Example usage for org.apache.commons.math3.distribution IntegerDistribution getNumericalMean

Introduction

In this page you can find the example usage for org.apache.commons.math3.distribution IntegerDistribution getNumericalMean.

Prototype

double getNumericalMean();

Source Link

Document

Use this method to get the numerical value of the mean of this distribution.

Usage

From source file:com.cloudera.oryx.common.OryxTest.java

/**
 * Asserts that the probability of sampling a value as or more extreme than the given value,
 * from the given discrete distribution, is at least 0.001.
 *
 * @param value sample value//from   w w  w .j a v  a  2s  .  c om
 * @param dist discrete distribution
 */
public static void checkDiscreteProbability(int value, IntegerDistribution dist) {
    double probAsExtreme = value <= dist.getNumericalMean() ? dist.cumulativeProbability(value)
            : (1.0 - dist.cumulativeProbability(value - 1));
    assertTrue(value + " is not likely (" + probAsExtreme + " ) to differ from expected value "
            + dist.getNumericalMean() + " by chance", probAsExtreme >= 0.001);
}

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From source file:com.wormsim.utils.Utils.java

/**
 * Returns a string representation of the provided distribution. TODO: Make
 * this complete TODO: Make this compatible with custom distributions (or just
 * more complex ones).// www .j a v  a2 s.  c om
 *
 * @param dist The distribution to translate
 *
 * @return The distribution as a string.
 */
public static String integerDistributionToString(IntegerDistribution dist) {
    if (dist instanceof EnumeratedIntegerDistribution) {
        return Double.toString(dist.getNumericalMean());
    } else if (dist instanceof UniformIntegerDistribution) {
        return "Uniform(" + dist.getSupportLowerBound() + "," + dist.getSupportUpperBound() + ")";
    } else if (dist instanceof BinomialDistribution) {
        BinomialDistribution dist2 = (BinomialDistribution) dist;
        return "Binomial(" + dist2.getNumberOfTrials() + "," + dist2.getProbabilityOfSuccess() + ")";
    } else {
        return dist.toString();
    }
}

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From source file:com.cloudera.oryx.ml.SimpleMLUpdateIT.java

@Test
public void testMLUpdate() throws Exception {
    Path tempDir = getTempDir();// w ww .  ja  va2 s  .  c  o  m
    Path dataDir = tempDir.resolve("data");
    Path modelDir = tempDir.resolve("model");
    Map<String, String> overlayConfig = new HashMap<>();
    overlayConfig.put("oryx.batch.update-class", MockMLUpdate.class.getName());
    ConfigUtils.set(overlayConfig, "oryx.batch.storage.data-dir", dataDir);
    ConfigUtils.set(overlayConfig, "oryx.batch.storage.model-dir", modelDir);
    overlayConfig.put("oryx.batch.generation-interval-sec", Integer.toString(GEN_INTERVAL_SEC));
    overlayConfig.put("oryx.batch.block-interval-sec", Integer.toString(BLOCK_INTERVAL_SEC));
    overlayConfig.put("oryx.ml.eval.test-fraction", Double.toString(TEST_FRACTION));
    Config config = ConfigUtils.overlayOn(overlayConfig, getConfig());

    startMessaging();

    List<Integer> trainCounts = new ArrayList<>();
    List<Integer> testCounts = new ArrayList<>();

    MockMLUpdate.setCountHolders(trainCounts, testCounts);

    startServerProduceConsumeTopics(config, DATA_TO_WRITE, WRITE_INTERVAL_MSEC);

    // If lists are unequal at this point, there must have been an empty test set
    // which yielded no call to evaluate(). Fill in the blank
    while (trainCounts.size() > testCounts.size()) {
        testCounts.add(0);
    }

    log.info("trainCounts = {}", trainCounts);
    log.info("testCounts = {}", testCounts);

    checkOutputData(dataDir, DATA_TO_WRITE);
    checkIntervals(trainCounts.size(), DATA_TO_WRITE, WRITE_INTERVAL_MSEC, GEN_INTERVAL_SEC);

    assertEquals(testCounts.size(), trainCounts.size());

    RandomGenerator random = RandomManager.getRandom();
    int lastTotalTrainCount = 0;
    int lastTestCount = 0;
    for (int i = 0; i < testCounts.size(); i++) {
        int totalTrainCount = trainCounts.get(i);
        int testCount = testCounts.get(i);
        int newTrainInGen = totalTrainCount - (lastTotalTrainCount + lastTestCount);
        if (newTrainInGen == 0) {
            continue;
        }
        lastTotalTrainCount = totalTrainCount;
        lastTestCount = testCount;
        int totalNew = testCount + newTrainInGen;

        IntegerDistribution dist = new BinomialDistribution(random, totalNew, TEST_FRACTION);
        double probability;
        if (testCount < dist.getNumericalMean()) {
            probability = dist.cumulativeProbability(testCount);
        } else {
            probability = 1.0 - dist.cumulativeProbability(testCount);
        }
        log.info("Probability of observing {} as {} sample of {}: {}", testCount, TEST_FRACTION, totalNew,
                probability);
        assertTrue(probability >= 0.001);
    }

}

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From source file:fr.obeo.emf.specimen.SpecimenGenerator.java

private boolean booleanInDistribution(IntegerDistribution distribution) {
    int sample = distribution.sample();
    return sample < distribution.getNumericalMean();
}

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From source file:fr.obeo.emf.specimen.DirectWriteSpecimenGenerator.java

protected boolean booleanInDistribution(IntegerDistribution distribution) {
    int sample = distribution.sample();
    return sample <= distribution.getNumericalMean();
}

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From source file:eu.opensourceprojects.mondo.benchmarks.transformationzoo.instantiator.SpecimenGenerator.java

private boolean booleanInDistribution(IntegerDistribution distribution) {
    int sample = distribution.sample();
    //System.out.println(sample < distribution.getNumericalMean());
    return sample < distribution.getNumericalMean();
}

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