/***********************************************************************
This file is part of KEEL-software, the Data Mining tool for regression,
classification, clustering, pattern mining and so on.
Copyright (C) 2004-2010
F. Herrera (herrera@decsai.ugr.es)
L. S�nchez (luciano@uniovi.es)
J. Alcal�-Fdez (jalcala@decsai.ugr.es)
S. Garc�a (sglopez@ujaen.es)
A. Fern�ndez (alberto.fernandez@ujaen.es)
J. Luengo (julianlm@decsai.ugr.es)
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 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 General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see http://www.gnu.org/licenses/
**********************************************************************/
/**
* <p>
* @author Written by Albert Orriols (La Salle, Ram�n Llull University - Barcelona) 28/03/2004
* @author Modified by Xavi Sol� (La Salle, Ram�n Llull University - Barcelona) 03/12/2008
* @version 1.1
* @since JDK1.2
* </p>
*/
package keel.Algorithms.Genetic_Rule_Learning.XCS;
import keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config;
import java.util.*;
import java.lang.*;
import java.util.*;
public class Specify {
/**
* <p>
* The class implement the specify operator proposed by Lanzi
* </p>
*/
///////////////////////////////////////
// operations
/**
* <p> It's the constructor of the class
*
*/
Specify(){}
/**
* <p>
* It applies the specify operator to the population. If the average
* prediction error of the action set is twice larger than the average
* prediction error of the population and the classifiers in the action set
* have been updated Nsp times (Config), then, a classifier is
* selected randomly from the action set (with probability proportional to
* its prediction error), and its don't care caracters are replaced with a
* probability Psp (Config) with the corresponding digit in the
* system input.
* </p>
* <p>
*
* @param pop is the Population
* </p>
* <p>
* @param actionSet is the action set of that iteration.
* </p>
* <p>
* @param envState is the environmental state (the input).
* </p>
*/
public void makeSpecify(Population pop, Population actionSet, double[] envState, int tStamp) {
int i=0;
if ( actionSet.getPredErrorAverage() >= 2. * pop.getPredErrorAverage() && actionSet.getExperienceAverage() > Config.Nspecify){
Roulette rul = new Roulette(actionSet.getMacroClSum());
for (i=0; i<actionSet.getMacroClSum(); i++){
rul.add (actionSet.getClassifier(i).getPredError() * actionSet.getClassifier(i).getNumerosity());
}
i = rul.selectRoulette();
Classifier cl = actionSet.getClassifier(i); // The classifier with the bigest prediction error is get.
cl.setPredError (cl.getPredError() * Config.predictionErrorReduction);
Classifier clOffspring = new Classifier(cl, tStamp); //Creates a copy of the classifier.
clOffspring.makeSpecify (envState); // It changes all don't care symbols with Psp probability.
clOffspring.calculateGenerality();
if (clOffspring.match(envState)) //If the new classifier matches with the environment.
pop.insertInPopulation(clOffspring,actionSet); //It inserts the new classifier in the population and deletes one if there isn't space enough.
else
pop.insertInPopulation(clOffspring,null);
} //else the specify operator has not to be applied
} // end doSpecify
} // end Specify