Package org.apache.commons.math3.distribution

Examples of org.apache.commons.math3.distribution.GammaDistribution

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    public void testNextGamma() {
        double[] quartiles;
        long[] counts;

        // Tests shape > 1, one case in the rejection sampling
        quartiles = TestUtils.getDistributionQuartiles(new GammaDistribution(4, 2));
        counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextGamma(4, 2);
            TestUtils.updateCounts(value, counts, quartiles);
        }
        TestUtils.assertChiSquareAccept(expected, counts, 0.001);

        // Tests shape <= 1, another case in the rejection sampling
        quartiles = TestUtils.getDistributionQuartiles(new GammaDistribution(0.3, 3));
        counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextGamma(0.3, 3);
            TestUtils.updateCounts(value, counts, quartiles);
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            assertEquals(x3, r.get("v3").get("value").asDouble(), 0);
        }

        // now compare against reference distributions to test accuracy of the observed step distributions
        NormalDistribution normalDistribution = new NormalDistribution();
        GammaDistribution gd1 = new GammaDistribution(0.2, 5);
        GammaDistribution gd2 = new GammaDistribution(1, 1);
        TDistribution tDistribution = new TDistribution(2);
        for (double q : new double[]{0.001, 0.01, 0.1, 0.2, 0.5, 0.8, 0.9, 0.99, 0.99}) {
            double uG1 = gd1.cumulativeProbability(tdG1.quantile(q));
            assertEquals(q, uG1, (1 - q) * q * 10e-2);

            double uG2 = gd2.cumulativeProbability(tdG2.quantile(q));
            assertEquals(q, uG2, (1 - q) * q * 10e-2);

            double u1 = normalDistribution.cumulativeProbability(td1.quantile(q));
            assertEquals(q, u1, (1 - q) * q * 10e-2);

 
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    public void testNextGamma() throws Exception {
        double[] quartiles;
        long[] counts;

        // Tests shape > 1, one case in the rejection sampling
        quartiles = TestUtils.getDistributionQuartiles(new GammaDistribution(4, 2));
        counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextGamma(4, 2);
            TestUtils.updateCounts(value, counts, quartiles);
        }
        TestUtils.assertChiSquareAccept(expected, counts, 0.001);

        // Tests shape <= 1, another case in the rejection sampling
        quartiles = TestUtils.getDistributionQuartiles(new GammaDistribution(0.3, 3));
        counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextGamma(0.3, 3);
            TestUtils.updateCounts(value, counts, quartiles);
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                // tests for termination and stringent tolerances
                if (FastMath.abs(actRed) <= TWO_EPS &&
                    preRed <= TWO_EPS &&
                    ratio <= 2.0) {
                    throw new ConvergenceException(LocalizedFormats.TOO_SMALL_COST_RELATIVE_TOLERANCE,
                                                   costRelativeTolerance);
                } else if (delta <= TWO_EPS * xNorm) {
                    throw new ConvergenceException(LocalizedFormats.TOO_SMALL_PARAMETERS_RELATIVE_TOLERANCE,
                                                   parRelativeTolerance);
                } else if (maxCosine <= TWO_EPS) {
                    throw new ConvergenceException(LocalizedFormats.TOO_SMALL_ORTHOGONALITY_TOLERANCE,
                                                   orthoTolerance);
                }
            }
        }
    }
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                for (int j = k; j < nR; ++j) {
                    double aki = weightedJacobian[j][permutation[i]];
                    norm2 += aki * aki;
                }
                if (Double.isInfinite(norm2) || Double.isNaN(norm2)) {
                    throw new ConvergenceException(LocalizedFormats.UNABLE_TO_PERFORM_QR_DECOMPOSITION_ON_JACOBIAN,
                                                   nR, nC);
                }
                if (norm2 > ak2) {
                    nextColumn = i;
                    ak2        = norm2;
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     * length.
     */
    protected double[] computeResiduals(double[] objectiveValue) {
        final double[] target = getTarget();
        if (objectiveValue.length != target.length) {
            throw new DimensionMismatchException(target.length,
                                                 objectiveValue.length);
        }

        final double[] residuals = new double[target.length];
        for (int i = 0; i < target.length; i++) {
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        /** {@inheritDoc} */
        public RealVector solve(final RealVector b) {
            final int m = lTData.length;
            if (b.getDimension() != m) {
                throw new DimensionMismatchException(b.getDimension(), m);
            }

            final double[] x = b.toArray();

            // Solve LY = b
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        /** {@inheritDoc} */
        public RealMatrix solve(RealMatrix b) {
            final int m = lTData.length;
            if (b.getRowDimension() != m) {
                throw new DimensionMismatchException(b.getRowDimension(), m);
            }

            final int nColB = b.getColumnDimension();
            final double[][] x = b.getData();

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     */
    public double correlation(final double[] xArray, final double[] yArray)
            throws DimensionMismatchException {

        if (xArray.length != yArray.length) {
            throw new DimensionMismatchException(xArray.length, yArray.length);
        }

        final int n = xArray.length;
        final long numPairs = sum(n - 1);

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     * @throws DimensionMismatchException if {@link #target} and
     * {@link #weightMatrix} have inconsistent dimensions.
     */
    private void checkParameters() {
        if (target.length != weightMatrix.getColumnDimension()) {
            throw new DimensionMismatchException(target.length,
                                                 weightMatrix.getColumnDimension());
        }
    }
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Related Classes of org.apache.commons.math3.distribution.GammaDistribution

  • org.apache.commons.math3.analysis.differentiation.DerivativeStructure
  • org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction
  • org.apache.commons.math3.analysis.function.HarmonicOscillator
  • org.apache.commons.math3.analysis.function.Sin
  • org.apache.commons.math3.analysis.function.Sinc
  • org.apache.commons.math3.analysis.MultivariateFunction
  • org.apache.commons.math3.analysis.QuinticFunction
  • org.apache.commons.math3.analysis.solvers.BrentSolver
  • org.apache.commons.math3.analysis.UnivariateFunction
  • org.apache.commons.math3.complex.Complex
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