Java Code Examples for org.apache.commons.math3.distribution.RealDistribution#density()

@Override
public double[] makeDensityTestValues() {
    final double[] testPoints = getCumulativeTestPoints();
    final double[] densityValues = new double[testPoints.length];
    final EmpiricalDistribution empiricalDistribution = (EmpiricalDistribution) makeDistribution();
    final double[] binBounds = empiricalDistribution.getUpperBounds();
    for (int i = 0; i < testPoints.length; i++) {
        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;
}
 

/** 
 * Modify test integration bounds from the default. Because the distribution
 * has discontinuities at bin boundaries, integrals spanning multiple bins
 * will face convergence problems.  Only test within-bin integrals and spans
 * across no more than 3 bin boundaries.
 */
@Override
@Test
public void testDensityIntegrals() {
    final RealDistribution distribution = makeDistribution();
    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++) {
        Assert.assertEquals(
                distribution.cumulativeProbability( 
                        lower[i], upper[i]),
                        integrator.integrate(
                                1000000, // Triangle integrals are very slow to converge
                                d, lower[i], upper[i]), tol);
    }
}
 

@Override
public double[] makeDensityTestValues() {
    final double[] testPoints = getCumulativeTestPoints();
    final double[] densityValues = new double[testPoints.length];
    final EmpiricalDistribution empiricalDistribution = (EmpiricalDistribution) makeDistribution();
    final double[] binBounds = empiricalDistribution.getUpperBounds();
    for (int i = 0; i < testPoints.length; i++) {
        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;
}
 

public static void addPDFSeries(Chart chart, RealDistribution distribution, String desc, int lowerBound, int upperBound) {
    // generates Log data
    List<Number> xData = new ArrayList<Number>();
    List<Number> yData = new ArrayList<Number>();
    int samples = 100;
    double stepSize = (upperBound - lowerBound) / (double) samples;
    for (double x = lowerBound; x <= upperBound; x += stepSize) {
        try {
            double density = distribution.density(x);
            if (! Double.isInfinite(density) && ! Double.isNaN(density)) {
                xData.add(x);
                yData.add(density);
            }
        } catch (Exception e) {
            // ignore
            // some distributions may reject certain values depending on the parameter settings
        }
    }

    Series series = chart.addSeries(desc, xData, yData);
    series.setMarker(SeriesMarker.NONE);
    series.setLineStyle(new BasicStroke(1.2f));
}
 

/** 
 * Modify test integration bounds from the default. Because the distribution
 * has discontinuities at bin boundaries, integrals spanning multiple bins
 * will face convergence problems.  Only test within-bin integrals and spans
 * across no more than 3 bin boundaries.
 */
@Override
@Test
public void testDensityIntegrals() {
    final RealDistribution distribution = makeDistribution();
    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++) {
        Assert.assertEquals(
                distribution.cumulativeProbability( 
                        lower[i], upper[i]),
                        integrator.integrate(
                                1000000, // Triangle integrals are very slow to converge
                                d, lower[i], upper[i]), tol);
    }
}
 

@Override
public double[] makeDensityTestValues() {
    final double[] testPoints = getCumulativeTestPoints();
    final double[] densityValues = new double[testPoints.length];
    final EmpiricalDistribution empiricalDistribution = (EmpiricalDistribution) makeDistribution();
    final double[] binBounds = empiricalDistribution.getUpperBounds();
    for (int i = 0; i < testPoints.length; i++) {
        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;
}
 

/** 
 * Modify test integration bounds from the default. Because the distribution
 * has discontinuities at bin boundaries, integrals spanning multiple bins
 * will face convergence problems.  Only test within-bin integrals and spans
 * across no more than 3 bin boundaries.
 */
@Override
@Test
public void testDensityIntegrals() {
    final RealDistribution distribution = makeDistribution();
    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++) {
        Assert.assertEquals(
                distribution.cumulativeProbability( 
                        lower[i], upper[i]),
                        integrator.integrate(
                                1000000, // Triangle integrals are very slow to converge
                                d, lower[i], upper[i]), tol);
    }
}
 

/** 
 * Modify test integration bounds from the default. Because the distribution
 * has discontinuities at bin boundaries, integrals spanning multiple bins
 * will face convergence problems.  Only test within-bin integrals and spans
 * across no more than 3 bin boundaries.
 */
@Override
@Test
public void testDensityIntegrals() {
    final RealDistribution distribution = makeDistribution();
    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++) {
        Assert.assertEquals(
                distribution.cumulativeProbability( 
                        lower[i], upper[i]),
                        integrator.integrate(
                                1000000, // Triangle integrals are very slow to converge
                                d, lower[i], upper[i]), tol);
    }
}
 

@Override
public double[] makeDensityTestValues() {
    final double[] testPoints = getCumulativeTestPoints();
    final double[] densityValues = new double[testPoints.length];
    final EmpiricalDistribution empiricalDistribution = (EmpiricalDistribution) makeDistribution();
    final double[] binBounds = empiricalDistribution.getUpperBounds();
    for (int i = 0; i < testPoints.length; i++) {
        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;
}
 

/** 
 * Modify test integration bounds from the default. Because the distribution
 * has discontinuities at bin boundaries, integrals spanning multiple bins
 * will face convergence problems.  Only test within-bin integrals and spans
 * across no more than 3 bin boundaries.
 */
@Override
@Test
public void testDensityIntegrals() {
    final RealDistribution distribution = makeDistribution();
    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++) {
        Assert.assertEquals(
                distribution.cumulativeProbability( 
                        lower[i], upper[i]),
                        integrator.integrate(
                                1000000, // Triangle integrals are very slow to converge
                                d, lower[i], upper[i]), tol);
    }
}
 

@Override
public double[] makeDensityTestValues() {
    final double[] testPoints = getCumulativeTestPoints();
    final double[] densityValues = new double[testPoints.length];
    final EmpiricalDistribution empiricalDistribution = (EmpiricalDistribution) makeDistribution();
    final double[] binBounds = empiricalDistribution.getUpperBounds();
    for (int i = 0; i < testPoints.length; i++) {
        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;
}
 

public static void addPDFSeries(Chart chart, RealDistribution distribution, String desc, int lowerBound, int upperBound) {
    // generates Log data
    List<Number> xData = new ArrayList<Number>();
    List<Number> yData = new ArrayList<Number>();
    int samples = 100;
    double stepSize = (upperBound - lowerBound) / (double) samples;
    for (double x = lowerBound; x <= upperBound; x += stepSize) {
        try {
            double density = distribution.density(x);
            if (! Double.isInfinite(density) && ! Double.isNaN(density)) {
                xData.add(x);
                yData.add(density);
            }
        } catch (Exception e) {
            // ignore
            // some distributions may reject certain values depending on the parameter settings
        }
    }

    Series series = chart.addSeries(desc, xData, yData);
    series.setMarker(SeriesMarker.NONE);
    series.setLineStyle(new BasicStroke(1.2f));
}
 

/**
 * {@inheritDoc}
 *
 * <p>Returns the kernel density normalized so that its integral over each bin
 * equals the bin mass.</p>
 *
 * <p>Algorithm description: <ol>
 * <li>Find the bin B that x belongs to.</li>
 * <li>Compute K(B) = the mass of B with respect to the within-bin kernel (i.e., the
 * integral of the kernel density over B).</li>
 * <li>Return k(x) * P(B) / K(B), where k is the within-bin kernel density
 * and P(B) is the mass of B.</li></ol></p>
 * @since 3.1
 */
public double density(double x) {
    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}
 *
 * <p>Returns the kernel density normalized so that its integral over each bin
 * equals the bin mass.</p>
 *
 * <p>Algorithm description: <ol>
 * <li>Find the bin B that x belongs to.</li>
 * <li>Compute K(B) = the mass of B with respect to the within-bin kernel (i.e., the
 * integral of the kernel density over B).</li>
 * <li>Return k(x) * P(B) / K(B), where k is the within-bin kernel density
 * and P(B) is the mass of B.</li></ol></p>
 * @since 3.1
 */
public double density(double x) {
    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}
 *
 * <p>Returns the kernel density normalized so that its integral over each bin
 * equals the bin mass.</p>
 *
 * <p>Algorithm description: <ol>
 * <li>Find the bin B that x belongs to.</li>
 * <li>Compute K(B) = the mass of B with respect to the within-bin kernel (i.e., the
 * integral of the kernel density over B).</li>
 * <li>Return k(x) * P(B) / K(B), where k is the within-bin kernel density
 * and P(B) is the mass of B.</li></ol></p>
 * @since 3.1
 */
public double density(double x) {
    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}
 *
 * <p>Returns the kernel density normalized so that its integral over each bin
 * equals the bin mass.</p>
 *
 * <p>Algorithm description: <ol>
 * <li>Find the bin B that x belongs to.</li>
 * <li>Compute K(B) = the mass of B with respect to the within-bin kernel (i.e., the
 * integral of the kernel density over B).</li>
 * <li>Return k(x) * P(B) / K(B), where k is the within-bin kernel density
 * and P(B) is the mass of B.</li></ol></p>
 * @since 3.1
 */
public double density(double x) {
    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}
 *
 * <p>Returns the kernel density normalized so that its integral over each bin
 * equals the bin mass.</p>
 *
 * <p>Algorithm description: <ol>
 * <li>Find the bin B that x belongs to.</li>
 * <li>Compute K(B) = the mass of B with respect to the within-bin kernel (i.e., the
 * integral of the kernel density over B).</li>
 * <li>Return k(x) * P(B) / K(B), where k is the within-bin kernel density
 * and P(B) is the mass of B.</li></ol></p>
 * @since 3.1
 */
public double density(double x) {
    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);
}
 

/**
 * Probability density function at x
 * @param dis Distribution.
 * @param x X.
 * @return Probability density value.
 */
public static double pdf(RealDistribution dis, Number x){
    return dis.density(x.doubleValue());
}