xorgenetic.java

VHDL制作的ann的code

/*
  * Encog Neural Network and Bot Library for Java
  * http://www.heatonresearch.com/encog/
  * http://code.google.com/p/encog-java/
  * 
  * Copyright 2008, Heaton Research Inc., and individual contributors.
  * See the copyright.txt in the distribution for a full listing of 
  * individual contributors.
  *
  * This is free software; you can redistribute it and/or modify it
  * under the terms of the GNU Lesser General Public License as
  * published by the Free Software Foundation; either version 2.1 of
  * the License, or (at your option) any later version.
  *
  * This software is distributed in the hope that it will be useful,
  * but WITHOUT ANY WARRANTY; without even the implied warranty of
  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
  * Lesser General Public License for more details.
  *
  * You should have received a copy of the GNU Lesser General Public
  * License along with this software; if not, write to the Free
  * Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
  * 02110-1301 USA, or see the FSF site: http://www.fsf.org.
  */
package org.encog.examples.neural.xorgenetic;

import org.encog.neural.data.NeuralData;
import org.encog.neural.data.NeuralDataPair;
import org.encog.neural.data.NeuralDataSet;
import org.encog.neural.data.basic.BasicNeuralDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.FeedforwardLayer;
import org.encog.neural.networks.training.genetic.TrainingSetNeuralGeneticAlgorithm;



/**
 * XOR-Genetic: This example solves the classic XOR operator neural
 * network problem.  However, it uses a genetic algorithm, rather than
 * backpropagation.
 * 
 * @author $Author$
 * @version $Revision$
 */
public class XorGenetic {
	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[]) {
		BasicNetwork network = new BasicNetwork();
		network.addLayer(new FeedforwardLayer(2));
		network.addLayer(new FeedforwardLayer(3));
		network.addLayer(new FeedforwardLayer(1));
		network.reset();

		NeuralDataSet trainingSet = new BasicNeuralDataSet(XOR_INPUT, XOR_IDEAL);
		
		// train the neural network
		final TrainingSetNeuralGeneticAlgorithm train = new TrainingSetNeuralGeneticAlgorithm(
				network, true, trainingSet, 5000, 0.1, 0.25);

		int epoch = 1;

		do {
			train.iteration();
			System.out
					.println("Epoch #" + epoch + " Error:" + train.getError());
			epoch++;
		} while ((epoch < 5000) && (train.getError() > 0.001));

		network = train.getNetwork();

		// test the neural network
		System.out.println("Neural Network Results:");
		for(NeuralDataPair pair: trainingSet ) {
			final NeuralData output = network.compute(pair.getInput());
			System.out.println(pair.getInput().getData(0) + "," + pair.getInput().getData(1)
					+ ", actual=" + output.getData(0) + ",ideal=" + pair.getIdeal().getData(0));
		}
	}
}