Package org.apache.commons.math.random

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

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        circle.addPoint(110.0, -20.0);
        circle.addPoint( 35.0,  15.0);
        circle.addPoint( 45.0,  97.0);
        NonLinearConjugateGradientOptimizer underlying =
            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);
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    @Test
    public void testSinMin() throws MathException {
        UnivariateRealFunction f = new SinFunction();
        UnivariateRealOptimizer underlying = new BrentOptimizer();
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(44428400075l);
        MultiStartUnivariateRealOptimizer minimizer =
            new MultiStartUnivariateRealOptimizer(underlying, 10, g);
        minimizer.optimize(f, GoalType.MINIMIZE, -100.0, 100.0);
        double[] optima = minimizer.getOptima();
        double[] optimaValues = minimizer.getOptimaValues();
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    public void testQuinticMin() throws MathException {
        // The quintic function has zeros at 0, +-0.5 and +-1.
        // The function has extrema (first derivative is zero) at 0.27195613 and 0.82221643,
        UnivariateRealFunction f = new QuinticFunction();
        UnivariateRealOptimizer underlying = new BrentOptimizer();
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(4312000053l);
        MultiStartUnivariateRealOptimizer minimizer =
            new MultiStartUnivariateRealOptimizer(underlying, 5, g);
        minimizer.setAbsoluteAccuracy(10 * minimizer.getAbsoluteAccuracy());
        minimizer.setRelativeAccuracy(10 * minimizer.getRelativeAccuracy());

 
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    Rosenbrock rosenbrock = new Rosenbrock();
    NelderMead underlying = new NelderMead();
    underlying.setStartConfiguration(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));
    MultiStartMultivariateRealOptimizer optimizer =
        new MultiStartMultivariateRealOptimizer(underlying, 10, generator);
    optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1, 1.0e-3));
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        circle.addPoint(110.0, -20.0);
        circle.addPoint( 35.0,  15.0);
        circle.addPoint( 45.0,  97.0);
        NonLinearConjugateGradientOptimizer underlying =
            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);
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    public void testTrivial() throws FunctionEvaluationException, OptimizationException {
        LinearProblem problem =
            new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
        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);
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    @Test(expected = OptimizationException.class)
    public void testNoOptimum() throws FunctionEvaluationException, OptimizationException {
        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);
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        correctRanks = new double[] { 2.5, 2.5, 2.5, 2.5 };
        TestUtils.assertEquals(correctRanks, ranks, 0d);
    }

    public void testNaNsFixedTiesRandom() {
        RandomGenerator randomGenerator = new JDKRandomGenerator();
        randomGenerator.setSeed(1000);
        NaturalRanking ranking = new NaturalRanking(NaNStrategy.FIXED,
                randomGenerator);
        double[] ranks = ranking.rank(exampleData);
        double[] correctRanks = { 5, 4, 6, 7, 3, 8, Double.NaN, 1, 4 };
        TestUtils.assertEquals(correctRanks, ranks, 0d);
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                assertTrue(minima[i-1].getCost() <= minima[i].getCost());
            }
        }
    }

    RandomGenerator rg = new JDKRandomGenerator();
    rg.setSeed(64453353l);
    RandomVectorGenerator rvg =
        new UncorrelatedRandomVectorGenerator(new double[] { 0.9, 1.1 },
                                              new double[] { 0.2, 0.2 },
                                              new UniformRandomGenerator(rg));
    optimum =
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        for (int i = 0; i < vertexA.length; ++i) {
            mean[i]              = 0.5 * (vertexA[i] + vertexB[i]);
            standardDeviation[i] = 0.5 * Math.abs(vertexA[i] - vertexB[i]);
        }

        RandomGenerator rg = new JDKRandomGenerator();
        rg.setSeed(seed);
        UniformRandomGenerator urg = new UniformRandomGenerator(rg);
        RandomVectorGenerator rvg =
            new UncorrelatedRandomVectorGenerator(mean, standardDeviation, urg);
        setMultiStart(starts, rvg);

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Related Classes of org.apache.commons.math.random.JDKRandomGenerator

  • org.apache.commons.math.optimization.DirectSearchOptimizer
  • org.apache.commons.math.optimization.MultiStartDifferentiableMultivariateRealOptimizerTest
  • org.apache.commons.math.optimization.MultiStartDifferentiableMultivariateVectorialOptimizerTest
  • org.apache.commons.math.optimization.MultiStartMultivariateRealOptimizerTest
  • org.apache.commons.math.optimization.MultiStartUnivariateRealOptimizerTest
  • org.apache.commons.math.optimization.NelderMeadTest
  • org.apache.commons.math.stat.ranking.NaturalRankingTest
  • org.apache.commons.math.stat.regression.GLSMultipleLinearRegressionTest
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