/*
* Encog(tm) Java Examples v3.2
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-examples
*
* Copyright 2008-2013 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.examples.neural.neat;
import org.encog.Encog;
import org.encog.ml.CalculateScore;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
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.training.TrainingSetScore;
import org.encog.util.simple.EncogUtility;
/**
* XOR-NEAT: This example solves the classic XOR operator neural
* network problem. However, it uses a NEAT evolving network.
*
* @author $Author$
* @version $Revision$
*/
public class XORNEAT {
public static double XOR_INPUT[][] = { { 0.0, 0.0 }, { 1.0, 0.0 },
{ 0.0, 1.0 }, { 1.0, 1.0 } };
public static double XOR_IDEAL[][] = { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } };
public static void main(final String args[]) {
MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, 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();
System.out.println("Epoch #" + train.getIteration() + " Error:" + train.getError()+ ", Species:" + pop.getSpecies().size());
} while(train.getError() > 0.01);
NEATNetwork network = (NEATNetwork)train.getCODEC().decode(train.getBestGenome());
// test the neural network
System.out.println("Neural Network Results:");
EncogUtility.evaluate(network, trainingSet);
Encog.getInstance().shutdown();
}
}