Examples of org.apache.commons.math3.distribution.RealDistribution.cumulativeProbability()

Package org.apache.commons.math3.distribution

Class org.apache.commons.math3.distribution.RealDistribution

Examples of org.apache.commons.math3.distribution.RealDistribution.cumulativeProbability()

org.apache.commons.math3.distribution.RealDistribution.cumulativeProbability()
For a random variable {@code X} whose values are distributed accordingto this distribution, this method returns {@code P(x0 < X <= x1)}. @param x0 the exclusive lower bound @param x1 the inclusive upper bound @return the probability that a random variable with this distributiontakes a value between {@code x0} and {@code x1}, excluding the lower and including the upper endpoint @throws NumberIsTooLargeException if {@code x0> x1} @deprecated As of 3.1. In 4.0, this method will be renamed{@code probability(double x0, double x1)}.

         *  evaluated at the random value from the distribution should match the uniform
         *  random value used to generate it, which is stored in the quantiles[] array.
         */
        for (int i = 0; i < 10; i++) {
            double value = betaDistribution.sample();
            Assert.assertEquals(betaDistribution.cumulativeProbability(value), quantiles[i], 10E-9);
        }
    }


    @Test
    public void testNextBeta() {

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         *  evaluated at the random value from the distribution should match the uniform
         *  random value used to generate it, which is stored in the quantiles[] array.
         */
        for (int i = 0; i < 10; i++) {
            double value = betaDistribution.sample();
            Assert.assertEquals(betaDistribution.cumulativeProbability(value), quantiles[i], 10E-9);
        }
    }


    @Test
    public void testNextBeta() {

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         *  evaluated at the random value from the distribution should match the uniform
         *  random value used to generate it, which is stored in the quantiles[] array.
         */
        for (int i = 0; i < 10; i++) {
            double value = betaDistribution.sample();
            Assert.assertEquals(betaDistribution.cumulativeProbability(value), quantiles[i], 10E-9);
        }
    }


    @Test
    public void testNextBeta() {

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        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final BinomialDistribution distribution = new BinomialDistribution(null, numberOfTrials, probability);
        switch (alternativeHypothesis) {
        case GREATER_THAN:
            return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1);
        case LESS_THAN:
            return distribution.cumulativeProbability(numberOfSuccesses);
        case TWO_SIDED:
            int criticalValueLow = 0;
            int criticalValueHigh = numberOfTrials;

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        final BinomialDistribution distribution = new BinomialDistribution(null, numberOfTrials, probability);
        switch (alternativeHypothesis) {
        case GREATER_THAN:
            return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1);
        case LESS_THAN:
            return distribution.cumulativeProbability(numberOfSuccesses);
        case TWO_SIDED:
            int criticalValueLow = 0;
            int criticalValueHigh = numberOfTrials;
            double pTotal = 0;

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        }


        final BinomialDistribution distribution = new BinomialDistribution(numberOfTrials, probability);
        switch (alternativeHypothesis) {
        case GREATER_THAN:
            return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1);
        case LESS_THAN:
            return distribution.cumulativeProbability(numberOfSuccesses);
        case TWO_SIDED:
            int criticalValueLow = 0;
            int criticalValueHigh = numberOfTrials;

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        final BinomialDistribution distribution = new BinomialDistribution(numberOfTrials, probability);
        switch (alternativeHypothesis) {
        case GREATER_THAN:
            return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1);
        case LESS_THAN:
            return distribution.cumulativeProbability(numberOfSuccesses);
        case TWO_SIDED:
            int criticalValueLow = 0;
            int criticalValueHigh = numberOfTrials;
            double pTotal = 0;

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            }


            final BinomialDistribution distribution = new BinomialDistribution(numberOfTrials, probability);
            switch (alternativeHypothesis) {
                case GREATER_THAN:
                    return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1);
                case LESS_THAN:
                    return distribution.cumulativeProbability(numberOfSuccesses);
                case TWO_SIDED:
                    int criticalValueLow = 0;
                    int criticalValueHigh = numberOfTrials;

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            final BinomialDistribution distribution = new BinomialDistribution(numberOfTrials, probability);
            switch (alternativeHypothesis) {
                case GREATER_THAN:
                    return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1);
                case LESS_THAN:
                    return distribution.cumulativeProbability(numberOfSuccesses);
                case TWO_SIDED:
                    int criticalValueLow = 0;
                    int criticalValueHigh = numberOfTrials;
                    double pTotal = 0;

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        DimensionMismatchException, MaxCountExceededException {


        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final ChiSquaredDistribution distribution =
            new ChiSquaredDistribution(null, expected.length - 1.0);
        return 1.0 - distribution.cumulativeProbability(chiSquare(expected, observed));
    }


    /**
     * Performs a <a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm">
     * Chi-square goodness of fit test</a> evaluating the null hypothesis that the

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