/*
* 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.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.neural.flat.FlatNetwork;
import org.encog.neural.flat.train.prop.TrainFlatNetworkResilient;
/**
* 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. This example uses
* a flat neural network.
*/
public class XORFlat {
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[]) {
FlatNetwork network = new FlatNetwork(2,4,0,1,false);
network.randomize();
MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
TrainFlatNetworkResilient train = new TrainFlatNetworkResilient(network,trainingSet);
//Encog.getInstance().initCL();
//train.setTargetDevice(Encog.getInstance().getCL().getDevices().get(0));
int epoch = 1;
do {
train.iteration();
System.out
.println("Epoch #" + epoch + " Error:" + train.getError());
epoch++;
} while(train.getError() > 0.01 );
double[] output = new double[1];
// test the neural network
System.out.println("Neural Network Results:");
for(MLDataPair pair: trainingSet ) {
double[] input = pair.getInput().getData();
network.compute(input, output);
System.out.println(input[0] + "," + input[1] + ":" + output[0]);
}
}
}