public class ProcessConfig
extends java.lang.Object
Class that process the configuration file for KEEL algorithms.
Modifier and Type | Field and Description |
---|---|
static int |
AUC
AUC measurement identifier.
|
static boolean |
Berg
Apply Berg flag.
|
static boolean |
Bon
Apply Bon flag.
|
static int |
curDataset
CUR dataset.
|
static java.lang.String |
dataMatrix
Data matrix order.
|
static java.lang.String |
dataTable1
Data table order 1.
|
static java.lang.String |
dataTable2
Data table order 2.
|
static java.lang.String |
dataTable3
Data table order 3.
|
static boolean |
Finner
Apply Finner flag.
|
static double |
fuzzyTolerance
Tolerance added to input examples in Fuzzy Symbolic Regression.
|
static int |
GMEAN
Geometric mean measurement identifier.
|
static boolean |
Hoch
Apply Hoch flag.
|
static boolean |
Holland
Apply Holland flag.
|
static boolean |
Holm
Apply Holm flag.
|
static boolean |
Hommel
Apply Hommel flag.
|
static boolean |
Iman
Apply Iman flag.
|
static int |
imbalancedMeasure
Type of imbalanced measurement used.
|
static int |
IndexTest
It ignores the repetition of train file
|
static int |
IndexTestKMeans
Test file in clustering algorithms
|
static int |
IndexTrain
First file in inputData (training)
|
static boolean |
Li
Apply Li flag.
|
static java.lang.String |
matrixConfussion
Confussion matrix flag.
|
static java.lang.String |
nameFile
File name.
|
static boolean |
Nem
Apply Nem flag.
|
static int |
numberLine
Line number.
|
static int |
numberLine1
Line number 1.
|
static int |
numberLine2
Line number 2.
|
static int |
numberLine3
Line number 3.
|
static int |
numDataset
Number of datasets.
|
static java.util.Vector |
outputData
All the output files are stored in this vector (in the doOutputData method)
|
static int |
parAlgorithmType
Algorithm to execute identifier.
|
static double |
parCrGAProb
GA Cross probability.
|
static int |
parCrossId1
Type of cross operator.
|
static int |
parCrossId2
Type of cross operator GAP.
|
static int |
parCrossId3
Type of cross operator GAP.
|
static double |
parDeltaFit
Waited fitness increment for a SAP overcrossing.
|
static int |
parFitnessType
Type of fitness.
|
static int |
parGALen
Number of parameters for GA string (GAP).
|
static java.util.Vector |
parInputData
Train name.
|
static double |
parIntraNicheProb
Crossing probability intra-niche.
|
static int |
parIslandNumber
Number of populations
|
static int |
parIterNumber
Number of iterations (generations of crosses).
|
static double |
parKernel
Kernel parameter.
|
static int |
parLoId
Local Optimization Algorithm identifier.
|
static int |
parLoIterNumber
NUmber of iterations for Local Optimization * used nvar.
|
static double |
parLoProb
Local Optimization probability.
|
static int |
parMaxHeigth
Maximum height for each individual
|
static int |
parMaxNiche
Maximum number of individuls by niche.
|
static double |
parMigProb
Migration probability.
|
static double |
parMuGAProb
GA Mutation probability.
|
static int |
parMutaId1
Type of mutation operator.
|
static int |
parMutaId2
Type of mutation operator GAP.
|
static int |
parMutaId3
Type of mutation operator GAP.
|
static double |
parMutAmpl
Mutation amplitude.
|
static double |
parMutProb
Mutation probability.
|
static int |
parNClusters
Number of cluster in clustering problems.
|
static int[] |
parNetTopo
Neural Network topology.
|
static boolean |
parNewFormat
Keel format or not flag.
|
static boolean |
parNiche
Using GA-P niches flag.
|
static int |
parNMeans
Number of means.
|
static int |
parNSUB
Number of iterations for each temperature SAP.
|
static java.util.Vector |
parOutputData
Test name.
|
static double |
parP0
Accepting probability -deltafit on 0 iteration SAP.
|
static double |
parP1
Accepting probability -deltafit on iteration SAP.
|
static int |
parPartitionLabelNum
Partition label number.
|
static int |
parPopSize
Population size
|
static java.lang.String |
parResultLabel
Results label.
|
static java.lang.String |
parResultName
Test results file
|
static java.lang.String |
parResultTrainName
Result file name for Trail file
|
static int |
parRuleNumber
Number of rules (Boosting and FSS98).
|
static long |
parSeed
Random seed.
|
static double |
parSigma
Initial Covariance in FSS98.
|
static double |
parSignificanceLevel
Contrast Significance Level.
|
static boolean |
parSteady
Steady or not flag.
|
static int |
parTourSize
Tournament size.
|
static boolean |
Rom
Apply Rom flag.
|
static boolean |
Scha
Apply Scha flag.
|
static int |
STANDARDACCURACY
Standard accuracy identifier.
|
static java.lang.String |
tableType1
Table type flag 1.
|
static java.lang.String |
tableType2
Table type flag 2.
|
static java.lang.String |
tableType3
Table type flag 3.
|
Constructor and Description |
---|
ProcessConfig()
Constructor that initializes input/output parameters.
|
Modifier and Type | Method and Description |
---|---|
int |
fileProcess(java.lang.String nfconfig)
method that process the configuration file nfconfig.
|
java.lang.String |
getRelation()
Returns the last token after read the last relation in results file.
|
void |
results(double[][] pattern,
int[] obtained)
Writes the result file with pattern and obtained data for clustering problems.
|
void |
results(double[] expected,
double[] obtained)
Writes the result file with expected and obtained data for modelling problems.
|
void |
results(int[] expected,
int[] obtained)
Writes the result file with expected and obtained data for classification problems.
|
java.lang.String[] |
skipHeader(java.io.BufferedReader in)
Skips the header of the results file.
|
void |
trainingResults(double[][] pattern,
int[] obtained)
Writes the training result file with pattern and obtained data for clustering problems.
|
void |
trainingResults(double[] expected,
double[] obtained)
Writes the training result file with expected and obtained data for modelling problems.
|
void |
trainingResults(int[] expected,
int[] obtained)
Writes the training result file with expected and obtained data for classification problems.
|
public static final int IndexTrain
public static final int IndexTestKMeans
public static final int IndexTest
public static boolean parNewFormat
public static int parAlgorithmType
public static java.util.Vector parInputData
public static java.util.Vector parOutputData
public static java.util.Vector outputData
public static java.lang.String parResultTrainName
public static java.lang.String parResultName
public static java.lang.String parResultLabel
public static int parPartitionLabelNum
public static int parPopSize
public static int parIslandNumber
public static boolean parSteady
public static int parIterNumber
public static int parTourSize
public static double parMutProb
public static double parMutAmpl
public static double parMigProb
public static double parLoProb
public static int parLoIterNumber
public static int parLoId
public static int parMaxHeigth
public static boolean parNiche
public static int parMaxNiche
public static double parIntraNicheProb
public static double parDeltaFit
public static double parP0
public static double parP1
public static int parNSUB
public static double parCrGAProb
public static double parMuGAProb
public static int parRuleNumber
public static int parCrossId1
public static int parMutaId1
public static int parCrossId2
public static int parMutaId2
public static int parCrossId3
public static int parMutaId3
public static int parFitnessType
public static int[] parNetTopo
public static double parKernel
public static int parNMeans
public static int parGALen
public static double parSigma
public static double parSignificanceLevel
public static long parSeed
public static int parNClusters
public static double fuzzyTolerance
public static java.lang.String tableType1
public static java.lang.String tableType2
public static java.lang.String tableType3
public static int numberLine
public static int numberLine1
public static int numberLine2
public static int numberLine3
public static java.lang.String dataTable1
public static java.lang.String dataTable2
public static java.lang.String dataTable3
public static java.lang.String dataMatrix
public static java.lang.String matrixConfussion
public static int numDataset
public static int curDataset
public static java.lang.String nameFile
public static boolean Iman
public static boolean Nem
public static boolean Bon
public static boolean Holm
public static boolean Hoch
public static boolean Hommel
public static boolean Scha
public static boolean Berg
public static boolean Holland
public static boolean Rom
public static boolean Finner
public static boolean Li
public static int imbalancedMeasure
public static final int AUC
public static final int GMEAN
public static final int STANDARDACCURACY
public ProcessConfig()
Constructor that initializes input/output parameters.
public int fileProcess(java.lang.String nfconfig)
method that process the configuration file nfconfig.
nfconfig
- name of the file to be configured.public java.lang.String[] skipHeader(java.io.BufferedReader in)
Skips the header of the results file.
in
- the stream to read.public java.lang.String getRelation()
Returns the last token after read the last relation in results file.
public void results(double[] expected, double[] obtained)
Writes the result file with expected and obtained data for modelling problems.
expected
- expected data vector.obtained
- expected data vectorpublic void results(int[] expected, int[] obtained)
Writes the result file with expected and obtained data for classification problems.
expected
- expected data vector.obtained
- expected data vectorpublic void results(double[][] pattern, int[] obtained)
Writes the result file with pattern and obtained data for clustering problems.
pattern
- expected data vector.obtained
- expected data vectorpublic void trainingResults(double[] expected, double[] obtained)
Writes the training result file with expected and obtained data for modelling problems.
expected
- expected data vector.obtained
- expected data vectorpublic void trainingResults(int[] expected, int[] obtained)
Writes the training result file with expected and obtained data for classification problems.
expected
- expected data vector.obtained
- expected data vectorpublic void trainingResults(double[][] pattern, int[] obtained)
Writes the training result file with pattern and obtained data for clustering problems.
pattern
- expected data vector.obtained
- expected data vector