forwardweighting.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.features.*;
import edu.udo.cs.yale.tools.LogService;

/** 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. 
 *   
 *  @version $Id: ForwardWeighting.java,v 1.2 2003/08/27 21:47:21 mierswa Exp $
 */
public class ForwardWeighting extends FeatureWeighting {

    public PopulationOperator getWeightingOperator(String parameter) {
	double[] weights = new double[] { 0.25d, 0.5d, 0.75d, 1.0d };
	if ((parameter != null) && (parameter.length() != 0)) {
	    try {
		String[] weightStrings = parameter.split(" ");
		weights = new double[weightStrings.length];
		for (int i = 0; i < weights.length; i++)
		    weights[i] = Double.parseDouble(weightStrings[i]);
	    } catch (Exception e) {
		LogService.logMessage("Could not create weights: " + e.getMessage(), LogService.ERROR);
		weights = new double[] { 0.25d, 0.5d, 0.75d, 1.0d };
	    }
	}
	return new SimpleWeighting(0.0d, weights);
    }

    public Population createInitialPopulation(ExampleSet es) {
	Population initPop = new Population();
	AttributeWeightedExampleSet nes = new AttributeWeightedExampleSet((ExampleSet)es.clone());
	for (int i = 0; i < es.getNumberOfAttributes(); i++)
	    nes.setWeight(i, 0.0d);
	for (int i = 0; i < es.getNumberOfAttributes(); i++) {
	    AttributeWeightedExampleSet forwardES = (AttributeWeightedExampleSet)nes.clone();
	    forwardES.setWeight(i, 1.0d);
	    initPop.add(forwardES);
	}
	return initPop;
    }
}