/* * 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.neural.neat.training.opp.links; import java.io.Serializable; import java.util.ArrayList; import java.util.List; import java.util.Random; import org.encog.ml.ea.train.EvolutionaryAlgorithm; import org.encog.neural.neat.training.NEATGenome; import org.encog.neural.neat.training.NEATLinkGene; /** * Select a random proportion of links to mutate. * * ----------------------------------------------------------------------------- * http://www.cs.ucf.edu/~kstanley/ Encog's NEAT implementation was drawn from * the following three Journal Articles. For more complete BibTeX sources, see * NEATNetwork.java. * * Evolving Neural Networks Through Augmenting Topologies * * Generating Large-Scale Neural Networks Through Discovering Geometric * Regularities * * Automatic feature selection in neuroevolution */ public class SelectProportion implements SelectLinks, Serializable { /** * The portion of the links to mutate. 0.0 for none, 1.0 for all. */ private double proportion; /** * The trainer. */ private EvolutionaryAlgorithm trainer; /** * Select based on proportion. * @param theProportion The proportion to select from. */ public SelectProportion(double theProportion) { this.proportion = theProportion; } /** * {@inheritDoc} */ @Override public void init(EvolutionaryAlgorithm theTrainer) { this.trainer = theTrainer; } /** * {@inheritDoc} */ @Override public List<NEATLinkGene> selectLinks(Random rnd, NEATGenome genome) { List<NEATLinkGene> result = new ArrayList<NEATLinkGene>(); boolean mutated = false; for (final NEATLinkGene linkGene : genome.getLinksChromosome()) { if (rnd.nextDouble() < this.proportion) { mutated = true; result.add(linkGene); } } if( !mutated ) { int idx = rnd.nextInt(genome.getLinksChromosome().size()); NEATLinkGene linkGene = genome.getLinksChromosome().get(idx); result.add(linkGene); } return result; } /** * {@inheritDoc} */ @Override public EvolutionaryAlgorithm getTrainer() { return trainer; } /** * {@inheritDoc} */ @Override public String toString() { StringBuilder result = new StringBuilder(); result.append("["); result.append(this.getClass().getSimpleName()); result.append(":proportion="); result.append(this.proportion); result.append("]"); return result.toString(); } }