the instances values of an attribute

variance(int) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances

Computes the variance for a numeric attribute.

variance(M5Attribute) - Method in class keel.Algorithms.Decision_Trees.M5.M5Instances

Computes the variance for a numeric attribute.

variance(double[]) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils

Computes the variance for an array of doubles.

variance(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Thrift.myDataset

It return the variance of an specific attribute

variance(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Computes the variance for a numeric attribute.

variance(AttributeWeka) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instances

Computes the variance for a numeric attribute.

variance(double[]) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils

Computes the variance for an array of doubles.

variance(int, MyDataset) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5

Returns the variance value of the itemsets values of an attribute

variance(double[]) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils

Computes the variance for an array of doubles.

variance(int) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

Computes the variance for a numeric attribute.

variance(MyAttribute) - Method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.MyDataset

Computes the variance for a numeric attribute.

variance(int) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Computes the variance for a numeric attribute.

variance(Attribute) - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instances

Computes the variance for a numeric attribute.

variance(double[]) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils

Computes the variance for an array of doubles.

variance(PrototypeSet, Prototype) - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet

Returns variance of prototype set to the center.

variance(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils

Computes the variance for an array of doubles.

variance(PrototypeSet, Prototype) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Returns variance of prototype set to the center.

variance(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils

Computes the variance for an array of doubles.

variance(double[]) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils

Computes the variance for an array of doubles.

variance(double[]) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils

Computes the variance for an array of doubles.

variance(int) - Method in class keel.Algorithms.SVM.SMO.core.Instances

Computes the variance for a numeric attribute.

variance(Attribute) - Method in class keel.Algorithms.SVM.SMO.core.Instances

Computes the variance for a numeric attribute.

variance(double[]) - Static method in class keel.Algorithms.SVM.SMO.core.Utils

Computes the variance for an array of doubles.

varName(int) - Method in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset

It returns the attribute name of a given variable

varName(int) - Method in class keel.Algorithms.Statistical_Classifiers.Naive_Bayes.myDataset

It gets the name of the variable

varNames() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.myDataset

It returns an array with the names of the input attributes

varNames() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.myDataset

It returns an array with the names of the input attributes

varNames() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.myDataset

It returns an array with the names of the input attributes

varNames() - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.myDataset

It returns an array with the names of the input attributes

varValues - Variable in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
 
vector2doubles(Vector) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils


 Turns vector of strings into a double array.

vector2doubles(Vector) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils


 Turns vector of strings into a double array.

vector2doubles(Vector) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils


 Turns vector of strings into a double array.

vector2doubles(Vector) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils


 Turns vector of strings into a double array.

vector2doubles(Vector) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils


 Turns vector of strings into a double array.

vector2doubles(Vector) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils


 Turns vector of strings into a double array.

vector2doubles(Vector) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils


 Turns vector of strings into a double array.

vector2Input(Vector, int) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils


 Turns vector of strings into a double array containing only the inputs
 

vector2Input(Vector, int) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils


 Turns vector of strings into a double array containing only the inputs
 

vector2Input(Vector, int) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils


 Turns vector of strings into a double array containing only the inputs
 

vector2Input(Vector, int) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils


 Turns vector of strings into a double array containing only the inputs
 

vector2Input(Vector, int) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils


 Turns vector of strings into a double array containing only the inputs
 

vector2Input(Vector, int) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils


 Turns vector of strings into a double array containing only the inputs
 

vector2Input(Vector, int) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils


 Turns vector of strings into a double array containing only the inputs
 

vector2InputOutput(Vector, int, double[], double[]) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils


 Turns vector of strings into two double arrays, one for inputs and other for outputs.

vector2InputOutput(Vector, int, double[], double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils


 Turns vector of strings into two double arrays, one for inputs and other for outputs.

vector2InputOutput(Vector, int, double[], double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils


 Turns vector of strings into two double arrays, one for inputs and other for outputs.

vector2InputOutput(Vector, int, double[], double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils


 Turns vector of strings into two double arrays, one for inputs and other for outputs.

vector2InputOutput(Vector, int, double[], double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils


 Turns vector of strings into two double arrays, one for inputs and other for outputs.

vector2InputOutput(Vector, int, double[], double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils


 Turns vector of strings into two double arrays, one for inputs and other for outputs.

vector2InputOutput(Vector, int, double[], double[]) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils


 Turns vector of strings into two double arrays, one for inputs and other for outputs.

vector2Output(Vector, int) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils


 Turns vector of strings into a double array containing only the outputs
 

vector2Output(Vector, int) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils


 Turns vector of strings into a double array containing only the outputs
 

vector2Output(Vector, int) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils


 Turns vector of strings into a double array containing only the outputs
 

vector2Output(Vector, int) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils


 Turns vector of strings into a double array containing only the outputs
 

vector2Output(Vector, int) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils


 Turns vector of strings into a double array containing only the outputs
 

vector2Output(Vector, int) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils


 Turns vector of strings into a double array containing only the outputs
 

vector2Output(Vector, int) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils


 Turns vector of strings into a double array containing only the outputs
 

vectordouble - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV
 
vectordouble - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE
vectordouble - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2
 
vectordouble - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0
 
vectorsNeighbors - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
 
vectorvar - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV
 
VectorVar - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE

Defines a list of variables

vectorvar - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2
 
vectorvar - Class in keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0
 
verbose - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Parameters

Verbose output

verbose(String) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils


 Prints the parameter without adding new line only if verbosity has been set to True.

verbose - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters

Verbose output

verbose - Variable in class keel.Algorithms.Neural_Networks.gmdh.Parameters

Verbose output

verbose - Variable in class keel.Algorithms.Neural_Networks.net.Parameters

Verbose output

verbose(String) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils


 Prints the parameter without adding new line only if verbosity has been set to True.

verbose(String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils


 Prints the parameter without adding new line only if verbosity has been set to True.

verbose(String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils


 Prints the parameter without adding new line only if verbosity has been set to True.

verbose(String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils


 Prints the parameter without adding new line only if verbosity has been set to True.

verbose(String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils


 Prints the parameter without adding new line only if verbosity has been set to True.

verbose(String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils


 Prints the parameter without adding new line only if verbosity has been set to True.

VERBOSE - Static variable in class keel.Algorithms.SVM.SMO.core.ClassDiscovery

whether to output some debug information

verboseln(String) - Static method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RBFUtils


 Prints the parameter and adds a new line only if verbosity has been set to True.

verboseln(String) - Static method in class keel.Algorithms.Neural_Networks.RBFN.RBFUtils


 Prints the parameter and adds a new line only if verbosity has been set to True.

verboseln(String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_CL.RBFUtils


 Prints the parameter and adds a new line only if verbosity has been set to True.

verboseln(String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental.RBFUtils


 Prints the parameter and adds a new line only if verbosity has been set to True.

verboseln(String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.RBFUtils


 Prints the parameter and adds a new line only if verbosity has been set to True.

verboseln(String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental.RBFUtils


 Prints the parameter and adds a new line only if verbosity has been set to True.

verboseln(String) - Static method in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.RBFUtils


 Prints the parameter and adds a new line only if verbosity has been set to True.

verifyInterval() - Method in interface keel.Algorithms.Genetic_Rule_Learning.UCS.Attribute

Does verify that the interval construction is correct

verifyInterval() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.RealRep


 Verifies if the interval is correct (if the upper value
 is greater or equal than the lower value).

verifyInterval() - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.TernaryRep
 
verifyInterval() - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Attribute

Does verify that the interval construction is correct

verifyInterval() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.IntegerRep


 Verifies if the interval is correct (if the upper value
 is greather or equal than the lower value).

verifyInterval() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RealRep


 Verifies if the interval is correct (if the upper value
 is greater or equal than the lower value).

verifyInterval() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.TernaryRep
 
vertices - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.FuzzyPartition
 
visit(Node, int) - Static method in class keel.Algorithms.Statistical_Tests.Shared.genLatex


 This method visits all the nodes in our DOM tree

visit(Node, int) - Static method in class keel.Algorithms.Statistical_Tests.Shared.outputFile


 This method visits all the nodes in our DOM tree

VisualizePanel - Class in keel.GraphInterKeel.datacf.visualizeData
VisualizePanel() - Constructor for class keel.GraphInterKeel.datacf.visualizeData.VisualizePanel


 Constructor that initializes the panel
 

VisualizePanelAttribute - Class in keel.GraphInterKeel.datacf.visualizeData
VisualizePanelAttribute() - Constructor for class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelAttribute


 Constructor that initializes the panel
 

VisualizePanelCharts2D - Class in keel.GraphInterKeel.datacf.visualizeData
VisualizePanelCharts2D() - Constructor for class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelCharts2D


 Constructor that initializes the panel
 

VisualizePanelDataset - Class in keel.GraphInterKeel.datacf.visualizeData
VisualizePanelDataset() - Constructor for class keel.GraphInterKeel.datacf.visualizeData.VisualizePanelDataset


 Constructor that initializes the panel
 

visualizeSelectionTree - Variable in class keel.GraphInterKeel.experiments.Experiments
 
vname - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.NameClasses

Classes names.

vname - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVE2.NameClasses

Classes names.

vname - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierSLAVEv0.NameClasses

Classes names.

volSphere(int) - Method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.Population


 VolSphere
 
 volSphere
 volSphere calculates the area of a sphere in n dimensions for use in the
        SPEA2 density calculation

volume() - Method in class keel.Algorithms.Hyperrectangles.EHS_CHC.Hyper
 
votingRule(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.ADE_CoForestGenerator

Predicted class of the given prototype.

votingRule(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.CLCCGenerator

Returns the predicted class for a given prototype.

votingRule(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.CoForestGenerator

Classifies the instance given.

votingRule(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.DE_TriTraining.DE_TriTrainingGenerator

Classifies the instance with the index given.

votingRule(int) - Method in class keel.Algorithms.Semi_Supervised_Learning.TriTraining.TriTrainingGenerator

Classifies a instance with the given index.

VQ - Static variable in class keel.Algorithms.Instance_Generation.HYB.HYBGenerator

VQGenerator title text

VQAlgorithm - Class in keel.Algorithms.Instance_Generation.VQ

VQ algorithm calling.

VQAlgorithm() - Constructor for class keel.Algorithms.Instance_Generation.VQ.VQAlgorithm
 
VQGenerator - Class in keel.Algorithms.Instance_Generation.VQ

Class that contains Vector Quantization algorithm

VQGenerator(PrototypeSet, int, int, double, int) - Constructor for class keel.Algorithms.Instance_Generation.VQ.VQGenerator

Constructs a new VQGenerator algorithm.

VQGenerator(PrototypeSet, int, double, double, int) - Constructor for class keel.Algorithms.Instance_Generation.VQ.VQGenerator

Constructs a new VQGenerator algorithm (using 1-Np rule).

VQGenerator(PrototypeSet, Parameters) - Constructor for class keel.Algorithms.Instance_Generation.VQ.VQGenerator

Constructs a new VQGenerator algorithm (using K-Np rule).

VSM - Class in keel.Algorithms.Instance_Selection.VSM

File: VSM.java
 
 The VSM Instance Selection algorithm.

VSM(String) - Constructor for class keel.Algorithms.Instance_Selection.VSM.VSM

Default constructor.

VSM - Class in keel.Algorithms.Preprocess.Instance_Selection.VSM

File: VSM.java
 
 The VSM Instance Selection algorithm.

VSM(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.VSM.VSM

Default constructor.

VWFuzzyKNN - Class in keel.Algorithms.Fuzzy_Instance_Based_Learning.VWFuzzyKNN

File: VWFuzzyKNN.java
 
 The VWFuzzyKNN algorithm.

VWFuzzyKNN(String) - Constructor for class keel.Algorithms.Fuzzy_Instance_Based_Learning.VWFuzzyKNN.VWFuzzyKNN

Main builder.





W

w - Variable in class keel.Algorithms.Fuzzy_Instance_Based_Learning.JFKNN.Triplet

Tripel weights.

w - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.MLPerceptronBackpropCS.Network

Matrix of weights

w - Variable in class keel.Algorithms.Neural_Networks.gann.Network

Matrix of weights

w - Variable in class keel.Algorithms.Neural_Networks.gmdh.Network

Matrix of weights

w - Variable in class keel.Algorithms.Neural_Networks.net.Network

Matrix of weights

W - Static variable in class keel.Algorithms.Rule_Learning.Ripper.Ripper

Flag ('Worth' metric)

W - Static variable in class keel.Algorithms.Rule_Learning.Slipper.Slipper

'Worth' metric

w_k - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters
 
w_k - Variable in class keel.Algorithms.Neural_Networks.gmdh.SetupParameters
 
w_mse - Variable in class keel.Algorithms.Neural_Networks.gann.Parameters
 
w_mse - Variable in class keel.Algorithms.Neural_Networks.gmdh.SetupParameters
 
w_range - Variable in class keel.Algorithms.Neural_Networks.gann.SetupParameters
 
wasCorrect() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.DECEnvironment


 Determines if the classification performed was good
 
 

wasCorrect() - Method in interface keel.Algorithms.Genetic_Rule_Learning.XCS.Environment


 Determines if the classification was good
 

wasCorrect() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MPEnvironment


 Determines if the classification was good
 
 

wasCorrect() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.MSEnvironment


 Determines if the classification was good
 
 

wasCorrect() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.PAREnvironment


 Determines if the classification was good
 
 

wasCorrect() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.POSEnvironment


 Determines if the classification was good
 
 

wasCorrect() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.RMPEnvironment


 Determines if the classification was good
 
 

wasCorrect() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.SSFileEnvironment


 Does determine if the classification was good
 

wcoef(int, int) - Static method in class keel.Algorithms.Statistical_Tests.Shared.StatTest


 Obtains an array of weights for calculating Shapiro Wilk statistic
 Translated from Fortran to C and from C to Java.

WECOA() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GFS_RB_MF.BaseR

Returns the centre of gravity weight by matching

weight - Variable in class keel.Algorithms.Decision_Trees.C45.Itemset

The weight of the itemset.

weight - Variable in class keel.Algorithms.Decision_Trees.DT_GA.C45.Itemset

The weight of the itemset.

weight - Variable in class keel.Algorithms.Decision_Trees.ID3.Itemset

The weight of the itemset.

weight() - Method in class keel.Algorithms.Decision_Trees.M5.M5Instance

Returns the instance's weight.

weight - Variable in class keel.Algorithms.Decision_Trees.SLIQ.Itemset

The weight of the itemset.

weight - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Node.IntDouble

Weight to be displayed.

weight() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.AttributeWeka

Returns the attribute's weight.

weight() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Instance

Returns the instance's weight.

weight - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.FuzzyRule

Weight of the rule.

weight - Variable in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.Itemset

The weight of the itemset.

weight - Variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.Itemset

The weight of the itemset.

weight - Variable in class keel.Algorithms.Genetic_Rule_Learning.PART.Itemset

The weight of the itemset.

weight - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Itemset

The weight of the itemset.

weight - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
 
weight - Variable in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Itemset

The weight of the itemset.

weight() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Attribute

Returns the attribute's weight.

weight() - Method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Instance

Returns the instance's weight.

weight - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.data.AbstractDataset.Instance

weight of this instance

weight - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.Link

Weight value

weight - Variable in class keel.Algorithms.Neural_Networks.RBFN_decremental_CL.Rbf

RBF Weights.

weight - Variable in class keel.Algorithms.Neural_Networks.RBFN_incremental_CL.Rbf

RBF weights.

weight - Variable in class keel.Algorithms.Rule_Learning.ART.Itemset

The weight of the itemset.

weight - Variable in class keel.Algorithms.Rule_Learning.C45Rules.Itemset

The weight of the itemset.

weight - Variable in class keel.Algorithms.Rule_Learning.C45RulesSA.Itemset

The weight of the itemset.

weight - Variable in class keel.Algorithms.Rule_Learning.DataSqueezer.Itemset

The weight of the itemset.

weight - Variable in class keel.Algorithms.Rule_Learning.PART.Itemset

The weight of the itemset.

weight - Variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Itemset

The weight of the itemset.

weight - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.Prototype

weight of instance.

weight() - Method in class keel.Algorithms.SVM.SMO.core.Instance

Returns the instance's weight.

weight - Variable in class org.libsvm.svm_parameter
 
Weight_Is_Negative_Covered(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
 
Weight_Is_Positive_Covered(int) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierNSLV.example_set
 
weight_label - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_parameter
 
weight_label - Variable in class org.libsvm.svm_parameter
 
WeightedInstancesHandler - Interface in keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core

Interface to something that makes use of the information provided
 by instance weights.

WeightedInstancesHandler - Interface in keel.Algorithms.Statistical_Classifiers.Logistic.core

Interface to something that makes use of the information provided
 by instance weights.

WeightedInstancesHandler - Interface in keel.Algorithms.SVM.SMO.core

Interface to something that makes use of the information provided
 by instance weights.

weightRange - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron

Weight range

weightRanges - Variable in class keel.Algorithms.Neural_Networks.NNEP_Common.AbstractNeuralNetSpecies

Weight ranges of each LinkedLayer of the neural nets

weightRelaxFactor - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
 
weightRelaxFactor - Static variable in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.Parameters
 
weights(Itemset) - Method in class keel.Algorithms.Decision_Trees.C45.Cut

Returns weights if itemset is assigned to more than one subset, null otherwise.

weights(Itemset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut

Returns weights if itemset is assigned to more than one subset, null otherwise.

weights(Itemset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut

Returns weights if itemset is assigned to more than one subset, null otherwise.

weights(Itemset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut

Returns weights if itemset is assigned to more than one subset, null otherwise.

weights(Itemset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut

Returns weights if itemset is assigned to more than one subset, null otherwise.

weights - Variable in class keel.Algorithms.Neural_Networks.ensemble.Ensemble

Ensemble weights.

weights(Itemset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut

Returns weights if itemset is assigned to more than one subset, null otherwise.

weights(Itemset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut

Returns weights if itemset is assigned to more than one subset, null otherwise.

weights(Itemset) - Method in class keel.Algorithms.Rule_Learning.PART.Cut

Returns weights if itemset is assigned to more than one subset, null otherwise.

weights(Itemset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut

Returns weights if itemset is assigned to more than one subset, null otherwise.

weightVector - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
 
WekaToKeel - Class in keel.Algorithms.Preprocess.Converter


  WekaToKeel 
 
 This class extends from the Importer class.

WekaToKeel() - Constructor for class keel.Algorithms.Preprocess.Converter.WekaToKeel
 
whichAtt - Variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.classifier_hyperrect_list_real
 
whichSubset(Itemset) - Method in class keel.Algorithms.Decision_Trees.C45.Cut

Returns index of subset itemset is assigned to.

whichSubset(Itemset) - Method in class keel.Algorithms.Decision_Trees.DT_GA.C45.Cut

Returns index of subset itemset is assigned to.

whichSubset(Itemset) - Method in class keel.Algorithms.Genetic_Rule_Learning.PART.Cut

Returns index of subset itemset is assigned to.

whichSubset(Itemset) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C45CS.Cut

Returns index of subset itemset is assigned to.

whichSubset(Itemset) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.C45.Cut

Returns index of subset itemset is assigned to.

whichSubset(Itemset) - Method in class keel.Algorithms.Rule_Learning.C45Rules.Cut

Returns index of subset itemset is assigned to.

whichSubset(Itemset) - Method in class keel.Algorithms.Rule_Learning.C45RulesSA.Cut

Returns index of subset itemset is assigned to.

whichSubset(Itemset) - Method in class keel.Algorithms.Rule_Learning.PART.Cut

Returns index of subset itemset is assigned to.

whichSubset(Itemset) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.C45.Cut

Returns index of subset itemset is assigned to.

width() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.ExponentialFormat

Returns the width of the exponential numbers format.

width() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FlexibleDecimalFormat

Returns the width of the flexible decimal numbers format.

width - Variable in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FloatingPointFormat

Total width of the integer number.

width() - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.FloatingPointFormat

Returns the width of the numbers format.

Wilcoxon - Class in keel.GraphInterKeel.statistical.tests

File: Wilcoxon.java.

Wilcoxon() - Constructor for class keel.GraphInterKeel.statistical.tests.Wilcoxon

Builder

WilcoxonC - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest

Classification Wilcoxon signed ranks Stat-test identifier.

WilcoxonDistribution - Class in keel.GraphInterKeel.statistical.tests

File: WilcoxonDistribution.java.

WilcoxonDistribution() - Constructor for class keel.GraphInterKeel.statistical.tests.WilcoxonDistribution
 
WilcoxonR - Static variable in class keel.Algorithms.Statistical_Tests.Shared.StatTest

Regression Wilcoxon signed ranks Stat-test identifier.

WilsonReduction - Class in keel.Algorithms.Genetic_Rule_Learning.XCS

This class implements the reduction Interface.

WilsonReduction() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.WilsonReduction


 Creates one WilsonReduction Object.

windowActivated(WindowEvent) - Method in class keel.GraphInterKeel.experiments.EducationalRun
 
windowClosed(WindowEvent) - Method in class keel.GraphInterKeel.experiments.EducationalRun
 
windowClosing(WindowEvent) - Method in class keel.GraphInterKeel.experiments.EducationalRun


 When the user close the window of
 partitions.

windowDeactivated(WindowEvent) - Method in class keel.GraphInterKeel.experiments.EducationalRun
 
windowDeiconified(WindowEvent) - Method in class keel.GraphInterKeel.experiments.EducationalRun
 
windowIconified(WindowEvent) - Method in class keel.GraphInterKeel.experiments.EducationalRun
 
windowing - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
 
windowing() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowing
 
Windowing - Class in keel.Algorithms.Genetic_Rule_Learning.GAssist

Manages the subset of training instances
 that is used at each iteration to perform the fitness computations

Windowing(InstanceWrapper[]) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.GAssist.Windowing
 
Windowing - Class in keel.Algorithms.Genetic_Rule_Learning.MPLCS

Manages the subset of training instances
 that is used at each iteration to perform the fitness computations

Windowing(InstanceWrapper[]) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Windowing
 
windowingGWS - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
 
windowingGWS() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowingGWS
 
windowingILAS - Class in keel.Algorithms.Genetic_Rule_Learning.BioHEL
 
windowingILAS() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.BioHEL.windowingILAS
 
windowingMethod - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
windowLowerBound - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQ2

Window width lower bound

windowOpened(WindowEvent) - Method in class keel.GraphInterKeel.experiments.EducationalRun
 
WindowsSize - Static variable in class keel.Algorithms.Genetic_Rule_Learning.Globals.Parameters
 
windowWidth - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQ2

Window width parameter

windowWidth - Variable in class keel.Algorithms.Instance_Generation.LVQ.LVQPRU

Window width of the LVQ2.1 algorithm.

winMethod - Static variable in class keel.Algorithms.Genetic_Rule_Learning.BioHEL.Parameters
 
WINNING_RULE - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.Fuzzy_Chi

Configuration flags.

WINNING_RULE - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Fuzzy_Ish

Configuration flags.

WINNING_RULE - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Fuzzy_Ish

Configuration flag (WINNING_RULE ).

WINNING_RULE - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.GP_COACH

Configuration flag (WINNING_RULE).

WINNING_RULE - Static variable in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.GP_COACH_H

Configuration flag (WINNING_RULE)

without(PrototypeSet) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet

Returns a copy of the set without an element.

without(Prototype) - Method in class keel.Algorithms.Instance_Generation.Basic.PrototypeSet

Returns a copy of the set without an prototype.

without(PrototypeSet) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Returns a copy of the set without an element.

without(Prototype) - Method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeSet

Returns a copy of the set without an prototype.

wknnImpute - Class in keel.Algorithms.Preprocess.Missing_Values.wknnImpute


 This class computes the mean (numerical) or mode (nominal) value of the attributes with missing values for the selected neighbours,
 weighting them according to the relative distance to the considered instance with missing values.

wknnImpute(String) - Constructor for class keel.Algorithms.Preprocess.Missing_Values.wknnImpute.wknnImpute

Creates a new instance of MostCommonValue

Worst_current_perf1 - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
 
Worst_current_perf2 - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
 
Worst_guy1 - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
 
Worst_guy2 - Variable in class keel.Algorithms.Genetic_Rule_Learning.Corcoran.Corcoran
 
WrapperManager - Class in keel.Algorithms.Genetic_Rule_Learning.olexGA
 
WrapperManager() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.olexGA.WrapperManager
 
wrapUp() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegOptimizer

wrap up various variables to save memeory and do some housekeeping after optimization
 has finished.

wrapUp() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMO

wrap up various variables to save memeory and do some housekeeping after optimization
 has finished.

wrapUp() - Method in class keel.Algorithms.SVM.SMO.supportVector.RegSMOImproved

wrap up various variables to save memeory and do some housekeeping after optimization
 has finished.

write(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Config
 
write(String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config
 
write(String, String) - Static method in class keel.Algorithms.Instance_Generation.utilities.KeelFile

Write in Keel-style a file.

write(String, double, double, Base, Poblacion) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.EscribeBCLing

It the function that writes

write(String, double, double, Base, Poblacion) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.EscribeBCLing

It the function that writes

write(String, double, double, Base, Poblacion) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.EscribeBCLing

It the function that writes

write(String, double, double, Base, Poblacion) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.EscribeBCLing

It the function that writes

write(String, double, double, Base, Poblacion) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.EscribeBCLing

It the function that writes

write(String, double, double, Base, Poblacion) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.EscribeBCLing

It the function that writes

write(String, double, double, Base, Poblacion) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.EscribeBCLing

It the function that writes

write(String, double, double, Base, Poblacion) - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.EscribeBCLing

It the function that writes

write(String, String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KeelFile

Write in Keel-style a file.

write(Writer) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.matrix.Matrix

Writes out a matrix.

write(Writer) - Method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Matrix

Deprecated.
Writes out a matrix.

Write(String) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.Calculate.Semantics


 This method writes the semantics of the linguistic variables at the file specified
 

Write(String) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.Calculate.Semantics


 This method writes the semantics of the linguistic variables at the file specified
 

Write(String) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.Calculate.Semantics


 This method writes the semantics of the linguistic variables at the file specified
 

write(String, Object) - Static method in class keel.Algorithms.SVM.SMO.core.SerializationHelper

serializes the given object to the specified file

write(OutputStream, Object) - Static method in class keel.Algorithms.SVM.SMO.core.SerializationHelper

serializes the given object to the specified stream

write() - Method in class keel.GraphInterKeel.experiments.Graph

Prints in the standard output the graph

write_results(String, String[][], InstanceSet) - Method in class keel.Algorithms.Preprocess.Missing_Values.BPCA.BPCA

Write data matrix X to disk, in KEEL format

write_results(String, String[][], InstanceSet) - Method in class keel.Algorithms.Preprocess.Missing_Values.EM.EM

Write data matrix X to disk, in KEEL format

write_results(String, String[][], InstanceSet) - Method in class keel.Algorithms.Preprocess.Missing_Values.LLSImpute.LLSImpute

Write data matrix X to disk, in KEEL format

write_results(String, String[][], InstanceSet) - Method in class keel.Algorithms.Preprocess.Missing_Values.SVDimpute.SVDimpute
 
writeAUCresults(String) - Method in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svmClassifierCost

Writes the AUC results in an aditional output file if the integral approximation of the AUC needs to be computed

writeChar(char) - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.FileManagement

Writes a char from file.

writeChar(char) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.FileManagement

Writes a char from file.

writeConfig(Algorithm, String, String, String, String, String, boolean, Joint, int, String, int, Vector, String, boolean, String) - Method in class keel.GraphInterKeel.experiments.Experiments

Write a configuration script for the method, employing its parameters

writeDataBase(String) - Method in class keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.GP_COACH_H

It writes the Data Base into an output file

writeEvo() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.IVTURS
 
writeExpectedTestOut(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Statistic


 It writes to the test statistics file the expected out compared with the
 output predicted by the system.

writeExpectedTestOut(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Statistic


 It writes to the test statistics file the expected out compared with the
 output predicted by the system.

writeExpectedTrainOut(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.UCS.Statistic


 It writes to the train statistics file the expected out compared with the
 output predicted by the system.

writeExpectedTrainOut(int, int) - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.Statistic


 It writes to the train statistics file the expected out compared with the
 output predicted by the system.

writeFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.DataBase

It writes the Data Base into an output file

writeFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Chi_RW.RuleBase

It writes the rule base into an ouput file

writeFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzySGERD.Population

It writes the best rule base obtained into file

writeFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.DataBase

It writes the Data Base into an output file

writeFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.Population

Writes the best population / RB into file

writeFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Fuzzy_Ish_Hybrid.RuleBase

It writes the rule base into an ouput file

writeFile(String) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.GP_COACH.DataBase

It writes the Data Base into an output file

writeFile(String, String) - Static method in class keel.GraphInterKeel.experiments.Files

Writes a text in a output file

writeFile(String, String) - Static method in class keel.GraphInterKeel.statistical.Files


 Writes data in the file, overwriting previous content 
 

writeFile(String, String) - Static method in class org.core.Files


 Writes data in the file, overwriting previous content 
 

writeLine(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.Globals.FileManagement

Write a line given.

writeLine(String) - Method in class keel.Algorithms.Genetic_Rule_Learning.MPLCS.Assistant.Globals.FileManagement

Writes a given line in the file.

WriteMyFile(String, String) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierMOGUL.MyFile


 Function for writing a String Object in a file
 

WriteOutDataset(String) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.MESDIF


 Dataset file writting to output file
 

WriteOutDataset(String) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.NMEEFSD


 Dataset file writting to output file
 

WriteOutDataset(String) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.SDIGA


 Dataset file writting to output file
 

writeOutput(String, int[][], int[][], Attribute[], Attribute, String) - Static method in class keel.Algorithms.Decision_Trees.C45_Binarization.KNN

Prints output files.

writeOutput(String, int[], int[]) - Method in class keel.Algorithms.Fuzzy_Instance_Based_Learning.FuzzyIBLAlgorithm

Prints KEEL standard output files.

writeOutput(String, String[], String[], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.COGIN.Cogin

Writes the output in KEEL format

writeOutput(String, String[], String[], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.CORE.Core

Writes the output in KEEL format

writeOutput(String, String[], String[], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.ILGA.Ilga

Writes the output in KEEL format

writeOutput(String, String[], String[], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.Genetic_Rule_Learning.OIGA.Oiga

Writes the output in KEEL format

writeOutput(AccAUC, AccAUC, String) - Method in class keel.Algorithms.ImbalancedClassification.Ensembles.multi_C45

It writes on a file the full ensemble (C4.5 rule sets)

writeOutput(String, int[][], int[][], Attribute[], Attribute, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.KNN

Prints output files.

writeOutput(String, int[][], int[][], Attribute[], Attribute, String) - Static method in class keel.Algorithms.Instance_Generation.Basic.PrototypeGenerationAlgorithm

Prints output files.

writeOutput(String, int[][], int[][], Attribute[], Attribute, String) - Static method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE

Prints output files.

writeOutput(String, int[][], int[][], Attribute[], Attribute, String) - Static method in class keel.Algorithms.Preprocess.Basic.KNN

Prints output files.

writeOutput(String, int[], int[]) - Method in class keel.Algorithms.RST_Learning.RSTAlgorithm

Prints KEEL standard output files.

writeOutput(String, String[], String[]) - Method in class keel.Algorithms.Rule_Learning.Swap1.swap1

Prints KEEL standard output files.

writeOutput(String, int[][], int[][], Attribute[], Attribute, String) - Static method in class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerationAlgorithm

Prints output files.

writeOutput(String, String[], String[], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.Logistic

Creates the output file in KEEL format of this method

writeOutput(String, String[], String[], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.SVM.SMO.SMO

Creates the output file in KEEL format of this method

writeParsedSVGBuffer(String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.AdministrativeStaff
 
writeResults(byte[][], byte[][]) - Method in class keel.Algorithms.Decision_Trees.CART.ResultPrinter

This method print the output file in Keel format for classification problems

writeResults(double[], double[]) - Method in class keel.Algorithms.Decision_Trees.CART.ResultPrinter

This method print the output file in Keel format for regression problems

writeResults() - Method in class keel.Algorithms.Rule_Learning.Swap1.swap1

Reports the results obtained

writeRules() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Farchd

Add all the rules generated by the classifier to fileRules file.

writeRules() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.IVTURS
 
writeRules() - Method in class keel.Algorithms.Hyperrectangles.Basic.HyperrectanglesAlgorithm

Writes the final ruleset obtained, in the ruleSetText variable.

writeRules() - Method in class keel.Algorithms.Hyperrectangles.BNGE.BNGE

Writes the final ruleset obtained, in the ruleSetText variable.

writeRules() - Method in class keel.Algorithms.Hyperrectangles.INNER.INNER

Writes the final ruleset obtained, in the ruleSetText variable.

writeRules() - Method in class keel.Algorithms.Hyperrectangles.RISE.RISE

Writes the final ruleset obtained, in the ruleSetText variable.

writeRules() - Method in class keel.Algorithms.Rule_Learning.DataSqueezer.DataSqueezer

Function to write the list of rules.

writeScienceMapToFile(String, List<String>, List<String>, List<Integer>, List<Integer>, List<List<Double>>, double) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.AdministrativeStaff
 
writeScripts(String, String, String, String, Vector, String, boolean, int, int, int) - Method in class keel.GraphInterKeel.experiments.Parameters

Write a configuration script for the method, employing its parameters

WriteSegDataset(String) - Static method in class keel.Algorithms.Subgroup_Discovery.MESDIF.MESDIF.MESDIF


 Dataset file writting to tracking file
 

WriteSegDataset(String) - Static method in class keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.NMEEFSD


 Dataset file writting to tracking file
 

WriteSegDataset(String) - Static method in class keel.Algorithms.Subgroup_Discovery.SDIGA.SDIGA.SDIGA


 Dataset file writting to tracking file
 

writeStringToFile(String, String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.AdministrativeStaff
 
writeTestOutput(String, InstanceSet, int[], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.OutputFS

General Test data output method.

writeTestOutput(String, InstanceSet, int[], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.Preprocess.Basic.OutputFS

General Test data output method.

writeTestScripts(String, String, String, String, Vector, String, boolean, String, String, int) - Method in class keel.GraphInterKeel.experiments.Parameters

Write a configuration script for the test, employing its parameters

writeTime() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA.CBA


 It writes the processing times in the output file
 

writeTime() - Method in class keel.Algorithms.Associative_Classification.ClassifierCBA2.CBA2

It prints the running time of the algorithm

writeTime() - Method in class keel.Algorithms.Associative_Classification.ClassifierCMAR.CMAR

It writes the time the algorithm takes on classify a given dataset.

writeTime() - Method in class keel.Algorithms.Associative_Classification.ClassifierCPAR.CPAR

It writes the running time of the algorithm

writeTime() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyCFAR.CFAR

It add the runtime to fileHora file

writeTime() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Farchd

It add the runtime to fileHora file.

writeTime() - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFCRA.FCRA

It add the runtime to fileHora file

writeTime() - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.IVTURS
 
writeTime() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Alatasetal.Alatasetal

Write the time of the execution on the output file.

writeTime() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Apriori.Apriori

Prints a line with the time taken by the algorithm's execution on the output file.

writeTime() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.ARMMGA.ARMMGA

Write the time of the execution on the output file.

writeTime() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.EARMGA.EARMGA

Writes the time taken to execute the algorithm.

writeTime() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.Eclat.Eclat

Prints a line with the time taken by the algorithm's execution on the output file.

writeTime() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GAR.GAR

Prints a line with the time taken by the algorithm's execution on the output file.

writeTime() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.GENAR.GENAR

Prints a line with the time taken by the algorithm's execution on the output file.

writeTime() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MODENAR.MODENAR

Prints a line with the time taken by the algorithm's execution on the output file.

writeTime() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_Ghosh

Prints a line with the time taken by the algorithm's execution on the output file.

writeTime() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.MOEA_Gosh

Prints a line with the time taken by the algorithm's execution on the output file.

writeTime() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.MOPNAR
 
writeTime() - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.QAR_CIP_NSGAII
 
writeToFileNetwork(List<List<Double>>, String, String) - Method in class keel.Algorithms.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.Fingrams.Fingrams
 
writeTrainOutput(String, double[][], int[][], boolean[][], int[], int[], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.ImbalancedClassification.Ensembles.Preprocess.Basic.OutputFS

General Train data output method.

writeTrainOutput(String, double[][], int[][], boolean[][], int[], int[], Attribute[], Attribute, int, String) - Static method in class keel.Algorithms.Preprocess.Basic.OutputFS

General Train data output method.

writeTree(Node, String) - Method in class keel.Algorithms.Decision_Trees.ID3.ID3

Function to write the decision tree in the form of rules.

writeTree(Node, String) - Method in class keel.Algorithms.Rule_Learning.ART.ART

Function to write the decision tree in the form of rules.





X

X - Variable in class keel.Algorithms.Decision_Trees.C45_Binarization.myDataset

examples array.

x - Variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Boosting.GenotypeBoosting
 
X - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForClassification
 
X - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForModels
 
X - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForSymbRegr
 
x - Variable in class keel.Algorithms.Hyperrectangles.EHS_CHC.Hyper
 
x - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_problem
 
x - Variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus

Input array

X - Variable in class keel.Algorithms.PSO_Learning.CPSO.Particle
 
X - Variable in class keel.Algorithms.PSO_Learning.LDWPSO.Particle
 
X - Variable in class keel.Algorithms.PSO_Learning.PSOLDA.Particle
 
X - Variable in class keel.Algorithms.PSO_Learning.REPSO.Particle
 
x - Variable in class org.libsvm.svm_problem
 
x0() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Difuso
 
x0() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Difuso
 
x0() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Difuso
 
x0() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Difuso
 
x0() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Difuso
 
x0() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Difuso
 
x0() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Difuso
 
x0() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Difuso
 
x1() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Difuso
 
x1() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Difuso
 
x1() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Difuso
 
x1() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Difuso
 
x1() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Difuso
 
x1() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Difuso
 
x1() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Difuso
 
x1() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Difuso
 
x2() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Difuso
 
x2() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Difuso
 
x2() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Difuso
 
x2() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Difuso
 
x2() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Difuso
 
x2() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Difuso
 
x2() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Difuso
 
x2() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Difuso
 
x3() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Difuso
 
x3() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Difuso
 
x3() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Difuso
 
x3() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Difuso
 
x3() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Difuso
 
x3() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Difuso
 
x3() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Difuso
 
x3() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Difuso
 
x_fix(Rbfn) - Method in class keel.Algorithms.Neural_Networks.EvRBF_CL.RbfnPopulation


 Performs the X_FIX crossover operator: replaces numNeurons neurons from
 _net, taking numNeurons from a randomly chosen net.

XCS - Class in keel.Algorithms.Genetic_Rule_Learning.XCS


 This is the main class of the XCS.

XCS(String) - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.XCS


 Initializes an XCS object.

XCSControl - Class in keel.Algorithms.Genetic_Rule_Learning.XCS

The XCSControl class creates an XCS object and makes a run, or makes a
 cross validation run.

XCSControl() - Constructor for class keel.Algorithms.Genetic_Rule_Learning.XCS.XCSControl
 
XCSControl() - Method in class keel.Algorithms.Genetic_Rule_Learning.XCS.XCSControl


 Creates an XCSControl object
 

XCSI - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
XCSI_L0 - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
XCSI_M0 - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
XCSI_R0 - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
XCSRun - Static variable in class keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config


 It represents the number of explores iterations that have to be 
 made to do a exploit


XCSRUN - Static variable in interface keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.ParserConstants
 
Xfuzzy - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForSymbRegr
 
xlogx(int) - Static method in class keel.Algorithms.Decision_Trees.M5.M5StaticUtils

Returns c*log2(c) for a given integer value c.

xlogx(int) - Static method in class keel.Algorithms.Fuzzy_Rule_Learning.Hybrid.FURIA.core.Utils

Returns c*log2(c) for a given integer value c.

xlogx(int) - Static method in class keel.Algorithms.Genetic_Rule_Learning.M5Rules.M5StaticUtils

Returns c*log2(c) for a given integer value c.

xlogx(int) - Static method in class keel.Algorithms.ImbalancedClassification.Resampling.SMOTE_RSB.Rough_Sets.Utils

Returns c*log2(c) for a given integer value c.

xlogx(int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.ADE_CoForest.Utils

Returns c*log2(c) for a given integer value c.

xlogx(int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CLCC.Utils

Returns c*log2(c) for a given integer value c.

xlogx(int) - Static method in class keel.Algorithms.Semi_Supervised_Learning.CoForest.Utils

Returns c*log2(c) for a given integer value c.

xlogx(int) - Static method in class keel.Algorithms.Statistical_Classifiers.Logistic.core.Utils

Returns c*log2(c) for a given integer value c.

xlogx(int) - Static method in class keel.Algorithms.SVM.SMO.core.Utils

Returns c*log2(c) for a given integer value c.

XML - Static variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.AbstractNeuralNet


 Marshal/Unmarshal input layer, hidden layers and output layer
 

XML - Static variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputLayer


 Marshal/Unmarshal maximum number of neurons and each neuron
 

XML - Static variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.InputNeuron


 Marshal/Unmarshal neuron index
 

XML - Static variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.Link


 Marshal/Unmarshal weight and state of the link
 

XML - Static variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedLayer


 Marshal/Unmarshal initial number of neurons, maximum number of
 neurons, each neuron, layer type, weigth range, and a boolean
 indicating if the layer is input-biased
 

XML - Static variable in class keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinkedNeuron


 Marshal/Unmarshal links and and a boolean indicating if the
 neuron is input-biased
 

XML - Static variable in class keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividual

Marshal individual fitness (inherited format).

XmlToKeel - Class in keel.Algorithms.Preprocess.Converter


  XmlToKeel 
 
 This class extends from the Importer class.

XmlToKeel(String, String) - Constructor for class keel.Algorithms.Preprocess.Converter.XmlToKeel

CsvToKeel class Constructor.

xnormi(double) - Static method in class keel.Algorithms.Statistical_Tests.Shared.nonParametric.CDF_Normal

This method calculates the normal cdf inverse function.

xnormi(double) - Static method in class keel.GraphInterKeel.statistical.tests.CDF_Normal

This method calculates the normal cdf inverse function.

XOVER_METHOD - Static variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
XOVER_RATE - Static variable in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
xOverMethodTipText() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
xOverRateTipText() - Method in class keel.Algorithms.Genetic_Rule_Learning.olexGA.OlexGA
 
xPC_BLX(Individual, double) - Method in class keel.Algorithms.Associative_Classification.ClassifierFuzzyFARCHD.Individual

Crosses the individuals using the BLX operator.

xPC_BLX(Individual, double) - Method in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS.Individual
 




Y

Y - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForModels
 
Y - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForSymbRegr
 
y - Variable in class keel.Algorithms.Hyperrectangles.EHS_CHC.Hyper
 
y - Variable in class keel.Algorithms.ImbalancedClassification.CSMethods.C_SVMCost.svm_problem
 
y - Variable in class keel.Algorithms.Neural_Networks.IRPropPlus_Clas.IRPropPlus

Output array

y() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_LatAmp_FRBSs.Difuso
 
y() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_T_Lateral_FRBSs.Difuso
 
y() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_LatAmp_FRBSs.Difuso
 
y() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_A_TS_Lateral_FRBSs.Difuso
 
y() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_LatAmp_FRBSs.Difuso
 
y() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_T_Lateral_FRBSs.Difuso
 
y() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_LatAmp_FRBSs.Difuso
 
y() - Method in class keel.Algorithms.RE_SL_Postprocess.Post_G_TS_Lateral_FRBSs.Difuso
 
y - Variable in class org.libsvm.svm_problem
 
YES - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.AdHoc.Fuzzy_Ish_Weighted.Fuzzy_Ish

Configuration flags.

yes - Static variable in class keel.Algorithms.Instance_Generation.Basic.AccuracyMeter

Posible yes variants of the command line.

yes - Static variable in class keel.Algorithms.Semi_Supervised_Learning.Basic.AccuracyMeter

Posible yes variants of the command line.

Yfuzzy - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForSymbRegr
 
Yo - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForModels
 
Yo - Static variable in class keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Individual.GeneticIndividualForSymbRegr
 
yulesQ - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.AssociationRule
 
yulesQ - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOEA_Ghosh.Chromosome
 
yulesQ - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.AssociationRule
 
yulesQ - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.MOPNAR.Chromosome
 
yulesQ - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.AssociationRule
 
yulesQ - Variable in class keel.Algorithms.UnsupervisedLearning.AssociationRules.IntervalRuleLearning.QAR_CIP_NSGAII.Chromosome
 




Z

z_score - Class in keel.Algorithms.Preprocess.Transformations.z_score


 This class performs the z-score transformation.

z_score(String) - Constructor for class keel.Algorithms.Preprocess.Transformations.z_score.z_score

Creates a new instance of z_score

zero - Variable in class keel.Algorithms.MIL.Diverse_Density.Optimization.Optimization
 
ZetaDiscretizer - Class in keel.Algorithms.Discretizers.Zeta_Discretizer


 This is the class with the operations of the Zeta based discretization.

ZetaDiscretizer() - Constructor for class keel.Algorithms.Discretizers.Zeta_Discretizer.ZetaDiscretizer
 
ZhangTS - Class in keel.Algorithms.Instance_Selection.ZhangTS

File: ZhangTS.java
 
 The ZhangTS Instance Selection algorithm.

ZhangTS(String) - Constructor for class keel.Algorithms.Instance_Selection.ZhangTS.ZhangTS

Default constructor.

ZhangTS - Class in keel.Algorithms.Preprocess.Instance_Selection.ZhangTS

File: ZhangTS.java
 
 The ZhangTS Instance Selection algorithm.

ZhangTS(String) - Constructor for class keel.Algorithms.Preprocess.Instance_Selection.ZhangTS.ZhangTS

Default constructor.

ZipFiles(String, Vector) - Static method in class keel.GraphInterKeel.experiments.FileUtils

Compress a list of files in a .zip file





_

_1nn(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.DROP3LVQ3.DROP3LVQ3

Implements the 1NN algorithm

_1nn(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.DROP3PSO.DROP3PSO

Implements the 1NN algorithm

_1nn(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.DROP3SFLSDE.DROP3SFLSDE

Implements the 1NN algorithm

_1nn(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.ICFLVQ3.ICFLVQ3

Implements the 1NN algorithm

_1nn(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.ICFPSO.ICFPSO

Implements the 1NN algorithm

_1nn(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.ICFSFLSDE.ICFSFLSDE

Implements the 1NN algorithm

_1nn(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.SSMALVQ3.SSMALVQ3

Implements the 1NN algorithm

_1nn(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.SSMAPSO.SSMAPSO

Implements the 1NN algorithm

_1nn(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.SSMASFLSDE.SSMASFLSDE

Implements the 1NN algorithm

_1nn(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Instance_Generation.utilities.KNN.KNN

Implements the 1NN algorithm

_1nn(Prototype, PrototypeSet) - Static method in class keel.Algorithms.Semi_Supervised_Learning.utilities.KNN.KNN

Implements the 1NN algorithm

_2LBGPartition(double) - Method in class keel.Algorithms.Instance_Generation.VQ.Cluster

Part the cluster by the LBG method.

_5x2 - Static variable in class keel.GraphInterKeel.datacf.partitionData.PartitionGenerator

Constant representing 5x2 of Dietterich partition

_DOBSCV_FOLD - Static variable in class keel.GraphInterKeel.datacf.partitionData.PartitionGenerator

Constant representing K-Fold Distribution Optimally Balanced Stratified Cross Validation partition

_HOLDOUT - Static variable in class keel.GraphInterKeel.datacf.partitionData.PartitionGenerator

Constant representing Holdout partition

_K_FOLD - Static variable in class keel.GraphInterKeel.datacf.partitionData.PartitionGenerator

Constant representing KFold partition


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