featureweighting.java

著名的开源仿真软件yale

/*
 *  YALE - Yet Another Learning Environment
 *  Copyright (C) 2002, 2003
 *      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.ExampleSet;
import edu.udo.cs.yale.example.AttributeWeightedExampleSet;
import edu.udo.cs.yale.operator.parameter.*;
import edu.udo.cs.yale.operator.features.*;
import edu.udo.cs.yale.operator.OperatorException;

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

/** This operator performs the weighting under the naive assumption that the features are independent from each other.
 *  Each attribute is weighted with a linear search. This approach may deliver good results after short time if 
 *  the features indeed are not highly correlated. <br />
 *  The ideas of forward selection and backward elimination can easily be used for the weighting with help 
 *  of a {@link SimpleWeighting}.
 *   
 *  @version $Id: FeatureWeighting.java,v 1.3 2003/08/27 21:47:21 mierswa Exp $
 */
public abstract class FeatureWeighting extends FeatureOperator {

    private List preOps  = new LinkedList();
    private List postOps = new LinkedList();
    private int generationsWOImp = 0;

    public abstract PopulationOperator getWeightingOperator(String parameter);

    public void initApply() throws OperatorException {
	super.initApply();
	generationsWOImp = getParameterAsInt("generations_without_improval");

	preOps = new LinkedList();
	preOps.add(new KeepBest(getParameterAsInt("keep_best")));
	preOps.add(getWeightingOperator(getParameterAsString("weights")));
	preOps.add(new RedundanceRemoval());

	postOps = new LinkedList();
    }

    public boolean solutionGoodEnough(Population population) {
	boolean stop = population.empty() || (population.getGenerationsWithoutImproval() >= generationsWOImp);
	return stop;
    }

    public List getPreEvaluationPopulationOperators() {
	return preOps;
    }

    public List getPostEvaluationPopulationOperators() {
	return postOps;
    }

    public List getParameterTypes() {
	List types = super.getParameterTypes();
	types.add(new ParameterTypeInt("keep_best", "Keep the best n individuals in each generation.", 1, Integer.MAX_VALUE, 1));
	types.add(new ParameterTypeInt("generations_without_improval", "Stop after n generations without improval of the performance.", 1, Integer.MAX_VALUE, 1));
	types.add(new ParameterTypeString("weights", "Use these weights for the creation of individuals in each generation.", true));
	return types;
    }
}