roulettewheel.java

一个很好的LIBSVM的JAVA源码。对于要研究和改进SVM算法的学者。可以参考。来自数据挖掘工具YALE工具包。

/*
 *  YALE - Yet Another Learning Environment
 *  Copyright (C) 2001-2004
 *      Simon Fischer, Ralf Klinkenberg, Ingo Mierswa, 
 *          Katharina Morik, Oliver Ritthoff
 *      Artificial Intelligence Unit
 *      Computer Science Department
 *      University of Dortmund
 *      44221 Dortmund,  Germany
 *  email: [e-mail]
 *  web:   http://yale.cs.uni-dortmund.de/
 *
 *  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 2 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, write to the Free Software
 *  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307
 *  USA.
 */
package edu.udo.cs.yale.operator.features.ga;

import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.example.AttributeWeightedExampleSet;
import edu.udo.cs.yale.operator.features.*;
import edu.udo.cs.yale.operator.performance.PerformanceVector;
import edu.udo.cs.yale.tools.RandomGenerator;

import java.util.List;
import java.util.LinkedList;
import java.util.ListIterator;

/** Selects a given fixed number of individuals by subdividing a roulette wheel in
 *  sections of size proportional to the individuals' fitness values. Optionally
 *  keep the best individual.
 *
 *  @author simon, ingo
 *  @version $Id: RouletteWheel.java,v 2.11 2004/08/27 11:57:36 ingomierswa Exp $
 */
public class RouletteWheel implements PopulationOperator {

    private int popSize;
    private boolean keepBest;

    public RouletteWheel(int popSize, boolean keepBest) {
	this.popSize = popSize;
	this.keepBest = keepBest;
    }

    /** The default implementation returns true for every generation. */
    public boolean performOperation(int generation) { return true; }

    public void operate(Population population) {
	List newGeneration = new LinkedList();
	if (keepBest) {
	    AttributeWeightedExampleSet best = (AttributeWeightedExampleSet)population.bestEver().clone();
	    newGeneration.add(best);
	}

	double fitnessSum = 0;
	for (int i = 0; i < population.getNumberOfIndividuals(); i++) {
	    fitnessSum += ((PerformanceVector)population.get(i).getUserData("performance")).getMainCriterion().getFitness();
	}

	while (newGeneration.size() < popSize) {
	    double r = fitnessSum*RandomGenerator.getGlobalRandomGenerator().nextDouble();
	    int j = 0;
	    double f = 0;
	    ExampleSet exampleSet = null;
	    do {
		exampleSet = population.get(j++);
		f += ((PerformanceVector)exampleSet.getUserData("performance")).getMainCriterion().getFitness();
	    } while (f < r);
	    newGeneration.add(exampleSet);
	}

	population.clear();
	ListIterator i = newGeneration.listIterator();
	while (i.hasNext()) {
	    population.add((AttributeWeightedExampleSet)i.next());
	}
    }
}