Examples of inverseCumulativeProbability()

Examples of org.apache.commons.math3.distribution.BinomialDistribution.inverseCumulativeProbability()

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            }
        }

        BinomialDistribution binomial = new BinomialDistribution(numberOfRuns, getConfidence());
        int lowerBound = binomial.inverseCumulativeProbability(0.01);
        int upperBound = binomial.inverseCumulativeProbability(0.99);
        assertTrue(lowerBound < inRange && inRange < upperBound, String.format("%d out of %d passed. Expected [%d, %d]", inRange, numberOfRuns, lowerBound, upperBound));
    }

    @Override
    protected void testAggregation(Object expectedValue, Block block)
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Examples of org.apache.commons.math3.distribution.ExponentialDistribution.inverseCumulativeProbability()

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                final long min = Long.parseLong(bounds[0]);
                final long max = Long.parseLong(bounds[1]);
                ExponentialDistribution findBounds = new ExponentialDistribution(1d);
                // max probability should be roughly equal to accuracy of (max-min) to ensure all values are visitable,
                // over entire range, but this results in overly skewed distribution, so take sqrt
                final double mean = (max - min) / findBounds.inverseCumulativeProbability(1d - Math.sqrt(1d/(max-min)));
                return new ExpFactory(min, max, mean);
            } catch (Exception _)
            {
                throw new IllegalArgumentException("Invalid parameter list for uniform distribution: " + params);
            }
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Examples of org.apache.commons.math3.distribution.ExponentialDistribution.inverseCumulativeProbability()

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                final long min = Long.parseLong(bounds[0]);
                final long max = Long.parseLong(bounds[1]);
                ExponentialDistribution findBounds = new ExponentialDistribution(1d);
                // max probability should be roughly equal to accuracy of (max-min) to ensure all values are visitable,
                // over entire range, but this results in overly skewed distribution, so take sqrt
                final double mean = (max - min) / findBounds.inverseCumulativeProbability(1d - Math.sqrt(1d/(max-min)));
                return new ExpFactory(min, max, mean);
            } catch (Exception _)
            {
                throw new IllegalArgumentException("Invalid parameter list for uniform distribution: " + params);
            }
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Examples of org.apache.commons.math3.distribution.FDistribution.inverseCumulativeProbability()

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        double upperBound = 0;
        final double alpha = (1.0 - confidenceLevel) / 2.0;

        final FDistribution distributionLowerBound = new FDistribution(2 * (numberOfTrials - numberOfSuccesses + 1),
                                                                       2 * numberOfSuccesses);
        final double fValueLowerBound = distributionLowerBound.inverseCumulativeProbability(1 - alpha);
        if (numberOfSuccesses > 0) {
            lowerBound = numberOfSuccesses /
                         (numberOfSuccesses + (numberOfTrials - numberOfSuccesses + 1) * fValueLowerBound);
        }

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Examples of org.apache.commons.math3.distribution.FDistribution.inverseCumulativeProbability()

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                         (numberOfSuccesses + (numberOfTrials - numberOfSuccesses + 1) * fValueLowerBound);
        }

        final FDistribution distributionUpperBound = new FDistribution(2 * (numberOfSuccesses + 1),
                                                                       2 * (numberOfTrials - numberOfSuccesses));
        final double fValueUpperBound = distributionUpperBound.inverseCumulativeProbability(1 - alpha);
        if (numberOfSuccesses > 0) {
            upperBound = (numberOfSuccesses + 1) * fValueUpperBound /
                         (numberOfTrials - numberOfSuccesses + (numberOfSuccesses + 1) * fValueUpperBound);
        }

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Examples of org.apache.commons.math3.distribution.FDistribution.inverseCumulativeProbability()

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        double upperBound = 0;
        final double alpha = (1.0 - confidenceLevel) / 2.0;

        final FDistribution distributionLowerBound = new FDistribution(2 * (numberOfTrials - numberOfSuccesses + 1),
                                                                       2 * numberOfSuccesses);
        final double fValueLowerBound = distributionLowerBound.inverseCumulativeProbability(1 - alpha);
        if (numberOfSuccesses > 0) {
            lowerBound = numberOfSuccesses /
                         (numberOfSuccesses + (numberOfTrials - numberOfSuccesses + 1) * fValueLowerBound);
        }

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Examples of org.apache.commons.math3.distribution.FDistribution.inverseCumulativeProbability()

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                         (numberOfSuccesses + (numberOfTrials - numberOfSuccesses + 1) * fValueLowerBound);
        }

        final FDistribution distributionUpperBound = new FDistribution(2 * (numberOfSuccesses + 1),
                                                                       2 * (numberOfTrials - numberOfSuccesses));
        final double fValueUpperBound = distributionUpperBound.inverseCumulativeProbability(1 - alpha);
        if (numberOfSuccesses > 0) {
            upperBound = (numberOfSuccesses + 1) * fValueUpperBound /
                         (numberOfTrials - numberOfSuccesses + (numberOfSuccesses + 1) * fValueUpperBound);
        }

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Examples of org.apache.commons.math3.distribution.NormalDistribution.inverseCumulativeProbability()

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                                             double confidenceLevel) {
        IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel);
        final double mean = (double) numberOfSuccesses / (double) numberOfTrials;
        final double alpha = (1.0 - confidenceLevel) / 2;
        final NormalDistribution normalDistribution = new NormalDistribution();
        final double difference = normalDistribution.inverseCumulativeProbability(1 - alpha) *
                                  FastMath.sqrt(1.0 / numberOfTrials * mean * (1 - mean));
        return new ConfidenceInterval(mean - difference, mean + difference, confidenceLevel);
    }

}
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Examples of org.apache.commons.math3.distribution.NormalDistribution.inverseCumulativeProbability()

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    /** {@inheritDoc} */
    public ConfidenceInterval createInterval(int numberOfTrials, int numberOfSuccesses, double confidenceLevel) {
        IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel);
        final double alpha = (1.0 - confidenceLevel) / 2;
        final NormalDistribution normalDistribution = new NormalDistribution();
        final double z = normalDistribution.inverseCumulativeProbability(1 - alpha);
        final double zSquared = FastMath.pow(z, 2);
        final double mean = (double) numberOfSuccesses / (double) numberOfTrials;

        final double factor = 1.0 / (1 + (1.0 / numberOfTrials) * zSquared);
        final double modifiedSuccessRatio = mean + (1.0 / (2 * numberOfTrials)) * zSquared;
 
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Examples of org.apache.commons.math3.distribution.NormalDistribution.inverseCumulativeProbability()

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    /** {@inheritDoc} */
    public ConfidenceInterval createInterval(int numberOfTrials, int numberOfSuccesses, double confidenceLevel) {
        IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel);
        final double alpha = (1.0 - confidenceLevel) / 2;
        final NormalDistribution normalDistribution = new NormalDistribution();
        final double z = normalDistribution.inverseCumulativeProbability(1 - alpha);
        final double zSquared = FastMath.pow(z, 2);
        final double modifiedNumberOfTrials = numberOfTrials + zSquared;
        final double modifiedSuccessesRatio = (1.0 / modifiedNumberOfTrials) * (numberOfSuccesses + 0.5 * zSquared);
        final double difference = z *
                                  FastMath.sqrt(1.0 / modifiedNumberOfTrials * modifiedSuccessesRatio *
 
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