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Java example source code file (KMeansPlusPlusClusterer.java)

This example Java source code file (KMeansPlusPlusClusterer.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

arraylist, cluster, clusterable, collection, convergenceexception, emptyclusterstrategy, farthest_point, kmeansplusplusclusterer, largest_points_number, list, mathillegalargumentexception, random, util, variance

The KMeansPlusPlusClusterer.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,
 * 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.
 */

package org.apache.commons.math3.stat.clustering;

import java.util.ArrayList;
import java.util.Collection;
import java.util.Collections;
import java.util.List;
import java.util.Random;

import org.apache.commons.math3.exception.ConvergenceException;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.stat.descriptive.moment.Variance;
import org.apache.commons.math3.util.MathUtils;

/**
 * Clustering algorithm based on David Arthur and Sergei Vassilvitski k-means++ algorithm.
 * @param <T> type of the points to cluster
 * @see <a href="http://en.wikipedia.org/wiki/K-means%2B%2B">K-means++ (wikipedia)
 * @since 2.0
 * @deprecated As of 3.2 (to be removed in 4.0),
 * use {@link org.apache.commons.math3.ml.clustering.KMeansPlusPlusClusterer} instead
 */
@Deprecated
public class KMeansPlusPlusClusterer<T extends Clusterable {

    /** Strategies to use for replacing an empty cluster. */
    public enum EmptyClusterStrategy {

        /** Split the cluster with largest distance variance. */
        LARGEST_VARIANCE,

        /** Split the cluster with largest number of points. */
        LARGEST_POINTS_NUMBER,

        /** Create a cluster around the point farthest from its centroid. */
        FARTHEST_POINT,

        /** Generate an error. */
        ERROR

    }

    /** Random generator for choosing initial centers. */
    private final Random random;

    /** Selected strategy for empty clusters. */
    private final EmptyClusterStrategy emptyStrategy;

    /** Build a clusterer.
     * <p>
     * The default strategy for handling empty clusters that may appear during
     * algorithm iterations is to split the cluster with largest distance variance.
     * </p>
     * @param random random generator to use for choosing initial centers
     */
    public KMeansPlusPlusClusterer(final Random random) {
        this(random, EmptyClusterStrategy.LARGEST_VARIANCE);
    }

    /** Build a clusterer.
     * @param random random generator to use for choosing initial centers
     * @param emptyStrategy strategy to use for handling empty clusters that
     * may appear during algorithm iterations
     * @since 2.2
     */
    public KMeansPlusPlusClusterer(final Random random, final EmptyClusterStrategy emptyStrategy) {
        this.random        = random;
        this.emptyStrategy = emptyStrategy;
    }

    /**
     * Runs the K-means++ clustering algorithm.
     *
     * @param points the points to cluster
     * @param k the number of clusters to split the data into
     * @param numTrials number of trial runs
     * @param maxIterationsPerTrial the maximum number of iterations to run the algorithm
     *     for at each trial run.  If negative, no maximum will be used
     * @return a list of clusters containing the points
     * @throws MathIllegalArgumentException if the data points are null or the number
     *     of clusters is larger than the number of data points
     * @throws ConvergenceException if an empty cluster is encountered and the
     * {@link #emptyStrategy} is set to {@code ERROR}
     */
    public List<Cluster cluster(final Collection points, final int k,
                                    int numTrials, int maxIterationsPerTrial)
        throws MathIllegalArgumentException, ConvergenceException {

        // at first, we have not found any clusters list yet
        List<Cluster best = null;
        double bestVarianceSum = Double.POSITIVE_INFINITY;

        // do several clustering trials
        for (int i = 0; i < numTrials; ++i) {

            // compute a clusters list
            List<Cluster clusters = cluster(points, k, maxIterationsPerTrial);

            // compute the variance of the current list
            double varianceSum = 0.0;
            for (final Cluster<T> cluster : clusters) {
                if (!cluster.getPoints().isEmpty()) {

                    // compute the distance variance of the current cluster
                    final T center = cluster.getCenter();
                    final Variance stat = new Variance();
                    for (final T point : cluster.getPoints()) {
                        stat.increment(point.distanceFrom(center));
                    }
                    varianceSum += stat.getResult();

                }
            }

            if (varianceSum <= bestVarianceSum) {
                // this one is the best we have found so far, remember it
                best            = clusters;
                bestVarianceSum = varianceSum;
            }

        }

        // return the best clusters list found
        return best;

    }

    /**
     * Runs the K-means++ clustering algorithm.
     *
     * @param points the points to cluster
     * @param k the number of clusters to split the data into
     * @param maxIterations the maximum number of iterations to run the algorithm
     *     for.  If negative, no maximum will be used
     * @return a list of clusters containing the points
     * @throws MathIllegalArgumentException if the data points are null or the number
     *     of clusters is larger than the number of data points
     * @throws ConvergenceException if an empty cluster is encountered and the
     * {@link #emptyStrategy} is set to {@code ERROR}
     */
    public List<Cluster cluster(final Collection points, final int k,
                                    final int maxIterations)
        throws MathIllegalArgumentException, ConvergenceException {

        // sanity checks
        MathUtils.checkNotNull(points);

        // number of clusters has to be smaller or equal the number of data points
        if (points.size() < k) {
            throw new NumberIsTooSmallException(points.size(), k, false);
        }

        // create the initial clusters
        List<Cluster clusters = chooseInitialCenters(points, k, random);

        // create an array containing the latest assignment of a point to a cluster
        // no need to initialize the array, as it will be filled with the first assignment
        int[] assignments = new int[points.size()];
        assignPointsToClusters(clusters, points, assignments);

        // iterate through updating the centers until we're done
        final int max = (maxIterations < 0) ? Integer.MAX_VALUE : maxIterations;
        for (int count = 0; count < max; count++) {
            boolean emptyCluster = false;
            List<Cluster newClusters = new ArrayList>();
            for (final Cluster<T> cluster : clusters) {
                final T newCenter;
                if (cluster.getPoints().isEmpty()) {
                    switch (emptyStrategy) {
                        case LARGEST_VARIANCE :
                            newCenter = getPointFromLargestVarianceCluster(clusters);
                            break;
                        case LARGEST_POINTS_NUMBER :
                            newCenter = getPointFromLargestNumberCluster(clusters);
                            break;
                        case FARTHEST_POINT :
                            newCenter = getFarthestPoint(clusters);
                            break;
                        default :
                            throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
                    }
                    emptyCluster = true;
                } else {
                    newCenter = cluster.getCenter().centroidOf(cluster.getPoints());
                }
                newClusters.add(new Cluster<T>(newCenter));
            }
            int changes = assignPointsToClusters(newClusters, points, assignments);
            clusters = newClusters;

            // if there were no more changes in the point-to-cluster assignment
            // and there are no empty clusters left, return the current clusters
            if (changes == 0 && !emptyCluster) {
                return clusters;
            }
        }
        return clusters;
    }

    /**
     * Adds the given points to the closest {@link Cluster}.
     *
     * @param <T> type of the points to cluster
     * @param clusters the {@link Cluster}s to add the points to
     * @param points the points to add to the given {@link Cluster}s
     * @param assignments points assignments to clusters
     * @return the number of points assigned to different clusters as the iteration before
     */
    private static <T extends Clusterable int
        assignPointsToClusters(final List<Cluster clusters, final Collection points,
                               final int[] assignments) {
        int assignedDifferently = 0;
        int pointIndex = 0;
        for (final T p : points) {
            int clusterIndex = getNearestCluster(clusters, p);
            if (clusterIndex != assignments[pointIndex]) {
                assignedDifferently++;
            }

            Cluster<T> cluster = clusters.get(clusterIndex);
            cluster.addPoint(p);
            assignments[pointIndex++] = clusterIndex;
        }

        return assignedDifferently;
    }

    /**
     * Use K-means++ to choose the initial centers.
     *
     * @param <T> type of the points to cluster
     * @param points the points to choose the initial centers from
     * @param k the number of centers to choose
     * @param random random generator to use
     * @return the initial centers
     */
    private static <T extends Clusterable List>
        chooseInitialCenters(final Collection<T> points, final int k, final Random random) {

        // Convert to list for indexed access. Make it unmodifiable, since removal of items
        // would screw up the logic of this method.
        final List<T> pointList = Collections.unmodifiableList(new ArrayList (points));

        // The number of points in the list.
        final int numPoints = pointList.size();

        // Set the corresponding element in this array to indicate when
        // elements of pointList are no longer available.
        final boolean[] taken = new boolean[numPoints];

        // The resulting list of initial centers.
        final List<Cluster resultSet = new ArrayList>();

        // Choose one center uniformly at random from among the data points.
        final int firstPointIndex = random.nextInt(numPoints);

        final T firstPoint = pointList.get(firstPointIndex);

        resultSet.add(new Cluster<T>(firstPoint));

        // Must mark it as taken
        taken[firstPointIndex] = true;

        // To keep track of the minimum distance squared of elements of
        // pointList to elements of resultSet.
        final double[] minDistSquared = new double[numPoints];

        // Initialize the elements.  Since the only point in resultSet is firstPoint,
        // this is very easy.
        for (int i = 0; i < numPoints; i++) {
            if (i != firstPointIndex) { // That point isn't considered
                double d = firstPoint.distanceFrom(pointList.get(i));
                minDistSquared[i] = d*d;
            }
        }

        while (resultSet.size() < k) {

            // Sum up the squared distances for the points in pointList not
            // already taken.
            double distSqSum = 0.0;

            for (int i = 0; i < numPoints; i++) {
                if (!taken[i]) {
                    distSqSum += minDistSquared[i];
                }
            }

            // Add one new data point as a center. Each point x is chosen with
            // probability proportional to D(x)2
            final double r = random.nextDouble() * distSqSum;

            // The index of the next point to be added to the resultSet.
            int nextPointIndex = -1;

            // Sum through the squared min distances again, stopping when
            // sum >= r.
            double sum = 0.0;
            for (int i = 0; i < numPoints; i++) {
                if (!taken[i]) {
                    sum += minDistSquared[i];
                    if (sum >= r) {
                        nextPointIndex = i;
                        break;
                    }
                }
            }

            // If it's not set to >= 0, the point wasn't found in the previous
            // for loop, probably because distances are extremely small.  Just pick
            // the last available point.
            if (nextPointIndex == -1) {
                for (int i = numPoints - 1; i >= 0; i--) {
                    if (!taken[i]) {
                        nextPointIndex = i;
                        break;
                    }
                }
            }

            // We found one.
            if (nextPointIndex >= 0) {

                final T p = pointList.get(nextPointIndex);

                resultSet.add(new Cluster<T> (p));

                // Mark it as taken.
                taken[nextPointIndex] = true;

                if (resultSet.size() < k) {
                    // Now update elements of minDistSquared.  We only have to compute
                    // the distance to the new center to do this.
                    for (int j = 0; j < numPoints; j++) {
                        // Only have to worry about the points still not taken.
                        if (!taken[j]) {
                            double d = p.distanceFrom(pointList.get(j));
                            double d2 = d * d;
                            if (d2 < minDistSquared[j]) {
                                minDistSquared[j] = d2;
                            }
                        }
                    }
                }

            } else {
                // None found --
                // Break from the while loop to prevent
                // an infinite loop.
                break;
            }
        }

        return resultSet;
    }

    /**
     * Get a random point from the {@link Cluster} with the largest distance variance.
     *
     * @param clusters the {@link Cluster}s to search
     * @return a random point from the selected cluster
     * @throws ConvergenceException if clusters are all empty
     */
    private T getPointFromLargestVarianceCluster(final Collection<Cluster clusters)
    throws ConvergenceException {

        double maxVariance = Double.NEGATIVE_INFINITY;
        Cluster<T> selected = null;
        for (final Cluster<T> cluster : clusters) {
            if (!cluster.getPoints().isEmpty()) {

                // compute the distance variance of the current cluster
                final T center = cluster.getCenter();
                final Variance stat = new Variance();
                for (final T point : cluster.getPoints()) {
                    stat.increment(point.distanceFrom(center));
                }
                final double variance = stat.getResult();

                // select the cluster with the largest variance
                if (variance > maxVariance) {
                    maxVariance = variance;
                    selected = cluster;
                }

            }
        }

        // did we find at least one non-empty cluster ?
        if (selected == null) {
            throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
        }

        // extract a random point from the cluster
        final List<T> selectedPoints = selected.getPoints();
        return selectedPoints.remove(random.nextInt(selectedPoints.size()));

    }

    /**
     * Get a random point from the {@link Cluster} with the largest number of points
     *
     * @param clusters the {@link Cluster}s to search
     * @return a random point from the selected cluster
     * @throws ConvergenceException if clusters are all empty
     */
    private T getPointFromLargestNumberCluster(final Collection<Cluster clusters) throws ConvergenceException {

        int maxNumber = 0;
        Cluster<T> selected = null;
        for (final Cluster<T> cluster : clusters) {

            // get the number of points of the current cluster
            final int number = cluster.getPoints().size();

            // select the cluster with the largest number of points
            if (number > maxNumber) {
                maxNumber = number;
                selected = cluster;
            }

        }

        // did we find at least one non-empty cluster ?
        if (selected == null) {
            throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
        }

        // extract a random point from the cluster
        final List<T> selectedPoints = selected.getPoints();
        return selectedPoints.remove(random.nextInt(selectedPoints.size()));

    }

    /**
     * Get the point farthest to its cluster center
     *
     * @param clusters the {@link Cluster}s to search
     * @return point farthest to its cluster center
     * @throws ConvergenceException if clusters are all empty
     */
    private T getFarthestPoint(final Collection<Cluster clusters) throws ConvergenceException {

        double maxDistance = Double.NEGATIVE_INFINITY;
        Cluster<T> selectedCluster = null;
        int selectedPoint = -1;
        for (final Cluster<T> cluster : clusters) {

            // get the farthest point
            final T center = cluster.getCenter();
            final List<T> points = cluster.getPoints();
            for (int i = 0; i < points.size(); ++i) {
                final double distance = points.get(i).distanceFrom(center);
                if (distance > maxDistance) {
                    maxDistance     = distance;
                    selectedCluster = cluster;
                    selectedPoint   = i;
                }
            }

        }

        // did we find at least one non-empty cluster ?
        if (selectedCluster == null) {
            throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
        }

        return selectedCluster.getPoints().remove(selectedPoint);

    }

    /**
     * Returns the nearest {@link Cluster} to the given point
     *
     * @param <T> type of the points to cluster
     * @param clusters the {@link Cluster}s to search
     * @param point the point to find the nearest {@link Cluster} for
     * @return the index of the nearest {@link Cluster} to the given point
     */
    private static <T extends Clusterable int
        getNearestCluster(final Collection<Cluster clusters, final T point) {
        double minDistance = Double.MAX_VALUE;
        int clusterIndex = 0;
        int minCluster = 0;
        for (final Cluster<T> c : clusters) {
            final double distance = point.distanceFrom(c.getCenter());
            if (distance < minDistance) {
                minDistance = distance;
                minCluster = clusterIndex;
            }
            clusterIndex++;
        }
        return minCluster;
    }

}

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