Package org.apache.commons.math.random

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

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        DifferentiableMultivariateVectorialOptimizer underlyingOptimizer =
            new GaussNewtonOptimizer(true);
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(16069223052l);
        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
        MultiStartDifferentiableMultivariateVectorialOptimizer optimizer =
            new MultiStartDifferentiableMultivariateVectorialOptimizer(underlyingOptimizer,
                                                                       10, generator);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
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        DifferentiableMultivariateVectorialOptimizer underlyingOptimizer =
            new GaussNewtonOptimizer(true);
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(12373523445l);
        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
        MultiStartDifferentiableMultivariateVectorialOptimizer optimizer =
            new MultiStartDifferentiableMultivariateVectorialOptimizer(underlyingOptimizer,
                                                                       10, generator);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
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        DifferentiableMultivariateVectorialOptimizer underlyingOptimizer =
            new GaussNewtonOptimizer(true);
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(16069223052l);
        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
        MultiStartDifferentiableMultivariateVectorialOptimizer optimizer =
            new MultiStartDifferentiableMultivariateVectorialOptimizer(underlyingOptimizer,
                                                                       10, generator);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
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        DifferentiableMultivariateVectorialOptimizer underlyingOptimizer =
            new GaussNewtonOptimizer(true);
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(12373523445l);
        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
        MultiStartDifferentiableMultivariateVectorialOptimizer optimizer =
            new MultiStartDifferentiableMultivariateVectorialOptimizer(underlyingOptimizer,
                                                                       10, generator);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
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        // 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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                                         { -1.2,  1.0 }, { 0.9, 1.2 } , {  3.5, -2.3 }
                                     });
    JDKRandomGenerator g = new JDKRandomGenerator();
    g.setSeed(16069223052l);
    RandomVectorGenerator generator =
        new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g));
    MultiStartMultivariateRealOptimizer optimizer =
        new MultiStartMultivariateRealOptimizer(underlying, 10, generator);
    optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1, 1.0e-3));
    optimizer.setMaxIterations(100);
    RealPointValuePair optimum =
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            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
        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));
        MultiStartDifferentiableMultivariateRealOptimizer optimizer =
            new MultiStartDifferentiableMultivariateRealOptimizer(underlying, 10, generator);
        optimizer.setMaxIterations(100);
        assertEquals(100, optimizer.getMaxIterations());
        optimizer.setMaxEvaluations(100);
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                                         { -1.2,  1.0 }, { 0.9, 1.2 } , {  3.5, -2.3 }
                                     });
    JDKRandomGenerator g = new JDKRandomGenerator();
    g.setSeed(16069223052l);
    RandomVectorGenerator generator =
        new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g));
    MultiStartMultivariateRealOptimizer optimizer =
        new MultiStartMultivariateRealOptimizer(underlying, 10, generator);
    optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1, 1.0e-3));
    optimizer.setMaxIterations(100);
    RealPointValuePair optimum =
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            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
        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));
        MultiStartDifferentiableMultivariateRealOptimizer optimizer =
            new MultiStartDifferentiableMultivariateRealOptimizer(underlying, 10, generator);
        optimizer.setMaxIterations(100);
        assertEquals(100, optimizer.getMaxIterations());
        optimizer.setMaxEvaluations(100);
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        DifferentiableMultivariateVectorialOptimizer underlyingOptimizer =
            new GaussNewtonOptimizer(true);
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(16069223052l);
        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
        MultiStartDifferentiableMultivariateVectorialOptimizer optimizer =
            new MultiStartDifferentiableMultivariateVectorialOptimizer(underlyingOptimizer,
                                                                       10, generator);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
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Related Classes of org.apache.commons.math.random.GaussianRandomGenerator

  • org.apache.commons.math.optimization.MultiStartDifferentiableMultivariateRealOptimizerTest
  • org.apache.commons.math.optimization.MultiStartDifferentiableMultivariateVectorialOptimizerTest
  • org.apache.commons.math.optimization.MultiStartMultivariateRealOptimizerTest
  • org.apache.commons.math.stat.regression.GLSMultipleLinearRegressionTest
  • org.apache.hadoop.hbase.regionserver.compactions.GaussianFileListGenerator
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