/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 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.
*
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package org.encog.neural.networks.neat.training.species;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import junit.framework.Assert;
import junit.framework.TestCase;
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.Test;
public class TestSortGenomesForSpecies extends TestCase {
@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);
}
}