/*
* 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.freeform;
import junit.framework.TestCase;
import org.encog.ml.CalculateScore;
import org.encog.ml.MLMethod;
import org.encog.ml.MethodFactory;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.ml.genetic.MLMethodGeneticAlgorithm;
import org.encog.ml.train.MLTrain;
import org.encog.neural.freeform.training.FreeformBackPropagation;
import org.encog.neural.freeform.training.FreeformResilientPropagation;
import org.encog.neural.networks.NetworkUtil;
import org.encog.neural.networks.XOR;
import org.encog.neural.networks.training.TrainingSetScore;
import org.encog.neural.networks.training.anneal.NeuralSimulatedAnnealing;
import org.junit.Test;
public class TestFreeformTraining extends TestCase {
@Test
public void testBPROP() throws Throwable
{
MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
FreeformNetwork network = NetworkUtil.createXORFreeformNetworkUntrained();
MLTrain bprop = new FreeformBackPropagation(network, trainingData, 0.7, 0.9);
NetworkUtil.testTraining(trainingData,bprop,0.01);
}
@Test
public void testRPROP() throws Throwable
{
MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
FreeformNetwork network = NetworkUtil.createXORFreeformNetworkUntrained();
MLTrain bprop = new FreeformResilientPropagation(network, trainingData);
NetworkUtil.testTraining(trainingData,bprop,0.01);
}
@Test
public void testAnneal() throws Throwable
{
MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
FreeformNetwork network = NetworkUtil.createXORFreeformNetworkUntrained();
CalculateScore score = new TrainingSetScore(trainingData);
NeuralSimulatedAnnealing anneal = new NeuralSimulatedAnnealing(network,score,10,2,100);
NetworkUtil.testTraining(trainingData,anneal,0.01);
}
@Test
public void testGenetic() throws Throwable
{
MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
CalculateScore score = new TrainingSetScore(trainingData);
MLMethodGeneticAlgorithm genetic = new MLMethodGeneticAlgorithm(new MethodFactory(){
@Override
public MLMethod factor() {
FreeformNetwork network = NetworkUtil.createXORFreeformNetworkUntrained();
network.reset();
return network;
}}, score, 500);
NetworkUtil.testTraining(trainingData,genetic,0.00001);
}
}