/***********************************************************************
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.Shared.Individual;
import keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Classifier.*;
import keel.Algorithms.Shared.Exceptions.*;
/**
* Class for management of genetic individuals in classification.
* Need: the examples with the class, the classifier and a variable for results
* <p>
* @author Written by Luciano S�nchez (University of Oviedo) 20/01/2004
* @author Modified by M.R. Su�rez (University of Oviedo) 18/12/2008
* @author Modified by Enrique A. de la Cal (University of Oviedo) 21/12/2008
* @version 1.0
* @since JDK1.5
* </p>
*/
public abstract class GeneticIndividualForClassification extends GeneticIndividual {
protected static double[][] X;
protected static int[] C;
protected Classifier c;
protected static int[]Co;
/**
* <p>
* Constructor. Initialize the type of fitness
* @param tf The type of fitness
*/
public GeneticIndividualForClassification(int tf) { super(tf); }
/**
* <p>
* This method calculate the classification error using the examples set
* </p>
* @return The classification error
* @throws invalidFitness Message if error
*/
public double fitness() throws invalidFitness {
if (fitnessType != STANDARD) throw new invalidFitness("Fitness no valido");
// Classification error is calculated using the samples set
double classificationError=0;
for (int i=0;i<X.length;i++) {
Co[i]=c.getMaximum(X[i]);
if (Co[i] != C[i]) classificationError++;
}
classificationError/=X.length;
return classificationError;
}
/**
* <p>
* This method is for debug
* </p>
*/
public void debug() {
g.debug();
c.debug();
}
/**
* <p>
* This method return the result of classification
* </p>
* @return The result of classification
*/
public int[] getCo() { return Co; }
/**
* <p>
* This method initialize the examples and create a new classifier
* </p>
* @param pX The set of examples
* @param pC The sets of classes
*/
public void setExamples(double[][] pX, int[] pC) {
X=pX; C=pC; Co=new int[pC.length];
}
}