Package org.apache.commons.math3.distribution

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

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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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            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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        final double[] binBounds = getUpperBounds();
        final double kB = kB(binIndex);
        final double lower = binIndex == 0 ? min : binBounds[binIndex - 1];
        final RealDistribution kernel = k(x);
        final double withinBinCum =
            (kernel.cumulativeProbability(x) -  kernel.cumulativeProbability(lower)) / kB;
        return pBminus + pB * withinBinCum;
    }

    /**
     * {@inheritDoc}
 
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        final double[] binBounds = getUpperBounds();
        final double kB = kB(binIndex);
        final double lower = binIndex == 0 ? min : binBounds[binIndex - 1];
        final RealDistribution kernel = k(x);
        final double withinBinCum =
            (kernel.cumulativeProbability(x) -  kernel.cumulativeProbability(lower)) / kB;
        return pBminus + pB * withinBinCum;
    }

    /**
     * {@inheritDoc}
 
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        final RealDistribution kernel = getKernel(binStats.get(i));
        final double kB = kB(i);
        final double[] binBounds = getUpperBounds();
        final double lower = i == 0 ? min : binBounds[i - 1];
        final double kBminus = kernel.cumulativeProbability(lower);
        final double pB = pB(i);
        final double pBminus = pBminus(i);
        final double pCrit = p - pBminus;
        if (pCrit <= 0) {
            return lower;
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     */
    @SuppressWarnings("deprecation")
    private double kB(int i) {
        final double[] binBounds = getUpperBounds();
        final RealDistribution kernel = getKernel(binStats.get(i));
        return i == 0 ? kernel.cumulativeProbability(min, binBounds[0]) :
            kernel.cumulativeProbability(binBounds[i - 1], binBounds[i]);
    }

    /**
     * The within-bin kernel of the bin that x belongs to.
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    @SuppressWarnings("deprecation")
    private double kB(int i) {
        final double[] binBounds = getUpperBounds();
        final RealDistribution kernel = getKernel(binStats.get(i));
        return i == 0 ? kernel.cumulativeProbability(min, binBounds[0]) :
            kernel.cumulativeProbability(binBounds[i - 1], binBounds[i]);
    }

    /**
     * The within-bin kernel of the bin that x belongs to.
     *
 
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            final double upper = binBounds[bin];
            // Compute bMinus = sum or mass of bins below the bin containing the point
            // First bin has mass 11 / 10000, the rest have mass 10 / 10000.
            final double bMinus = bin == 0 ? 0 : (bin - 1) * binMass + firstBinMass;
            final RealDistribution kernel = findKernel(lower, upper);
            final double withinBinKernelMass = kernel.cumulativeProbability(lower, upper);
            final double kernelCum = kernel.cumulativeProbability(lower, testPoints[i]);
            cumValues[i] = bMinus + (bin == 0 ? firstBinMass : binMass) * kernelCum/withinBinKernelMass;
        }
        return cumValues;
    }
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