ProcessConfig

java.lang.Object
- keel.Algorithms.Preprocess.NoiseFilters.ANR.ProcessConfig

public class ProcessConfig
extends java.lang.Object

Author:: sergio

Field Summary

Fields
Modifier and Type	Field and Description
`static boolean`	`Bon` Bon boolean
`static int`	`curDataset` Dataset
`static java.lang.String`	`dataMatrix` Data matrix
`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 double`	`fuzzyTolerance` Tolerance added to input examples in Fuzzy Symbolic Regression
`static boolean`	`Hoch` Hoch boolean
`static boolean`	`Holm` Holm boolean
`static boolean`	`Hommel` Hommel boolean
`static boolean`	`Iman` Iman boolean
`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
`static java.lang.String`	`matrixConfussion` Confussion matrix (YES-NO)
`static java.lang.String`	`nameFile` file name
`static boolean`	`Nem` Nem boolean
`static int`	`numberLine` Number of line
`static int`	`numberLine1` Number of line 1
`static int`	`numberLine2` Number of line 2
`static int`	`numberLine3` Number of line 3
`static java.util.Vector`	`outputData` NEW: All the output files are stored in this vector (in the doOutputData method)
`static int`	`parAlgorithmType` Algorithm to execute
`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` Input Train and Test names
`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
`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
`static boolean`	`parNiche` Using GA-P niches
`static int`	`parNMeans` Number of means
`static int`	`parNSUB` Number of iterations for each temperature SAP
`static java.util.Vector`	`parOutputData` Output Train and Test names
`static double`	`parP0` Accepting probability -deltafit on 0 iteration SAP
`static double`	`parP1` Accepting probability -deltafit on iteration SAP
`static int`	`parPartitionLabelNum` Partition label
`static int`	`parPopSize` Population size
`static java.lang.String`	`parResultLabel` Results label
`static java.lang.String`	`parResultName` Global 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
`static int`	`parTourSize` Tournament size
`static java.lang.String`	`tableType1` tableType1 (YES or NO)
`static java.lang.String`	`tableType2` tableType2 (YES or NO)
`static java.lang.String`	`tableType3` tableType3 (YES or NO)

Constructor Summary

Constructors
Constructor and Description

ProcessConfig()
Constructor that initializes input/output parameters.

Constructors
Constructor and Description
`ProcessConfig()` Constructor that initializes input/output parameters.

Method Summary

All Methods Instance Methods Concrete Methods
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.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - IndexTrain
```
public static final int IndexTrain
```
    First file in inputData
    
    See Also:
    
    Constant Field Values
  - IndexTestKMeans
```
public static final int IndexTestKMeans
```
    Test file in clustering algorithms
    
    See Also:
    
    Constant Field Values
  - IndexTest
```
public static final int IndexTest
```
    It ignores the repetition of train file
    
    See Also:
    
    Constant Field Values
  - parNewFormat
```
public static boolean parNewFormat
```
    Keel format or not
  - parAlgorithmType
```
public static int parAlgorithmType
```
    Algorithm to execute
  - parInputData
```
public static java.util.Vector parInputData
```
    Input Train and Test names
  - parOutputData
```
public static java.util.Vector parOutputData
```
    Output Train and Test names
  - outputData
```
public static java.util.Vector outputData
```
    NEW: All the output files are stored in this vector (in the doOutputData method)
  - parResultTrainName
```
public static java.lang.String parResultTrainName
```
    Result file name for Trail file
  - parResultName
```
public static java.lang.String parResultName
```
    Global results file
  - parResultLabel
```
public static java.lang.String parResultLabel
```
    Results label
  - parPartitionLabelNum
```
public static int parPartitionLabelNum
```
    Partition label
  - parPopSize
```
public static int parPopSize
```
    Population size
  - parIslandNumber
```
public static int parIslandNumber
```
    Number of populations
  - parSteady
```
public static boolean parSteady
```
    Steady or not
  - parIterNumber
```
public static int parIterNumber
```
    Number of iterations (generations of crosses)
  - parTourSize
```
public static int parTourSize
```
    Tournament size
  - parMutProb
```
public static double parMutProb
```
    Mutation probability
  - parMutAmpl
```
public static double parMutAmpl
```
    Mutation amplitude
  - parMigProb
```
public static double parMigProb
```
    Migration probability
  - parLoProb
```
public static double parLoProb
```
    Local Optimization probability
  - parLoIterNumber
```
public static int parLoIterNumber
```
    NUmber of iterations for Local Optimization * used nvar
  - parLoId
```
public static int parLoId
```
    Local Optimization Algorithm
  - parMaxHeigth
```
public static int parMaxHeigth
```
    Maximum height for each individual
  - parNiche
```
public static boolean parNiche
```
    Using GA-P niches
  - parMaxNiche
```
public static int parMaxNiche
```
    Maximum number of individuls by niche
  - parIntraNicheProb
```
public static double parIntraNicheProb
```
    Crossing probability intra-niche
  - parDeltaFit
```
public static double parDeltaFit
```
    Waited fitness increment for a SAP overcrossing
  - parP0
```
public static double parP0
```
    Accepting probability -deltafit on 0 iteration SAP
  - parP1
```
public static double parP1
```
    Accepting probability -deltafit on iteration SAP
  - parNSUB
```
public static int parNSUB
```
    Number of iterations for each temperature SAP
  - parCrGAProb
```
public static double parCrGAProb
```
    GA Cross probability
  - parMuGAProb
```
public static double parMuGAProb
```
    GA Mutation probability
  - parRuleNumber
```
public static int parRuleNumber
```
    Number of rules (Boosting and FSS98)
  - parCrossId1
```
public static int parCrossId1
```
    Type of cross operator
  - parMutaId1
```
public static int parMutaId1
```
    Type of mutation operator
  - parCrossId2
```
public static int parCrossId2
```
    Type of cross operator GAP
  - parMutaId2
```
public static int parMutaId2
```
    Type of mutation operator GAP
  - parCrossId3
```
public static int parCrossId3
```
    Type of cross operator GAP
  - parMutaId3
```
public static int parMutaId3
```
    Type of mutation operator GAP
  - parFitnessType
```
public static int parFitnessType
```
    Type of fitness
  - parNetTopo
```
public static int[] parNetTopo
```
    Neural Network topology
  - parKernel
```
public static double parKernel
```
    Kernel parameter
  - parNMeans
```
public static int parNMeans
```
    Number of means
  - parGALen
```
public static int parGALen
```
    Number of parameters for GA string (GAP)
  - parSigma
```
public static double parSigma
```
    Initial Covariance in FSS98
  - parSignificanceLevel
```
public static double parSignificanceLevel
```
    Contrast Significance Level
  - parSeed
```
public static long parSeed
```
    Random seed
  - parNClusters
```
public static int parNClusters
```
    Number of cluster in clustering problems
  - fuzzyTolerance
```
public static double fuzzyTolerance
```
    Tolerance added to input examples in Fuzzy Symbolic Regression
  - tableType1
```
public static java.lang.String tableType1
```
    tableType1 (YES or NO)
  - tableType2
```
public static java.lang.String tableType2
```
    tableType2 (YES or NO)
  - tableType3
```
public static java.lang.String tableType3
```
    tableType3 (YES or NO)
  - numberLine
```
public static int numberLine
```
    Number of line
  - numberLine1
```
public static int numberLine1
```
    Number of line 1
  - numberLine2
```
public static int numberLine2
```
    Number of line 2
  - numberLine3
```
public static int numberLine3
```
    Number of line 3
  - dataTable1
```
public static java.lang.String dataTable1
```
    Data table order 1
  - dataTable2
```
public static java.lang.String dataTable2
```
    Data table order 2
  - dataTable3
```
public static java.lang.String dataTable3
```
    Data table order 3
  - dataMatrix
```
public static java.lang.String dataMatrix
```
    Data matrix
  - matrixConfussion
```
public static java.lang.String matrixConfussion
```
    Confussion matrix (YES-NO)
  - curDataset
```
public static int curDataset
```
    Dataset
  - nameFile
```
public static java.lang.String nameFile
```
    file name
  - Iman
```
public static boolean Iman
```
    Iman boolean
  - Nem
```
public static boolean Nem
```
    Nem boolean
  - Bon
```
public static boolean Bon
```
    Bon boolean
  - Holm
```
public static boolean Holm
```
    Holm boolean
  - Hoch
```
public static boolean Hoch
```
    Hoch boolean
  - Hommel
```
public static boolean Hommel
```
    Hommel boolean
- Constructor Detail
  - ProcessConfig
```
public ProcessConfig()
```
    Constructor that initializes input/output parameters.
- Method Detail
  - fileProcess
```
public int fileProcess(java.lang.String nfconfig)
```
    method that process the configuration file nfconfig.
    
    Parameters:
    
    nfconfig - name of the file to be configured.
    
    Returns:
    
    a value of 0 if function returns successfully, On an unsuccessful call, a negative value is returned.
  - skipHeader
```
public java.lang.String[] skipHeader(java.io.BufferedReader in)
```
    Skips the header of the results file.
    
    Parameters:
    
    in - the stream to read.
    
    Returns:
    
    rest of the results file without hearder.
  - getRelation
```
public java.lang.String getRelation()
```
    Returns the last token after read the last relation in results file.
    
    Returns:
    
    the last token after read the last relation in results file.
  - results
```
public void results(double[] expected,
                    double[] obtained)
```
    Writes the result file with expected and obtained data for modelling problems.
    
    Parameters:
    
    expected - expected data vector.
    
    obtained - expected data vector
  - results
```
public void results(int[] expected,
                    int[] obtained)
```
    Writes the result file with expected and obtained data for classification problems.
    
    Parameters:
    
    expected - expected data vector.
    
    obtained - expected data vector
  - results
```
public void results(double[][] pattern,
                    int[] obtained)
```
    Writes the result file with pattern and obtained data for clustering problems.
    
    Parameters:
    
    pattern - patterns given to write.
    
    obtained - obtained data given to write.
  - trainingResults
```
public void trainingResults(double[] expected,
                            double[] obtained)
```
    Writes the training result file with expected and obtained data for modelling problems.
    
    Parameters:
    
    expected - expected data vector.
    
    obtained - expected data vector
  - trainingResults
```
public void trainingResults(int[] expected,
                            int[] obtained)
```
    Writes the training result file with expected and obtained data for classification problems.
    
    Parameters:
    
    expected - expected data vector.
    
    obtained - expected data vector
  - trainingResults
```
public void trainingResults(double[][] pattern,
                            int[] obtained)
```
    Writes the training result file with pattern and obtained data for clustering problems.
    
    Parameters:
    
    pattern - patterns given to write.
    
    obtained - obtained data given to write.

Class ProcessConfig

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

IndexTrain

IndexTestKMeans

IndexTest

parNewFormat

parAlgorithmType

parInputData

parOutputData

outputData

parResultTrainName

parResultName

parResultLabel

parPartitionLabelNum

parPopSize

parIslandNumber

parSteady

parIterNumber

parTourSize

parMutProb

parMutAmpl

parMigProb

parLoProb

parLoIterNumber

parLoId

parMaxHeigth

parNiche

parMaxNiche

parIntraNicheProb

parDeltaFit

parP0

parP1

parNSUB

parCrGAProb

parMuGAProb

parRuleNumber

parCrossId1

parMutaId1

parCrossId2

parMutaId2

parCrossId3

parMutaId3

parFitnessType

parNetTopo

parKernel

parNMeans

parGALen

parSigma

parSignificanceLevel

parSeed

parNClusters

fuzzyTolerance

tableType1

tableType2

tableType3

numberLine

numberLine1

numberLine2

numberLine3

dataTable1

dataTable2

dataTable3

dataMatrix

matrixConfussion

curDataset

nameFile

Iman

Nem

Bon

Holm

Hoch

Hommel

Constructor Detail

ProcessConfig

Method Detail

fileProcess