/*
* Encog(tm) Core Unit Tests v3.0 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2011 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.persist;
import java.io.File;
import java.io.IOException;
import junit.framework.Assert;
import junit.framework.TestCase;
import org.encog.engine.network.activation.ActivationStep;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.ml.genetic.population.Population;
import org.encog.neural.neat.NEATPopulation;
import org.encog.neural.neat.training.NEATTraining;
import org.encog.neural.networks.XOR;
import org.encog.neural.networks.training.CalculateScore;
import org.encog.neural.networks.training.TrainingSetScore;
import org.encog.util.TempDir;
import org.encog.util.obj.SerializeObject;
public class TestPersistPopulation extends TestCase {
public final TempDir TEMP_DIR = new TempDir();
public final File EG_FILENAME = TEMP_DIR.createFile("encogtest.eg");
public final File SERIAL_FILENAME = TEMP_DIR.createFile("encogtest.ser");
private NEATPopulation generate()
{
MLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT, XOR.XOR_IDEAL);
CalculateScore score = new TrainingSetScore(trainingSet);
// train the neural network
ActivationStep step = new ActivationStep();
step.setCenter(0.5);
NEATTraining train = new NEATTraining(
score, 2, 1, 10);
//train.setOutputActivationFunction(step);
return (NEATPopulation)train.getPopulation();
}
public void testPersistEG()
{
Population pop = generate();
EncogDirectoryPersistence.saveObject((EG_FILENAME), pop);
NEATPopulation pop2 = (NEATPopulation)EncogDirectoryPersistence.loadObject((EG_FILENAME));
validate(pop2);
}
public void testPersistSerial() throws IOException, ClassNotFoundException
{
NEATPopulation pop = generate();
SerializeObject.save(SERIAL_FILENAME, pop);
NEATPopulation pop2 = (NEATPopulation)SerializeObject.load(SERIAL_FILENAME);
validate(pop2);
}
private void validate(NEATPopulation pop)
{
Assert.assertEquals(0.3,pop.getOldAgePenalty());
Assert.assertEquals(50,pop.getOldAgeThreshold());
Assert.assertEquals(10,pop.getPopulationSize());
Assert.assertEquals(0.2,pop.getSurvivalRate());
Assert.assertEquals(10,pop.getYoungBonusAgeThreshold());
Assert.assertEquals(0.3,pop.getYoungScoreBonus());
// see if the population can actually be used to train
MLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT, XOR.XOR_IDEAL);
CalculateScore score = new TrainingSetScore(trainingSet);
NEATTraining train = new NEATTraining(score,pop);
train.iteration();
}
@Override
protected void tearDown() throws Exception {
super.tearDown();
TEMP_DIR.dispose();
}
}