/*
* Encog(tm) Unit Tests v2.5 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2010 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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*/
package org.encog.neural.networks.training;
import org.encog.mathutil.randomize.ConsistentRandomizer;
import org.encog.mathutil.randomize.Randomizer;
import org.encog.neural.data.NeuralDataSet;
import org.encog.neural.data.basic.BasicNeuralDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.NetworkUtil;
import org.encog.neural.networks.XOR;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
import org.encog.util.logging.Logging;
import org.encog.util.simple.EncogUtility;
import junit.framework.Assert;
import junit.framework.TestCase;
public class TrainComplete extends TestCase {
public void testCompleteTrain()
{
Logging.stopConsoleLogging();
NeuralDataSet trainingData = new BasicNeuralDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
BasicNetwork network = EncogUtility.simpleFeedForward(2, 5, 7, 1, true);
Randomizer randomizer = new ConsistentRandomizer(-1, 1, 19);
//randomizer.randomize(network);
System.out.println(network.dumpWeights());
Train rprop = new ResilientPropagation(network, trainingData);
int iteration = 0;
do {
rprop.iteration();
System.out.println(rprop.getError());
iteration++;
} while( iteration<5000 && rprop.getError()>0.01);
System.out.println(iteration);
Assert.assertTrue(iteration<40);
}
}