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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        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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     * Verifies that Monte Carlo simulation gives results close to exact p values. This test is a
     * little long-running (more than two minutes on a fast machine), so is disabled by default.
     */
    // @Test
    public void testTwoSampleMonteCarlo() {
        final KolmogorovSmirnovTest test = new KolmogorovSmirnovTest(new Well19937c(1000));
        final int sampleSize = 14;
        final double tol = .001;
        final double[] shortUniform = new double[sampleSize];
        System.arraycopy(uniform, 0, shortUniform, 0, sampleSize);
        final double[] shortGaussian = new double[sampleSize];
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    /**
     * Construct a KolmogorovSmirnovTest instance with a default random data generator.
     */
    public KolmogorovSmirnovTest() {
        rng = new Well19937c();
    }
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            setLayout(new GridBagLayout());
           
            int nSamples = 1500;
           
            RandomGenerator rng = new Well19937c(0);
            List<List<DoublePoint>> datasets = new ArrayList<List<DoublePoint>>();

            datasets.add(normalize(makeCircles(nSamples, true, 0.04, 0.5, rng), -1, 1, -1, 1));
            datasets.add(normalize(makeMoons(nSamples, true, 0.04, rng), -1, 2, -1, 1));
            datasets.add(normalize(makeBlobs(nSamples, 3, 1.0, -10, 10, true, rng), -12, 12, -12, 12));
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     * @throws NotStrictlyPositiveException if {@code p <= 0}.
     * @since 2.1
     */
    public PoissonDistribution(double p, double epsilon, int maxIterations)
    throws NotStrictlyPositiveException {
        this(new Well19937c(), p, epsilon, maxIterations);
    }
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     * @throws MathArithmeticException all of the probabilities are 0.
     */
    public EnumeratedRealDistribution(final double[] singletons, final double[] probabilities)
    throws DimensionMismatchException, NotPositiveException, MathArithmeticException,
           NotFiniteNumberException, NotANumberException {
        this(new Well19937c(), singletons, probabilities);
    }
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        public VoltMeter(double voltage, double processNoise, double measurementNoise, int seed) {
            this.initialVoltage = voltage;
            this.voltage = voltage;
            this.processNoise = processNoise;
            this.measurementNoise = measurementNoise;
            rng = new Well19937c(seed);
        }
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     * @param p Probability of success.
     * @throws NotPositiveException if {@code trials < 0}.
     * @throws OutOfRangeException if {@code p < 0} or {@code p > 1}.
     */
    public BinomialDistribution(int trials, double p) {
        this(new Well19937c(), trials, p);
    }
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            int maxDiffNDs,
            int[] minIndices,
            int[] maxIndices,
            boolean withSymmetries,
            long seed) {
        this.random = new Well19937c();
        this.random.setSeed(seed = random.nextLong());
        this.minIndices = minIndices;
        this.maxIndices = maxIndices;
        this.withSymmetries = withSymmetries;
        int di = 1, t;
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            int minDiffNDs,
            int maxDiffNDs,
            int[] minIndices,
            int[] maxIndices,
            boolean withSymmetries) {
        this(minDiffNDs, maxDiffNDs, minIndices, maxIndices, withSymmetries, new Well19937c());
    }
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