Examples of org.apache.commons.math.distribution.PoissonDistributionImpl.cumulativeProbability()

Package org.apache.commons.math.distribution

Class org.apache.commons.math.distribution.PoissonDistributionImpl

Examples of org.apache.commons.math.distribution.PoissonDistributionImpl.cumulativeProbability()

org.apache.commons.math.distribution.PoissonDistributionImpl.cumulativeProbability()
The probability distribution function P(X <= x) for a Poisson distribution. @param x the value at which the PDF is evaluated. @return Poisson distribution function evaluated at x @throws MathException if the cumulative probability can not be computeddue to convergence or other numerical errors.

        int binWidth = 1;
        boolean widthSufficient = false;
        double lowerBinMass = 0;
        double upperBinMass = 0;
        while (!widthSufficient) {
            lowerBinMass = poissonDistribution.cumulativeProbability(lower, lower + binWidth - 1);
            upperBinMass = poissonDistribution.cumulativeProbability(upper - binWidth + 1, upper);
            widthSufficient = FastMath.min(lowerBinMass, upperBinMass) * sampleSize >= minExpectedCount;
            binWidth++;
        }

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        boolean widthSufficient = false;
        double lowerBinMass = 0;
        double upperBinMass = 0;
        while (!widthSufficient) {
            lowerBinMass = poissonDistribution.cumulativeProbability(lower, lower + binWidth - 1);
            upperBinMass = poissonDistribution.cumulativeProbability(upper - binWidth + 1, upper);
            widthSufficient = FastMath.min(lowerBinMass, upperBinMass) * sampleSize >= minExpectedCount;
            binWidth++;
        }


        /*

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        // Bottom bin
        observed[0] = 0;
        for (int i = 0; i < lower; i++) {
            observed[0] += frequency.getCount(i);
        }
        expected[0] = poissonDistribution.cumulativeProbability(lower - 1) * sampleSize;


        // Top bin
        observed[binCount - 1] = 0;
        for (int i = upper; i <= maxObservedValue; i++) {
            observed[binCount - 1] += frequency.getCount(i);

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        // Top bin
        observed[binCount - 1] = 0;
        for (int i = upper; i <= maxObservedValue; i++) {
            observed[binCount - 1] += frequency.getCount(i);
        }
        expected[binCount - 1] = (1 - poissonDistribution.cumulativeProbability(upper - 1)) * sampleSize;


        // Interior bins
        for (int i = 1; i < binCount - 1; i++) {
            observed[i] = 0;
            for (int j = binBounds.get(i - 1); j < binBounds.get(i); j++) {

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        for (int i = 1; i < binCount - 1; i++) {
            observed[i] = 0;
            for (int j = binBounds.get(i - 1); j < binBounds.get(i); j++) {
                observed[i] += frequency.getCount(j);
            } // Expected count is (mass in [binBounds[i], binBounds[i+1])) * sampleSize
            expected[i] = (poissonDistribution.cumulativeProbability(binBounds.get(i) - 1) -
                poissonDistribution.cumulativeProbability(binBounds.get(i - 1) -1)) * sampleSize;
        }


        // Use chisquare test to verify that generated values are poisson(mean)-distributed
        ChiSquareTest chiSquareTest = new ChiSquareTestImpl();

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            observed[i] = 0;
            for (int j = binBounds.get(i - 1); j < binBounds.get(i); j++) {
                observed[i] += frequency.getCount(j);
            } // Expected count is (mass in [binBounds[i], binBounds[i+1])) * sampleSize
            expected[i] = (poissonDistribution.cumulativeProbability(binBounds.get(i) - 1) -
                poissonDistribution.cumulativeProbability(binBounds.get(i - 1) -1)) * sampleSize;
        }


        // Use chisquare test to verify that generated values are poisson(mean)-distributed
        ChiSquareTest chiSquareTest = new ChiSquareTestImpl();
        try {

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