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

Introduction

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

Prototype

public FDistributionImpl(double numeratorDegreesOfFreedom, double denominatorDegreesOfFreedom) 

Source Link

Document

Create a F distribution using the given degrees of freedom.

Usage

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

FDistribution getFDistribution(double param, double param2) {
    if (f == null)
        f = new FDistributionImpl(param, param2);
    else {//from w  w w.  ja  v  a2 s.  c om
        f.setNumeratorDegreesOfFreedom(param);
        f.setDenominatorDegreesOfFreedom(param2);
    }
    return f;
}

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From source file:edu.utexas.cs.tactex.servercustomers.factoredcustomer.ProbabilityDistribution.java

ProbabilityDistribution(FactoredCustomerService service, Element xml) {
    if (null == randomSeedRepo)
        randomSeedRepo = (RandomSeedRepo) SpringApplicationContext.getBean("randomSeedRepo");

    type = Enum.valueOf(DistType.class, xml.getAttribute("distribution"));
    switch (type) {
    case POINTMASS:
    case DEGENERATE:
        param1 = Double.parseDouble(xml.getAttribute("value"));
        sampler = new DegenerateSampler(param1);
        break;/* w  w w  .ja v a  2  s . co m*/
    case UNIFORM:
        param1 = Double.parseDouble(xml.getAttribute("low"));
        param2 = Double.parseDouble(xml.getAttribute("high"));
        sampler = new UniformSampler(param1, param2);
        break;
    case INTERVAL:
        param1 = Double.parseDouble(xml.getAttribute("mean"));
        param2 = Double.parseDouble(xml.getAttribute("stdDev"));
        param3 = Double.parseDouble(xml.getAttribute("low"));
        param4 = Double.parseDouble(xml.getAttribute("high"));
        sampler = new IntervalSampler(param1, param2, param3, param4);
        break;
    case NORMAL:
    case GAUSSIAN:
        param1 = Double.parseDouble(xml.getAttribute("mean"));
        param2 = Double.parseDouble(xml.getAttribute("stdDev"));
        sampler = new ContinuousSampler(new NormalDistributionImpl(param1, param2));
        break;
    case STDNORMAL:
        param1 = 0;
        param2 = 1;
        sampler = new ContinuousSampler(new NormalDistributionImpl(param1, param2));
        break;
    case LOGNORMAL:
        param1 = Double.parseDouble(xml.getAttribute("expMean"));
        param2 = Double.parseDouble(xml.getAttribute("expStdDev"));
        sampler = new LogNormalSampler(param1, param2);
        break;
    case CAUCHY:
        param1 = Double.parseDouble(xml.getAttribute("median"));
        param2 = Double.parseDouble(xml.getAttribute("scale"));
        sampler = new ContinuousSampler(new CauchyDistributionImpl(param1, param2));
        break;
    case BETA:
        param1 = Double.parseDouble(xml.getAttribute("alpha"));
        param2 = Double.parseDouble(xml.getAttribute("beta"));
        sampler = new ContinuousSampler(new BetaDistributionImpl(param1, param2));
        break;
    case BINOMIAL:
        param1 = Double.parseDouble(xml.getAttribute("trials"));
        param2 = Double.parseDouble(xml.getAttribute("success"));
        sampler = new DiscreteSampler(new BinomialDistributionImpl((int) param1, param2));
        break;
    case POISSON:
        param1 = Double.parseDouble(xml.getAttribute("lambda"));
        sampler = new DiscreteSampler(new PoissonDistributionImpl(param1));
        break;
    case CHISQUARED:
        param1 = Double.parseDouble(xml.getAttribute("dof"));
        sampler = new ContinuousSampler(new ChiSquaredDistributionImpl(param1));
        break;
    case EXPONENTIAL:
        param1 = Double.parseDouble(xml.getAttribute("mean"));
        sampler = new ContinuousSampler(new ExponentialDistributionImpl(param1));
        break;
    case GAMMA:
        param1 = Double.parseDouble(xml.getAttribute("alpha"));
        param2 = Double.parseDouble(xml.getAttribute("beta"));
        sampler = new ContinuousSampler(new GammaDistributionImpl(param1, param2));
        break;
    case WEIBULL:
        param1 = Double.parseDouble(xml.getAttribute("alpha"));
        param2 = Double.parseDouble(xml.getAttribute("beta"));
        sampler = new ContinuousSampler(new WeibullDistributionImpl(param1, param2));
        break;
    case STUDENT:
        param1 = Double.parseDouble(xml.getAttribute("dof"));
        sampler = new ContinuousSampler(new TDistributionImpl(param1));
        break;
    case SNEDECOR:
        param1 = Double.parseDouble(xml.getAttribute("d1"));
        param2 = Double.parseDouble(xml.getAttribute("d2"));
        sampler = new ContinuousSampler(new FDistributionImpl(param1, param2));
        break;
    default:
        throw new Error("Invalid probability distribution type!");
    }
    sampler.reseedRandomGenerator(service.getRandomSeedRepo()
            .getRandomSeed("factoredcustomer.ProbabilityDistribution", SeedIdGenerator.getId(), "Sampler")
            .getValue());
}

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

/**
 * @param param// w w  w .  j a v a  2  s .  c o  m
 *            degrees of freedom (numerator)
 * @param param2
 *            degrees of freedom (denominator)
 * @return F-distribution
 */
protected FDistribution getFDistribution(double param, double param2) {
    if (f == null || f.getDenominatorDegreesOfFreedom() != param2 || f.getNumeratorDegreesOfFreedom() != param)
        f = new FDistributionImpl(param, param2);

    return f;
}

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From source file:org.renjin.Distributions.java

public static double df(final double x, final double df1, final double df2, boolean log) {
    return d(new FDistributionImpl(df1, df2), x, log);
}

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From source file:org.renjin.Distributions.java

public static double pf(final double q, final double df1, final double df2, boolean lowerTail, boolean logP) {
    return p(new FDistributionImpl(df1, df2), q, lowerTail, logP);
}

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From source file:org.renjin.Distributions.java

public static double qf(final double p, final double df1, final double df2, boolean lowerTail, boolean logP) {
    return q(new FDistributionImpl(df1, df2), p, lowerTail, logP);
}

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From source file:org.renjin.primitives.random.Distributions.java

public static double df(@Recycle double x, @Recycle double df1, @Recycle double df2, boolean log) {
    return d(new FDistributionImpl(df1, df2), x, log);
}

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From source file:org.renjin.primitives.random.Distributions.java

public static double pf(@Recycle double q, @Recycle double df1, @Recycle double df2, boolean lowerTail,
        boolean logP) {
    return p(new FDistributionImpl(df1, df2), q, lowerTail, logP);
}

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From source file:org.renjin.primitives.random.Distributions.java

public static double qf(@Recycle double p, @Recycle double df1, @Recycle double df2, boolean lowerTail,
        boolean logP) {
    return q(new FDistributionImpl(df1, df2), p, lowerTail, logP);
}

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From source file:org.renjin.stats.internals.Distributions.java

@DataParallel
@Internal//from  w  w w  .  j  a  va  2 s  .com
public static double df(@Recycle double x, @Recycle double df1, @Recycle double df2, boolean log) {
    return d(new FDistributionImpl(df1, df2), x, log);
}

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