Package org.apache.commons.math3.random

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

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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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    static final int[] sampleSizes= {TINY , SMALL , NOMINAL , MEDIUM ,
            STANDARD, BIG };

    @Test
    public void testStoredVsDirect() {
        final RandomGenerator rand= new JDKRandomGenerator();
        rand.setSeed(Long.MAX_VALUE);
        for (final int sampleSize:sampleSizes) {
            final double[] data = new NormalDistribution(rand,4000, 50)
                                .sample(sampleSize);
            for (final double p:new double[] {50d,95d}) {
                for (final Percentile.EstimationType e : Percentile.EstimationType.values()) {
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    @Test(expected=NullPointerException.class)
    public void testGetOptimaBeforeOptimize() {

        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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        TestUtils.assertEquals(correctRanks, ranks, 0d);
    }

    @Test
    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, 3, 6, 7, 3, 8, Double.NaN, 1, 2 };
        TestUtils.assertEquals(correctRanks, ranks, 0d);
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                    }
                }
                return super.optimize(filtered);
            }
        };
        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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     */
    @Test(expected=TestException.class)
    public void testNoOptimum() {
        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),
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     * @param maxIterations the maximum number of iterations to run the algorithm for.
     *   If negative, no maximum will be used.
     * @param measure the distance measure to use
     */
    public KMeansPlusPlusClusterer(final int k, final int maxIterations, final DistanceMeasure measure) {
        this(k, maxIterations, measure, new JDKRandomGenerator());
    }
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     * @throws NumberIsTooSmallException if {@code fuzziness <= 1.0}
     */
    public FuzzyKMeansClusterer(final int k, final double fuzziness,
                                final int maxIterations, final DistanceMeasure measure)
            throws NumberIsTooSmallException {
        this(k, fuzziness, maxIterations, measure, DEFAULT_EPSILON, new JDKRandomGenerator());
    }
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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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