Example usage for org.apache.commons.math.distribution PoissonDistributionImpl PoissonDistributionImpl

Introduction

In this page you can find the example usage for org.apache.commons.math.distribution PoissonDistributionImpl PoissonDistributionImpl.

Prototype

public PoissonDistributionImpl(double p) 

Source Link

Document

Create a new Poisson distribution with the given the mean.

Usage

From source file:geogebra.kernel.statistics.AlgoDistribution.java

PoissonDistribution getPoissonDistribution(double param) {
    if (poisson == null)
        poisson = new PoissonDistributionImpl(param);
    else {/*from   w  w w . j a  v a2 s. c  om*/
        poisson.setMean(param);
    }
    return poisson;
}

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From source file:geogebra.common.kernel.statistics.AlgoDistribution.java

/**
 * @param param//from www  . j a v a 2  s.  c om
 *            mean
 * @return Poisson distribution
 */
protected PoissonDistribution getPoissonDistribution(double param) {
    if (poisson == null || poisson.getMean() != param)
        poisson = new PoissonDistributionImpl(param);
    return poisson;
}

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From source file:geogebra.common.kernel.algos.AlgoBarChart.java

/**
 * Prepares list1 and list2 for use with probability distribution bar charts
 *///from  www  . j av a  2  s  .  c o m
private boolean prepareDistributionLists() {
    IntegerDistribution dist = null;
    int first = 0, last = 0;
    try {
        // get the distribution and the first, last list values for given
        // distribution type
        switch (type) {
        case TYPE_BARCHART_BINOMIAL:
            if (!(p1geo.isDefined() && p2geo.isDefined()))
                return false;
            int n = (int) Math.round(p1.getDouble());
            double p = p2.getDouble();
            dist = new BinomialDistributionImpl(n, p);
            first = 0;
            last = n;
            break;

        case TYPE_BARCHART_PASCAL:
            if (!(p1geo.isDefined() && p2geo.isDefined()))
                return false;
            n = (int) Math.round(p1.getDouble());
            p = p2.getDouble();
            dist = new PascalDistributionImpl(n, p);

            first = 0;
            last = (int) Math.max(1, (kernel).getXmax() + 1);
            break;
        case TYPE_BARCHART_ZIPF:
            if (!(p1geo.isDefined() && p2geo.isDefined()))
                return false;
            n = (int) Math.round(p1.getDouble());
            p = p2.getDouble();
            dist = new ZipfDistributionImpl(n, p);

            first = 0;
            last = n;
            break;
        case TYPE_BARCHART_POISSON:
            if (!p1geo.isDefined())
                return false;
            double lambda = p1.getDouble();
            dist = new PoissonDistributionImpl(lambda);
            first = 0;
            last = (int) Math.max(1, kernel.getXmax() + 1);
            break;

        case TYPE_BARCHART_HYPERGEOMETRIC:
            if (!(p1geo.isDefined() && p2geo.isDefined() && p3geo.isDefined()))
                return false;
            int pop = (int) p1.getDouble();
            int successes = (int) p2.getDouble();
            int sample = (int) p3.getDouble();
            dist = new HypergeometricDistributionImpl(pop, successes, sample);
            first = Math.max(0, successes + sample - pop);
            last = Math.min(successes, sample);
            break;
        }

        // load class list and probability list
        loadDistributionLists(first, last, dist);
    }

    catch (Exception e) {
        App.debug(e.getMessage());
        return false;
    }

    return true;
}

---

From source file:geogebra.kernel.AlgoFunctionAreaSums.java

/**
 * Prepares list1 and list2 for use with probability distribution bar charts
 *//*from  w w  w  .  j av  a2  s  . c o m*/
private boolean prepareDistributionLists() {
    IntegerDistribution dist = null;
    int first = 0, last = 0;
    try {
        // get the distribution and the first, last list values for given distribution type
        switch (type) {
        case TYPE_BARCHART_BINOMIAL:
            if (!(p1geo.isDefined() && p2geo.isDefined()))
                return false;
            int n = (int) Math.round(p1.getDouble());
            double p = p2.getDouble();
            dist = new BinomialDistributionImpl(n, p);
            first = 0;
            last = n;
            break;

        case TYPE_BARCHART_PASCAL:
            if (!(p1geo.isDefined() && p2geo.isDefined()))
                return false;
            n = (int) Math.round(p1.getDouble());
            p = p2.getDouble();
            dist = new PascalDistributionImpl(n, p);

            first = 0;
            last = (int) Math.max(1, kernel.getXmax() + 1);
            break;
        case TYPE_BARCHART_ZIPF:
            if (!(p1geo.isDefined() && p2geo.isDefined()))
                return false;
            n = (int) Math.round(p1.getDouble());
            p = p2.getDouble();
            dist = new ZipfDistributionImpl(n, p);

            first = 0;
            last = n;
            break;
        case TYPE_BARCHART_POISSON:
            if (!p1geo.isDefined())
                return false;
            double lambda = p1.getDouble();
            dist = new PoissonDistributionImpl(lambda);
            first = 0;
            last = (int) Math.max(1, kernel.getXmax() + 1);
            break;

        case TYPE_BARCHART_HYPERGEOMETRIC:
            if (!(p1geo.isDefined() && p2geo.isDefined() && p3geo.isDefined()))
                return false;
            int pop = (int) p1.getDouble();
            int successes = (int) p2.getDouble();
            int sample = (int) p3.getDouble();
            dist = new HypergeometricDistributionImpl(pop, successes, sample);
            first = Math.max(0, successes + sample - pop);
            last = Math.min(successes, sample);
            break;
        }

        // load class list and probability list
        loadDistributionLists(first, last, dist);
    }

    catch (Exception e) {
        Application.debug(e.getMessage());
        return false;
    }

    return true;
}

---

From source file:geogebra.common.kernel.algos.AlgoFunctionAreaSums.java

/**
 * Prepares list1 and list2 for use with probability distribution bar charts
 *//*from   w  w w  . j av  a  2  s  .  c  o m*/
private boolean prepareDistributionLists() {
    IntegerDistribution dist = null;
    int first = 0, last = 0;
    try {
        // get the distribution and the first, last list values for given
        // distribution type
        switch (type) {
        case BARCHART_BINOMIAL:
            if (!(p1geo.isDefined() && p2geo.isDefined()))
                return false;
            int n = (int) Math.round(p1.getDouble());
            double p = p2.getDouble();
            dist = new BinomialDistributionImpl(n, p);
            first = 0;
            last = n;
            break;

        case BARCHART_PASCAL:
            if (!(p1geo.isDefined() && p2geo.isDefined()))
                return false;
            n = (int) Math.round(p1.getDouble());
            p = p2.getDouble();
            dist = new PascalDistributionImpl(n, p);

            first = 0;
            last = (int) Math.max(1, (kernel).getXmax() + 1);
            break;
        case BARCHART_ZIPF:
            if (!(p1geo.isDefined() && p2geo.isDefined()))
                return false;
            n = (int) Math.round(p1.getDouble());
            p = p2.getDouble();
            dist = new ZipfDistributionImpl(n, p);

            first = 0;
            last = n;
            break;
        case BARCHART_POISSON:
            if (!p1geo.isDefined())
                return false;
            double lambda = p1.getDouble();
            dist = new PoissonDistributionImpl(lambda);
            first = 0;
            last = (int) Math.max(1, kernel.getXmax() + 1);
            break;

        case BARCHART_HYPERGEOMETRIC:
            if (!(p1geo.isDefined() && p2geo.isDefined() && p3geo.isDefined()))
                return false;
            int pop = (int) p1.getDouble();
            int successes = (int) p2.getDouble();
            int sample = (int) p3.getDouble();
            dist = new HypergeometricDistributionImpl(pop, successes, sample);
            first = Math.max(0, successes + sample - pop);
            last = Math.min(successes, sample);
            break;
        }

        // load class list and probability list
        loadDistributionLists(first, last, dist);
    }

    catch (Exception e) {
        App.debug(e.getMessage());
        return false;
    }

    return true;
}

---

From source file:org.apache.mahout.clustering.lda.TestLDAInference.java

/**
 * Generate random document vector//from  w  w  w.  j a v  a  2  s.c o m
 * 
 * @param numWords
 *          int number of words in the vocabulary
 * @param numWords
 *          E[count] for each word
 */
private Vector generateRandomDoc(int numWords, double sparsity) throws MathException {
    Vector v = new DenseVector(numWords);
    IntegerDistribution dist = new PoissonDistributionImpl(sparsity);
    for (int i = 0; i < numWords; i++) {
        // random integer
        v.setQuick(i, dist.inverseCumulativeProbability(random.nextDouble()) + 1);
    }
    return v;
}

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From source file:org.apache.mahout.clustering.lda.TestMapReduce.java

/**
 * Generate random document vector/*from w  ww.ja va2  s .  co m*/
 * @param numWords int number of words in the vocabulary
 * @param numWords E[count] for each word
 */
private RandomAccessSparseVector generateRandomDoc(int numWords, double sparsity) throws MathException {
    RandomAccessSparseVector v = new RandomAccessSparseVector(numWords, (int) (numWords * sparsity));
    IntegerDistribution dist = new PoissonDistributionImpl(sparsity);
    for (int i = 0; i < numWords; i++) {
        // random integer
        v.set(i, dist.inverseCumulativeProbability(random.nextDouble()) + 1);
    }
    return v;
}

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From source file:org.apache.mahout.knn.generate.PoissonSampler.java

public PoissonSampler(double lambda) {
    gen = RandomUtils.getRandom();
    pd = new PoissonDistributionImpl(lambda);
}

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From source file:org.apache.mahout.knn.generate.PoissonSamplerTest.java

private void checkDistribution(PoissonSampler pd, double alpha) {
    int[] count = new int[(int) Math.max(10, 5 * alpha)];
    for (int i = 0; i < 10000; i++) {
        count[pd.sample().intValue()]++;
    }//  w w w . java 2  s  .c  om

    PoissonDistribution ref = new PoissonDistributionImpl(alpha);
    for (int i = 0; i < count.length; i++) {
        assertEquals(ref.probability(i), count[i] / 10000.0, 2e-2);
    }
}

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From source file:org.renjin.Distributions.java

public static double dpois(final double x, final double lambda, boolean log) {
    return d(new PoissonDistributionImpl(lambda), x, log);
}

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