weightingmutation.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.example.Attribute;
import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.example.AttributeWeightedExampleSet;
import edu.udo.cs.yale.tools.RandomGenerator;
import edu.udo.cs.yale.operator.features.*;

import java.util.LinkedList;
import java.util.List;
import java.util.Random;

/** Changes the weight for all attributes by multiplying them with a gaussian distribution.
 *
 *  @version $Id: WeightingMutation.java,v 1.8 2004/08/27 11:57:37 ingomierswa Exp $
 */
public class WeightingMutation extends IndividualOperator {

    private double variance;
    private boolean bounded;
    private Random random;

    public WeightingMutation(double variance, boolean bounded) {
	this.variance = variance;
	this.bounded  = bounded;
	this.random   = RandomGenerator.getGlobalRandomGenerator();
    }

    public void setVariance(double variance) {
	this.variance = variance;
    }

    public double getVariance() {
	return variance;
    }

    public List operate(AttributeWeightedExampleSet exampleSet) {
	List l = new LinkedList();
	AttributeWeightedExampleSet clone = (AttributeWeightedExampleSet)exampleSet.clone();
	for (int i = 0; i < exampleSet.getNumberOfAttributes(); i++) {
	    Attribute attribute = exampleSet.getAttribute(i);
	    double weight = exampleSet.getWeight(i);
	    weight = weight + random.nextGaussian() * variance;
	    if ((!bounded) || ((weight >= 0) && (weight <= 1)))
		exampleSet.setWeightForBlock(attribute, weight);
	}
	if (exampleSet.getNumberOfUsedAttributes() > 0)
	    l.add(exampleSet);
	return l;
    }
}