/*
* RapidMiner
*
* Copyright (C) 2001-2008 by Rapid-I and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapid-i.com
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License as published by
* the Free Software Foundation, either version 3 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 Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.features.weighting;
import java.util.LinkedList;
import java.util.List;
import java.util.Random;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.set.AttributeWeightedExampleSet;
import com.rapidminer.operator.features.Individual;
import com.rapidminer.operator.features.IndividualOperator;
/**
* Changes the weight for all attributes by multiplying them with a gaussian
* distribution.
*
* @author Ingo Mierswa
* @version $Id: WeightingMutation.java,v 1.16 2006/03/27 13:22:00 ingomierswa
* Exp $
*/
public class WeightingMutation extends IndividualOperator {
private double variance;
private boolean bounded;
private Random random;
public WeightingMutation(double variance, boolean bounded, Random random) {
this.variance = variance;
this.bounded = bounded;
this.random = random;
}
public void setVariance(double variance) {
this.variance = variance;
}
public double getVariance() {
return variance;
}
public List<Individual> operate(Individual individual) {
AttributeWeightedExampleSet exampleSet = individual.getExampleSet();
List<Individual> l = new LinkedList<Individual>();
for (Attribute attribute : exampleSet.getAttributes()) {
double weight = exampleSet.getWeight(attribute);
weight = weight + random.nextGaussian() * variance;
if ((!bounded) || ((weight >= 0) && (weight <= 1)))
exampleSet.setWeight(attribute, weight);
}
if (exampleSet.getNumberOfUsedAttributes() > 0)
l.add(new Individual(exampleSet));
return l;
}
}