Example usage for org.apache.commons.math3.distribution PascalDistribution PascalDistribution

Introduction

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

Prototype

public PascalDistribution(RandomGenerator rng, int r, double p)
        throws NotStrictlyPositiveException, OutOfRangeException 

Source Link

Document

Create a Pascal distribution with the given number of successes and probability of success.

Usage

From source file:net.myrrix.common.collection.SamplingLongPrimitiveIterator.java

public SamplingLongPrimitiveIterator(RandomGenerator random, LongPrimitiveIterator delegate,
        double samplingRate) {
    Preconditions.checkNotNull(random);//from ww  w  .j av  a 2s .co m
    Preconditions.checkNotNull(delegate);
    Preconditions.checkArgument(samplingRate > 0.0 && samplingRate <= 1.0);
    // Geometric distribution is special case of negative binomial (aka Pascal) with r=1:
    geometricDistribution = new PascalDistribution(random, 1, samplingRate);
    this.delegate = delegate;
    this.hasNext = true;
    doNext();
}

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From source file:com.cloudera.oryx.common.collection.SamplingLongPrimitiveIterator.java

/**
 *
 * @param random random number generator to re-use
 * @param delegate iterator to sample from
 * @param samplingRate sampling rate in (0,1]
 *//*from  w  w w. ja  va 2s . c  om*/
public SamplingLongPrimitiveIterator(RandomGenerator random, LongPrimitiveIterator delegate,
        double samplingRate) {
    Preconditions.checkNotNull(random);
    Preconditions.checkNotNull(delegate);
    Preconditions.checkArgument(samplingRate > 0.0 && samplingRate <= 1.0);
    // Geometric distribution is special case of negative binomial (aka Pascal) with r=1:
    geometricDistribution = new PascalDistribution(random, 1, samplingRate);
    this.delegate = delegate;
    this.hasNext = true;
    doNext();
}

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From source file:de.fosd.jdime.matcher.cost_model.CMParameters.java

/**
 * Constructs a new <code>CMParameters</code> configured from the given <code>MergeContext</code>.
 *
 * @param context/* w  w w .  j  a  va  2s  .  com*/
 *         the <code>MergeContext</code> to use
 */
public CMParameters(MergeContext context) {
    setNoMatchWeight(context.getWn());
    setRenamingWeight(context.getWr());
    setAncestryViolationWeight(context.getWa());
    setSiblingGroupBreakupWeight(context.getWs());
    setOrderingWeight(context.getWo());
    rng = new RandomAdaptor(context.getSeed().map(Well19937c::new).orElse(new Well19937c()));
    assignDist = new PascalDistribution(rng, 1, context.getpAssign());
    setPAssign(context.getpAssign());
    setFixLower(context.getFixLower());
    setFixUpper(context.getFixUpper());
    setBeta(30);
    setParallel(context.isCmMatcherParallel());
    setFixRandomPercentage(context.isCmMatcherFixRandomPercentage());
    lcaCache = new ConcurrentHashMap<>();
    siblingCache = new ConcurrentHashMap<>();
    otherSiblingsCache = new ConcurrentHashMap<>();
    exactContainsCache = new ConcurrentHashMap<>();
    boundContainsCache = new ConcurrentHashMap<>();
}

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From source file:io.coala.random.impl.RandomDistributionFactoryImpl.java

@Override
public RandomNumberDistribution<Integer> getPascal(final RandomNumberStream rng, final Number r,
        final Number p) {
    final IntegerDistribution dist = new PascalDistribution(
            RandomNumberStream.Util.asCommonsRandomGenerator(rng), r.intValue(), p.doubleValue());
    return new RandomNumberDistribution<Integer>() {
        @Override/*from   w  ww  .ja v a 2 s  .  c  om*/
        public Integer draw() {
            return dist.sample();
        }
    };
}

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From source file:net.myrrix.online.som.SelfOrganizingMaps.java

/**
 * @return map of initialized {@link Node}s, where each node is empty and initialized to a randomly chosen
 *  input vector normalized to unit length
 *//*  ww w. ja  v a 2 s .  c  o  m*/
private static Node[][] buildInitialMap(FastByIDMap<float[]> vectors, int mapSize) {

    double p = ((double) mapSize * mapSize) / vectors.size(); // Choose mapSize^2 out of # vectors
    IntegerDistribution pascalDistribution;
    if (p >= 1.0) {
        // No sampling at all, we can't fill the map with one pass even
        pascalDistribution = null;
    } else {
        // Number of un-selected elements to skip between selections is geometrically distributed with
        // parameter p; this is the same as a negative binomial / Pascal distribution with r=1:
        pascalDistribution = new PascalDistribution(RandomManager.getRandom(), 1, p);
    }

    LongPrimitiveIterator keyIterator = vectors.keySetIterator();
    Node[][] map = new Node[mapSize][mapSize];
    for (Node[] mapRow : map) {
        for (int j = 0; j < mapSize; j++) {
            if (pascalDistribution != null) {
                keyIterator.skip(pascalDistribution.sample());
            }
            while (!keyIterator.hasNext()) {
                keyIterator = vectors.keySetIterator(); // Start over, a little imprecise but affects it not much
                Preconditions.checkState(keyIterator.hasNext());
                if (pascalDistribution != null) {
                    keyIterator.skip(pascalDistribution.sample());
                }
            }
            float[] sampledVector = vectors.get(keyIterator.nextLong());
            mapRow[j] = new Node(sampledVector);
        }
    }
    return map;
}

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From source file:org.apache.mahout.cf.taste.impl.common.SamplingLongPrimitiveIterator.java

public SamplingLongPrimitiveIterator(RandomWrapper random, LongPrimitiveIterator delegate,
        double samplingRate) {
    Preconditions.checkNotNull(delegate);
    Preconditions.checkArgument(samplingRate > 0.0 && samplingRate <= 1.0,
            "Must be: 0.0 < samplingRate <= 1.0");
    // Geometric distribution is special case of negative binomial (aka Pascal) with r=1:
    geometricDistribution = new PascalDistribution(random.getRandomGenerator(), 1, samplingRate);
    this.delegate = delegate;
    this.hasNext = true;
    doNext();// ww  w .j  a v a2s .co  m
}

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From source file:org.apache.mahout.common.iterator.SamplingIterator.java

public SamplingIterator(RandomWrapper random, Iterator<? extends T> delegate, double samplingRate) {
    Preconditions.checkNotNull(delegate);
    Preconditions.checkArgument(samplingRate > 0.0 && samplingRate <= 1.0,
            "Must be: 0.0 < samplingRate <= 1.0. But samplingRate = " + samplingRate);
    // Geometric distribution is special case of negative binomial (aka Pascal) with r=1:
    geometricDistribution = new PascalDistribution(random.getRandomGenerator(), 1, samplingRate);
    this.delegate = delegate;
}

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From source file:org.codelibs.elasticsearch.taste.common.SamplingLongPrimitiveIterator.java

public SamplingLongPrimitiveIterator(final RandomWrapper random, final LongPrimitiveIterator delegate,
        final double samplingRate) {
    Preconditions.checkNotNull(delegate);
    Preconditions.checkArgument(samplingRate > 0.0 && samplingRate <= 1.0,
            "Must be: 0.0 < samplingRate <= 1.0");
    // Geometric distribution is special case of negative binomial (aka Pascal) with r=1:
    geometricDistribution = new PascalDistribution(random.getRandomGenerator(), 1, samplingRate);
    this.delegate = delegate;
    hasNext = true;//  w  w  w.  j a  va  2 s. c  o m
    doNext();
}

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