/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.ml.factory.train;
import java.util.Map;
import org.encog.ml.CalculateScore;
import org.encog.ml.MLEncodable;
import org.encog.ml.MLMethod;
import org.encog.ml.MLResettable;
import org.encog.ml.MethodFactory;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.factory.MLTrainFactory;
import org.encog.ml.factory.parse.ArchitectureParse;
import org.encog.ml.genetic.MLMethodGeneticAlgorithm;
import org.encog.ml.train.MLTrain;
import org.encog.neural.networks.training.TrainingError;
import org.encog.neural.networks.training.TrainingSetScore;
import org.encog.util.ParamsHolder;
import org.encog.util.obj.ObjectCloner;
/**
* A factory to create genetic algorithm trainers.
*/
public class GeneticFactory {
/**
* Create an annealing 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) {
if (!(method instanceof MLEncodable)) {
throw new TrainingError(
"Invalid method type, requires an encodable MLMethod");
}
final CalculateScore score = new TrainingSetScore(training);
final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
final ParamsHolder holder = new ParamsHolder(args);
final int populationSize = holder.getInt(
MLTrainFactory.PROPERTY_POPULATION_SIZE, false, 5000);
MLTrain train = new MLMethodGeneticAlgorithm(new MethodFactory(){
@Override
public MLMethod factor() {
final MLMethod result = (MLMethod) ObjectCloner.deepCopy(method);
((MLResettable)result).reset();
return result;
}}, score, populationSize);
return train;
}
}