Package org.apache.commons.math3.random

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

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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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    @Test
    public void testSinMin() {
        UnivariateFunction f = new Sin();
        UnivariateOptimizer underlying = new BrentOptimizer(1e-10, 1e-14);
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(44428400075l);
        UnivariateMultiStartOptimizer<UnivariateFunction> optimizer =
            new UnivariateMultiStartOptimizer<UnivariateFunction>(underlying, 10, g);
        optimizer.optimize(300, f, GoalType.MINIMIZE, -100.0, 100.0);
        UnivariatePointValuePair[] optima = optimizer.getOptima();
        for (int i = 1; i < optima.length; ++i) {
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    public void testQuinticMin() {
        // 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,
        UnivariateFunction f = new QuinticFunction();
        UnivariateOptimizer underlying = new BrentOptimizer(1e-9, 1e-14);
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(4312000053L);
        UnivariateMultiStartOptimizer<UnivariateFunction> optimizer =
            new UnivariateMultiStartOptimizer<UnivariateFunction>(underlying, 5, g);

        UnivariatePointValuePair optimum
            = optimizer.optimize(300, f, GoalType.MINIMIZE, -0.3, -0.2);
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                    }
                    return 0;
                }
            };
        UnivariateOptimizer underlying = new BrentOptimizer(1e-9, 1e-14);
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(4312000053L);
        UnivariateMultiStartOptimizer<UnivariateFunction> optimizer =
            new UnivariateMultiStartOptimizer<UnivariateFunction>(underlying, 5, g);
        try {
            optimizer.optimize(300, f, GoalType.MINIMIZE, -0.3, -0.2);
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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[] { FastMath.pow(dt, 2d) / 2d, dt });

        // 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
            double mNoise = measurementNoise * rand.nextGaussian();

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

            filter.correct(z);
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    }

    @Test
    public void testGetters() {
        final DistanceMeasure measure = new CanberraDistance();
        final RandomGenerator random = new JDKRandomGenerator();
        final FuzzyKMeansClusterer<DoublePoint> clusterer =
                new FuzzyKMeansClusterer<DoublePoint>(3, 2.0, 100, measure, 1e-6, random);

        Assert.assertEquals(3, clusterer.getK());
        Assert.assertEquals(2.0, clusterer.getFuzziness(), 1e-6);
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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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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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