Examples of cern.jet.random.engine.RandomEngine

cern.jet.random.engine.RandomEngine
Abstract base class for uniform pseudo-random number generating engines.
Most probability distributions are obtained by using a uniform pseudo-random number generation engine followed by a transformation to the desired distribution. Thus, subclasses of this class are at the core of computational statistics, simulations, Monte Carlo methods, etc.
Subclasses produce uniformly distributed int's and long's in the closed intervals [Integer.MIN_VALUE,Integer.MAX_VALUE] and [Long.MIN_VALUE,Long.MAX_VALUE], respectively, as well as float's and double's in the open unit intervals (0.0f,1.0f) and (0.0,1.0), respectively.
Subclasses need to override one single method only: nextInt(). All other methods generating different data types or ranges are usually layered upon nextInt(). long's are formed by concatenating two 32 bit int's. float's are formed by dividing the interval [0.0f,1.0f] into 2³² sub intervals, then randomly choosing one subinterval. double's are formed by dividing the interval [0.0,1.0] into 2⁶⁴ sub intervals, then randomly choosing one subinterval.
Note that this implementation is not synchronized. @author wolfgang.hoschek@cern.ch @version 1.0, 09/24/99 @see MersenneTwister @see MersenneTwister64 @see java.util.Random

   * @param calculateVariance true if the variance of the result is to be calculated
   */
  public EquityVarianceSwapMonteCarloCalculator(final int seed, final boolean useAntithetics, final boolean calculateVariance) {
    _useAntithetics = useAntithetics;
    _calculateVariance = calculateVariance;
    final RandomEngine random = new MersenneTwister64(seed);
    _norm = new NormalDistribution(0, 1.0, random);
  }

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        }
        return res;
      }
    };


    final RandomEngine ran = new MersenneTwister64(MersenneTwister.DEFAULT_SEED);
    final double[] temp = new double[nWeights];
    for (int i = 0; i < nWeights; i++) {
      temp[i] = ran.nextDouble();
    }
    final DoubleMatrix1D start = new DoubleMatrix1D(temp);


    final LeastSquareResults lsRes = NLLSWP.solve(new DoubleMatrix1D(obs), new DoubleMatrix1D(n, 0.01), func, jac, start, penalty);
    if(print) {

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  }


  @Test
  //TODO if this interpolator cannot get the answer right then an exception should be thrown
  public void testFlat() {
    final RandomEngine random = new MersenneTwister64(MersenneTwister.DEFAULT_SEED);
    final double x1 = 10 * random.nextDouble();
    final double x2 = 10 * random.nextDouble();
    final double x3 = 10 * random.nextDouble();
    // Fails utterly for flat surface since the variogram function will be zero for all r
    final InterpolatorND interpolator = new KrigingInterpolatorND(1.99);
    final InterpolatorNDDataBundle dataBundle = interpolator.getDataBundle(FLAT_DATA);
    assertEquals(INTERPOLATOR.interpolate(dataBundle, new double[] {x1, x2, x3}), 0, 0);
  }

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