Examples with Random org.nd4j.linalg.api.rng.Random used on opensource projects

Example 16 with Random

use of org.nd4j.linalg.api.rng.Random 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);
}

Example 17 with Random

use of org.nd4j.linalg.api.rng.Random in project nd4j by deeplearning4j.

the class RandomTests method testStepOver2.

@Test
public void testStepOver2() throws Exception {
    Random random = Nd4j.getRandomFactory().getNewRandomInstance(119);
    if (random instanceof NativeRandom) {
        NativeRandom rng = (NativeRandom) random;
        assertTrue(rng.getBufferSize() > 1000000L);
        assertEquals(0, rng.getPosition());
        rng.nextLong();
        assertEquals(1, rng.getPosition());
        assertEquals(1, rng.getGeneration());
        for (long e = 0; e <= rng.getBufferSize(); e++) {
            rng.nextLong();
        }
        assertEquals(2, rng.getPosition());
        assertEquals(2, rng.getGeneration());
        rng.reSeed(8792);
        assertEquals(2, rng.getGeneration());
        assertEquals(2, rng.getPosition());
    } else
        log.warn("Not a NativeRandom object received, skipping test");
}

Example 18 with Random

use of org.nd4j.linalg.api.rng.Random in project nd4j by deeplearning4j.

the class RandomTests method testStepOver4.

@Test
public void testStepOver4() throws Exception {
    Random random1 = Nd4j.getRandomFactory().getNewRandomInstance(119, 100000);
    Random random2 = Nd4j.getRandomFactory().getNewRandomInstance(119, 100000);
    for (int x = 0; x < 1000; x++) {
        INDArray z1 = Nd4j.rand(1, 10000, random1);
        INDArray z2 = Nd4j.rand(1, 10000, random2);
        assertEquals(z1, z2);
    }
}

Example 19 with Random

use of org.nd4j.linalg.api.rng.Random in project deeplearning4j by deeplearning4j.

the class TestOptimizers method testSphereFnMultipleStepsHelper.

private static void testSphereFnMultipleStepsHelper(OptimizationAlgorithm oa, int nOptIter, int maxNumLineSearchIter) {
    double[] scores = new double[nOptIter + 1];
    for (int i = 0; i <= nOptIter; i++) {
        Random rng = new DefaultRandom(12345L);
        org.nd4j.linalg.api.rng.distribution.Distribution dist = new org.nd4j.linalg.api.rng.distribution.impl.UniformDistribution(rng, -10, 10);
        NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().maxNumLineSearchIterations(maxNumLineSearchIter).iterations(i).learningRate(0.1).layer(new DenseLayer.Builder().nIn(1).nOut(1).updater(Updater.SGD).build()).build();
        //Normally done by ParamInitializers, but obviously that isn't done here
        conf.addVariable("W");
        Model m = new SphereFunctionModel(100, dist, conf);
        if (i == 0) {
            m.computeGradientAndScore();
            //Before optimization
            scores[0] = m.score();
        } else {
            ConvexOptimizer opt = getOptimizer(oa, conf, m);
            opt.optimize();
            m.computeGradientAndScore();
            scores[i] = m.score();
            assertTrue(!Double.isNaN(scores[i]) && !Double.isInfinite(scores[i]));
        }
    }
    if (PRINT_OPT_RESULTS) {
        System.out.println("Multiple optimization iterations (" + nOptIter + " opt. iter.) score vs iteration, maxNumLineSearchIter=" + maxNumLineSearchIter + ": " + oa);
        System.out.println(Arrays.toString(scores));
    }
    for (int i = 1; i < scores.length; i++) {
        assertTrue(scores[i] <= scores[i - 1]);
    }
    //Very easy function, expect score ~= 0 with any reasonable number of steps/numLineSearchIter
    assertTrue(scores[scores.length - 1] < 1.0);
}

Example 20 with Random

use of org.nd4j.linalg.api.rng.Random in project deeplearning4j by deeplearning4j.

the class DM method inferSequence.

/**
     * This method does training on previously unseen paragraph, and returns inferred vector
     *
     * @param sequence
     * @param nr
     * @param learningRate
     * @return
     */
@Override
public INDArray inferSequence(Sequence<T> sequence, long nr, double learningRate, double minLearningRate, int iterations) {
    AtomicLong nextRandom = new AtomicLong(nr);
    if (sequence.isEmpty())
        return null;
    Random random = Nd4j.getRandomFactory().getNewRandomInstance(configuration.getSeed() * sequence.hashCode(), lookupTable.layerSize() + 1);
    INDArray ret = Nd4j.rand(new int[] { 1, lookupTable.layerSize() }, random).subi(0.5).divi(lookupTable.layerSize());
    for (int iter = 0; iter < iterations; iter++) {
        for (int i = 0; i < sequence.size(); i++) {
            nextRandom.set(Math.abs(nextRandom.get() * 25214903917L + 11));
            dm(i, sequence, (int) nextRandom.get() % window, nextRandom, learningRate, null, true, ret);
        }
        learningRate = ((learningRate - minLearningRate) / (iterations - iter)) + minLearningRate;
    }
    finish();
    return ret;
}