Package org.apache.mahout.math.jet.random

Examples of org.apache.mahout.math.jet.random.AbstractContinousDistribution

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    @Test
    public void testNarrowNormal() {
        // this mixture of a uniform and normal distribution has a very narrow peak which is centered
        // near the median.  Our system should be scale invariant and work well regardless.
        final Random gen = RandomUtils.getRandom();
        AbstractContinousDistribution mix = new AbstractContinousDistribution() {
            AbstractContinousDistribution normal = new Normal(0, 1e-5, gen);
            AbstractContinousDistribution uniform = new Uniform(-1, 1, gen);

            @Override
            public double nextDouble() {
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    @Test
    public void testRepeatedValues() {
        final Random gen = RandomUtils.getRandom();

        // 5% of samples will be 0 or 1.0.  10% for each of the values 0.1 through 0.9
        AbstractContinousDistribution mix = new AbstractContinousDistribution() {
            @Override
            public double nextDouble() {
                return Math.rint(gen.nextDouble() * 10) / 10.0;
            }
        };

        TDigest dist = new TDigest((double) 1000, gen);
        long t0 = System.nanoTime();
        List<Double> data = Lists.newArrayList();
        for (int i1 = 0; i1 < 100000; i1++) {
            double x = mix.nextDouble();
            data.add(x);
            dist.add(x);
        }

        System.out.printf("# %fus per point\n", (System.nanoTime() - t0) * 1e-3 / 100000);
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    @Test
    public void testSequentialPoints() {
        Random gen = RandomUtils.getRandom();
        for (int i = 0; i < repeats(); i++) {
            runTest(new AbstractContinousDistribution() {
                double base = 0;

                @Override
                public double nextDouble() {
                    base += Math.PI * 1e-5;
 
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  }

  private static OnlineSummarizer gamma(int n, double shape) {
    OnlineSummarizer x = new OnlineSummarizer();
    Random gen = RandomUtils.getRandom();
    AbstractContinousDistribution gamma = new Gamma(shape, shape, gen);
    for (int i = 0; i < n; i++) {
      x.add(gamma.nextDouble());
    }
    return x;
  }
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    @Test
    public void testNarrowNormal() {
        // this mixture of a uniform and normal distribution has a very narrow peak which is centered
        // near the median.  Our system should be scale invariant and work well regardless.
        final RandomWrapper gen = RandomUtils.getRandom();
        AbstractContinousDistribution mix = new AbstractContinousDistribution() {
            AbstractContinousDistribution normal = new Normal(0, 1e-5, gen);
            AbstractContinousDistribution uniform = new Uniform(-1, 1, gen);

            @Override
            public double nextDouble() {
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    @Test
    public void testRepeatedValues() {
        final RandomWrapper gen = RandomUtils.getRandom();

        // 5% of samples will be 0 or 1.0.  10% for each of the values 0.1 through 0.9
        AbstractContinousDistribution mix = new AbstractContinousDistribution() {
            @Override
            public double nextDouble() {
                return Math.rint(gen.nextDouble() * 10) / 10.0;
            }
        };

        TDigest dist = new TDigest((double) 1000);
        long t0 = System.nanoTime();
        Multiset<Double> data = HashMultiset.create();
        for (int i1 = 0; i1 < 100000; i1++) {
            double x = mix.nextDouble();
            data.add(x);
            dist.add(x);
        }

        System.out.printf("# %fus per point\n", (System.nanoTime() - t0) * 1e-3 / 100000);
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    }

    @Test
    public void testSequentialPoints() {
        for (int i = 0; i < 5; i++) {
            runTest(new AbstractContinousDistribution() {
                double base = 0;

                @Override
                public double nextDouble() {
                    base += Math.PI * 1e-5;
 
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  }

  private static double[] gamma(int n, double shape) {
    double[] r = new double[n];
    Random gen = RandomUtils.getRandom();
    AbstractContinousDistribution gamma = new Gamma(shape, shape, gen);
    for (int i = 0; i < n; i++) {
      r[i] = gamma.nextDouble();
    }
    return r;
  }
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    @Test
    public void testNarrowNormal() {
        // this mixture of a uniform and normal distribution has a very narrow peak which is centered
        // near the median.  Our system should be scale invariant and work well regardless.
        final Random gen = RandomUtils.getRandom();
        AbstractContinousDistribution mix = new AbstractContinousDistribution() {
            AbstractContinousDistribution normal = new Normal(0, 1e-5, gen);
            AbstractContinousDistribution uniform = new Uniform(-1, 1, gen);

            @Override
            public double nextDouble() {
View Full Code Here
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    @Test
    public void testRepeatedValues() {
        final Random gen = RandomUtils.getRandom();

        // 5% of samples will be 0 or 1.0.  10% for each of the values 0.1 through 0.9
        AbstractContinousDistribution mix = new AbstractContinousDistribution() {
            @Override
            public double nextDouble() {
                return Math.rint(gen.nextDouble() * 10) / 10.0;
            }
        };

        TreeDigest dist = new TreeDigest((double) 1000);
        List<Double> data = Lists.newArrayList();
        for (int i1 = 0; i1 < 100000; i1++) {
            double x = mix.nextDouble();
            data.add(x);
        }

        long t0 = System.nanoTime();
        for (double x : data) {
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Related Classes of org.apache.mahout.math.jet.random.AbstractContinousDistribution

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  • com.mapr.stats.random.BetaBinomialDistribution
  • com.mapr.stats.random.BinomialDistributionSampler
  • com.mapr.stats.random.GammaNormalDistributionTest
  • com.tdunning.math.stats.ArrayDigestTest
  • com.tdunning.math.stats.AVLTreeDigestTest
  • com.tdunning.math.stats.TreeDigestTest
  • org.apache.mahout.math.stats.OnlineSummarizerTest
  • org.apache.mahout.math.stats.TDigestTest
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