/*
* Encog(tm) Examples v2.4
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
*
* Copyright 2008-2010 by Heaton Research Inc.
*
* Released under the LGPL.
*
* 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.
*
* Encog and Heaton Research are Trademarks of Heaton Research, Inc.
* For information on Heaton Research trademarks, visit:
*
* http://www.heatonresearch.com/copyright.html
*/
package org.encog.examples.neural.radial;
import org.encog.neural.data.NeuralDataSet;
import org.encog.neural.data.basic.BasicNeuralDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.training.svd.SVDTraining;
import org.encog.neural.pattern.RadialBasisPattern;
import org.encog.util.logging.Logging;
import org.encog.util.simple.EncogUtility;
/**
* XOR: This example is essentially the "Hello World" of neural network
* programming. This example shows how to construct an Encog neural network to
* predict the output from the XOR operator using a RBF neural network. This
* example uses RPROP to train.
*
* This example uses an RBF neural network with an SVD trainer.
*/
public class XorSVD {
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[]) {
Logging.stopConsoleLogging();
RadialBasisPattern pattern = new RadialBasisPattern();
pattern.setInputNeurons(2);
pattern.addHiddenLayer(4);
pattern.setOutputNeurons(1);
BasicNetwork network = pattern.generate();
network.getStructure().finalizeStructure();
final NeuralDataSet trainingSet = new BasicNeuralDataSet(
XorSVD.XOR_INPUT, XorSVD.XOR_IDEAL);
SVDTraining train = new SVDTraining(network,trainingSet);
// train the neural network
EncogUtility.trainToError(train, network, trainingSet, 0.01);
// test the neural network
System.out.println("Neural Network Results:");
EncogUtility.evaluate(network, trainingSet);
}
}