/*
* 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.train.strategy.Greedy;
import org.encog.ml.train.strategy.HybridStrategy;
import org.encog.ml.train.strategy.ResetStrategy;
import org.encog.ml.train.strategy.StopTrainingStrategy;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.XOR;
import org.encog.neural.networks.training.strategy.SmartLearningRate;
import org.encog.neural.networks.training.strategy.SmartMomentum;
import org.encog.neural.pattern.FeedForwardPattern;
import org.junit.Assert;
public class TestStrategy extends TestCase{
public void testDone()
{
StopTrainingStrategy strategy = new StopTrainingStrategy(0.01,2);
MockTrain mock = new MockTrain();
mock.addStrategy(strategy);
mock.setError(0.05);
Assert.assertFalse(strategy.shouldStop());
mock.iteration();
mock.iteration();
mock.iteration();
mock.iteration();
Assert.assertTrue(strategy.shouldStop());
}
public void testGreedy()
{
FeedForwardPattern pattern = new FeedForwardPattern();
pattern.setInputNeurons(1);
pattern.setOutputNeurons(1);
BasicNetwork network = (BasicNetwork)pattern.generate();
MockTrain.setFirstElement(3.0,network);
MockTrain mock = new MockTrain();
mock.setNetwork(network);
Greedy strategy = new Greedy();
mock.addStrategy(strategy);
// simulate an improvement
mock.setError(0.01);
mock.simulate(0.04, 5.0);
Assert.assertEquals(5.0, MockTrain.getFirstElement(network),0.1);
// simulate a reverse
mock.simulate(0.07, 6.0);
Assert.assertEquals(5.0, MockTrain.getFirstElement(network),0.1);
}
public void testHybrid()
{
MockTrain alt = new MockTrain();
HybridStrategy strategy = new HybridStrategy(alt,0.001,2,5 );
MockTrain mock = new MockTrain();
mock.addStrategy(strategy);
mock.setError(0.05);
mock.iteration();
mock.iteration();
mock.iteration();
mock.iteration();
Assert.assertTrue(alt.wasUsed());
}
public void testReset()
{
FeedForwardPattern pattern = new FeedForwardPattern();
pattern.setInputNeurons(1);
pattern.setOutputNeurons(1);
BasicNetwork network = (BasicNetwork)pattern.generate();
ResetStrategy strategy = new ResetStrategy(0.95,2);
MockTrain mock = new MockTrain();
mock.setNetwork(network);
mock.addStrategy(strategy);
mock.setError(0.07);
MockTrain.setFirstElement(30.0,network);
mock.iteration();
Assert.assertTrue(MockTrain.getFirstElement(network)>20);
mock.setError(0.99);
mock.iteration();
mock.iteration();
mock.iteration();
Assert.assertTrue(MockTrain.getFirstElement(network)<20);
}
public void testSmart()
{
FeedForwardPattern pattern = new FeedForwardPattern();
pattern.setInputNeurons(1);
pattern.setOutputNeurons(1);
BasicNetwork network = (BasicNetwork)pattern.generate();
SmartLearningRate strategy1 = new SmartLearningRate();
SmartMomentum strategy2 = new SmartMomentum();
MockTrain mock = new MockTrain();
mock.setNetwork(network);
mock.setTraining(XOR.createXORDataSet());
mock.addStrategy(strategy1);
mock.addStrategy(strategy2);
mock.setError(0.05);
mock.simulate(0.04, 1);
Assert.assertEquals(0.25, mock.getLearningRate(),0.1);
mock.simulate(0.03, 1);
mock.simulate(0.05, 1);
Assert.assertEquals(0.2475, mock.getLearningRate(),0.1);
}
}