Package org.apache.commons.math3.random

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

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        final List<T> parent2Rep = second.getRepresentation();
        // and of the children
        final ArrayList<T> child1Rep = new ArrayList<T>(first.getLength());
        final ArrayList<T> child2Rep = new ArrayList<T>(second.getLength());

        final RandomGenerator random = GeneticAlgorithm.getRandomGenerator();

        ArrayList<T> c1 = child1Rep;
        ArrayList<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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     * @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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        final List<T> child2 = new ArrayList<T>(length);
        // sets of already inserted items for quick access
        final Set<T> child1Set = new HashSet<T>(length);
        final Set<T> child2Set = new HashSet<T>(length);

        final RandomGenerator random = GeneticAlgorithm.getRandomGenerator();
        // choose random points, making sure that lb < ub.
        int a = random.nextInt(length);
        int b;
        do {
            b = random.nextInt(length);
        } while (a == b);
        // determine the lower and upper bounds
        final int lb = FastMath.min(a, b);
        final int ub = FastMath.max(a, b);
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        final List<T> parent2Rep = second.getRepresentation();
        // and of the children
        final List<T> child1Rep = new ArrayList<T>(first.getLength());
        final List<T> child2Rep = new ArrayList<T>(second.getLength());

        final RandomGenerator random = GeneticAlgorithm.getRandomGenerator();

        for (int index = 0; index < length; index++) {

            if (random.nextDouble() < ratio) {
                // swap the bits -> take other parent
                child1Rep.add(parent2Rep.get(index));
                child2Rep.add(parent1Rep.get(index));
            } else {
                child1Rep.add(parent1Rep.get(index));
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                                      double y,
                                      double radius,
                                      double xSigma,
                                      double ySigma,
                                      long seed) {
        final RandomGenerator rng = new Well44497b(seed);
        this.radius = radius;
        cX = new NormalDistribution(rng, x, xSigma,
                                    NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        cY = new NormalDistribution(rng, y, ySigma,
                                    NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
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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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    public void testValueWithInverseFunction() {
        final double lo = 2;
        final double hi = 3;
        final Logit f = new Logit(lo, hi);
        final Sigmoid g = new Sigmoid(lo, hi);
        RandomGenerator random = new Well1024a(0x49914cdd9f0b8db5l);
        final UnivariateDifferentiableFunction id = FunctionUtils.compose((UnivariateDifferentiableFunction) g,
                                                                (UnivariateDifferentiableFunction) f);

        for (int i = 0; i < 10; i++) {
            final double x = lo + random.nextDouble() * (hi - lo);
            Assert.assertEquals(x, id.value(new DerivativeStructure(1, 1, 0, x)).getValue(), EPS);
        }

        Assert.assertEquals(lo, id.value(new DerivativeStructure(1, 1, 0, lo)).getValue(), EPS);
        Assert.assertEquals(hi, id.value(new DerivativeStructure(1, 1, 0, hi)).getValue(), EPS);
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        double[] epsilon = new double[] { 1.0e-20, 4.0e-16, 3.0e-15, 2.0e-11, 3.0e-9, 1.0e-6 };
        final double lo = 2;
        final double hi = 3;
        final Logit f = new Logit(lo, hi);
        final Sigmoid g = new Sigmoid(lo, hi);
        RandomGenerator random = new Well1024a(0x96885e9c1f81cea5l);
        final UnivariateDifferentiableFunction id =
                FunctionUtils.compose((UnivariateDifferentiableFunction) g, (UnivariateDifferentiableFunction) f);
        for (int maxOrder = 0; maxOrder < 6; ++maxOrder) {
            double max = 0;
            for (int i = 0; i < 10; i++) {
                final double x = lo + random.nextDouble() * (hi - lo);
                final DerivativeStructure dsX = new DerivativeStructure(1, maxOrder, 0, x);
                max = FastMath.max(max, FastMath.abs(dsX.getPartialDerivative(maxOrder) -
                                                     id.value(dsX).getPartialDerivative(maxOrder)));
                Assert.assertEquals(dsX.getPartialDerivative(maxOrder),
                                    id.value(dsX).getPartialDerivative(maxOrder),
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                                      double y,
                                      double radius,
                                      double xSigma,
                                      double ySigma,
                                      long seed) {
        final RandomGenerator rng = new Well44497b(seed);
        this.radius = radius;
        cX = new NormalDistribution(rng, x, xSigma,
                                    NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        cY = new NormalDistribution(rng, y, ySigma,
                                    NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
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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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Related Classes of org.apache.commons.math3.random.RandomGenerator

  • net.myrrix.online.factorizer.als.AlternatingLeastSquares
  • 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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