Package org.apache.commons.math3.random

Examples of org.apache.commons.math3.random.Well19937c

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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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            int maxDiffNDs,
            int[] minIndices,
            int[] maxIndices,
            boolean withSymmetries,
            boolean generateNewDescriptors) {
        this(minDiffNDs, maxDiffNDs, minIndices, maxIndices, withSymmetries, generateNewDescriptors, new Well19937c());
    }
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            case WELL1024A:
                return new RandomAdaptor(randomSeed == null ? new Well1024a() : new Well1024a(randomSeed));
            case WELL19937A:
                return new RandomAdaptor(randomSeed == null ? new Well19937a() : new Well19937a(randomSeed));
            case WELL19937C:
                return new RandomAdaptor(randomSeed == null ? new Well19937c() : new Well19937c(randomSeed));
            case WELL44497A:
                return new RandomAdaptor(randomSeed == null ? new Well44497a() : new Well44497a(randomSeed));
            case WELL44497B:
                return new RandomAdaptor(randomSeed == null ? new Well44497b() : new Well44497b(randomSeed));
            default:
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            int maxDiffNDs,
            int[] minIndices,
            int[] maxIndices,
            boolean withSymmetries,
            long seed) {
        this.random = new Well19937c();
        this.random.setSeed(seed);
        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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     * @throws NotStrictlyPositiveException if {@code degreesOfFreedom <= 0}
     * @since 2.1
     */
    public TDistribution(double degreesOfFreedom, double inverseCumAccuracy)
        throws NotStrictlyPositiveException {
        this(new Well19937c(), degreesOfFreedom, inverseCumAccuracy);
    }
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     * @throws NumberIsTooLargeException if {@code numberOfSuccesses > populationSize},
     * or {@code sampleSize > populationSize}.
     */
    public HypergeometricDistribution(int populationSize, int numberOfSuccesses, int sampleSize)
    throws NotPositiveException, NotStrictlyPositiveException, NumberIsTooLargeException {
        this(new Well19937c(), populationSize, numberOfSuccesses, sampleSize);
    }
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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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