varianceadaption.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.weighting;

import edu.udo.cs.yale.operator.features.*;

/** 
 *
 *  @version $Id: VarianceAdaption.java,v 1.5 2004/08/27 11:57:37 ingomierswa Exp $
 */
public class VarianceAdaption implements PopulationOperator {

    /** Factor for determining the number of changes. */
    private static final int INTERVAL_SIZE = 10;
    /** Counter for good changes. */
    private int goodCount = 0;
    /** The weighting mutation. */
    private WeightingMutation weighting = null;

    public VarianceAdaption(WeightingMutation weighting) {
	this.weighting = weighting;
    }

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

    public void operate(Population population) {
	if (population.getGenerationsWithoutImproval() == 0)
	    goodCount++;
	if (population.getNumberOfIndividuals() != 0) {
	    int delta = population.get(0).getNumberOfAttributes() * INTERVAL_SIZE;
	    if ((population.getGeneration() % delta) == 0) {
		// next adaption point
		if (((double)goodCount / (double)delta) < 0.2) {
		    weighting.setVariance(weighting.getVariance() / 2.0d);
		} else {
		    weighting.setVariance(weighting.getVariance() * 2.0d);
		}
		goodCount = 0;
	    }
	}
    }
}