Package com.cloudera.cdk.morphline.shaded.org.apache.commons.math3.random

Examples of com.cloudera.cdk.morphline.shaded.org.apache.commons.math3.random.Well19937c

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     * cumulative probability estimates (defaults to
     * {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
     * @since 2.1
     */
    public BetaDistribution(double alpha, double beta, double inverseCumAccuracy) {
        this(new Well19937c(), alpha, beta, inverseCumAccuracy);
    }
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     * @throws NotStrictlyPositiveException if {@code sd <= 0}.
     * @since 2.1
     */
    public NormalDistribution(double mean, double sd, double inverseCumAccuracy)
        throws NotStrictlyPositiveException {
        this(new Well19937c(), mean, sd, inverseCumAccuracy);
    }
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     * associated weights.
     *
     * @param components List of (weight, distribution) pairs from which to sample.
     */
    public MixtureMultivariateRealDistribution(List<Pair<Double, T>> components) {
        this(new Well19937c(), components);
    }
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     * @param inverseCumAccuracy Inverse cumulative probability accuracy.
     * @throws NotStrictlyPositiveException if {@code shape <= 0}.
     */
    public LogNormalDistribution(double scale, double shape, double inverseCumAccuracy)
        throws NotStrictlyPositiveException {
        this(new Well19937c(), scale, shape, inverseCumAccuracy);
    }
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        final ProcessModel pm = new DefaultProcessModel(A, B, Q, initialState, initialErrorCovariance);
        final MeasurementModel mm = new DefaultMeasurementModel(H, R);
        final KalmanFilter filter = new KalmanFilter(pm, mm);

        final RandomGenerator rng = new Well19937c(1000);
        final NormalDistribution dist = new NormalDistribution(rng, 0, measurementNoise);

        for (int i = 0; i < iterations; i++) {
            // get the "real" cannonball position
            double x = cannonball.getX();
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     * or the beginning (if {@link Position#HEAD}) of the array.
     */
    public static void shuffle(int[] list,
                               int start,
                               Position pos) {
        shuffle(list, start, pos, new Well19937c());
    }
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     * @see #shuffle(int[],int,Position,RandomGenerator)
     *
     * @param list Array whose entries will be shuffled (in-place).
     */
    public static void shuffle(int[] list) {
        shuffle(list, new Well19937c());
    }
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        final BivariateFunction bcf
            = new BicubicSplineInterpolatingFunction(xval, yval, zval,
                                                     dZdX, dZdY, dZdXdY);
        double x, y;

        final RandomGenerator rng = new Well19937c(1234567L); // "tol" depends on the seed.
        final UniformRealDistribution distX
            = new UniformRealDistribution(rng, xval[0], xval[xval.length - 1]);
        final UniformRealDistribution distY
            = new UniformRealDistribution(rng, yval[0], yval[yval.length - 1]);
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        BivariateFunction bcf = new BicubicSplineInterpolatingFunction(xval, yval, zval,
                                                                       dZdX, dZdY, dZdXdY);
        double x, y;

        final RandomGenerator rng = new Well19937c(1234567L); // "tol" depends on the seed.
        final UniformRealDistribution distX
            = new UniformRealDistribution(rng, xval[0], xval[xval.length - 1]);
        final UniformRealDistribution distY
            = new UniformRealDistribution(rng, yval[0], yval[yval.length - 1]);
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        BivariateGridInterpolator interpolator = new BicubicSplineInterpolator();
        BivariateFunction p = interpolator.interpolate(xval, yval, zval);
        double x, y;

        final RandomGenerator rng = new Well19937c(1234567L); // "tol" depends on the seed.
        final UniformRealDistribution distX
            = new UniformRealDistribution(rng, xval[0], xval[xval.length - 1]);
        final UniformRealDistribution distY
            = new UniformRealDistribution(rng, yval[0], yval[yval.length - 1]);
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