Example usage for org.apache.commons.math.random RandomDataImpl RandomDataImpl

Introduction

In this page you can find the example usage for org.apache.commons.math.random RandomDataImpl RandomDataImpl.

Prototype

public RandomDataImpl(RandomGenerator rand) 

Source Link

Document

Construct a RandomDataImpl using the supplied RandomGenerator as the source of (non-secure) random data.

Usage

From source file:com.milaboratory.util.MathTest.java

@Test
public void testBinomialLog() throws Exception {
    RandomData rdg = new RandomDataImpl(new Well19937c());
    long n, k;//from www  .  j a  v  a2s  .c  om
    double func, exact, avrg;
    for (int i = 0; i < 100000; ++i) {
        n = rdg.nextLong(1, 100);
        k = rdg.nextLong(0, n);
        func = Math.binomialCoefficientLog(n, k);
        exact = exactBinomialCoefficientLog(n, k);
        avrg = (func + exact) / 2;
        assertTrue(func == exact || (java.lang.Math.abs(func - exact) / avrg) <= .001);
    }
}

---

From source file:datafu.pig.hash.lsh.RepeatingLSH.java

public RepeatingLSH(List<LSH> lshList) throws MathException {
    super(lshList.get(0).getDim(), lshList.get(0).getRandomGenerator());
    this.lshList = lshList;
    RandomGenerator rg = lshList.get(0).getRandomGenerator();
    RandomData rd = new RandomDataImpl(rg);
    /*/*from w  w  w.  ja va 2s .  c o  m*/
     * Compute a random vector of lshList.size() with each component taken from U(0,10)
     */
    randomVec = new ArrayRealVector(lshList.size());
    for (int i = 0; i < randomVec.getDimension(); ++i) {
        randomVec.setEntry(i, rd.nextUniform(0, 10.0));
    }
}

---

From source file:com.milaboratory.util.MathTest.java

@Test
public void testBinomialLogBig() throws Exception {
    RandomData rdg = new RandomDataImpl(new Well19937c());
    long n, k;//  ww w. j a  va 2  s.  co m
    double func, exact, avrg;
    for (int i = 0; i < 5000; ++i) {
        n = rdg.nextLong(1, 10000);
        k = rdg.nextLong(0, n);
        func = Math.binomialCoefficientLog(n, k);
        exact = exactBinomialCoefficientLog(n, k);
        avrg = (func + exact) / 2;
        assertTrue(func == exact || (java.lang.Math.abs(func - exact) / avrg) <= .001);
    }
}

---

From source file:datafu.pig.hash.lsh.p_stable.AbstractStableDistributionFunction.java

public void reset(int dim, double w) throws MathException {
    RandomDataImpl dataSampler = new RandomDataImpl(rg);
    Sampler sampler = getSampler();//from  w ww  .  j a  v  a 2s .c o  m
    this.a = new double[dim];
    this.dim = dim;
    this.w = w;
    for (int i = 0; i < dim; ++i) {
        a[i] = sampler.sample(dataSampler);
    }
    b = dataSampler.nextUniform(0, w);
}

---

From source file:jmona.random.RandomUtilsTester.java

/**
 * Test method for {@link jmona.random.RandomUtils#setRandomData(RandomData)}.
 *///from   w ww . ja v a 2s  .co  m
@Test
public void testSetRandomData() {
    final RandomData newRandomData = new RandomDataImpl(new MersenneTwister());
    RandomUtils.setRandomData(newRandomData);
    assertSame(newRandomData, RandomUtils.randomData());
}

---

From source file:lfsom.visualization.clustering.LFSKMeans.java

/**
 * cluster centres are initialised by equally sized random chunks of the
 * input data when there's 150 instances, we assign 50 chosen randomly to
 * each cluster and calculate its centre from these (the last cluster might
 * be larger if numInstances mod k < 0)
 *//* ww  w .j  a v a 2  s.c o  m*/
private void initClustersEqualNumbers() {
    HashSet<Integer> usedIndices = new HashSet<Integer>();
    int limit = numberOfInstances / k;
    // FIXME: Test clustering with new permutation generator!
    // int[] randPermIndices = RandomTools.permutation(new
    // Random(RANDOM_SEED), this.numberOfInstances);
    JDKRandomGenerator rg = new JDKRandomGenerator();
    rg.setSeed(RANDOM_SEED);
    int[] randPermIndices = new RandomDataImpl(rg).nextPermutation(this.numberOfInstances,
            this.numberOfInstances);
    for (int clusterIndex = 0; clusterIndex < k; clusterIndex++) {
        LFSCluster c = new LFSCluster(new double[data[0].length]);
        // System.out.println("cluster: " + clusterIndex);
        for (int randPermIndice : randPermIndices) {
            int currentIndex = randPermIndice;
            if ((c.getNumberOfInstances() < limit || clusterIndex == k - 1)
                    && !usedIndices.contains(currentIndex)) {
                c.addIndex(currentIndex);
                usedIndices.add(currentIndex);
                // System.out.print(" " + currentIndex);
            }
        }
        // System.out.println();
        c.calculateCentroid(data);
        // clusters[clusterIndex] = c;
        clusters[clusterIndex] = new LFSCluster(c.getCentroid());

    }
}

---