/* * 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.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.manhattan.ManhattanPropagation; import org.encog.util.ParamsHolder; /** * A factory for Manhattan training. * */ public class ManhattanFactory { /** * Create a Manhattan 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<String, String> args = ArchitectureParse.parseParams(argsStr); final ParamsHolder holder = new ParamsHolder(args); final double learningRate = holder.getDouble( MLTrainFactory.PROPERTY_LEARNING_RATE, false, 0.1); return new ManhattanPropagation((BasicNetwork) method, training, learningRate); } }