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Java example source code file (KohonenUpdateAction.java)

This example Java source code file (KohonenUpdateAction.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.

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Java - Java tags/keywords

arrayrealvector, atomiclong, collection, distancemeasure, gaussian, hashset, kohonenupdateaction, learningfactorfunction, neighbourhoodsizefunction, neuron, util

The KohonenUpdateAction.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 java.util.Collection;
import java.util.HashSet;
import java.util.concurrent.atomic.AtomicLong;

import org.apache.commons.math3.analysis.function.Gaussian;
import org.apache.commons.math3.linear.ArrayRealVector;
import org.apache.commons.math3.ml.distance.DistanceMeasure;
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.UpdateAction;

 * Update formula for <a href="http://en.wikipedia.org/wiki/Kohonen">
 * Kohonen's Self-Organizing Map</a>.
 * <br/>
 * The {@link #update(Network,double[]) update} method modifies the
 * features {@code w} of the "winning" neuron and its neighbours
 * according to the following rule:
 * <code>
 *  w<sub>new = wold + α e(-d / σ) * (sample - wold)
 * </code>
 * where
 * <ul>
 *  <li>α is the current learning rate, 
 *  <li>σ is the current neighbourhood size, and
 *  <li>{@code d} is the number of links to traverse in order to reach
 *   the neuron from the winning neuron.</li>
 * </ul>
 * <br/>
 * This class is thread-safe as long as the arguments passed to the
 * {@link #KohonenUpdateAction(DistanceMeasure,LearningFactorFunction,
 * NeighbourhoodSizeFunction) constructor} are instances of thread-safe
 * classes.
 * <br/>
 * Each call to the {@link #update(Network,double[]) update} method
 * will increment the internal counter used to compute the current
 * values for
 * <ul>
 *  <li>the learning rate, and
 *  <li>the neighbourhood size.
 * </ul>
 * Consequently, the function instances that compute those values (passed
 * to the constructor of this class) must take into account whether this
 * class's instance will be shared by multiple threads, as this will impact
 * the training process.
 * @since 3.3
public class KohonenUpdateAction implements UpdateAction {
    /** Distance function. */
    private final DistanceMeasure distance;
    /** Learning factor update function. */
    private final LearningFactorFunction learningFactor;
    /** Neighbourhood size update function. */
    private final NeighbourhoodSizeFunction neighbourhoodSize;
    /** Number of calls to {@link #update(Network,double[])}. */
    private final AtomicLong numberOfCalls = new AtomicLong(0);

     * @param distance Distance function.
     * @param learningFactor Learning factor update function.
     * @param neighbourhoodSize Neighbourhood size update function.
    public KohonenUpdateAction(DistanceMeasure distance,
                               LearningFactorFunction learningFactor,
                               NeighbourhoodSizeFunction neighbourhoodSize) {
        this.distance = distance;
        this.learningFactor = learningFactor;
        this.neighbourhoodSize = neighbourhoodSize;

     * {@inheritDoc}
    public void update(Network net,
                       double[] features) {
        final long numCalls = numberOfCalls.incrementAndGet() - 1;
        final double currentLearning = learningFactor.value(numCalls);
        final Neuron best = findAndUpdateBestNeuron(net,

        final int currentNeighbourhood = neighbourhoodSize.value(numCalls);
        // The farther away the neighbour is from the winning neuron, the
        // smaller the learning rate will become.
        final Gaussian neighbourhoodDecay
            = new Gaussian(currentLearning,

        if (currentNeighbourhood > 0) {
            // Initial set of neurons only contains the winning neuron.
            Collection<Neuron> neighbours = new HashSet();
            // Winning neuron must be excluded from the neighbours.
            final HashSet<Neuron> exclude = new HashSet();

            int radius = 1;
            do {
                // Retrieve immediate neighbours of the current set of neurons.
                neighbours = net.getNeighbours(neighbours, exclude);

                // Update all the neighbours.
                for (Neuron n : neighbours) {
                    updateNeighbouringNeuron(n, features, neighbourhoodDecay.value(radius));

                // Add the neighbours to the exclude list so that they will
                // not be update more than once per training step.
            } while (radius <= currentNeighbourhood);

     * Retrieves the number of calls to the {@link #update(Network,double[]) update}
     * method.
     * @return the current number of calls.
    public long getNumberOfCalls() {
        return numberOfCalls.get();

     * Tries to update a neuron.
     * @param n Neuron to be updated.
     * @param features Training data.
     * @param learningRate Learning factor.
     * @return {@code true} if the update succeeded, {@code true} if a
     * concurrent update has been detected.
    private boolean attemptNeuronUpdate(Neuron n,
                                        double[] features,
                                        double learningRate) {
        final double[] expect = n.getFeatures();
        final double[] update = computeFeatures(expect,

        return n.compareAndSetFeatures(expect, update);

     * Atomically updates the given neuron.
     * @param n Neuron to be updated.
     * @param features Training data.
     * @param learningRate Learning factor.
    private void updateNeighbouringNeuron(Neuron n,
                                          double[] features,
                                          double learningRate) {
        while (true) {
            if (attemptNeuronUpdate(n, features, learningRate)) {

     * Searches for the neuron whose features are closest to the given
     * sample, and atomically updates its features.
     * @param net Network.
     * @param features Sample data.
     * @param learningRate Current learning factor.
     * @return the winning neuron.
    private Neuron findAndUpdateBestNeuron(Network net,
                                           double[] features,
                                           double learningRate) {
        while (true) {
            final Neuron best = MapUtils.findBest(features, net, distance);

            if (attemptNeuronUpdate(best, features, learningRate)) {
                return best;

            // If another thread modified the state of the winning neuron,
            // it may not be the best match anymore for the given training
            // sample: Hence, the winner search is performed again.

     * Computes the new value of the features set.
     * @param current Current values of the features.
     * @param sample Training data.
     * @param learningRate Learning factor.
     * @return the new values for the features.
    private double[] computeFeatures(double[] current,
                                     double[] sample,
                                     double learningRate) {
        final ArrayRealVector c = new ArrayRealVector(current, false);
        final ArrayRealVector s = new ArrayRealVector(sample, false);
        // c + learningRate * (s - c)
        return s.subtract(c).mapMultiplyToSelf(learningRate).add(c).toArray();

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