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

Package org.apache.commons.math3.distribution

Class org.apache.commons.math3.distribution.NormalDistribution

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

org.apache.commons.math3.distribution.NormalDistribution.cumulativeProbability()
{@inheritDoc}If {@code x} is more than 40 standard deviations from the mean, 0 or 1is returned, as in these cases the actual value is within {@code Double.MIN_VALUE} of 0 or 1.

            // 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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            final int bin = findBin(testPoints[i]);
            final double lower = bin == 0 ? empiricalDistribution.getSupportLowerBound() :
                binBounds[bin - 1];
            final double upper = binBounds[bin];
            final RealDistribution kernel = findKernel(lower, upper);
            final double withinBinKernelMass = kernel.cumulativeProbability(lower, upper);
            final double density = kernel.density(testPoints[i]);
            densityValues[i] = density * (bin == 0 ? firstBinMass : binMass) / withinBinKernelMass;   
        }
        return densityValues;
    }

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        };
        final double[] lower = {0, 5, 1000, 5001, 9995};
        final double[] upper = {5, 12, 1030, 5010, 10000};
        for (int i = 1; i < 5; i++) {
            Assert.assertEquals(
                    distribution.cumulativeProbability( 
                            lower[i], upper[i]),
                            integrator.integrate(
                                    1000000, // Triangle integrals are very slow to converge
                                    d, lower[i], upper[i]), tol);
        }

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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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            // 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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            final int bin = findBin(testPoints[i]);
            final double lower = bin == 0 ? empiricalDistribution.getSupportLowerBound() :
                binBounds[bin - 1];
            final double upper = binBounds[bin];
            final RealDistribution kernel = findKernel(lower, upper);
            final double withinBinKernelMass = kernel.cumulativeProbability(lower, upper);
            final double density = kernel.density(testPoints[i]);
            densityValues[i] = density * (bin == 0 ? firstBinMass : binMass) / withinBinKernelMass;   
        }
        return densityValues;
    }

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        };
        final double[] lower = {0, 5, 1000, 5001, 9995};
        final double[] upper = {5, 12, 1030, 5010, 10000};
        for (int i = 1; i < 5; i++) {
            Assert.assertEquals(
                    distribution.cumulativeProbability( 
                            lower[i], upper[i]),
                            integrator.integrate(
                                    1000000, // Triangle integrals are very slow to converge
                                    d, lower[i], upper[i]), tol);
        }

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        if (n < 3) {
            return Double.NaN;
        }
        // No advertised NotStrictlyPositiveException here - will return NaN above
        TDistribution distribution = new TDistribution(n - 2);
        return 2d * (1.0 - distribution.cumulativeProbability(
                    FastMath.abs(getSlope()) / getSlopeStdErr()));
    }


    // ---------------------Private methods-----------------------------------

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                if (i == j) {
                    out[i][j] = 0d;
                } else {
                    double r = correlationMatrix.getEntry(i, j);
                    double t = FastMath.abs(r * FastMath.sqrt((nObs - 2)/(1 - r * r)));
                    out[i][j] = 2 * tDistribution.cumulativeProbability(-t);
                }
            }
        }
        return new BlockRealMatrix(out);
    }

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        throws MaxCountExceededException, MathIllegalArgumentException {


        final double t = FastMath.abs(t(m, mu, v, n));
        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final TDistribution distribution = new TDistribution(null, n - 1);
        return 2.0 * distribution.cumulativeProbability(-t);


    }


    /**
     * Computes p-value for 2-sided, 2-sample t-test.

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