/*
* 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.neural.networks.training;
import java.io.File;
import junit.framework.Assert;
import junit.framework.TestCase;
import org.encog.Encog;
import org.encog.ml.CalculateScore;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLData;
import org.encog.ml.data.basic.BasicMLDataPair;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.ml.data.buffer.BufferedMLDataSet;
import org.encog.ml.ea.train.EvolutionaryAlgorithm;
import org.encog.neural.neat.NEATNetwork;
import org.encog.neural.neat.NEATPopulation;
import org.encog.neural.neat.NEATUtil;
import org.encog.neural.networks.XOR;
import org.encog.util.TempDir;
import org.encog.util.simple.EncogUtility;
public class TestNEAT extends TestCase {
public final TempDir TEMP_DIR = new TempDir();
public final File EGB_FILENAME = TEMP_DIR.createFile("encogtest.egb");
public void testNEATBuffered() {
BufferedMLDataSet buffer = new BufferedMLDataSet(EGB_FILENAME);
buffer.beginLoad(2, 1);
for(int i=0;i<XOR.XOR_INPUT.length;i++) {
buffer.add(new BasicMLDataPair(
new BasicMLData(XOR.XOR_INPUT[i]),
new BasicMLData(XOR.XOR_IDEAL[i])));
}
buffer.endLoad();
NEATPopulation pop = new NEATPopulation(2,1,1000);
pop.setInitialConnectionDensity(1.0);// not required, but speeds training
pop.reset();
CalculateScore score = new TrainingSetScore(buffer);
// train the neural network
final EvolutionaryAlgorithm train = NEATUtil.constructNEATTrainer(pop,score);
do {
train.iteration();
} while(train.getError() > 0.01 && train.getIteration()<10000);
Encog.getInstance().shutdown();
NEATNetwork network = (NEATNetwork)train.getCODEC().decode(train.getBestGenome());
Assert.assertTrue(train.getError()<0.01);
Assert.assertTrue(network.calculateError(buffer)<0.01);
}
public void testNEAT() {
MLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT, XOR.XOR_IDEAL);
NEATPopulation pop = new NEATPopulation(2,1,1000);
pop.setInitialConnectionDensity(1.0);// not required, but speeds training
pop.reset();
CalculateScore score = new TrainingSetScore(trainingSet);
// train the neural network
final EvolutionaryAlgorithm train = NEATUtil.constructNEATTrainer(pop,score);
do {
train.iteration();
} while(train.getError() > 0.01);
// test the neural network
Encog.getInstance().shutdown();
Assert.assertTrue(train.getError()<0.01);
NEATNetwork network = (NEATNetwork)train.getCODEC().decode(train.getBestGenome());
Assert.assertTrue(network.calculateError(trainingSet)<0.01);
}
}