directedattributegenerator.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.ga;

import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.tools.RandomGenerator;
import edu.udo.cs.yale.generator.*;
import edu.udo.cs.yale.example.Attribute;
import edu.udo.cs.yale.example.Tools;
import edu.udo.cs.yale.operator.features.*;
import edu.udo.cs.yale.operator.AttributeInformationGain;

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

/** In contrast to its superclass this AttributeGenerator selects the arguments for the 
 *  generation of new attributes according to their information gain. The probability
 *  is determined similar to the calculation in DirectedMutation.
 *
 *  @author ingo
 *  @version $Id: DirectedAttributeGenerator.java,v 2.2 2003/04/04 11:59:28 fischer Exp $
 */
public class DirectedAttributeGenerator extends AttributeGenerator {


    /** Left margin 
     */
    double leftBound;

    /** Right margin
     */
    double rightBound;


    /** Constructs a new <tt>DirectedAttributeGenerator</tt>. Parameters for the generation probability, maximal
     *  number of new attribtues and a list of applicable generators. Information gain values are scaled to
     *  the given bounds.
     */
    public DirectedAttributeGenerator(double pGenerate, int numberOfNewAttributes, ArrayList generatorList, 
				      double leftBound, double rightBound) {
	super(pGenerate, numberOfNewAttributes, generatorList);
	this.leftBound  = leftBound;
	this.rightBound = rightBound;
	
    }

    Attribute[] getArguments(ExampleSet exampleSet, Attribute[] expectedInputAttributes) {
	Attribute[] arguments = new Attribute[expectedInputAttributes.length];

	double smallest = ((Double)exampleSet.getUserData(AttributeInformationGain.SMALLEST_INFORMATION_GAIN_KEY)).doubleValue();
	double greatest = 1;
	double sum = 0;
	double[] shouldBeUsed = new double[exampleSet.getNumberOfAttributes()];
	for (int i = 0 ; i < exampleSet.getNumberOfAttributes(); i++) {
	    double[] informationGain = (double[])exampleSet.getUserData(AttributeInformationGain.SMALLEST_INFORMATION_GAIN_KEY);
	    shouldBeUsed[i] = 
		(((informationGain[i] - smallest) / 
		  (greatest - smallest))
		 * (rightBound - leftBound)) 
		+ leftBound;
	    sum += shouldBeUsed[i];
	}

	for (int i = 0 ; i < exampleSet.getNumberOfAttributes(); i++) 
	    shouldBeUsed[i] /= sum;

	
	for (int j = 0 ; j < expectedInputAttributes.length ; j++){
	    Attribute attribute = expectedInputAttributes[j];
	    Attribute[] compatibleAttributes = Tools.getCompatibleAttributes(exampleSet, attribute);

	    double currentProb = RandomGenerator.getGlobalRandomGenerator().nextDouble(); 
	    double bound = 0; 
	    for (int i = 0; i < compatibleAttributes.length; i++) {
		bound += shouldBeUsed[i];
		if (currentProb < bound) {
		    arguments[j] = compatibleAttributes[i];
		    break;
		}
	    }

	}
	return arguments;
    }
}