Example usage for org.apache.commons.math3.random Well44497b Well44497b

Introduction

In this page you can find the example usage for org.apache.commons.math3.random Well44497b Well44497b.

Prototype

public Well44497b() 

Source Link

Document

Creates a new random number generator.

Usage

From source file:cc.redberry.core.context.NameManager.java

NameManager(Long seed, String kronecker, String metric) {
    if (seed == null) {
        random = new Well44497b();
        random.setSeed(this.seed = random.nextLong());
    } else/*from ww  w . j av a 2  s . co  m*/
        random = new Well44497b(this.seed = seed);
    kroneckerAndMetricNames[0] = kronecker;
    kroneckerAndMetricNames[1] = metric;
}

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From source file:gdsc.smlm.ij.plugins.pcpalm.PCPALMFitting.java

private PointValuePair runBoundedOptimiser(double[][] gr, double[] initialSolution, double[] lB, double[] uB,
        SumOfSquaresModelFunction function) {
    // Create the functions to optimise
    ObjectiveFunction objective = new ObjectiveFunction(new SumOfSquaresMultivariateFunction(function));
    ObjectiveFunctionGradient gradient = new ObjectiveFunctionGradient(
            new SumOfSquaresMultivariateVectorFunction(function));

    final boolean debug = false;

    // Try a BFGS optimiser since this will produce a deterministic solution and can respect bounds.
    PointValuePair optimum = null;// w w  w .j a va 2s  .c o  m
    boundedEvaluations = 0;
    final MaxEval maxEvaluations = new MaxEval(2000);
    MultivariateOptimizer opt = null;
    for (int iteration = 0; iteration <= fitRestarts; iteration++) {
        try {
            opt = new BFGSOptimizer();
            final double relativeThreshold = 1e-6;

            // Configure maximum step length for each dimension using the bounds
            double[] stepLength = new double[lB.length];
            for (int i = 0; i < stepLength.length; i++)
                stepLength[i] = (uB[i] - lB[i]) * 0.3333333;

            // The GoalType is always minimise so no need to pass this in
            optimum = opt.optimize(maxEvaluations, gradient, objective,
                    new InitialGuess((optimum == null) ? initialSolution : optimum.getPointRef()),
                    new SimpleBounds(lB, uB), new BFGSOptimizer.GradientTolerance(relativeThreshold),
                    new BFGSOptimizer.StepLength(stepLength));
            if (debug)
                System.out.printf("BFGS Iter %d = %g (%d)\n", iteration, optimum.getValue(),
                        opt.getEvaluations());
        } catch (TooManyEvaluationsException e) {
            break; // No need to restart
        } catch (RuntimeException e) {
            break; // No need to restart
        } finally {
            boundedEvaluations += opt.getEvaluations();
        }
    }

    // Try a CMAES optimiser which is non-deterministic. To overcome this we perform restarts.

    // CMAESOptimiser based on Matlab code:
    // https://www.lri.fr/~hansen/cmaes.m
    // Take the defaults from the Matlab documentation
    double stopFitness = 0; //Double.NEGATIVE_INFINITY;
    boolean isActiveCMA = true;
    int diagonalOnly = 0;
    int checkFeasableCount = 1;
    RandomGenerator random = new Well44497b(); //Well19937c();
    boolean generateStatistics = false;
    ConvergenceChecker<PointValuePair> checker = new SimpleValueChecker(1e-6, 1e-10);
    // The sigma determines the search range for the variables. It should be 1/3 of the initial search region.
    double[] range = new double[lB.length];
    for (int i = 0; i < lB.length; i++)
        range[i] = (uB[i] - lB[i]) / 3;
    OptimizationData sigma = new CMAESOptimizer.Sigma(range);
    OptimizationData popSize = new CMAESOptimizer.PopulationSize(
            (int) (4 + Math.floor(3 * Math.log(initialSolution.length))));
    SimpleBounds bounds = new SimpleBounds(lB, uB);

    opt = new CMAESOptimizer(maxEvaluations.getMaxEval(), stopFitness, isActiveCMA, diagonalOnly,
            checkFeasableCount, random, generateStatistics, checker);
    // Restart the optimiser several times and take the best answer.
    for (int iteration = 0; iteration <= fitRestarts; iteration++) {
        try {
            // Start from the initial solution
            PointValuePair constrainedSolution = opt.optimize(new InitialGuess(initialSolution), objective,
                    GoalType.MINIMIZE, bounds, sigma, popSize, maxEvaluations);
            if (debug)
                System.out.printf("CMAES Iter %d initial = %g (%d)\n", iteration,
                        constrainedSolution.getValue(), opt.getEvaluations());
            boundedEvaluations += opt.getEvaluations();
            if (optimum == null || constrainedSolution.getValue() < optimum.getValue()) {
                optimum = constrainedSolution;
            }
        } catch (TooManyEvaluationsException e) {
        } catch (TooManyIterationsException e) {
        } finally {
            boundedEvaluations += maxEvaluations.getMaxEval();
        }
        if (optimum == null)
            continue;
        try {
            // Also restart from the current optimum
            PointValuePair constrainedSolution = opt.optimize(new InitialGuess(optimum.getPointRef()),
                    objective, GoalType.MINIMIZE, bounds, sigma, popSize, maxEvaluations);
            if (debug)
                System.out.printf("CMAES Iter %d restart = %g (%d)\n", iteration,
                        constrainedSolution.getValue(), opt.getEvaluations());
            if (constrainedSolution.getValue() < optimum.getValue()) {
                optimum = constrainedSolution;
            }
        } catch (TooManyEvaluationsException e) {
        } catch (TooManyIterationsException e) {
        } finally {
            boundedEvaluations += maxEvaluations.getMaxEval();
        }
    }
    return optimum;
}

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From source file:org.optaplanner.core.impl.solver.random.DefaultRandomFactory.java

@Override
public Random createRandom() {
    switch (randomType) {
    case JDK://from  www.  j  a  v  a 2  s.  com
        return randomSeed == null ? new Random() : new Random(randomSeed);
    case MERSENNE_TWISTER:
        return new RandomAdaptor(randomSeed == null ? new MersenneTwister() : new MersenneTwister(randomSeed));
    case WELL512A:
        return new RandomAdaptor(randomSeed == null ? new Well512a() : new Well512a(randomSeed));
    case WELL1024A:
        return new RandomAdaptor(randomSeed == null ? new Well1024a() : new Well1024a(randomSeed));
    case WELL19937A:
        return new RandomAdaptor(randomSeed == null ? new Well19937a() : new Well19937a(randomSeed));
    case WELL19937C:
        return new RandomAdaptor(randomSeed == null ? new Well19937c() : new Well19937c(randomSeed));
    case WELL44497A:
        return new RandomAdaptor(randomSeed == null ? new Well44497a() : new Well44497a(randomSeed));
    case WELL44497B:
        return new RandomAdaptor(randomSeed == null ? new Well44497b() : new Well44497b(randomSeed));
    default:
        throw new IllegalStateException("The randomType (" + randomType + ") is not implemented.");
    }
}

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