Example usage for org.apache.commons.math.exception ConvergenceException ConvergenceException

Introduction

In this page you can find the example usage for org.apache.commons.math.exception ConvergenceException ConvergenceException.

Prototype

public ConvergenceException(Localizable specific) 

Source Link

Document

Construct the exception with a specific context.

Usage

From source file:org.basketball.MyKMeansPlusPlusClusterer.java

/**
 * Runs the K-means++ clustering algorithm.
 *
 * @param points the points to cluster/*  w  ww  .  ja  v a 2  s  . co m*/
 * @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
 */
public List<Cluster<T>> cluster(final Collection<T> points, final int k, final int maxIterations) {
    // create the initial clusters
    List<Cluster<T>> clusters = chooseInitialCenters(points, k, random);
    assignPointsToClusters(clusters, points);

    // 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 clusteringChanged = false;
        List<Cluster<T>> newClusters = new ArrayList<Cluster<T>>();
        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;
                case IGNORE:
                    continue;
                default:
                    throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
                }
                clusteringChanged = true;
            } else {
                newCenter = cluster.getCenter().centroidOf(cluster.getPoints());
                if (!newCenter.equals(cluster.getCenter())) {
                    clusteringChanged = true;
                }
            }
            newClusters.add(new Cluster<T>(newCenter));
        }
        if (!clusteringChanged) {
            return clusters;
        }
        assignPointsToClusters(newClusters, points);
        clusters = newClusters;
    }
    return clusters;
}

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From source file:org.basketball.MyKMeansPlusPlusClusterer.java

/**
 * 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
 *//*from w  w  w  .  j a v  a  2 s .c  om*/
private T getPointFromLargestVarianceCluster(final Collection<Cluster<T>> clusters) {

    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()));

}

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From source file:org.basketball.MyKMeansPlusPlusClusterer.java

/**
 * 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
 */// w  ww .j a v  a  2s . c om
private T getPointFromLargestNumberCluster(final Collection<Cluster<T>> clusters) {

    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()));

}

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From source file:org.basketball.MyKMeansPlusPlusClusterer.java

/**
 * Get the point farthest to its cluster center
 *
 * @param clusters the {@link Cluster}s to search
 * @return point farthest to its cluster center
 *//*from www  .  ja  v  a  2  s.  c o  m*/
private T getFarthestPoint(final Collection<Cluster<T>> clusters) {

    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);

}

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