Example usage for org.apache.commons.math.stat.descriptive.moment Variance getResult

Introduction

In this page you can find the example usage for org.apache.commons.math.stat.descriptive.moment Variance getResult.

Prototype

@Override
public double getResult() 

Source Link

Usage

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
 *///  ww  w. jav  a  2 s. co  m
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.beedraz.semantics_II.MathBugDemonstration.java

/**
 * @param args/*from  w ww .j a v  a 2 s. co m*/
 */
public static void main(String[] args) {

    // difference between getResult and evaluate
    //      double[] values = new double[] {1.0, 2.0, Double.POSITIVE_INFINITY};
    //      Variance var1 = new Variance();
    //      double value1 = var1.evaluate(values);
    //      Variance var2 = new Variance();
    //      var2.incrementAll(values);
    //      double value2 = var2.getResult();
    //      System.out.println(value1);
    //      System.out.println(value2);

    // difference between getResult and evaluate
    double[] values = new double[] { 1.0, 2.0, Double.NEGATIVE_INFINITY };
    Variance var1 = new Variance();
    double value1 = var1.evaluate(values);
    Variance var2 = new Variance();
    var2.incrementAll(values);
    double value2 = var2.getResult();
    var2.evaluate(values);
    System.out.println(value1);
    System.out.println(value2);

    //    // evaluate method changes internal representation
    //    double[] values = new double[] {1.0, 2.0, Double.NEGATIVE_INFINITY};
    //    Variance var1 = new Variance();
    //    var1.incrementAll(values);
    //    System.out.println(var1.getN());
    //    System.out.println(var1.getResult());
    //    double value1 = var1.evaluate(values);
    //    System.out.println(var1.getN());
    //    System.out.println(var1.getResult());
    //    System.out.println(value1);

}

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