/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 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.ml.MLRegression;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.folded.FoldedDataSet;
import org.encog.ml.train.MLTrain;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.NetworkUtil;
import org.encog.neural.networks.XOR;
import org.encog.neural.networks.training.cross.CrossValidationKFold;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
import org.encog.util.simple.EncogUtility;
import org.junit.Test;
public class TestFolded extends TestCase {
@Test
public void testRPROP() throws Throwable
{
MLDataSet trainingData = XOR.createNoisyXORDataSet(10);
BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
final FoldedDataSet folded = new FoldedDataSet(trainingData);
final MLTrain train = new ResilientPropagation(network, folded);
final CrossValidationKFold trainFolded = new CrossValidationKFold(train,4);
EncogUtility.trainToError(trainFolded, 0.2);
XOR.verifyXOR((MLRegression)trainFolded.getMethod(), 0.2);
}
}