/*
* 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
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* http://www.apache.org/licenses/LICENSE-2.0
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* distributed under the License is distributed on an "AS IS" BASIS,
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package org.encog.neural.networks.training.competitive;
import junit.framework.TestCase;
import org.encog.mathutil.matrices.Matrix;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLData;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.neural.som.SOM;
import org.encog.neural.som.training.basic.BasicTrainSOM;
import org.encog.neural.som.training.basic.neighborhood.NeighborhoodSingle;
import org.junit.Assert;
import org.junit.Test;
public class TestCompetitive extends TestCase {
public static double SOM_INPUT[][] = { { 0.0, 0.0, 1.0, 1.0 },
{ 1.0, 1.0, 0.0, 0.0 } };
// Just a random starting matrix, but it gives us a constant starting point
public static final double[][] MATRIX_ARRAY = {
{0.9950675732277183, -0.09315692732658198,0.9840257865083011,0.5032129897356723},
{-0.8738960119753589, -0.48043680531294997,-0.9455207768842442, -0.8612565984447569}
};
@Test
public void testSOM() {
// create the training set
final MLDataSet training = new BasicMLDataSet(
TestCompetitive.SOM_INPUT, null);
// Create the neural network.
SOM network = new SOM(4,2);
network.setWeights(new Matrix(MATRIX_ARRAY));
final BasicTrainSOM train = new BasicTrainSOM(network, 0.4,
training, new NeighborhoodSingle());
train.setForceWinner(true);
int iteration = 0;
for (iteration = 0; iteration <= 100; iteration++) {
train.iteration();
}
final MLData data1 = new BasicMLData(
TestCompetitive.SOM_INPUT[0]);
final MLData data2 = new BasicMLData(
TestCompetitive.SOM_INPUT[1]);
int result1 = network.classify(data1);
int result2 = network.classify(data2);
Assert.assertTrue(result1!=result2);
}
}