/***********************************************************************
This file is part of KEEL-software, the Data Mining tool for regression,
classification, clustering, pattern mining and so on.
Copyright (C) 2004-2010
F. Herrera (herrera@decsai.ugr.es)
L. S�nchez (luciano@uniovi.es)
J. Alcal�-Fdez (jalcala@decsai.ugr.es)
S. Garc�a (sglopez@ujaen.es)
A. Fern�ndez (alberto.fernandez@ujaen.es)
J. Luengo (julianlm@decsai.ugr.es)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see http://www.gnu.org/licenses/
**********************************************************************/
package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.ClassifierFuzzyGAP;
import keel.Algorithms.Shared.Parsing.*;
import keel.Algorithms.Fuzzy_Rule_Learning.Shared.Fuzzy.*;
import keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms.*;
import keel.Algorithms.Shared.Exceptions.*;
import java.io.*;
import org.core.*;
/**
* <p>
* ClassifierFuzzyGAP is intended to generate a Fuzzy Rule Based System
* (FRBS) classifier using an Genetic Algorithm and Programming (GAP).
*
* This class makes used of the following classes:
* {@link FuzzyGAPClassifier}: the classifier to be learned
* {@link GeneticAlgorithm}: to optimize following the GAP rules, The concrete
* algorithm used depends on the Steady and Niches
* parameters, varying between the {@link AlgorithmGAPGen}, the
* {@link AlgorithmGAPNiches} and the {@link AlgorithmGAPSteady}.
*
* Detailed in:
*
* L. S�nchez, I. Couso, J.A. Corrales. Combining GP Operators With SA Search To
* Evolve Fuzzy Rule Based Classifiers. Information Sciences 136:1-4 (2001)
* 175-192.
*
* </p>
*
* <p>
* @author Written by Luciano Sanchez (University of Oviedo) 21/07/2005
* @author Modified by J.R. Villar (University of Oviedo) 19/12/2008
* @version 1.0
* @since JDK1.4
* </p>
*/
public class ClassifierFuzzyGAP {
//The Randomize object used in this class
static Randomize rand;
//The maximum number of Fuzzy Rules to be learned.
final static int MAXFUZZYRULES=1000;
/**
* <p>
* This private static method extract 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 classifier --which is a
* FuzzyGAPClassifier instance-- using the GAP algorithm --which is an instance
* of the GeneticAlgorithm class-- and print out the results with the validation
* dataset.
*
* If the parameter Steady is not fixed then the GAP used is the AlgorithmGAPGen.
* If that parameter is fixed, if the parameter Niches is fixed then the GAP
* used is the AlgorithmGAPNiches. If the latter parameter is not fixed then the
* GAP used is the AlgorithmGAPSteady.
*
* </p>
* @param tty unused boolean parameter, kept for compatibility
* @param pc ProcessConfig object to obtain the train and test datasets
* and the method's parameters.
*/
private static void fuzzyGAPClassifier(boolean tty, ProcessConfig pc) {
try {
String readALine=new String();
int defaultNumberInputPartitions=0;
int numberOfCrossovers=0;
ProcessDataset pd=new ProcessDataset();
readALine=(String)pc.parInputData.get(ProcessConfig.IndexTrain);
if (pc.parNewFormat) pd.processClassifierDataset(readALine,true);
else pd.oldClusteringProcess(readALine);
int nData=pd.getNdata(); // Number of examples
int nVariables=pd.getNvariables(); // Number of variables
int nInputs=pd.getNinputs(); // Number of inputs
System.out.println("Number of examples="+nData);
System.out.println("Number of inputs="+nInputs);
double[][] X = pd.getX(); // Input data
int[] C = pd.getC(); // Output data
int nClasses = pd.getNclasses(); // Number of classes
int [] Ct=new int[C.length];
double[] inputMaximum = pd.getImaximum(); // Maximum and Minimum for input data
double[] inputMinimum = pd.getIminimum();
int[] nInputPartitions=new int[nInputs];
pd.showDatasetStatistics();
// Partitions definition
FuzzyPartition[] inputPartitions=new FuzzyPartition[nInputs];
for (int i=0;i<nInputs;i++) {
nInputPartitions[i]=pc.parPartitionLabelNum;
inputPartitions[i]=new FuzzyPartition(inputMinimum[i],inputMaximum[i],nInputPartitions[i]);
}
FuzzyPartition outputPartitions=new FuzzyPartition(nClasses);
int localnPopulations=pc.parIslandNumber;
int lPopulation=pc.parPopSize;
System.out.println("Pop. size="+lPopulation);
System.out.println("Islands="+localnPopulations);
int lOption=0;
boolean STEADY=pc.parSteady;
int localHeight=10; //the desired maximum height of the tree
localHeight=pc.parMaxHeigth;
int nIterations=0; //the number if iterations
nIterations=pc.parIterNumber;
//the geneticalgorithm and programming parameters
int lTournament=4;
double lmutationProb=0.05;
double lmutationAmpl=0.1;
double migrationProb=0.001;
double localOptProb=0.0;
int localOptIterations=0;
double gaCrossoverProb=0.5;
double gaMutationProb=0.5;
lTournament=pc.parTourSize;
lmutationProb=pc.parMutProb;
lmutationAmpl=pc.parMutAmpl;
migrationProb=pc.parMigProb;
localOptProb=pc.parLoProb;
localOptIterations=pc.parLoIterNumber;
gaCrossoverProb=pc.parCrGAProb;
gaMutationProb=pc.parMuGAProb;
boolean lNumNiches=false;
if (STEADY) {
lNumNiches=pc.parNiche;
}
// Genetic Algorithm optimization
FuzzyGAPClassifier p = new FuzzyGAPClassifier(inputPartitions,outputPartitions,localHeight,pc.parFitnessType,rand);
p.setExamples(X,C);
GeneticAlgorithm AG;
int gaCrossoverID=OperatorIdent.GAPCROSSGA; int gpCrossoverID=OperatorIdent.GAPCROSSGP;
int gaMutationID=OperatorIdent.GAPMUTAGA; int gpMutationID=OperatorIdent.GAPMUTAGP;
if (STEADY) {
if (lNumNiches) AG=new AlgorithmGAPNiches(p,lPopulation,lTournament,lmutationProb,lmutationAmpl,
migrationProb,gaCrossoverProb,gaMutationProb,localnPopulations,localOptProb,localOptIterations,OperatorIdent.AMEBA,
rand,gaCrossoverID,gpCrossoverID,gaMutationID,gpMutationID);
else AG=new AlgorithmGAPSteady(p,lPopulation,lTournament,lmutationProb,lmutationAmpl,migrationProb,
gaCrossoverProb,gaMutationProb,localnPopulations,localOptProb,localOptIterations,OperatorIdent.AMEBA,
rand,gaCrossoverID,gpCrossoverID,gaMutationID,gpMutationID);
}
else AG=new AlgorithmGAPGen(p,lPopulation,localnPopulations,lmutationProb,lmutationAmpl,migrationProb,
gaCrossoverProb,gaMutationProb,localOptProb,localOptIterations,OperatorIdent.AMEBA,rand,
gaCrossoverID,gpCrossoverID,gaMutationID,gpMutationID);
p=(FuzzyGAPClassifier)AG.evolve(nIterations);
// Result is printed
p.debug();
System.out.println("Train error= "+p.fitness());
pc.trainingResults(C,p.getCo());
ProcessDataset pdt = new ProcessDataset();
int nTest,nTestInputs,nTestVariables;
readALine=(String)pc.parInputData.get(ProcessConfig.IndexTest);
if (pc.parNewFormat) pdt.processClassifierDataset(readALine,false);
else pdt.oldClusteringProcess(readALine);
nTest = pdt.getNdata();
nTestVariables = pdt.getNvariables();
nTestInputs = pdt.getNinputs();
if (nTestInputs!=nInputs) throw new IOException("IOError in test file");
double[][] Xp=pdt.getX(); int [] Cp=pdt.getC();
pdt.showDatasetStatistics();
p.setExamples(Xp,Cp);
System.out.println("Test error "+p.fitness());
pc.results(Cp,p.getCo());
} catch(FileNotFoundException e) {
System.err.println(e+" Train file not found");
} catch(IOException e) {
System.err.println(e+" Read Error");
} catch(invalidFitness e) {
System.err.println(e);
} catch(invalidCrossover e) {
System.err.println(e);
} catch(invalidMutation e) {
System.err.println(e);
} catch(invalidOptim e) {
System.err.println(e);
}
}
/**
* <p>
* This public static method runs the algorithm that this class concerns with.
* </p>
* @param args Array of strings to sent parameters to the main program. The
* path of the algorithm's parameters file must be given.
*/
public static void main(String args[]) {
boolean tty=false;
ProcessConfig pc=new ProcessConfig();
System.out.println("Reading configuration file: "+args[0]);
if (pc.fileProcess(args[0])<0) return;
int algo=pc.parAlgorithmType;
rand=new Randomize();
rand.setSeed(pc.parSeed);
ClassifierFuzzyGAP pi=new ClassifierFuzzyGAP();
pi.fuzzyGAPClassifier(tty,pc);
}
}