/*
* Encog(tm) Examples 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.examples.neural.xor;
import org.encog.engine.network.activation.ActivationStep;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.neural.neat.NEATNetwork;
import org.encog.neural.neat.NEATPopulation;
import org.encog.neural.neat.training.NEATTraining;
import org.encog.neural.networks.training.CalculateScore;
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);
CalculateScore score = new TrainingSetScore(trainingSet);
// train the neural network
ActivationStep step = new ActivationStep();
step.setCenter(0.5);
pop.setOutputActivationFunction(step);
final NEATTraining train = new NEATTraining(score,pop);
EncogUtility.trainToError(train, 0.01);
NEATNetwork network = (NEATNetwork)train.getMethod();
network.clearContext();
// test the neural network
System.out.println("Neural Network Results:");
EncogUtility.evaluate(network, trainingSet);
}
}