home | career | drupal | java | mac | mysql | perl | scala | uml | unix  

Java example source code file (KohonenUpdateActionTest.java)

This example Java source code file (KohonenUpdateActionTest.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

distancemeasure, euclideandistance, featureinitializer, kohonenupdateaction, kohonenupdateactiontest, learningfactorfunction, neighbourhoodsizefunction, network, neuron, neuronstring, test

The KohonenUpdateActionTest.java Java example source code

 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You 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,
 * See the License for the specific language governing permissions and
 * limitations under the License.

package org.apache.commons.math3.ml.neuralnet.sofm;

import org.apache.commons.math3.ml.distance.DistanceMeasure;
import org.apache.commons.math3.ml.distance.EuclideanDistance;
import org.apache.commons.math3.ml.neuralnet.FeatureInitializer;
import org.apache.commons.math3.ml.neuralnet.FeatureInitializerFactory;
import org.apache.commons.math3.ml.neuralnet.MapUtils;
import org.apache.commons.math3.ml.neuralnet.Network;
import org.apache.commons.math3.ml.neuralnet.Neuron;
import org.apache.commons.math3.ml.neuralnet.OffsetFeatureInitializer;
import org.apache.commons.math3.ml.neuralnet.UpdateAction;
import org.apache.commons.math3.ml.neuralnet.oned.NeuronString;
import org.apache.commons.math3.util.Precision;
import org.junit.Assert;
import org.junit.Test;

 * Tests for {@link KohonenUpdateAction} class.
public class KohonenUpdateActionTest {
     * Test assumes that the network is
     *  0-----1-----2
    public void testUpdate() {
        final FeatureInitializer init
            = new OffsetFeatureInitializer(FeatureInitializerFactory.uniform(0, 0.1));
        final FeatureInitializer[] initArray = { init };

        final int netSize = 3;
        final Network net = new NeuronString(netSize, false, initArray).getNetwork();
        final DistanceMeasure dist = new EuclideanDistance();
        final LearningFactorFunction learning
            = LearningFactorFunctionFactory.exponentialDecay(1, 0.1, 100);
        final NeighbourhoodSizeFunction neighbourhood
            = NeighbourhoodSizeFunctionFactory.exponentialDecay(3, 1, 100);
        final UpdateAction update = new KohonenUpdateAction(dist, learning, neighbourhood);

        // The following test ensures that, after one "update",
        // 1. when the initial learning rate equal to 1, the best matching
        //    neuron's features are mapped to the input's features,
        // 2. when the initial neighbourhood is larger than the network's size,
        //    all neuron's features get closer to the input's features.

        final double[] features = new double[] { 0.3 };
        final double[] distancesBefore = new double[netSize];
        int count = 0;
        for (Neuron n : net) {
            distancesBefore[count++] = dist.compute(n.getFeatures(), features);
        final Neuron bestBefore = MapUtils.findBest(features, net, dist);

        // Initial distance from the best match is larger than zero.
        Assert.assertTrue(dist.compute(bestBefore.getFeatures(), features) >= 0.2);

        update.update(net, features);

        final double[] distancesAfter = new double[netSize];
        count = 0;
        for (Neuron n : net) {
            distancesAfter[count++] = dist.compute(n.getFeatures(), features);
        final Neuron bestAfter = MapUtils.findBest(features, net, dist);

        Assert.assertEquals(bestBefore, bestAfter);
        // Distance is now zero.
        Assert.assertEquals(0, dist.compute(bestAfter.getFeatures(), features), Precision.EPSILON);

        for (int i = 0; i < netSize; i++) {
            // All distances have decreased.
            Assert.assertTrue(distancesAfter[i] < distancesBefore[i]);

Other Java examples (source code examples)

Here is a short list of links related to this Java KohonenUpdateActionTest.java source code file:

my book on functional programming


new blog posts


Copyright 1998-2021 Alvin Alexander, alvinalexander.com
All Rights Reserved.

A percentage of advertising revenue from
pages under the /java/jwarehouse URI on this website is
paid back to open source projects.