TrainComplete.java

/*
 * Encog(tm) Core v3.4 - Java Version
 * http://www.heatonresearch.com/encog/
 * https://github.com/encog/encog-java-core
 
 * 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.
 *   
 * For more information on Heaton Research copyrights, licenses 
 * and trademarks visit:
 * http://www.heatonresearch.com/copyright
 */
package org.encog.neural.networks.training;

import junit.framework.TestCase;
import org.encog.mathutil.randomize.ConsistentRandomizer;
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.XOR;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
import org.encog.util.simple.EncogUtility;
import org.junit.Assert;
import org.junit.Test;

public class TrainComplete extends TestCase {

	@Test
	public void testCompleteTrain()
	{
		MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
		
		BasicNetwork network = EncogUtility.simpleFeedForward(2, 5, 7, 1, true);
		(new ConsistentRandomizer(-1,1)).randomize(network);
		MLTrain rprop = new ResilientPropagation(network, trainingData);
		int iteration = 0;
		do {
			rprop.iteration();
			iteration++;
		} while( iteration<5000 && rprop.getError()>0.01);
		Assert.assertTrue(iteration<40);
	}
	
}