/*
* Encog(tm) Core v3.4 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2016 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
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* and trademarks visit:
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*/
package org.encog.ml.factory.train;
import org.encog.ml.CalculateScore;
import org.encog.ml.MLMethod;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.ea.score.adjust.ComplexityAdjustedScore;
import org.encog.ml.ea.train.basic.TrainEA;
import org.encog.ml.prg.PrgCODEC;
import org.encog.ml.prg.opp.ConstMutation;
import org.encog.ml.prg.opp.SubtreeCrossover;
import org.encog.ml.prg.opp.SubtreeMutation;
import org.encog.ml.prg.species.PrgSpeciation;
import org.encog.ml.prg.train.PrgPopulation;
import org.encog.ml.prg.train.rewrite.RewriteAlgebraic;
import org.encog.ml.prg.train.rewrite.RewriteConstants;
import org.encog.ml.train.MLTrain;
import org.encog.neural.networks.training.TrainingSetScore;
public class EPLGAFactory {
/**
* Create an EPL GA trainer.
*
* @param method
* The method to use.
* @param training
* The training data to use.
* @param argsStr
* The arguments to use.
* @return The newly created trainer.
*/
public MLTrain create(final MLMethod method,
final MLDataSet training, final String argsStr) {
PrgPopulation pop = (PrgPopulation)method;
final CalculateScore score = new TrainingSetScore(training);
TrainEA train = new TrainEA(pop, score);
pop.getRules().addRewriteRule(new RewriteConstants());
pop.getRules().addRewriteRule(new RewriteAlgebraic());
train.setCODEC(new PrgCODEC());
train.addOperation(0.8, new SubtreeCrossover());
train.addOperation(0.1, new SubtreeMutation(pop.getContext(),4));
train.addOperation(0.1, new ConstMutation(pop.getContext(),0.5,1.0));
train.addScoreAdjuster(new ComplexityAdjustedScore());
train.setSpeciation(new PrgSpeciation());
return train;
}
}