XORNEAT.java

/*
 * Encog(tm) Java Examples v3.4
 * http://www.heatonresearch.com/encog/
 * https://github.com/encog/encog-java-examples
 *
 * Copyright 2008-2016 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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 * and trademarks visit:
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 */
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();
	}
}