/*
* 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.
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package org.encog.mathutil.matrixes.hessian;
import java.util.Arrays;
import junit.framework.Assert;
import junit.framework.TestCase;
import org.encog.engine.network.activation.ActivationSigmoid;
import org.encog.mathutil.matrices.hessian.ComputeHessian;
import org.encog.mathutil.matrices.hessian.HessianCR;
import org.encog.mathutil.matrices.hessian.HessianFD;
import org.encog.mathutil.randomize.ConsistentRandomizer;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.XOR;
import org.encog.neural.networks.layers.BasicLayer;
public class TestHessian extends TestCase {
private void dump(ComputeHessian hess, String name) {
System.out.println(name);
double[][] h = hess.getHessian();
System.out.println("Gradients: " + Arrays.toString(hess.getGradients()));
for(int i=0;i<h.length;i++) {
System.out.println(Arrays.toString(h[i]));
}
}
public void testSingleOutput() {
BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(null,true,2));
network.addLayer(new BasicLayer(new ActivationSigmoid(),true,2));
network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1));
network.getStructure().finalizeStructure();
(new ConsistentRandomizer(-1,1)).randomize(network);
MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
HessianFD testFD = new HessianFD();
testFD.init(network, trainingData);
testFD.compute();
HessianCR testCR = new HessianCR();
testCR.init(network, trainingData);
testCR.compute();
//dump(testFD, "FD");
//dump(testCR, "CR");
Assert.assertTrue(testCR.getHessianMatrix().equals(testFD.getHessianMatrix(), 4));
}
public void testDualOutput() {
BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(null,true,2));
network.addLayer(new BasicLayer(new ActivationSigmoid(),true,2));
network.addLayer(new BasicLayer(new ActivationSigmoid(),false,2));
network.getStructure().finalizeStructure();
(new ConsistentRandomizer(-1,1)).randomize(network);
MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL2);
HessianFD testFD = new HessianFD();
testFD.init(network, trainingData);
testFD.compute();
//dump(testFD, "FD");
HessianCR testCR = new HessianCR();
testCR.init(network, trainingData);
testCR.compute();
//dump(testCR, "CR");
Assert.assertTrue(testCR.getHessianMatrix().equals(testFD.getHessianMatrix(), 4));
}
}