List of density() Examples

Examples of density()

ch.akuhn.hapax.index.TermDocumentMatrix.density()
org.apache.commons.math3.distribution.NormalDistribution.density()
{@inheritDoc}
org.apache.commons.math3.distribution.RealDistribution.density()
Returns the probability density function (PDF) of this distribution evaluated at the specified point {@code x}. In general, the PDF is the derivative of the {@link #cumulativeProbability(double) CDF}. If the derivative does not exist at {@code x}, then an appropriate replacement should be returned, e.g. {@code Double.POSITIVE_INFINITY}, {@code Double.NaN}, or the limit inferior or limit superior of the difference quotient. @param x the point at which the PDF is evaluated @return the value of the probability density function at point {@code x}

Examples of org.apache.commons.math3.distribution.RealDistribution.density()

        final double tol = 1.0e-9;
        final BaseAbstractUnivariateIntegrator integrator =
            new IterativeLegendreGaussIntegrator(5, 1.0e-12, 1.0e-10);
        final UnivariateFunction d = new UnivariateFunction() {
            public double value(double x) {
                return distribution.density(x);
            }
        };
        final double[] lower = {0, 5, 1000, 5001, 9995};
        final double[] upper = {5, 12, 1030, 5010, 10000};
        for (int i = 1; i < 5; i++) {

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Examples of org.apache.commons.math3.distribution.RealDistribution.density()

            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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Examples of org.apache.commons.math3.distribution.RealDistribution.density()

        final double tol = 1.0e-9;
        final BaseAbstractUnivariateIntegrator integrator =
            new IterativeLegendreGaussIntegrator(5, 1.0e-12, 1.0e-10);
        final UnivariateFunction d = new UnivariateFunction() {
            public double value(double x) {
                return distribution.density(x);
            }
        };
        final double[] lower = {0, 5, 1000, 5001, 9995};
        final double[] upper = {5, 12, 1030, 5010, 10000};
        for (int i = 1; i < 5; i++) {

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Examples of org.apache.commons.math3.distribution.RealDistribution.density()

        if (x < min || x > max) {
            return 0d;
        }
        final int binIndex = findBin(x);
        final RealDistribution kernel = getKernel(binStats.get(binIndex));
        return kernel.density(x) * pB(binIndex) / kB(binIndex);
    }


    /**
     * {@inheritDoc}
     *

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Examples of org.apache.commons.math3.distribution.RealDistribution.density()

        if (x < min || x > max) {
            return 0d;
        }
        final int binIndex = findBin(x);
        final RealDistribution kernel = getKernel(binStats.get(binIndex));
        return kernel.density(x) * pB(binIndex) / kB(binIndex);
    }


    /**
     * {@inheritDoc}
     *

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Examples of org.apache.commons.math3.distribution.RealDistribution.density()

            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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Examples of org.apache.commons.math3.distribution.RealDistribution.density()

        final double tol = 1.0e-9;
        final BaseAbstractUnivariateIntegrator integrator =
            new IterativeLegendreGaussIntegrator(5, 1.0e-12, 1.0e-10);
        final UnivariateFunction d = new UnivariateFunction() {
            public double value(double x) {
                return distribution.density(x);
            }
        };
        final double[] lower = {0, 5, 1000, 5001, 9995};
        final double[] upper = {5, 12, 1030, 5010, 10000};
        for (int i = 1; i < 5; i++) {

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