/*
 * Encog(tm) Core v3.1 - Java Version
 * http://www.heatonresearch.com/encog/
 * http://code.google.com/p/encog-java/
 
 * Copyright 2008-2012 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.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.propagation.back.Backpropagation;
import org.encog.util.ParamsHolder;

/**
 * A factory for backpropagation training.
 * 
 */
public class BackPropFactory {

	/**
	 * Create a backpropagation 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 final MLTrain create(final MLMethod method,
			final MLDataSet training, final String argsStr) {

		final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
		final ParamsHolder holder = new ParamsHolder(args);

		final double learningRate = holder.getDouble(
				MLTrainFactory.PROPERTY_LEARNING_RATE, false, 0.7);
		final double momentum = holder.getDouble(
				MLTrainFactory.PROPERTY_LEARNING_MOMENTUM, false, 0.3);

		return new Backpropagation((BasicNetwork) method, training,
				learningRate, momentum);
	}
}
