Package org.apache.commons.math3.random

Examples of org.apache.commons.math3.random.Well44497b

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    for (SiteWithPolynomial site : sites) {
     
      List<SiteWithPolynomial> nearestSites =
          nearestSiteMap.get(site);
     
      RealVector vector = new ArrayRealVector(SITES_FOR_APPROX);
      RealMatrix matrix = new Array2DRowRealMatrix(
          SITES_FOR_APPROX, DefaultPolynomial.NUM_COEFFS);
     
      for (int row = 0; row < SITES_FOR_APPROX; row++) {
        SiteWithPolynomial nearSite = nearestSites.get(row);
        DefaultPolynomial.populateMatrix(matrix, row, nearSite.pos.x, nearSite.pos.z);
        vector.setEntry(row, nearSite.pos.y);
      }
     
      QRDecomposition qr = new QRDecomposition(matrix);
      RealVector solution = qr.getSolver().solve(vector);
       
      double[] coeffs = solution.toArray();
     
      for (double coeff : coeffs) {
        if (coeff > 10e3) {
          continue calculatePolynomials;
        }
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                return Double.compare(weightedResidual(o1),
                                      weightedResidual(o2));
            }

            private double weightedResidual(final PointVectorValuePair pv) {
                final RealVector v = new ArrayRealVector(pv.getValueRef(), false);
                final RealVector r = target.subtract(v);
                return r.dotProduct(weight.operate(r));
            }
        };
    }
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        // Multi-start loop.
        for (int i = 0; i < starts; i++) {
            // CHECKSTYLE: stop IllegalCatch
            try {
                // Decrease number of allowed evaluations.
                optimData[maxEvalIndex] = new MaxEval(maxEval - totalEvaluations);
                // New start value.
                final double s = (i == 0) ?
                    startValue :
                    min + generator.nextDouble() * (max - min);
                optimData[searchIntervalIndex] = new SearchInterval(min, max, s);
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     * {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
     * @throws NotStrictlyPositiveException if {@code mean <= 0}.
     * @since 2.1
     */
    public ExponentialDistribution(double mean, double inverseCumAccuracy) {
        this(new Well19937c(), mean, inverseCumAccuracy);
    }
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     * @param upper Upper bound (inclusive) of this distribution.
     * @throws NumberIsTooLargeException if {@code lower >= upper}.
     */
    public UniformIntegerDistribution(int lower, int upper)
        throws NumberIsTooLargeException {
        this(new Well19937c(), lower, upper);
    }
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     * @throws NumberIsTooLargeException if {@code a >= b} or if {@code c > b}.
     * @throws NumberIsTooSmallException if {@code c < a}.
     */
    public TriangularDistribution(double a, double c, double b)
        throws NumberIsTooLargeException, NumberIsTooSmallException {
        this(new Well19937c(), a, c, b);
    }
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                                            double b,
                                            double sigma,
                                            double lo,
                                            double hi,
                                            long seed) {
        final RandomGenerator rng = new Well44497b(seed);
        slope = a;
        intercept = b;
        error = new NormalDistribution(rng, 0, sigma,
                                       NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        x = new UniformRealDistribution(rng, lo, hi,
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                                      double y,
                                      double radius,
                                      double xSigma,
                                      double ySigma,
                                      long seed) {
        final RandomGenerator rng = new Well44497b(seed);
        this.radius = radius;
        cX = new NormalDistribution(rng, x, xSigma,
                                    NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        cY = new NormalDistribution(rng, y, ySigma,
                                    NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
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                                      double y,
                                      double radius,
                                      double xSigma,
                                      double ySigma,
                                      long seed) {
        final RandomGenerator rng = new Well44497b(seed);
        this.radius = radius;
        cX = new NormalDistribution(rng, x, xSigma,
                                    NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        cY = new NormalDistribution(rng, y, ySigma,
                                    NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
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     * @param cityList List of cities to visit in a single travel.
     * @param numNeuronsPerCity Number of neurons per city.
     */
    public TravellingSalesmanSolver(City[] cityList,
                                    double numNeuronsPerCity) {
        this(cityList, numNeuronsPerCity, new Well44497b().nextLong());
    }
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