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

Introduction

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

Prototype

public UniformRandomGenerator(RandomGenerator generator) 

Source Link

Document

Create a new generator.

Usage

From source file:inputHandling.DataGenCorrCommons.java

@Override
public TupleList genData(int dimensions, int tupleCount) {
    logger.info("Generating uniform correlated Data with " + tupleCount + " Tuples in " + dimensions
            + " dimensions, Coeff.: -" + this.coeff);
    genMatrices(dimensions);//from   w  w  w  .j  a  v a  2  s  . co m
    RealMatrix covariance = MatrixUtils.createRealMatrix(cov);
    RandomGenerator rg = new JDKRandomGenerator(Math.round(seed));
    UniformRandomGenerator rawGenerator = new UniformRandomGenerator(rg);
    double small = 1.0e-12 * covariance.getNorm();
    CorrelatedRandomVectorGenerator generator = new CorrelatedRandomVectorGenerator(mean, covariance, small,
            rawGenerator);

    // Generate the Tuples
    TupleList tupleList = new TupleList(dimensions);
    for (int j = 0; j < tupleCount; j++) {
        double[] randomVector = generator.nextVector();
        Tuple tuple = new Tuple(randomVector);
        tupleList.add(tuple);
    }
    return tupleList;
}

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From source file:inputHandling.DataGenAntiCorrCommons.java

@Override
public TupleList genData(int dimensions, int tupleCount) {
    logger.info("Generating uniform anti-correlated Data with " + tupleCount + " Tuples in " + dimensions
            + " dimensions, Coeff.: -" + this.coeff);
    genMatrices(dimensions);/*  w  ww. j a  v  a2s .c  o m*/
    RealMatrix covariance = MatrixUtils.createRealMatrix(cov);
    RandomGenerator rg = new JDKRandomGenerator(Math.round(seed));
    UniformRandomGenerator rawGenerator = new UniformRandomGenerator(rg);
    double small = 1.0e-12 * covariance.getNorm();
    CorrelatedRandomVectorGenerator generator = new CorrelatedRandomVectorGenerator(mean, covariance, small,
            rawGenerator);

    TupleList tupleList = new TupleList(dimensions);
    // Invert the Values, to receive Anti-Correlation
    for (int j = 0; j < tupleCount; j++) {
        double[] randomVector = generator.nextVector();
        for (int i = 0; i < dimensions; i++) {
            if (j % 2 == 0 && i % 2 == 0)
                randomVector[i] = 2 * mean[i] - randomVector[i];
            else if (j % 2 != 0 && i % 2 != 0)
                randomVector[i] = 2 * mean[i] - randomVector[i];
            else
                randomVector[i] = randomVector[i];
        }
        Tuple tuple = new Tuple(randomVector);
        tupleList.add(tuple);
    }
    return tupleList;
}

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From source file:objenome.goal.numeric.OptimizeMultivariate.java

@Override
public synchronized void solve(Objenome o, List<SetNumericValue> variables) {

    this.variables = variables;
    objenome = o;// w  w w . ja v  a2 s.  c om

    if (numStarts == -1)
        numStarts = variables.size() * 2;

    double[] lower = new double[variables.size()];
    double[] upper = new double[variables.size()];
    double[] mid = new double[variables.size()];
    int j = 0;
    for (SetNumericValue n : variables) {
        lower[j] = n.getMin().doubleValue();
        upper[j] = n.getMax().doubleValue();
        mid[j] = (lower[j] + upper[j]) * 0.5f;
        j++;
    }

    //TODO add MultiStart

    MultivariateOptimizer optimize = new BOBYQAOptimizer(variables.size() * 2);
    //new PowellOptimizer(0.01, 0.05);

    RandomGenerator rng = getRandomGenerator();

    MultiStartMultivariateOptimizer multiOptimize = new MultiStartMultivariateOptimizer(optimize, numStarts,
            new UncorrelatedRandomVectorGenerator(variables.size(), new UniformRandomGenerator(rng)));

    PointValuePair result = multiOptimize.optimize(new MaxEval(evaluations), new SimpleBounds(lower, upper),
            goal, new InitialGuess(mid), new ObjectiveFunction(this));

    apply(result.getPointRef());

    bestValue = result.getValue();
}

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From source file:org.briljantframework.benchmark.DataFrameSortPerformance.java

@Setup(Level.Iteration)
public void setupDataFrame() {
    DataFrame.Builder builder = new MixedDataFrame.Builder();
    UniformRandomGenerator gen = new UniformRandomGenerator(new Well1024a());
    for (int i = 0; i < 1000_000; i++) {
        builder.setRecord(String.valueOf(i), Vector.of(gen.nextNormalizedDouble(), i, 20.0, 30.0));
    }/*from  w ww .ja  v  a 2 s  .co  m*/
    builder.setColumnIndex("First", "Second", "Third", "Fourth");
    dataFrame = builder.build();
}

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From source file:org.briljantframework.benchmark.VectorSortPerformance.java

@Setup(Level.Iteration)
public void setupDataFrame() {
    Vector.Builder builder = Vector.Builder.of(Double.class);
    UniformRandomGenerator gen = new UniformRandomGenerator(new Well1024a());
    for (int i = 0; i < 1000_000; i++) {
        builder.add(gen.nextNormalizedDouble());
    }//from w ww .j av a2 s  .com
    vector = builder.build();
}

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