Example usage for org.apache.commons.math3.distribution EnumeratedRealDistribution sample

Introduction

In this page you can find the example usage for org.apache.commons.math3.distribution EnumeratedRealDistribution sample.

Prototype

public double[] sample(int sampleSize) 

Source Link

Document

The default implementation generates the sample by calling #sample() in a loop.

Usage

From source file:cz.cuni.mff.spl.evaluator.statistics.KolmogorovSmirnovTestFlag.java

/**
 * Estimates the <i>p-value</i> of a two-sample
 * <a href="http://en.wikipedia.org/wiki/Kolmogorov-Smirnov_test"> Kolmogorov-Smirnov test</a>
 * evaluating the null hypothesis that {@code x} and {@code y} are samples drawn from the same
 * probability distribution. This method estimates the p-value by repeatedly sampling sets of size
 * {@code x.length} and {@code y.length} from the empirical distribution of the combined sample.
 * When {@code strict} is true, this is equivalent to the algorithm implemented in the R function
 * {@code ks.boot}, described in <pre>
 * Jasjeet S. Sekhon. 2011. 'Multivariate and Propensity Score Matching
 * Software with Automated Balance Optimization: The Matching package for R.'
 * Journal of Statistical Software, 42(7): 1-52.
 * </pre>/*from   w  ww  .  ja v  a2  s.c  o  m*/
 * @param x first sample
 * @param y second sample
 * @param iterations number of bootstrap resampling iterations
 * @param strict whether or not the null hypothesis is expressed as a strict inequality
 * @return estimated p-value
 */
public double bootstrap(double[] x, double[] y, int iterations, boolean strict) {
    final int xLength = x.length;
    final int yLength = y.length;
    final double[] combined = new double[xLength + yLength];
    System.arraycopy(x, 0, combined, 0, xLength);
    System.arraycopy(y, 0, combined, xLength, yLength);
    final EnumeratedRealDistribution dist = new EnumeratedRealDistribution(rng, combined);
    final long d = integralKolmogorovSmirnovStatistic(x, y);
    int greaterCount = 0;
    int equalCount = 0;
    double[] curX;
    double[] curY;
    long curD;
    for (int i = 0; i < iterations; i++) {
        curX = dist.sample(xLength);
        curY = dist.sample(yLength);
        curD = integralKolmogorovSmirnovStatistic(curX, curY);
        if (curD > d) {
            greaterCount++;
        } else if (curD == d) {
            equalCount++;
        }
    }
    return strict ? greaterCount / (double) iterations : (greaterCount + equalCount) / (double) iterations;
}

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