/* * Java Genetic Algorithm Library (@__identifier__@). * Copyright (c) @__year__@ Franz Wilhelmstötter * * 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. * * Author: * Franz Wilhelmstötter (franz.wilhelmstoetter@gmx.at) */ package org.jenetics; import static java.lang.String.format; import static org.jenetics.stat.StatisticsAssert.assertDistribution; import static org.jenetics.util.RandomRegistry.using; import java.util.Arrays; import java.util.stream.IntStream; import org.testng.annotations.DataProvider; import org.testng.annotations.Test; import org.jenetics.internal.util.Named; import org.jenetics.stat.Histogram; import org.jenetics.util.Factory; import org.jenetics.util.LCG64ShiftRandom; import org.jenetics.util.TestData; /** * @author <a href="mailto:franz.wilhelmstoetter@gmx.at">Franz Wilhelmstötter</a> */ public class BoltzmannSelectorTest extends ProbabilitySelectorTester<BoltzmannSelector<DoubleGene, Double>> { @Override protected boolean isSorted() { return false; } @Override protected Factory<BoltzmannSelector<DoubleGene, Double>> factory() { return BoltzmannSelector::new; } @Test public void parameters() { final BoltzmannSelector<DoubleGene, Double> selector = new BoltzmannSelector<>(2); // Create population with zero fitness. final Population<DoubleGene, Double> population = TestUtils.newDoublePopulation(20, 0, 0); // Must select without exception. selector.probabilities(population, 10); } @Test(dataProvider = "expectedDistribution", groups = {"statistics"}) public void selectDistribution( final Double b, final Named<double[]> expected, final Optimize opt ) { retry(3, () -> { final int loops = 50; final int npopulation = POPULATION_COUNT; final ThreadLocal<LCG64ShiftRandom> random = new LCG64ShiftRandom.ThreadLocal(); using(random, r -> { final Histogram<Double> distribution = SelectorTester.distribution( new BoltzmannSelector<>(b), opt, npopulation, loops ); assertDistribution(distribution, expected.value, 0.001, 5); }); }); } @DataProvider(name = "expectedDistribution") public Object[][] expectedDistribution() { final String resource = "/org/jenetics/selector/distribution/BoltzmannSelector"; return Arrays.stream(Optimize.values()) .flatMap(opt -> { final TestData data = TestData.of(resource, opt.toString()); final double[][] csv = data.stream() .map(TestData::toDouble) .toArray(double[][]::new); return IntStream.range(0, csv[0].length) .mapToObj(i -> new Object[]{ csv[0][i], Named.of( format("distribution[%f]", csv[0][i]), expected(csv, i) ), opt }); }).toArray(Object[][]::new); } private static double[] expected(final double[][] csv, final int c) { final double[] col = new double[csv.length - 1]; for (int i = 0; i < col.length; ++i) { col[i] = Math.max(csv[i + 1][c], Double.MIN_VALUE); } return col; } /* public static void main(final String[] args) { writeDistributionData(Optimize.MAXIMUM); writeDistributionData(Optimize.MINIMUM); } */ private static void writeDistributionData(final Optimize opt) { final ThreadLocal<LCG64ShiftRandom> random = new LCG64ShiftRandom.ThreadLocal(); using(random, r -> { final int npopulation = POPULATION_COUNT; //final int loops = 2_500_000; final int loops = 100_000; printDistributions( System.out, Arrays.asList(-4.0, -2.0, 0.0, 2.0, 3.0, 5.0), BoltzmannSelector::new, opt, npopulation, loops ); }); } }