Package org.apache.commons.math3.random

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

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        assertVectorEquals(expectedInitialState, filter.getStateEstimation());

        RealVector pNoise = new ArrayRealVector(1);
        RealVector mNoise = new ArrayRealVector(1);

        RandomGenerator rand = new JDKRandomGenerator();
        // iterate 60 steps
        for (int i = 0; i < 60; i++) {
            filter.predict();

            // Simulate the process
            pNoise.setEntry(0, processNoise * rand.nextGaussian());

            // x = A * x + p_noise
            x = A.operate(x).add(pNoise);

            // Simulate the measurement
            mNoise.setEntry(0, measurementNoise * rand.nextGaussian());

            // z = H * x + m_noise
            RealVector z = H.operate(x).add(mNoise);

            filter.correct(z);
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        // check the initial state
        double[] expectedInitialState = new double[] { 0.0, 0.0 };
        assertVectorEquals(expectedInitialState, filter.getStateEstimation());

        RandomGenerator rand = new JDKRandomGenerator();

        RealVector tmpPNoise = new ArrayRealVector(
                new double[] { Math.pow(dt, 2d) / 2d, dt });

        RealVector mNoise = new ArrayRealVector(1);

        // iterate 60 steps
        for (int i = 0; i < 60; i++) {
            filter.predict(u);

            // Simulate the process
            RealVector pNoise = tmpPNoise.mapMultiply(accelNoise * rand.nextGaussian());

            // x = A * x + B * u + pNoise
            x = A.operate(x).add(B.operate(u)).add(pNoise);

            // Simulate the measurement
            mNoise.setEntry(0, measurementNoise * rand.nextGaussian());

            // z = H * x + m_noise
            RealVector z = H.operate(x).add(mNoise);

            filter.correct(z);
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            = new SimplexOptimizer(new SimpleValueChecker(-1, 1.0e-3));
        NelderMeadSimplex simplex = new NelderMeadSimplex(new double[][] {
                { -1.2,  1.0 }, { 0.9, 1.2 } , {  3.5, -2.3 }
            });
        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 =
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            public ConvergenceChecker<PointValuePair> getConvergenceChecker() {
                return cg.getConvergenceChecker();
            }
        };
        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);
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        // version 3.1 of the library. It should be removed when NonLinearConjugateGradientOptimizer
        // will officially be declared as implementing MultivariateDifferentiableOptimizer
        GradientMultivariateOptimizer underlying
            = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                      new SimpleValueChecker(1e-10, 1e-10));
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(753289573253l);
        RandomVectorGenerator generator
            = new UncorrelatedRandomVectorGenerator(new double[] { 50, 50 },
                                                    new double[] { 10, 10 },
                                                    new GaussianRandomGenerator(g));
        MultiStartMultivariateOptimizer optimizer
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        NelderMeadSimplex simplex = new NelderMeadSimplex(new double[][] {
                { -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));
        MultiStartMultivariateOptimizer optimizer
            = new MultiStartMultivariateOptimizer(underlying, 10, generator);
        PointValuePair optimum
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            public ConvergenceChecker<PointVectorValuePair> getConvergenceChecker() {
                return gn.getConvergenceChecker();
            }
        };
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(16069223052l);
        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
        MultivariateDifferentiableVectorMultiStartOptimizer optimizer =
            new MultivariateDifferentiableVectorMultiStartOptimizer(underlyingOptimizer,
                                                                       10, generator);
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            public ConvergenceChecker<PointVectorValuePair> getConvergenceChecker() {
                return gn.getConvergenceChecker();
            }
        };
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(12373523445l);
        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
        MultivariateDifferentiableVectorMultiStartOptimizer optimizer =
            new MultivariateDifferentiableVectorMultiStartOptimizer(underlyingOptimizer,
                                                                       10, generator);
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    public void testGetOptimaBeforeOptimize() {
        LinearProblem problem
            = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
        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);
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    public void testTrivial() {
        LinearProblem problem
            = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
        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);
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Related Classes of org.apache.commons.math3.random.JDKRandomGenerator

  • org.apache.cassandra.stress.settings.OptionDistribution$ExpFactory
  • org.apache.cassandra.stress.settings.OptionDistribution$ExtremeFactory
  • org.apache.cassandra.stress.settings.OptionDistribution$GaussianFactory
  • org.apache.cassandra.stress.settings.OptionDistribution$UniformFactory
  • org.apache.commons.math3.analysis.differentiation.DerivativeStructure
  • org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction
  • org.apache.commons.math3.analysis.function.HarmonicOscillator
  • org.apache.commons.math3.analysis.function.Sin
  • org.apache.commons.math3.analysis.function.Sinc
  • org.apache.commons.math3.analysis.MultivariateFunction
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