Examples of ZipfDistribution

Examples of com.facebook.LinkBench.distributions.ZipfDistribution

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    return 5;
  }

  @Override
  public ProbabilityDistribution getDist() {
    return new ZipfDistribution();
  }
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Examples of com.facebook.LinkBench.distributions.ZipfDistribution

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    testSanityAccessDist(shuffled, min, max);
  }

  @Test
  public void testZipf() {
    ZipfDistribution z = new ZipfDistribution();
    Properties props = new Properties();
    props.setProperty("shape", "0.5");
    int min = 100, max = 200;
    z.init(min, max, props, "");
    ProbAccessDistribution unshuffled = new ProbAccessDistribution(z, null);
    testSanityAccessDist(unshuffled, min, max);

    ProbAccessDistribution shuffled = new ProbAccessDistribution(z,
                        new InvertibleShuffler(13, 25, max - min));
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Examples of org.apache.commons.math3.distribution.ZipfDistribution

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     * @return random value sampled from the Zipf(numberOfElements, exponent) distribution
     * @exception NotStrictlyPositiveException if {@code numberOfElements <= 0}
     * or {@code exponent <= 0}.
     */
    public int nextZipf(int numberOfElements, double exponent) throws NotStrictlyPositiveException {
        return new ZipfDistribution(getRandomGenerator(), numberOfElements, exponent).sample();
    }
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Examples of org.apache.commons.math3.distribution.ZipfDistribution

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        ZipfDistributionTest testInstance = new ZipfDistributionTest();
        int[] densityPoints = testInstance.makeDensityTestPoints();
        double[] densityValues = testInstance.makeDensityTestValues();
        int sampleSize = 1000;
        int length = TestUtils.eliminateZeroMassPoints(densityPoints, densityValues);
        ZipfDistribution distribution = (ZipfDistribution) testInstance.makeDistribution();
        double[] expectedCounts = new double[length];
        long[] observedCounts = new long[length];
        for (int i = 0; i < length; i++) {
            expectedCounts[i] = sampleSize * densityValues[i];
        }
        randomData.reSeed(1000);
        for (int i = 0; i < sampleSize; i++) {
          int value = randomData.nextZipf(distribution.getNumberOfElements(), distribution.getExponent());
          for (int j = 0; j < length; j++) {
              if (value == densityPoints[j]) {
                  observedCounts[j]++;
              }
          }
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Examples of org.apache.commons.math3.distribution.ZipfDistribution

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     * @param exponent the exponent of the ZipfDistribution
     * @return random value sampled from the Zipf(numberOfElements, exponent) distribution
     * @since 2.2
     */
    public int nextZipf(int numberOfElements, double exponent) {
        return nextInversionDeviate(new ZipfDistribution(numberOfElements, exponent));
    }
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Examples of org.apache.commons.math3.distribution.ZipfDistribution

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     * @return random value sampled from the Zipf(numberOfElements, exponent) distribution
     * @exception NotStrictlyPositiveException if {@code numberOfElements <= 0}
     * or {@code exponent <= 0}.
     */
    public int nextZipf(int numberOfElements, double exponent) throws NotStrictlyPositiveException {
        return new ZipfDistribution(getRandomGenerator(), numberOfElements, exponent).sample();
    }
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Examples of org.apache.commons.math3.distribution.ZipfDistribution

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        ZipfDistributionTest testInstance = new ZipfDistributionTest();
        int[] densityPoints = testInstance.makeDensityTestPoints();
        double[] densityValues = testInstance.makeDensityTestValues();
        int sampleSize = 1000;
        int length = TestUtils.eliminateZeroMassPoints(densityPoints, densityValues);
        ZipfDistribution distribution = (ZipfDistribution) testInstance.makeDistribution();
        double[] expectedCounts = new double[length];
        long[] observedCounts = new long[length];
        for (int i = 0; i < length; i++) {
            expectedCounts[i] = sampleSize * densityValues[i];
        }
        randomData.reSeed(1000);
        for (int i = 0; i < sampleSize; i++) {
          int value = randomData.nextZipf(distribution.getNumberOfElements(), distribution.getExponent());
          for (int j = 0; j < length; j++) {
              if (value == densityPoints[j]) {
                  observedCounts[j]++;
              }
          }
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Examples of org.apache.commons.math3.distribution.ZipfDistribution

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        ZipfDistributionTest testInstance = new ZipfDistributionTest();
        int[] densityPoints = testInstance.makeDensityTestPoints();
        double[] densityValues = testInstance.makeDensityTestValues();
        int sampleSize = 1000;
        int length = TestUtils.eliminateZeroMassPoints(densityPoints, densityValues);
        ZipfDistribution distribution = (ZipfDistribution) testInstance.makeDistribution();
        double[] expectedCounts = new double[length];
        long[] observedCounts = new long[length];
        for (int i = 0; i < length; i++) {
            expectedCounts[i] = sampleSize * densityValues[i];
        }
        randomData.reSeed(1000);
        for (int i = 0; i < sampleSize; i++) {
          int value = randomData.nextZipf(distribution.getNumberOfElements(), distribution.getExponent());
          for (int j = 0; j < length; j++) {
              if (value == densityPoints[j]) {
                  observedCounts[j]++;
              }
          }
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Examples of org.apache.commons.math3.distribution.ZipfDistribution

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     * @return random value sampled from the Zipf(numberOfElements, exponent) distribution
     * @exception NotStrictlyPositiveException if {@code numberOfElements <= 0}
     * or {@code exponent <= 0}.
     */
    public int nextZipf(int numberOfElements, double exponent) throws NotStrictlyPositiveException {
        return new ZipfDistribution(getRandomGenerator(), numberOfElements, exponent).sample();
    }
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Examples of org.apache.commons.math3.distribution.ZipfDistribution

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     * @return random value sampled from the Zipf(numberOfElements, exponent) distribution
     * @exception NotStrictlyPositiveException if {@code numberOfElements <= 0}
     * or {@code exponent <= 0}.
     */
    public int nextZipf(int numberOfElements, double exponent) throws NotStrictlyPositiveException {
        return new ZipfDistribution(getRan(), numberOfElements, exponent).sample();
    }
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