/*
* 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.Iterator;
import java.util.LinkedList;
import com.rapidminer.operator.features.Population;
import com.rapidminer.operator.features.PopulationOperator;
/**
* Implements the 1/5-Rule for dynamic parameter adaption of the variance of a
* {@link WeightingMutation}.
*
* @author Ingo Mierswa
* @version $Id: VarianceAdaption.java,v 1.12 2006/03/27 13:22:00 ingomierswa
* Exp $
*/
public class VarianceAdaption implements PopulationOperator {
/** The weighting mutation. */
private WeightingMutation weightingMutation = null;
/** The interval size in which the new variance is calculated. */
private int intervalSize = 400;
/** Remember for all positions if an improval was found. */
private LinkedList<Boolean> successList = new LinkedList<Boolean>();
/**
* The interval size should be as big as the changeable components, i.e. the
* number of attributes.
*/
public VarianceAdaption(WeightingMutation weightingMutation, int intervalSize) {
this.weightingMutation = weightingMutation;
this.intervalSize = intervalSize;
}
/** The default implementation returns true for every generation. */
public boolean performOperation(int generation) {
return true;
}
public void operate(Population population) {
if (population.getGenerationsWithoutImproval() < 2)
successList.add(true);
else
successList.add(false);
if (population.getGeneration() >= 10 * intervalSize) {
successList.removeFirst();
if ((population.getGeneration() % intervalSize) == 0) {
int successCount = 0;
Iterator<Boolean> i = successList.iterator();
while (i.hasNext())
if (i.next())
successCount++;
if ((successCount / (10.0d * intervalSize)) < 0.2) {
weightingMutation.setVariance(weightingMutation.getVariance() * 0.85d);
} else {
weightingMutation.setVariance(weightingMutation.getVariance() / 0.85d);
}
}
}
}
}