fi.smaa.common.RandomUtil.java Source code

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Here is the source code for fi.smaa.common.RandomUtil.java

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/*
   This file is part of JSMAA.
   (c) Tommi Tervonen, 2009-2010   
    
JSMAA is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
    
JSMAA is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.
    
You should have received a copy of the GNU General Public License
along with JSMAA.  If not, see <http://www.gnu.org/licenses/>.
*/

package fi.smaa.common;

import java.util.Arrays;

import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.RealVector;
import org.apache.commons.math3.random.JDKRandomGenerator;
import org.apache.commons.math3.random.MersenneTwister;
import org.apache.commons.math3.random.RandomDataImpl;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.random.RandomVectorGenerator;

public class RandomUtil {
    public static RandomUtil createWithFixedSeed() {
        JDKRandomGenerator engine = new JDKRandomGenerator();
        engine.setSeed(666);
        return new RandomUtil(engine);
    }

    public static RandomUtil createWithRandomSeed() {
        return new RandomUtil(new MersenneTwister());
    }

    private RandomDataImpl random;
    private RandomGenerator engine;

    private RandomUtil(RandomGenerator engine) {
        this.random = new RandomDataImpl(engine);
        this.engine = engine;
    }

    /**
     * Generates a Gaussian distributed number.
     * 
     * @param mean mean of the number
     * @param stdev standard deviation of the number
     * @return a value sampled from the gaussian distribution
     */
    public double createGaussian(double mean, double stddev) {
        return stddev == 0 ? mean : random.nextGaussian(mean, stddev);
    }

    /**
     * Generates a sample from uniform distribution in interval [0.0, 1.0].
     * 
     * @return the sampled number
     */
    public double createUnif01() {
        return random.nextUniform(0.0, 1.0);
    }

    /**
     * Creates an array of random numbers that sum to a given amount.
     * 
     * @param dest the array to create the numbers to.
     * @param sumTo The amount the numbers must sum to. Must be >= 0.0
     * @throws NullPointerException if dest == null
     */
    public void createSumToRand(double[] dest, double sumTo) throws NullPointerException {
        assert (sumTo >= 0.0);
        if (dest == null) {
            throw new NullPointerException("destination array null");
        }

        int len = dest.length;
        for (int i = 0; i < len - 1; i++) {
            dest[i] = createUnif01() * sumTo;
        }

        dest[len - 1] = sumTo;

        Arrays.sort(dest);

        double last = 0.0;
        for (int i = 0; i < len; i++) {
            double t = dest[i];
            dest[i] = t - last;
            last = t;
        }
    }

    /**
     * Creates random numbers that sum to 1.0.
     * 
     * @param dest the destination array to create the random numbers to
     * @throws NullPointerException if dest == null
     */
    public void createSumToOneRand(double[] dest) throws NullPointerException {
        createSumToRand(dest, 1.0);
    }

    /**
     * Creates random numbers that sum to 1.0 and are sorted in ascending order.
     * 
     * @param dest the destination array to create the random numbers to
     * @throws NullPointerException if dest == null
     */
    public void createSumToOneSorted(double[] dest) throws NullPointerException {
        createSumToOneRand(dest);
        Arrays.sort(dest);
    }

    public double createBeta(Double min, Double max, Double alpha, Double beta) {
        double r = random.nextBeta(alpha, beta);
        return r * (max - min) + min;
    }

    public RandomVectorGenerator createMultivariateGaussian(RealVector mean, RealMatrix covariance) {
        return new MultivariateGaussianGenerator(mean, covariance, this.engine);
    }
}