/*
* 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.xor;
import org.encog.Encog;
import org.encog.mathutil.randomize.NguyenWidrowRandomizer;
import org.encog.mathutil.randomize.Randomizer;
import org.encog.ml.CalculateScore;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.ml.train.MLTrain;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.training.TrainingSetScore;
import org.encog.neural.networks.training.pso.NeuralPSO;
import org.encog.util.simple.EncogUtility;
/**
* XOR-PSO: This example solves the classic XOR operator neural
* network problem. However, it uses PSO training.
*
* @author $Author$
* @version $Revision$
*/
public class XORPSO {
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);
BasicNetwork network = EncogUtility.simpleFeedForward(2, 2, 0, 1, false);
CalculateScore score = new TrainingSetScore(trainingSet);
Randomizer randomizer = new NguyenWidrowRandomizer();
final MLTrain train = new NeuralPSO(network,randomizer,score,20);
EncogUtility.trainToError(train, 0.01);
network = (BasicNetwork)train.getMethod();
// test the neural network
System.out.println("Neural Network Results:");
EncogUtility.evaluate(network, trainingSet);
Encog.getInstance().shutdown();
}
}