Package org.apache.commons.math3.random

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

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                                            double b,
                                            double sigma,
                                            double lo,
                                            double hi,
                                            long seed) {
        final RandomGenerator rng = new Well44497b(seed);
        slope = a;
        intercept = b;
        error = new NormalDistribution(rng, 0, sigma,
                                       NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        x = new UniformRealDistribution(rng, lo, hi,
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                actual = interpolation.value( currentX, currentY );
                assertTrue( Precision.equals( expected, actual ) );
            }
        }

        final RandomGenerator rng = new Well19937c( 1234567L ); // "tol" depends on the seed.
        final UniformRealDistribution distX =
            new UniformRealDistribution( rng, xValues[0], xValues[xValues.length - 1] );
        final UniformRealDistribution distY =
            new UniformRealDistribution( rng, yValues[0], yValues[yValues.length - 1] );
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        BivariateGridInterpolator interpolator = new BicubicSplineInterpolator();
        BivariateFunction p = interpolator.interpolate(xval, yval, zval);
        double x, y;

        final RandomGenerator rng = new Well19937c(1234567L); // "tol" depends on the seed.
        final UniformRealDistribution distX
            = new UniformRealDistribution(rng, xval[0], xval[xval.length - 1]);
        final UniformRealDistribution distY
            = new UniformRealDistribution(rng, yval[0], yval[yval.length - 1]);
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        BivariateGridInterpolator interpolator = new BicubicSplineInterpolator();
        BivariateFunction p = interpolator.interpolate(xval, yval, zval);
        double x, y;

        final RandomGenerator rng = new Well19937c(1234567L); // "tol" depends on the seed.
        final UniformRealDistribution distX
            = new UniformRealDistribution(rng, xval[0], xval[xval.length - 1]);
        final UniformRealDistribution distY
            = new UniformRealDistribution(rng, yval[0], yval[yval.length - 1]);
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     * @return the population for the next generation.
     */
    public Population nextGeneration(final Population current) {
        Population nextGeneration = current.nextGeneration();

        RandomGenerator randGen = getRandomGenerator();

        while (nextGeneration.getPopulationSize() < nextGeneration.getPopulationLimit()) {
            // select parent chromosomes
            ChromosomePair pair = getSelectionPolicy().select(current);

            // crossover?
            if (randGen.nextDouble() < getCrossoverRate()) {
                // apply crossover policy to create two offspring
                pair = getCrossoverPolicy().crossover(pair.getFirst(), pair.getSecond());
            }

            // mutation?
            if (randGen.nextDouble() < getMutationRate()) {
                // apply mutation policy to the chromosomes
                pair = new ChromosomePair(
                    getMutationPolicy().mutate(pair.getFirst()),
                    getMutationPolicy().mutate(pair.getSecond()));
            }
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        Assert.assertTrue(ball.getRadius() > 0);
    }

    @Test
    public void testLargeSamples() throws IOException {
        RandomGenerator random = new Well1024a(0x35ddecfc78131e1dl);
        final UnitSphereRandomVectorGenerator sr = new UnitSphereRandomVectorGenerator(3, random);
        for (int k = 0; k < 50; ++k) {

            // define the reference sphere we want to compute
            double d = 25 * random.nextDouble();
            double refRadius = 10 * random.nextDouble();
            Vector3D refCenter = new Vector3D(d, new Vector3D(sr.nextVector()));
            // set up a large sample inside the reference sphere
            int nbPoints = random.nextInt(1000);
            List<Vector3D> points = new ArrayList<Vector3D>();
            for (int i = 0; i < nbPoints; ++i) {
                double r = refRadius * random.nextDouble();
                points.add(new Vector3D(1.0, refCenter, r, new Vector3D(sr.nextVector())));
            }

            // test we find a sphere at most as large as the one used for random drawings
            checkSphere(points, refRadius);
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        final List<T> parent2Rep = second.getRepresentation();
        // and of the children
        final List<T> child1Rep = new ArrayList<T>(length);
        final List<T> child2Rep = new ArrayList<T>(length);

        final RandomGenerator random = GeneticAlgorithm.getRandomGenerator();

        List<T> c1 = child1Rep;
        List<T> c2 = child2Rep;

        int remainingPoints = crossoverPoints;
        int lastIndex = 0;
        for (int i = 0; i < crossoverPoints; i++, remainingPoints--) {
            // select the next crossover point at random
            final int crossoverIndex = 1 + lastIndex + random.nextInt(length - lastIndex - remainingPoints);

            // copy the current segment
            for (int j = lastIndex; j < crossoverIndex; j++) {
                c1.add(parent1Rep.get(j));
                c2.add(parent2Rep.get(j));
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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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        final ProcessModel pm = new DefaultProcessModel(A, B, Q, initialState, initialErrorCovariance);
        final MeasurementModel mm = new DefaultMeasurementModel(H, R);
        final KalmanFilter filter = new KalmanFilter(pm, mm);

        final RandomGenerator rng = new Well19937c(1000);
        final NormalDistribution dist = new NormalDistribution(rng, 0, measurementNoise);

        for (int i = 0; i < iterations; i++) {
            // get the "real" cannonball position
            double x = cannonball.getX();
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Related Classes of org.apache.commons.math3.random.RandomGenerator

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  • net.myrrix.online.som.SelfOrganizingMaps
  • 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.LogitTest
  • org.apache.commons.math3.analysis.function.Sin
  • org.apache.commons.math3.analysis.function.Sinc
  • org.apache.commons.math3.analysis.interpolation.BicubicSplineInterpolatingFunctionTest
  • org.apache.commons.math3.analysis.interpolation.BicubicSplineInterpolatorTest
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