Package org.apache.commons.math3.distribution

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

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            successes += approximateAggregationWithinErrorBound(function, 1, 0.5, sum, 0, builder.build()) ? 1 : 0;
        }

        // Since we used a confidence of 0.5, successes should have a binomial distribution B(n=20, p=0.5)
        assertTrue(binomial.inverseCumulativeProbability(0.01) < successes && successes < binomial.inverseCumulativeProbability(0.99));
    }
}
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            successes += approximateAggregationWithinErrorBound(function, 1, 0.5, sum, 0, builder.build()) ? 1 : 0;
        }

        // Since we used a confidence of 0.5, successes should have a binomial distribution B(n=20, p=0.5)
        assertTrue(binomial.inverseCumulativeProbability(0.01) < successes && successes < binomial.inverseCumulativeProbability(0.99));
    }
}
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                inRange++;
            }
        }

        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
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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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                inRange++;
            }
        }

        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
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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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            successes += approximateAggregationWithinErrorBound(function, 1, 0.5, (double) sum, builder.build()) ? 1 : 0;
        }

        // Since we used a confidence of 0.5, successes should have a binomial distribution B(n=20, p=0.5)
        assertTrue(binomial.inverseCumulativeProbability(0.01) < successes && successes < binomial.inverseCumulativeProbability(0.99));
    }

    private static class DeterministicBootstrappedAggregation
            extends BootstrappedAggregation
    {
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            successes += approximateAggregationWithinErrorBound(function, 1, 0.5, (double) sum, builder.build()) ? 1 : 0;
        }

        // Since we used a confidence of 0.5, successes should have a binomial distribution B(n=20, p=0.5)
        assertTrue(binomial.inverseCumulativeProbability(0.01) < successes && successes < binomial.inverseCumulativeProbability(0.99));
    }

    private static class DeterministicBootstrappedAggregation
            extends BootstrappedAggregation
    {
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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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                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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