yagga.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.operator.OperatorException;
import edu.udo.cs.yale.operator.parameter.*;
import edu.udo.cs.yale.generator.*;
import edu.udo.cs.yale.operator.features.*;
import edu.udo.cs.yale.tools.LogService;
import edu.udo.cs.yale.example.ExampleSet;

import java.util.ArrayList;
import java.util.List;
import java.util.LinkedList;

/** YAGGA is an acronym for Yet Another Generating Genetic Algorithm. 
 *  Its approach to generating new attributes differs from the original one. 
 *  The (generating) mutation can do one of the following things with 
 *  different probabilities:
 *  <ul>
 *    <li>Probability {@yale.math p/4}: Add a newly generated attribute to the feature vector</li>
 *    <li>Probability {@yale.math p/4}: Add a randomly chosen original attribute to the feature vector</li>
 *    <li>Probability {@yale.math p/2}: Remove a randomly chosen attribute from the feature vector</li>
 *  </ul>
 *  Thus it is guaranteed that the length of the feature vector can both
 *  grow and shrink. On average it will keep its original length, unless
 *  longer or shorter individuals prove to have a better fitness.
 *
 *  Since this operator does not contain algorithms to extract features from value series, it is restricted
 *  to example sets with only single attributes. For (automatic) feature extraction from values series the 
 *  value series plugin for Yale written by Ingo Mierswa should be used. It is available at 
 *  <a href="http://yale.cs.uni-dortmund.de">http://yale.cs.uni-dortmund.de</a>.
 *
 *  @yale.xmlclass YAGGA
 *  @author ingo, simon
 *  @version $Id: YAGGA.java,v 2.16 2004/08/27 11:57:36 ingomierswa Exp $
 */
public class YAGGA extends AbstractGeneratingGeneticAlgorithm {

    /** Since the mutation of YAGGA also creates new attributes this method returns null. */
    protected PopulationOperator getGeneratingPopulationOperator() { return null; }

    /** Returns the generating mutation <code>PopulationOperator</code>. */
    protected PopulationOperator getMutationPopulationOperator() throws OperatorException {	
	List generators = getGenerators();
	if (generators.size()==0) {
	    LogService.logMessage("No FeatureGenerators specified for " + getName() + ".", LogService.WARNING);
	}
	ExampleSet eSet = (ExampleSet)getInput(ExampleSet.class, false);
	List attributes = new LinkedList();
	for (int i = 0; i < eSet.getNumberOfAttributes(); i++) {
	    attributes.add(eSet.getAttribute(i));
	}
	double pMutation = getParameterAsDouble("p_mutation");
 	return new GeneratingMutation(attributes, pMutation, generators);
    }    

    /** Creates a initial population. */
    public Population createInitialPopulation(ExampleSet es) {
	Population population = super.createInitialPopulation(es);
	Population popRemovedDeselected = new Population();
	for (int i = 0; i < population.getNumberOfIndividuals(); i++) {
	    popRemovedDeselected.add(population.get(i).createCleanExampleSet());
	}
	return popRemovedDeselected;
    }

    public List getParameterTypes() {
	List types = super.getParameterTypes();
	ParameterType type = new ParameterTypeDouble("p_mutation", 
						     "Probability for mutation.", 
						     0, 1, 0.5);
	type.setExpert(false);
	types.add(type);
	return types;
    }
}