Examples of GaussianRandomGenerator

org.apache.commons.math.random.GaussianRandomGenerator
This class is a gaussian normalized random generator for scalars.
This class is a simple wrapper around the {@link RandomGenerator#nextGaussian} method.
@version $Revision: 811827 $ $Date: 2009-09-06 17:32:50 +0200 (dim. 06 sept. 2009) $ @since 1.2
org.apache.commons.math3.random.GaussianRandomGenerator
This class is a gaussian normalized random generator for scalars.
This class is a simple wrapper around the {@link RandomGenerator#nextGaussian} method.
@since 1.2

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

    // random package here)
    RandomGenerator rg = new JDKRandomGenerator();
    rg.setSeed(17399225432l); // Fixed seed means same results every time


    // Create a GassianRandomGenerator using rg as its source of randomness
    GaussianRandomGenerator rawGenerator = new GaussianRandomGenerator(rg);


    // Create a CorrelatedRandomVectorGenerator using rawGenerator for the
    // components
    CorrelatedRandomVectorGenerator generator = new CorrelatedRandomVectorGenerator(mean, covariance, 1.0e-12 * covariance.getNorm(), rawGenerator);

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

            new GaussNewtonOptimizer(true,
                                     new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(16069223052l);
        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
        DifferentiableMultivariateVectorMultiStartOptimizer optimizer =
            new DifferentiableMultivariateVectorMultiStartOptimizer(underlyingOptimizer,
                                                                       10, generator);


        // no optima before first optimization attempt

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

            new GaussNewtonOptimizer(true,
                                     new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(12373523445l);
        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
        DifferentiableMultivariateVectorMultiStartOptimizer optimizer =
            new DifferentiableMultivariateVectorMultiStartOptimizer(underlyingOptimizer,
                                                                       10, generator);
        optimizer.optimize(100, new DifferentiableMultivariateVectorFunction() {
                public MultivariateMatrixFunction jacobian() {

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

                                                    new SimpleValueChecker(1.0e-10, 1.0e-10));
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(753289573253l);
        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(new double[] { 50.0, 50.0 }, new double[] { 10.0, 10.0 },
                                                  new GaussianRandomGenerator(g));
        DifferentiableMultivariateMultiStartOptimizer optimizer =
            new DifferentiableMultivariateMultiStartOptimizer(underlying, 10, generator);
        PointValuePair optimum =
            optimizer.optimize(200, circle, GoalType.MINIMIZE, new double[] { 98.680, 47.345 });
        Assert.assertEquals(200, optimizer.getMaxEvaluations());

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

        
        // Get covariance matrix for columns
        RealMatrix cov = (new Covariance(errorSeeds)).getCovarianceMatrix();
          
        // Create a CorrelatedRandomVectorGenerator to use to generate correlated errors
        GaussianRandomGenerator rawGenerator = new GaussianRandomGenerator(rg);
        double[] errorMeans = new double[nObs];  // Counting on init to 0 here
        CorrelatedRandomVectorGenerator gen = new CorrelatedRandomVectorGenerator(errorMeans, cov,
         1.0e-12 * cov.getNorm(), rawGenerator);
        
        // Now start generating models.  Use Longley X matrix on LHS

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