Example usage for org.apache.commons.math3.random GaussianRandomGenerator nextNormalizedDouble

Introduction

In this page you can find the example usage for org.apache.commons.math3.random GaussianRandomGenerator nextNormalizedDouble.

Prototype

public double nextNormalizedDouble() 

Source Link

Document

Generate a random scalar with null mean and unit standard deviation.

Usage

From source file:org.apache.metron.statistics.OnlineStatisticsProviderTest.java

@Test
public void testNormallyDistributedRandomData() {
    List<Double> values = new ArrayList<>();
    GaussianRandomGenerator gaussian = new GaussianRandomGenerator(new MersenneTwister(0L));
    for (int i = 0; i < 1000000; ++i) {
        double d = gaussian.nextNormalizedDouble();
        values.add(d);//from   w w  w.  j  a  v  a  2 s  . c om
    }
    validateEquality(values);
}

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From source file:org.apache.metron.statistics.OnlineStatisticsProviderTest.java

@Test
public void testNormallyDistributedRandomDataShifted() {
    List<Double> values = new ArrayList<>();
    GaussianRandomGenerator gaussian = new GaussianRandomGenerator(new MersenneTwister(0L));
    for (int i = 0; i < 1000000; ++i) {
        double d = gaussian.nextNormalizedDouble() + 10;
        values.add(d);//w  w  w  .  java  2  s  .  c  om
    }
    validateEquality(values);
}

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From source file:org.apache.metron.statistics.OnlineStatisticsProviderTest.java

@Test
public void testNormallyDistributedRandomDataShiftedBackwards() {
    List<Double> values = new ArrayList<>();
    GaussianRandomGenerator gaussian = new GaussianRandomGenerator(new MersenneTwister(0L));
    for (int i = 0; i < 1000000; ++i) {
        double d = gaussian.nextNormalizedDouble() - 10;
        values.add(d);//from   w  ww  . j  av  a2  s .  co m
    }
    validateEquality(values);
}

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From source file:org.apache.metron.statistics.OnlineStatisticsProviderTest.java

@Test
public void testNormallyDistributedRandomDataSkewed() {
    List<Double> values = new ArrayList<>();
    GaussianRandomGenerator gaussian = new GaussianRandomGenerator(new MersenneTwister(0L));
    for (int i = 0; i < 1000000; ++i) {
        double d = (gaussian.nextNormalizedDouble() + 10000) / 1000;
        values.add(d);/*w ww.j a  va  2  s  . c o  m*/
    }
    validateEquality(values);
}

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From source file:org.apache.metron.statistics.OnlineStatisticsProviderTest.java

@Test
public void testNormallyDistributedRandomDataAllNegative() {
    List<Double> values = new ArrayList<>();
    GaussianRandomGenerator gaussian = new GaussianRandomGenerator(new MersenneTwister(0L));
    for (int i = 0; i < 1000000; ++i) {
        double d = -1 * gaussian.nextNormalizedDouble();
        values.add(d);/*from  w ww . j  av  a  2 s  .co  m*/
    }
    validateEquality(values);
}

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From source file:org.apache.metron.statistics.outlier.MedianAbsoluteDeviationTest.java

@Test
public void test() {

    GaussianRandomGenerator gaussian = new GaussianRandomGenerator(new MersenneTwister(0L));
    DescriptiveStatistics stats = new DescriptiveStatistics();
    List<MedianAbsoluteDeviationFunctions.State> states = new ArrayList<>();
    MedianAbsoluteDeviationFunctions.State currentState = null;
    //initialize the state
    currentState = (MedianAbsoluteDeviationFunctions.State) run("OUTLIER_MAD_STATE_MERGE(states, NULL)",
            ImmutableMap.of("states", states));
    for (int i = 0, j = 0; i < 10000; ++i, ++j) {
        Double d = gaussian.nextNormalizedDouble();
        stats.addValue(d);//from w  w  w .j  a v  a2s  .  co m
        run("OUTLIER_MAD_ADD(currentState, data)", ImmutableMap.of("currentState", currentState, "data", d));
        if (j >= 1000) {
            j = 0;
            List<MedianAbsoluteDeviationFunctions.State> stateWindow = new ArrayList<>();
            for (int stateIndex = Math.max(0, states.size() - 5); stateIndex < states.size(); ++stateIndex) {
                stateWindow.add(states.get(stateIndex));
            }
            currentState = (MedianAbsoluteDeviationFunctions.State) run(
                    "OUTLIER_MAD_STATE_MERGE(states, currentState)",
                    ImmutableMap.of("states", stateWindow, "currentState", currentState));
        }
    }
    {
        Double score = (Double) run("OUTLIER_MAD_SCORE(currentState, value)",
                ImmutableMap.of("currentState", currentState, "value", stats.getMin()));
        Assert.assertTrue("Score: " + score + " is not an outlier despite being a minimum.", score > 3.5);
    }
    {
        Double score = (Double) run("OUTLIER_MAD_SCORE(currentState, value)",
                ImmutableMap.of("currentState", currentState, "value", stats.getMax()));
        Assert.assertTrue("Score: " + score + " is not an outlier despite being a maximum", score > 3.5);
    }
    {
        Double score = (Double) run("OUTLIER_MAD_SCORE(currentState, value)", ImmutableMap.of("currentState",
                currentState, "value", stats.getMean() + 4 * stats.getStandardDeviation()));
        Assert.assertTrue(
                "Score: " + score + " is not an outlier despite being 4 std deviations away from the mean",
                score > 3.5);
    }
    {
        Double score = (Double) run("OUTLIER_MAD_SCORE(currentState, value)", ImmutableMap.of("currentState",
                currentState, "value", stats.getMean() - 4 * stats.getStandardDeviation()));
        Assert.assertTrue(
                "Score: " + score + " is not an outlier despite being 4 std deviations away from the mean",
                score > 3.5);
    }
    {
        Double score = (Double) run("OUTLIER_MAD_SCORE(currentState, value)",
                ImmutableMap.of("currentState", currentState, "value", stats.getMean()));
        Assert.assertFalse("Score: " + score + " is an outlier despite being the mean", score > 3.5);
    }
}

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From source file:org.apache.metron.statistics.sampling.UniformSamplerTest.java

@BeforeClass
public static void beforeClass() {
    Random rng = new Random(0);
    GaussianRandomGenerator gen = new GaussianRandomGenerator(new MersenneTwister(0));
    for (int i = 0; i < SAMPLE_SIZE; ++i) {
        double us = 10 * rng.nextDouble();
        uniformSample.add(us);//from www.j  a v a  2 s  .  c o m
        uniformStats.addValue(us);
        double gs = 10 * gen.nextNormalizedDouble();
        gaussianSample.add(gs);
        gaussianStats.addValue(gs);
    }
}

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From source file:org.apache.metron.statistics.StatisticalBinningPerformanceDriver.java

public static void main(String... argv) {
    DescriptiveStatistics perfStats = new DescriptiveStatistics();
    OnlineStatisticsProvider statsProvider = new OnlineStatisticsProvider();
    List<Double> values = new ArrayList<>();
    GaussianRandomGenerator gaussian = new GaussianRandomGenerator(new MersenneTwister(0L));
    for (int i = 0; i < NUM_DATA_POINTS; ++i) {
        //get the data point out of the [0,1] range
        double d = 1000 * gaussian.nextNormalizedDouble();
        values.add(d);//  w  ww.  ja  va2 s.c  o m
        statsProvider.addValue(d);
    }

    for (int perfRun = 0; perfRun < NUM_RUNS; ++perfRun) {
        StellarStatisticsFunctions.StatsBin bin = new StellarStatisticsFunctions.StatsBin();
        long start = System.currentTimeMillis();
        Random r = new Random(0);
        for (int i = 0; i < TRIALS_PER_RUN; ++i) {
            //grab a random value and fuzz it a bit so we make sure there's no cheating via caching in t-digest.
            bin.apply(ImmutableList.of(statsProvider, values.get(r.nextInt(values.size())) - 3.5, PERCENTILES));
        }
        perfStats.addValue(System.currentTimeMillis() - start);
    }
    System.out.println("Min/25th/50th/75th/Max Milliseconds: " + perfStats.getMin() + " / "
            + perfStats.getPercentile(25) + " / " + perfStats.getPercentile(50) + " / "
            + perfStats.getPercentile(75) + " / " + perfStats.getMax());
}

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From source file:org.apache.metron.statistics.StellarStatisticsFunctionsTest.java

@Test
public void testMergeProviders() throws Exception {
    List<StatisticsProvider> providers = new ArrayList<>();
    /*/*from  w w  w. jav a  2 s .c om*/
    Create 10 providers, each with a sample drawn from a gaussian distribution.
    Update the reference stats from commons math to ensure we are
     */
    GaussianRandomGenerator gaussian = new GaussianRandomGenerator(new MersenneTwister(1L));
    SummaryStatistics sStatistics = new SummaryStatistics();
    DescriptiveStatistics dStatistics = new DescriptiveStatistics();
    for (int i = 0; i < 10; ++i) {
        List<Double> sample = new ArrayList<>();
        for (int j = 0; j < 100; ++j) {
            double s = gaussian.nextNormalizedDouble();
            sample.add(s);
            sStatistics.addValue(s);
            dStatistics.addValue(s);
        }
        StatisticsProvider provider = (StatisticsProvider) run(
                "STATS_ADD(STATS_INIT(), " + Joiner.on(",").join(sample) + ")", new HashMap<>());
        providers.add(provider);
    }

    /*
    Merge the providers and validate
     */
    Map<String, Object> providerVariables = new HashMap<>();
    for (int i = 0; i < providers.size(); ++i) {
        providerVariables.put("provider_" + i, providers.get(i));
    }
    StatisticsProvider mergedProvider = (StatisticsProvider) run(
            "STATS_MERGE([" + Joiner.on(",").join(providerVariables.keySet()) + "])", providerVariables);
    OnlineStatisticsProviderTest.validateStatisticsProvider(mergedProvider, sStatistics, dStatistics);

}

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