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

            });
        underlying.setSimplex(simplex);
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(16069223052l);
        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g));
        MultivariateMultiStartOptimizer optimizer =
            new MultivariateMultiStartOptimizer(underlying, 10, generator);
        PointValuePair optimum =
            optimizer.optimize(1100, rosenbrock, GoalType.MINIMIZE, new double[] { -1.2, 1.0 });

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

        };
        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));
        MultivariateDifferentiableMultiStartOptimizer optimizer =
            new MultivariateDifferentiableMultiStartOptimizer(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

        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(753289573253l);
        RandomVectorGenerator generator
            = new UncorrelatedRandomVectorGenerator(new double[] { 50, 50 },
                                                    new double[] { 10, 10 },
                                                    new GaussianRandomGenerator(g));
        MultiStartMultivariateOptimizer optimizer
            = new MultiStartMultivariateOptimizer(underlying, 10, generator);
        PointValuePair optimum
            = optimizer.optimize(new MaxEval(200),
                                 circle.getObjectiveFunction(),

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

                {  3.5, -2.3 }
            });
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(16069223052l);
        RandomVectorGenerator generator
            = new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g));
        MultiStartMultivariateOptimizer optimizer
            = new MultiStartMultivariateOptimizer(underlying, 10, generator);
        PointValuePair optimum
            = optimizer.optimize(new MaxEval(1100),
                                 new ObjectiveFunction(rosenbrock),

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

            }
        };
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(16069223052l);
        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
        MultivariateDifferentiableVectorMultiStartOptimizer optimizer =
            new MultivariateDifferentiableVectorMultiStartOptimizer(underlyingOptimizer,
                                                                       10, generator);


        // no optima before first optimization attempt

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

            }
        };
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(12373523445l);
        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
        MultivariateDifferentiableVectorMultiStartOptimizer optimizer =
            new MultivariateDifferentiableVectorMultiStartOptimizer(underlyingOptimizer,
                                                                       10, generator);
        optimizer.optimize(100, new MultivariateDifferentiableVectorFunction() {
            public double[] value(double[] point) {

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

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


        optimizer.getOptima();
    }

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

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


        PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(100),

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

        JacobianMultivariateVectorOptimizer underlyingOptimizer
            = new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6));
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(12373523445l);
        RandomVectorGenerator generator
            = new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
        MultiStartMultivariateVectorOptimizer optimizer
            = new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);
        optimizer.optimize(new MaxEval(100),
                           new Target(new double[] { 0 }),
                           new Weight(new double[] { 1 }),

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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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