Examples with NormalDistribution org.nd4j.linalg.api.rng.distribution.impl.NormalDistribution used on opensource projects

Example 1 with NormalDistribution

use of org.nd4j.linalg.api.rng.distribution.impl.NormalDistribution in project nd4j by deeplearning4j.

the class RandomTests method testLegacyDistribution1.

@Test
public void testLegacyDistribution1() throws Exception {
    NormalDistribution distribution = new NormalDistribution(new DefaultRandom(), 0.0, 1.0);
    INDArray z1 = distribution.sample(new int[] { 1, 30000000 });
    assertEquals(0.0, z1.meanNumber().doubleValue(), 0.01);
    assertEquals(1.0, z1.stdNumber().doubleValue(), 0.01);
}

Example 2 with NormalDistribution

use of org.nd4j.linalg.api.rng.distribution.impl.NormalDistribution in project nd4j by deeplearning4j.

the class RandomTests method testAndersonDarling.

/**
 * Uses a test of Gaussianity for testing the values out of GaussianDistribution
 * See https://en.wikipedia.org/wiki/Anderson%E2%80%93Darling_test
 *
 * @throws Exception
 */
@Test
public void testAndersonDarling() throws Exception {
    Random random1 = Nd4j.getRandomFactory().getNewRandomInstance(119);
    INDArray z1 = Nd4j.create(1000);
    GaussianDistribution op1 = new GaussianDistribution(z1, 0.0, 1.0);
    Nd4j.getExecutioner().exec(op1, random1);
    int n = z1.length();
    // using this just for the cdf
    Distribution nd = new NormalDistribution(random1, 0.0, 1.0);
    Nd4j.sort(z1, true);
    System.out.println("Data for Anderson-Darling: " + z1);
    for (int i = 0; i < n; i++) {
        Double res = nd.cumulativeProbability(z1.getDouble(i));
        assertTrue(res >= 0.0);
        assertTrue(res <= 1.0);
        // avoid overflow when taking log later.
        if (res == 0)
            res = 0.0000001;
        if (res == 1)
            res = 0.9999999;
        z1.putScalar(i, res);
    }
    double A = 0.0;
    for (int i = 0; i < n; i++) {
        A -= (2 * i + 1) * (Math.log(z1.getDouble(i)) + Math.log(1 - z1.getDouble(n - i - 1)));
    }
    A = A / n - n;
    A *= (1 + 4.0 / n - 25.0 / (n * n));
    assertTrue("Critical (max) value for 1000 points and confidence α = 0.0001 is 1.8692, received: " + A, A < 1.8692);
}