Examples of HypergeometricDistribution

Examples of org.apache.commons.math3.distribution.HypergeometricDistribution

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     * or {@code sampleSize > populationSize}.
     * @throws NotStrictlyPositiveException if {@code populationSize <= 0}.
     * @throws NotPositiveException  if {@code numberOfSuccesses < 0}.
     */
    public int nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize) throws NotPositiveException, NotStrictlyPositiveException, NumberIsTooLargeException {
        return new HypergeometricDistribution(getRandomGenerator(),populationSize,
                numberOfSuccesses, sampleSize).sample();
    }
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Examples of org.apache.commons.math3.distribution.HypergeometricDistribution

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        HypergeometricDistributionTest testInstance = new HypergeometricDistributionTest();
        int[] densityPoints = testInstance.makeDensityTestPoints();
        double[] densityValues = testInstance.makeDensityTestValues();
        int sampleSize = 1000;
        int length = TestUtils.eliminateZeroMassPoints(densityPoints, densityValues);
        HypergeometricDistribution distribution = (HypergeometricDistribution) 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.nextHypergeometric(distribution.getPopulationSize(),
                  distribution.getNumberOfSuccesses(), distribution.getSampleSize());
          for (int j = 0; j < length; j++) {
              if (value == densityPoints[j]) {
                  observedCounts[j]++;
              }
          }
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Examples of org.apache.commons.math3.distribution.HypergeometricDistribution

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     * @param sampleSize the sample size of the Hypergeometric distribution
     * @return random value sampled from the Hypergeometric(numberOfSuccesses, sampleSize) distribution
     * @since 2.2
     */
    public int nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize) {
        return nextInversionDeviate(new HypergeometricDistribution(populationSize, numberOfSuccesses, sampleSize));
    }
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Examples of org.apache.commons.math3.distribution.HypergeometricDistribution

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     * or {@code sampleSize > populationSize}.
     * @throws NotStrictlyPositiveException if {@code populationSize <= 0}.
     * @throws NotPositiveException  if {@code numberOfSuccesses < 0}.
     */
    public int nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize) throws NotPositiveException, NotStrictlyPositiveException, NumberIsTooLargeException {
        return new HypergeometricDistribution(getRandomGenerator(),populationSize,
                numberOfSuccesses, sampleSize).sample();
    }
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Examples of org.apache.commons.math3.distribution.HypergeometricDistribution

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        HypergeometricDistributionTest testInstance = new HypergeometricDistributionTest();
        int[] densityPoints = testInstance.makeDensityTestPoints();
        double[] densityValues = testInstance.makeDensityTestValues();
        int sampleSize = 1000;
        int length = TestUtils.eliminateZeroMassPoints(densityPoints, densityValues);
        HypergeometricDistribution distribution = (HypergeometricDistribution) 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.nextHypergeometric(distribution.getPopulationSize(),
                  distribution.getNumberOfSuccesses(), distribution.getSampleSize());
          for (int j = 0; j < length; j++) {
              if (value == densityPoints[j]) {
                  observedCounts[j]++;
              }
          }
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Examples of org.apache.commons.math3.distribution.HypergeometricDistribution

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        HypergeometricDistributionTest testInstance = new HypergeometricDistributionTest();
        int[] densityPoints = testInstance.makeDensityTestPoints();
        double[] densityValues = testInstance.makeDensityTestValues();
        int sampleSize = 1000;
        int length = TestUtils.eliminateZeroMassPoints(densityPoints, densityValues);
        HypergeometricDistribution distribution = (HypergeometricDistribution) 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.nextHypergeometric(distribution.getPopulationSize(),
                  distribution.getNumberOfSuccesses(), distribution.getSampleSize());
          for (int j = 0; j < length; j++) {
              if (value == densityPoints[j]) {
                  observedCounts[j]++;
              }
          }
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Examples of org.apache.commons.math3.distribution.HypergeometricDistribution

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     * or {@code sampleSize > populationSize}.
     * @throws NotStrictlyPositiveException if {@code populationSize <= 0}.
     * @throws NotPositiveException  if {@code numberOfSuccesses < 0}.
     */
    public int nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize) throws NotPositiveException, NotStrictlyPositiveException, NumberIsTooLargeException {
        return new HypergeometricDistribution(getRandomGenerator(),populationSize,
                numberOfSuccesses, sampleSize).sample();
    }
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Examples of org.apache.commons.math3.distribution.HypergeometricDistribution

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     * or {@code sampleSize > populationSize}.
     * @throws NotStrictlyPositiveException if {@code populationSize <= 0}.
     * @throws NotPositiveException  if {@code numberOfSuccesses < 0}.
     */
    public int nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize) throws NotPositiveException, NotStrictlyPositiveException, NumberIsTooLargeException {
        return new HypergeometricDistribution(getRan(),populationSize,
                numberOfSuccesses, sampleSize).sample();
    }
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Examples of org.apache.commons.math3.distribution.HypergeometricDistribution

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        HypergeometricDistributionTest testInstance = new HypergeometricDistributionTest();
        int[] densityPoints = testInstance.makeDensityTestPoints();
        double[] densityValues = testInstance.makeDensityTestValues();
        int sampleSize = 1000;
        int length = TestUtils.eliminateZeroMassPoints(densityPoints, densityValues);
        HypergeometricDistribution distribution = (HypergeometricDistribution) 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.nextHypergeometric(distribution.getPopulationSize(),
                  distribution.getNumberOfSuccesses(), distribution.getSampleSize());
          for (int j = 0; j < length; j++) {
              if (value == densityPoints[j]) {
                  observedCounts[j]++;
              }
          }
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Examples of org.apache.commons.math3.distribution.HypergeometricDistribution

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        HypergeometricDistributionTest testInstance = new HypergeometricDistributionTest();
        int[] densityPoints = testInstance.makeDensityTestPoints();
        double[] densityValues = testInstance.makeDensityTestValues();
        int sampleSize = 1000;
        int length = TestUtils.eliminateZeroMassPoints(densityPoints, densityValues);
        HypergeometricDistribution distribution = (HypergeometricDistribution) 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.nextHypergeometric(distribution.getPopulationSize(),
                  distribution.getNumberOfSuccesses(), distribution.getSampleSize());
          for (int j = 0; j < length; j++) {
              if (value == densityPoints[j]) {
                  observedCounts[j]++;
              }
          }
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