/*
 * 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.
 *   
 * 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.mathutil.randomize.NguyenWidrowRandomizer;
import org.encog.mathutil.randomize.Randomizer;
import org.encog.ml.CalculateScore;
import org.encog.ml.MLMethod;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.factory.MLTrainFactory;
import org.encog.ml.factory.parse.ArchitectureParse;
import org.encog.ml.train.MLTrain;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.training.TrainingSetScore;
import org.encog.neural.networks.training.pso.NeuralPSO;
import org.encog.util.ParamsHolder;

/**
 * A factory for quick propagation training.
 * 
 */
public class PSOFactory {

	/**
	 * Create a PSO 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) {

		final Map args = ArchitectureParse.parseParams(argsStr);
		final ParamsHolder holder = new ParamsHolder(args);

		final int particles = holder.getInt(
				MLTrainFactory.PROPERTY_PARTICLES, false, 20);
		
		CalculateScore score = new TrainingSetScore(training);
		Randomizer randomizer = new NguyenWidrowRandomizer();
		
		final MLTrain train = new NeuralPSO((BasicNetwork)method,randomizer,score,particles);
		
		return train;
	}
}