unbalancedcrossover.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.Attribute;
import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.example.AttributeWeightedExampleSet;
import edu.udo.cs.yale.tools.RandomGenerator;

import java.util.List;
import java.util.Iterator;
import java.util.LinkedList;

/** This <tt>PopulationOperator</tt> applies a crossover on two example sets. 
 *  Crossover type can be ONE_POINT or UNIFORM. In difference to SelectionCrossover 
 *  the attribute vectors can have different lengths. <br>
 *  This crossover type should only be used for SINGLE_VALUEs, 
 *  i.e. attributes without a block number!
 *
 *  @author ingo
 *  @version $Id: UnbalancedCrossover.java,v 2.9 2004/08/27 11:57:36 ingomierswa Exp $
 */
public class UnbalancedCrossover extends SelectionCrossover {

    private class AttributeWeightContainer {
	private Attribute attribute;
	private double weight;
	public AttributeWeightContainer(Attribute attribute, double weight) {
	    this.attribute = attribute;
	    this.weight    = weight;
	}
	public Attribute getAttribute() { return attribute; }
	public double getWeight() { return weight; }
	public String toString() {
	    return attribute.getName() + "(" + weight + ")";
	}
    }

    /** Creates a new generating crossover with the given type which will
     *  be applied with the given probability. */
    public UnbalancedCrossover(int type, double prob) {
	super(type, prob);
    }

    /** Applies the crossover. Works directly on the given example sets. */
    public void crossover(AttributeWeightedExampleSet es1, AttributeWeightedExampleSet es2) {
	LinkedList dummyList1 = new LinkedList();
	LinkedList dummyList2 = new LinkedList();
	int maxSize = Math.max(es1.getNumberOfAttributes(), es2.getNumberOfAttributes());
	if (maxSize < 2) return;

	switch (getType()) {
	case SelectionCrossover.ONE_POINT:
	    int splitPoint = 1 + RandomGenerator.getGlobalRandomGenerator().nextInt(maxSize - 2);
	    while (es1.getNumberOfAttributes() > splitPoint) {
		double weight = es1.getWeight(es1.getAttribute(splitPoint));
		Attribute attribute = es1.removeAttribute(splitPoint);
		dummyList1.add(new AttributeWeightContainer(attribute, weight));
	    }
	    while (es2.getNumberOfAttributes() > splitPoint) {
		double weight = es2.getWeight(es2.getAttribute(splitPoint));
		Attribute attribute = es2.removeAttribute(splitPoint);
		dummyList2.add(new AttributeWeightContainer(attribute, weight));
	    }
	    break;

	case SelectionCrossover.UNIFORM:
	    for (int i = es1.getNumberOfAttributes() - 1; i >= 0; i--) {
		if (RandomGenerator.getGlobalRandomGenerator().nextBoolean()) {
		    Attribute attribute = es1.getAttribute(i);
		    double weight = es1.getWeight(attribute);
		    es1.removeAttribute(attribute);
		    dummyList1.add(new AttributeWeightContainer(attribute, weight));
		}
	    }
	    for (int i = es2.getNumberOfAttributes() - 1; i >= 0; i--) {
		if (RandomGenerator.getGlobalRandomGenerator().nextBoolean()) {
		    Attribute attribute = es2.getAttribute(i);
		    double weight = es2.getWeight(attribute);
		    es2.removeAttribute(attribute);
		    dummyList2.add(new AttributeWeightContainer(attribute, weight));
		}
	    }
	    break;
	default:
	}

	mergeAttributes(es1, dummyList2);
	mergeAttributes(es2, dummyList1);
    }
    
    private void mergeAttributes(AttributeWeightedExampleSet exampleSet, List attributeWeights) {
	Iterator i = attributeWeights.iterator();
	while (i.hasNext()) {
	    AttributeWeightContainer attributeWeight = (AttributeWeightContainer)i.next();
	    Attribute attribute = attributeWeight.getAttribute();
	    if (exampleSet.getAttribute(attribute.getName()) == null) {
		exampleSet.addAttribute(attribute);
	    }
	    exampleSet.setWeight(attribute, attributeWeight.getWeight());
	}
    }
}