/*
* 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
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.CalculateScore;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.ml.ea.population.Population;
import org.encog.ml.ea.train.EvolutionaryAlgorithm;
import org.encog.neural.neat.NEATPopulation;
import org.encog.neural.neat.NEATUtil;
import org.encog.neural.networks.XOR;
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);
EvolutionaryAlgorithm train = NEATUtil.constructNEATTrainer(
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();
validate(pop);
SerializeObject.save(SERIAL_FILENAME, pop);
NEATPopulation pop2 = (NEATPopulation)SerializeObject.load(SERIAL_FILENAME);
validate(pop2);
}
private void validate(NEATPopulation pop)
{
Assert.assertEquals(10,pop.getPopulationSize());
Assert.assertEquals(0.2,pop.getSurvivalRate());
// 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);
EvolutionaryAlgorithm train = NEATUtil.constructNEATTrainer(pop, score);
train.iteration();
}
@Override
protected void tearDown() throws Exception {
super.tearDown();
TEMP_DIR.dispose();
}
}