Package org.apache.commons.math3.distribution

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

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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-1], binBounds[i])) * 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 ChiSquareTest();
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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-1], binBounds[i])) * 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 ChiSquareTest();
            // Fail if we can reject null hypothesis that distributions are the same
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         *  Start with upper and lower tail bins.
         *  Lower bin = [0, lower); Upper bin = [upper, +inf).
         */
        PoissonDistribution poissonDistribution = new PoissonDistribution(mean);
        int lower = 1;
        while (poissonDistribution.cumulativeProbability(lower - 1) * sampleSize < minExpectedCount) {
            lower++;
        }
        int upper = (int) (5 * mean);  // Even for mean = 1, not much mass beyond 5
        while ((1 - poissonDistribution.cumulativeProbability(upper - 1)) * sampleSize < minExpectedCount) {
            upper--;
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        int lower = 1;
        while (poissonDistribution.cumulativeProbability(lower - 1) * sampleSize < minExpectedCount) {
            lower++;
        }
        int upper = (int) (5 * mean);  // Even for mean = 1, not much mass beyond 5
        while ((1 - poissonDistribution.cumulativeProbability(upper - 1)) * sampleSize < minExpectedCount) {
            upper--;
        }

        // Set bin width for interior bins.  For poisson, only need to look at end bins.
        int binWidth = 0;
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        boolean widthSufficient = false;
        double lowerBinMass = 0;
        double upperBinMass = 0;
        while (!widthSufficient) {
            binWidth++;
            lowerBinMass = poissonDistribution.cumulativeProbability(lower - 1, lower + binWidth - 1);
            upperBinMass = poissonDistribution.cumulativeProbability(upper - binWidth - 1, upper - 1);
            widthSufficient = FastMath.min(lowerBinMass, upperBinMass) * sampleSize >= minExpectedCount;
        }

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

        /*
         *  Determine interior bin bounds.  Bins are
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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-1], binBounds[i])) * 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 ChiSquareTest();
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