/* * 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.genetic; import org.encog.ml.MLEncodable; import org.encog.ml.genetic.genome.DoubleArrayGenome; /** * Implements a genome that allows a feedforward neural network to be trained * using a genetic algorithm. The chromosome for a feed forward neural network * is the weight and bias matrix. */ public class MLMethodGenome extends DoubleArrayGenome { /** * Serial id. */ private static final long serialVersionUID = 1L; /** * The phenome. */ private MLEncodable phenotype; /** * Construct a neural genome. * * @param thePhenotype * The phenotype to use. */ public MLMethodGenome(final MLEncodable thePhenotype) { super(thePhenotype.encodedArrayLength()); this.phenotype = thePhenotype; this.phenotype.encodeToArray(getData()); } /** * Decode the phenotype. */ public void decode() { this.phenotype.decodeFromArray(getData()); } /** * @return the phenotype */ public MLEncodable getPhenotype() { return this.phenotype; } /** * @param phenotype * the phenotype to set */ public void setPhenotype(final MLEncodable phenotype) { this.phenotype = phenotype; } }