Example usage for org.apache.commons.math.distribution GammaDistribution inverseCumulativeProbability

Introduction

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

Prototype

double inverseCumulativeProbability(double p) throws MathException;

Source Link

Document

For this distribution, X, this method returns x such that P(X < x) = p.

Usage

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

protected final void compute() {

    if (input[0].isDefined() && input[1].isDefined() && input[2].isDefined()) {
        double param = a.getDouble();
        double param2 = b.getDouble();
        double val = c.getDouble();
        try {//from  ww  w .  ja  va2  s. co m
            GammaDistribution dist = getGammaDistribution(param, param2);
            num.setValue(dist.inverseCumulativeProbability(val)); // P(T <= val)

        } catch (Exception e) {
            num.setUndefined();
        }
    } else
        num.setUndefined();
}

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

@Override
public final void compute() {

    if (input[0].isDefined() && input[1].isDefined() && input[2].isDefined()) {
        double param = a.getDouble();
        double param2 = b.getDouble();
        double val = c.getDouble();
        try {// w w  w  .  j  a  va 2s.  c  om
            GammaDistribution dist = getGammaDistribution(param, param2);
            num.setValue(dist.inverseCumulativeProbability(val)); // P(T <=
            // val)

        } catch (Exception e) {
            num.setUndefined();
        }
    } else
        num.setUndefined();
}

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From source file:desmoj.core.dist.ContDistErlang.java

/**
 * Abstract method to map a double <code>p</code> from 0...1 to the 
 * distribution's domain by determining the value x that satisfies
 * <code>P(X &lt; x) = p</code>.
 * /*from   w w  w  .ja va 2  s  .  c  om*/
 * @param p double: A value between 0 and 1
 * 
 * @return Double : The value x that satisfies <code>P(X &lt; x) = p</code>
 */
public Double getInverseOfCumulativeProbabilityFunction(final double p) {

    double newSample = StatisticObject.UNDEFINED; //
    org.apache.commons.math.distribution.GammaDistribution gammadist = new org.apache.commons.math.distribution.GammaDistributionImpl(
            k, this.mean); // special case 

    try {
        newSample = gammadist.inverseCumulativeProbability(p);
    } catch (MathException e) {
    }

    return newSample;
}

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From source file:desmoj.core.dist.ContDistGamma.java

/**
 * Returns the next sample from this distribution. The value depends upon
 * the seed, the number of values taken from the stream by using this method
 * before and the alpha and beta parameters specified for this distribution.
 * /* w ww .j  a  v  a2 s  .  c o  m*/
 * @return Double : The next gamma distributed sample from this
 *         distribution.
 */
public Double sample() {

    double newSample = -1; //
    double randomNumber = randomGenerator.nextDouble();
    org.apache.commons.math.distribution.GammaDistribution gammadist = new org.apache.commons.math.distribution.GammaDistributionImpl(
            alpha, beta);
    incrementObservations(); // increase count of samples

    if (isAntithetic()) {

        try {
            newSample = gammadist.inverseCumulativeProbability(1 - randomNumber);
        } catch (MathException e) {
        }
    } else {
        try {
            newSample = gammadist.inverseCumulativeProbability(randomNumber);
        } catch (MathException e) {
        }
    }

    if (this.currentlySendTraceNotes())
        this.traceLastSample(Double.toString(newSample));

    return newSample;
}

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From source file:desmoj.core.dist.ContDistBeta.java

/**
 * Returns the next sample from this distribution. The value depends upon
 * the seed, the number of values taken from the stream by using this method
 * before and the alpha and beta parameters specified for this distribution.
 * //w  ww . j  a  v  a 2s.  co  m
 * @return Double : The next Beta distributed sample from this distribution.
 */
public Double sample() {

    double newSample = -1; //
    double randomNumber1 = randomGenerator.nextDouble();
    double randomNumber2 = randomGenerator.nextDouble();
    org.apache.commons.math.distribution.GammaDistribution gammadist1 = new org.apache.commons.math.distribution.GammaDistributionImpl(
            alpha, 1);
    org.apache.commons.math.distribution.GammaDistribution gammadist2 = new org.apache.commons.math.distribution.GammaDistributionImpl(
            beta, 1);
    incrementObservations(); // increase count of samples

    if (isAntithetic()) {

        try {
            double gammaval1 = gammadist1.inverseCumulativeProbability(1 - randomNumber1);
            double gammaval2 = gammadist2.inverseCumulativeProbability(1 - randomNumber2);
            newSample = gammaval1 / (gammaval1 + gammaval2);
        } catch (MathException e) {
        }
    } else {
        try {
            double gammaval1 = gammadist1.inverseCumulativeProbability(randomNumber1);
            double gammaval2 = gammadist2.inverseCumulativeProbability(randomNumber2);
            newSample = gammaval1 / (gammaval1 + gammaval2);
        } catch (MathException e) {
        }
    }

    if (this.currentlySendTraceNotes())
        this.traceLastSample(Double.toString(newSample));

    return newSample;
}

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From source file:beast.evolution.sitemodel.SiteModel.java

/**
 * discretisation of gamma distribution with equal proportions in each
 * category/*  w  w  w . j a va2  s .c  o  m*/
 * @param node
 */
protected void calculateCategoryRates(final Node node) {
    double propVariable = 1.0;
    int cat = 0;

    if (/*invarParameter != null && */invarParameter.getValue() > 0) {
        if (hasPropInvariantCategory) {
            categoryRates[0] = 0.0;
            categoryProportions[0] = invarParameter.getValue();
        }
        propVariable = 1.0 - invarParameter.getValue();
        if (hasPropInvariantCategory) {
            cat = 1;
        }
    }

    if (shapeParameter != null) {

        final double a = shapeParameter.getValue();
        double mean = 0.0;
        final int gammaCatCount = categoryCount - cat;

        final GammaDistribution g = new GammaDistributionImpl(a, 1.0 / a);
        for (int i = 0; i < gammaCatCount; i++) {
            try {
                // RRB: alternative implementation that seems equally good in
                // the first 5 significant digits, but uses a standard distribution object
                if (useBeast1StyleGamma) {
                    categoryRates[i + cat] = GammaDistributionQuantile((2.0 * i + 1.0) / (2.0 * gammaCatCount),
                            a, 1.0 / a);
                } else {
                    categoryRates[i + cat] = g
                            .inverseCumulativeProbability((2.0 * i + 1.0) / (2.0 * gammaCatCount));
                }

            } catch (Exception e) {
                e.printStackTrace();
                Log.err.println("Something went wrong with the gamma distribution calculation");
                System.exit(-1);
            }
            mean += categoryRates[i + cat];

            categoryProportions[i + cat] = propVariable / gammaCatCount;
        }

        mean = (propVariable * mean) / gammaCatCount;

        for (int i = 0; i < gammaCatCount; i++) {

            categoryRates[i + cat] /= mean;
        }
    } else {
        categoryRates[cat] = 1.0 / propVariable;
        categoryProportions[cat] = propVariable;
    }

    ratesKnown = true;
}

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From source file:yabby.evolution.sitemodel.SiteModel.java

/**
 * discretisation of gamma distribution with equal proportions in each
 * category/*from w  w  w. j  a  v a2 s  .  c  o m*/
 * @param node
 */
protected void calculateCategoryRates(final Node node) {
    double propVariable = 1.0;
    int cat = 0;

    if (/*invarParameter != null && */invarParameter.getValue() > 0) {
        if (hasPropInvariantCategory) {
            categoryRates[0] = 0.0;
            categoryProportions[0] = invarParameter.getValue();
        }
        propVariable = 1.0 - invarParameter.getValue();
        if (hasPropInvariantCategory) {
            cat = 1;
        }
    }

    if (shapeParameter != null) {

        final double a = shapeParameter.getValue();
        double mean = 0.0;
        final int gammaCatCount = categoryCount - cat;

        final GammaDistribution g = new GammaDistributionImpl(a, 1.0 / a);
        for (int i = 0; i < gammaCatCount; i++) {
            try {
                // RRB: alternative implementation that seems equally good in
                // the first 5 significant digits, but uses a standard distribution object
                if (useBeast1StyleGamma) {
                    categoryRates[i + cat] = GammaDistributionQuantile((2.0 * i + 1.0) / (2.0 * gammaCatCount),
                            a, 1.0 / a);
                } else {
                    categoryRates[i + cat] = g
                            .inverseCumulativeProbability((2.0 * i + 1.0) / (2.0 * gammaCatCount));
                }

            } catch (Exception e) {
                e.printStackTrace();
                System.err.println("Something went wrong with the gamma distribution calculation");
                System.exit(-1);
            }
            mean += categoryRates[i + cat];

            categoryProportions[i + cat] = propVariable / gammaCatCount;
        }

        mean = (propVariable * mean) / gammaCatCount;

        for (int i = 0; i < gammaCatCount; i++) {

            categoryRates[i + cat] /= mean;
        }
    } else {
        categoryRates[cat] = 1.0 / propVariable;
        categoryProportions[cat] = propVariable;
    }

    ratesKnown = true;
}

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