/* * 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.networks.neat.training.species; import java.util.ArrayList; import java.util.Collections; import java.util.List; import org.encog.ml.CalculateScore; import org.encog.ml.data.MLDataSet; import org.encog.ml.data.basic.BasicMLDataSet; import org.encog.ml.ea.sort.SortGenomesForSpecies; import org.encog.ml.ea.train.EvolutionaryAlgorithm; import org.encog.neural.neat.NEATPopulation; import org.encog.neural.neat.NEATUtil; import org.encog.neural.neat.training.NEATGenome; import org.encog.neural.networks.XOR; import org.encog.neural.networks.training.TrainingSetScore; import org.junit.Assert; import org.junit.Test; public class TestSortGenomesForSpecies { @Test public void testSort1() { MLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL); NEATPopulation pop = new NEATPopulation(2,1,100); pop.reset(); CalculateScore score = new TrainingSetScore(trainingSet); final EvolutionaryAlgorithm train = NEATUtil.constructNEATTrainer(pop,score); NEATGenome genome1 = new NEATGenome(); genome1.setAdjustedScore(3.0); NEATGenome genome2 = new NEATGenome(); genome2.setAdjustedScore(2.0); NEATGenome genome3 = new NEATGenome(); genome3.setAdjustedScore(1.0); List<NEATGenome> list = new ArrayList<NEATGenome>(); list.add(genome1); list.add(genome2); list.add(genome3); Collections.sort(list,new SortGenomesForSpecies(train)); Assert.assertTrue(list.get(0)==genome3); Assert.assertTrue(list.get(1)==genome2); Assert.assertTrue(list.get(2)==genome1); } @Test public void testSort2() { MLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL); NEATPopulation pop = new NEATPopulation(2,1,100); pop.reset(); CalculateScore score = new TrainingSetScore(trainingSet); final EvolutionaryAlgorithm train = NEATUtil.constructNEATTrainer(pop,score); NEATGenome genome1 = new NEATGenome(); genome1.setAdjustedScore(3.0); NEATGenome genome2 = new NEATGenome(); genome2.setAdjustedScore(2.0); genome2.setBirthGeneration(200); NEATGenome genome3 = new NEATGenome(); genome3.setAdjustedScore(2.0); genome3.setBirthGeneration(100); List<NEATGenome> list = new ArrayList<NEATGenome>(); list.add(genome1); list.add(genome2); list.add(genome3); Collections.sort(list,new SortGenomesForSpecies(train)); Assert.assertTrue(list.get(0)==genome2); Assert.assertTrue(list.get(1)==genome3); Assert.assertTrue(list.get(2)==genome1); } }