/*
* 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.prune;
import junit.framework.TestCase;
import org.encog.ml.data.basic.BasicMLData;
import org.encog.neural.flat.FlatNetwork;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.XOR;
import org.encog.neural.networks.structure.NetworkCODEC;
import org.encog.util.simple.EncogUtility;
import org.junit.Assert;
public class TestPruneSelective extends TestCase {
private BasicNetwork obtainNetwork()
{
BasicNetwork network = EncogUtility.simpleFeedForward(2,3,0,4,false);
double[] weights = { 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25 };
NetworkCODEC.arrayToNetwork(weights, network);
Assert.assertEquals(1.0, network.getWeight(1, 0, 0),0.01);
Assert.assertEquals(2.0, network.getWeight(1, 1, 0),0.01);
Assert.assertEquals(3.0, network.getWeight(1, 2, 0),0.01);
Assert.assertEquals(4.0, network.getWeight(1, 3, 0),0.01);
Assert.assertEquals(5.0, network.getWeight(1, 0, 1),0.01);
Assert.assertEquals(6.0, network.getWeight(1, 1, 1),0.01);
Assert.assertEquals(7.0, network.getWeight(1, 2, 1),0.01);
Assert.assertEquals(8.0, network.getWeight(1, 3, 1),0.01);
Assert.assertEquals(9.0, network.getWeight(1, 0, 2),0.01);
Assert.assertEquals(10.0, network.getWeight(1, 1, 2),0.01);
Assert.assertEquals(11.0, network.getWeight(1, 2, 2),0.01);
Assert.assertEquals(12.0, network.getWeight(1, 3, 2),0.01);
Assert.assertEquals(13.0, network.getWeight(1, 0, 3),0.01);
Assert.assertEquals(14.0, network.getWeight(1, 1, 3),0.01);
Assert.assertEquals(15.0, network.getWeight(1, 2, 3),0.01);
Assert.assertEquals(16.0, network.getWeight(1, 3, 3),0.01);
Assert.assertEquals(17.0, network.getWeight(0, 0, 0),0.01);
Assert.assertEquals(18.0, network.getWeight(0, 1, 0),0.01);
Assert.assertEquals(19.0, network.getWeight(0, 2, 0),0.01);
Assert.assertEquals(20.0, network.getWeight(0, 0, 1),0.01);
Assert.assertEquals(21.0, network.getWeight(0, 1, 1),0.01);
Assert.assertEquals(22.0, network.getWeight(0, 2, 1),0.01);
Assert.assertEquals(20.0, network.getWeight(0, 0, 1),0.01);
Assert.assertEquals(21.0, network.getWeight(0, 1, 1),0.01);
Assert.assertEquals(22.0, network.getWeight(0, 2, 1),0.01);
Assert.assertEquals(23.0, network.getWeight(0, 0, 2),0.01);
Assert.assertEquals(24.0, network.getWeight(0, 1, 2),0.01);
Assert.assertEquals(25.0, network.getWeight(0, 2, 2),0.01);
return network;
}
private void checkWithModel(FlatNetwork model, FlatNetwork pruned)
{
Assert.assertEquals(model.getWeights().length, pruned.getWeights().length);
Assert.assertArrayEquals(model.getContextTargetOffset(),pruned.getContextTargetOffset());
Assert.assertArrayEquals(model.getContextTargetSize(),pruned.getContextTargetSize());
Assert.assertArrayEquals(model.getLayerCounts(),pruned.getLayerCounts());
Assert.assertArrayEquals(model.getLayerFeedCounts(),pruned.getLayerFeedCounts());
Assert.assertArrayEquals(model.getLayerIndex(),pruned.getLayerIndex());
Assert.assertEquals(model.getLayerOutput().length,pruned.getLayerOutput().length);
Assert.assertArrayEquals(model.getWeightIndex(),pruned.getWeightIndex());
}
public void testPruneNeuronInput()
{
BasicNetwork network = obtainNetwork();
Assert.assertEquals(2, network.getInputCount());
PruneSelective prune = new PruneSelective(network);
prune.prune(0, 1);
Assert.assertEquals(22, network.encodedArrayLength());
Assert.assertEquals(1,network.getLayerNeuronCount(0));
Assert.assertEquals("1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,19,20,22,23,25", network.dumpWeights());
BasicNetwork model = EncogUtility.simpleFeedForward(1,3,0,4,false);
checkWithModel(model.getStructure().getFlat(),network.getStructure().getFlat());
Assert.assertEquals(1, network.getInputCount());
}
public void testPruneNeuronHidden()
{
BasicNetwork network = obtainNetwork();
PruneSelective prune = new PruneSelective(network);
prune.prune(1, 1);
Assert.assertEquals(18, network.encodedArrayLength());
Assert.assertEquals(2,network.getLayerNeuronCount(1));
Assert.assertEquals("1,3,4,5,7,8,9,11,12,13,15,16,17,18,19,23,24,25", network.dumpWeights());
BasicNetwork model = EncogUtility.simpleFeedForward(2,2,0,4,false);
checkWithModel(model.getStructure().getFlat(),network.getStructure().getFlat());
}
public void testPruneNeuronOutput()
{
BasicNetwork network = obtainNetwork();
Assert.assertEquals(4, network.getOutputCount());
PruneSelective prune = new PruneSelective(network);
prune.prune(2, 1);
Assert.assertEquals(21, network.encodedArrayLength());
Assert.assertEquals(3,network.getLayerNeuronCount(2));
Assert.assertEquals("1,2,3,4,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25", network.dumpWeights());
BasicNetwork model = EncogUtility.simpleFeedForward(2,3,0,3,false);
checkWithModel(model.getStructure().getFlat(),network.getStructure().getFlat());
Assert.assertEquals(3, network.getOutputCount());
}
public void testNeuronSignificance()
{
BasicNetwork network = obtainNetwork();
PruneSelective prune = new PruneSelective(network);
double inputSig = prune.determineNeuronSignificance(0, 1);
double hiddenSig = prune.determineNeuronSignificance(1, 1);
double outputSig = prune.determineNeuronSignificance(2, 1);
Assert.assertEquals(63.0, inputSig,0.01);
Assert.assertEquals(95.0, hiddenSig,0.01);
Assert.assertEquals(26.0, outputSig,0.01);
}
public void testIncreaseNeuronCountHidden()
{
BasicNetwork network = XOR.createTrainedXOR();
Assert.assertTrue( XOR.verifyXOR(network, 0.10) );
PruneSelective prune = new PruneSelective(network);
prune.changeNeuronCount(1, 5);
BasicNetwork model = EncogUtility.simpleFeedForward(2,5,0,1,false);
checkWithModel(model.getStructure().getFlat(),network.getStructure().getFlat());
Assert.assertTrue( XOR.verifyXOR(network, 0.10) );
}
public void testIncreaseNeuronCountHidden2()
{
BasicNetwork network = EncogUtility.simpleFeedForward(5,6,0,2,true);
PruneSelective prune = new PruneSelective(network);
prune.changeNeuronCount(1, 60);
BasicMLData input = new BasicMLData(5);
BasicNetwork model = EncogUtility.simpleFeedForward(5,60,0,2,true);
checkWithModel(model.getStructure().getFlat(),network.getStructure().getFlat());
model.compute(input);
network.compute(input);
}
public void testRandomizeNeuronInput()
{
double[] d = { 0,0,0,0,0,0,0,0,0,0,0,0,0 };
BasicNetwork network = EncogUtility.simpleFeedForward(2,3,0,1,false);
NetworkCODEC.arrayToNetwork(d, network);
PruneSelective prune = new PruneSelective(network);
prune.randomizeNeuron(100, 100, 0,1);
Assert.assertEquals("0,0,0,0,0,100,0,0,100,0,0,100,0", network.dumpWeights());
}
public void testRandomizeNeuronHidden()
{
double[] d = { 0,0,0,0,0,0,0,0,0,0,0,0,0 };
BasicNetwork network = EncogUtility.simpleFeedForward(2,3,0,1,false);
NetworkCODEC.arrayToNetwork(d, network);
PruneSelective prune = new PruneSelective(network);
prune.randomizeNeuron(100, 100, 1,1);
Assert.assertEquals("0,100,0,0,0,0,0,100,100,100,0,0,0", network.dumpWeights());
}
public void testRandomizeNeuronOutput()
{
double[] d = { 0,0,0,0,0,0,0,0,0,0,0,0,0 };
BasicNetwork network = EncogUtility.simpleFeedForward(2,3,0,1,false);
NetworkCODEC.arrayToNetwork(d, network);
PruneSelective prune = new PruneSelective(network);
prune.randomizeNeuron(100, 100, 2,0);
Assert.assertEquals("100,100,100,100,0,0,0,0,0,0,0,0,0", network.dumpWeights());
}
}