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## org.apache.commons.math.distribution
Class HypergeometricDistributionImpl

    java.lang.Object
      org.apache.commons.math.distribution.AbstractDistribution
          org.apache.commons.math.distribution.AbstractIntegerDistribution
              org.apache.commons.math.distribution.HypergeometricDistributionImpl

All Implemented Interfaces:
  Serializable, DiscreteDistribution, Distribution, HypergeometricDistribution, IntegerDistribution

    public class HypergeometricDistributionImpl

extends AbstractIntegerDistribution
implements HypergeometricDistribution, Serializable

The default implementation of `HypergeometricDistribution`.

Version:
  $Revision: 1054524 $ $Date: 2011-01-03 05:59:18 +0100 (lun. 03 janv. 2011) $
See Also:
  Serialized Form

Field Summary
`private int` | `numberOfSuccesses` The number of successes in the population.
`private int` | `populationSize` The population size.
`private int` | `sampleSize` The sample size.
`private static long` | `serialVersionUID` Serializable version identifier

Fields inherited from class org.apache.commons.math.distribution.AbstractIntegerDistribution
`randomData`

Constructor Summary
`HypergeometricDistributionImpl(int populationSize, int numberOfSuccesses, int sampleSize)` Construct a new hypergeometric distribution with the given the population size, the number of successes in the population, and the sample size.

Method Summary
`double` | `cumulativeProbability(int x)` For this distribution, X, this method returns P(X ≤ x).
`private int[]` | `getDomain(int n, int m, int k)` Return the domain for the given hypergeometric distribution parameters.
`protected int` | `getDomainLowerBound(double p)` Access the domain value lower bound, based on `p`, used to bracket a PDF root.
`protected int` | `getDomainUpperBound(double p)` Access the domain value upper bound, based on `p`, used to bracket a PDF root.
`private int` | `getLowerDomain(int n, int m, int k)` Return the lowest domain value for the given hypergeometric distribution parameters.
`int` | `getNumberOfSuccesses()` Access the number of successes.
`protected double` | `getNumericalMean()` Returns the mean.
`double` | `getNumericalVariance()` Returns the variance.
`int` | `getPopulationSize()` Access the population size.
`int` | `getSampleSize()` Access the sample size.
`int` | `getSupportLowerBound()` Returns the lower bound for the support for the distribution.
`int` | `getSupportUpperBound()` Returns the upper bound for the support of the distribution.
`private int` | `getUpperDomain(int m, int k)` Return the highest domain value for the given hypergeometric distribution parameters.
`private double` | `innerCumulativeProbability(int x0, int x1, int dx, int n, int m, int k)` For this distribution, X, this method returns P(x0 ≤ X ≤ x1).
`double` | `probability(int x)` For this distribution, X, this method returns P(X = x).
`private double` | `probability(int n, int m, int k, int x)` For the distribution, X, defined by the given hypergeometric distribution parameters, this method returns P(X = x).
`void` | `setNumberOfSuccesses(int num)` Deprecated. as of 2.1 (class will become immutable in 3.0)
`private void` | `setNumberOfSuccessesInternal(int num)` Modify the number of successes.
`void` | `setPopulationSize(int size)` Deprecated. as of 2.1 (class will become immutable in 3.0)
`private void` | `setPopulationSizeInternal(int size)` Modify the population size.
`void` | `setSampleSize(int size)` Deprecated. as of 2.1 (class will become immutable in 3.0)
`private void` | `setSampleSizeInternal(int size)` Modify the sample size.
`double` | `upperCumulativeProbability(int x)` For this distribution, X, this method returns P(X ≥ x).

Methods inherited from class org.apache.commons.math.distribution.AbstractIntegerDistribution
`cumulativeProbability, cumulativeProbability, cumulativeProbability, inverseCumulativeProbability, isSupportLowerBoundInclusive, isSupportUpperBoundInclusive, probability, reseedRandomGenerator, sample, sample`

Methods inherited from class java.lang.Object
`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Methods inherited from interface org.apache.commons.math.distribution.IntegerDistribution
`cumulativeProbability, inverseCumulativeProbability`

Methods inherited from interface org.apache.commons.math.distribution.DiscreteDistribution
`probability`

Methods inherited from interface org.apache.commons.math.distribution.Distribution
`cumulativeProbability, cumulativeProbability`

Field Detail

### serialVersionUID

    private static final long serialVersionUID

  Serializable version identifier

  See Also:
    Constant Field Values

### numberOfSuccesses

    private int numberOfSuccesses

  The number of successes in the population.

### populationSize

    private int populationSize

  The population size.

### sampleSize

    private int sampleSize

  The sample size.

Constructor Detail

### HypergeometricDistributionImpl

    public HypergeometricDistributionImpl(int populationSize,
                                          int numberOfSuccesses,
                                          int sampleSize)

  Construct a new hypergeometric distribution with the given the population size, the number of successes in the population, and the sample size.

  Parameters:
    `populationSize` - the population size.
    `numberOfSuccesses` - number of successes in the population.
    `sampleSize` - the sample size.

Method Detail

### cumulativeProbability

    public double cumulativeProbability(int x)

  For this distribution, X, this method returns P(X ≤ x).

  Specified by:
    `cumulativeProbability` in interface `IntegerDistribution`
  Specified by:
    `cumulativeProbability` in class `AbstractIntegerDistribution`
  Parameters:
    `x` - the value at which the PDF is evaluated.
  Returns:
    PDF for this distribution.

### getDomain

    private int[] getDomain(int n,
                            int m,
                            int k)

  Return the domain for the given hypergeometric distribution parameters.

  Parameters:
    `n` - the population size.
    `m` - number of successes in the population.
    `k` - the sample size.
  Returns:
    a two element array containing the lower and upper bounds of the hypergeometric distribution.

### getDomainLowerBound

    protected int getDomainLowerBound(double p)

  Access the domain value lower bound, based on `p`, used to bracket a PDF root.

  Specified by:
    `getDomainLowerBound` in class `AbstractIntegerDistribution`
  Parameters:
    `p` - the desired probability for the critical value
  Returns:
    domain value lower bound, i.e. P(X < lower bound) < `p`

### getDomainUpperBound

    protected int getDomainUpperBound(double p)

  Access the domain value upper bound, based on `p`, used to bracket a PDF root.

  Specified by:
    `getDomainUpperBound` in class `AbstractIntegerDistribution`
  Parameters:
    `p` - the desired probability for the critical value
  Returns:
    domain value upper bound, i.e. P(X < upper bound) > `p`

### getLowerDomain

    private int getLowerDomain(int n,
                               int m,
                               int k)

  Return the lowest domain value for the given hypergeometric distribution parameters.

  Parameters:
    `n` - the population size.
    `m` - number of successes in the population.
    `k` - the sample size.
  Returns:
    the lowest domain value of the hypergeometric distribution.

### getNumberOfSuccesses

    public int getNumberOfSuccesses()

  Access the number of successes.

  Specified by:
    `getNumberOfSuccesses` in interface `HypergeometricDistribution`
  Returns:
    the number of successes.

### getPopulationSize

    public int getPopulationSize()

  Access the population size.

  Specified by:
    `getPopulationSize` in interface `HypergeometricDistribution`
  Returns:
    the population size.

### getSampleSize

    public int getSampleSize()

  Access the sample size.

  Specified by:
    `getSampleSize` in interface `HypergeometricDistribution`
  Returns:
    the sample size.

### getUpperDomain

    private int getUpperDomain(int m,
                               int k)

  Return the highest domain value for the given hypergeometric distribution parameters.

  Parameters:
    `m` - number of successes in the population.
    `k` - the sample size.
  Returns:
    the highest domain value of the hypergeometric distribution.

### probability

    public double probability(int x)

  For this distribution, X, this method returns P(X = x).

  Specified by:
    `probability` in interface `IntegerDistribution`
  Parameters:
    `x` - the value at which the PMF is evaluated.
  Returns:
    PMF for this distribution.

### probability

    private double probability(int n,
                               int m,
                               int k,
                               int x)

  For the distribution, X, defined by the given hypergeometric distribution parameters, this method returns P(X = x).

  Parameters:
    `n` - the population size.
    `m` - number of successes in the population.
    `k` - the sample size.
    `x` - the value at which the PMF is evaluated.
  Returns:
    PMF for the distribution.

### setNumberOfSuccesses

    @Deprecated
    public void setNumberOfSuccesses(int num)

  Deprecated. as of 2.1 (class will become immutable in 3.0)

  Modify the number of successes.

  Specified by:
    `setNumberOfSuccesses` in interface `HypergeometricDistribution`
  Parameters:
    `num` - the new number of successes.
  Throws:
    `IllegalArgumentException` - if `num` is negative.

### setNumberOfSuccessesInternal

    private void setNumberOfSuccessesInternal(int num)

  Modify the number of successes.

  Parameters:
    `num` - the new number of successes.
  Throws:
    `IllegalArgumentException` - if `num` is negative.

### setPopulationSize

    @Deprecated
    public void setPopulationSize(int size)

  Deprecated. as of 2.1 (class will become immutable in 3.0)

  Modify the population size.

  Specified by:
    `setPopulationSize` in interface `HypergeometricDistribution`
  Parameters:
    `size` - the new population size.
  Throws:
    `IllegalArgumentException` - if `size` is not positive.

### setPopulationSizeInternal

    private void setPopulationSizeInternal(int size)

  Modify the population size.

  Parameters:
    `size` - the new population size.
  Throws:
    `IllegalArgumentException` - if `size` is not positive.

### setSampleSize

    @Deprecated
    public void setSampleSize(int size)

  Deprecated. as of 2.1 (class will become immutable in 3.0)

  Modify the sample size.

  Specified by:
    `setSampleSize` in interface `HypergeometricDistribution`
  Parameters:
    `size` - the new sample size.
  Throws:
    `IllegalArgumentException` - if `size` is negative.

### setSampleSizeInternal

    private void setSampleSizeInternal(int size)

  Modify the sample size.

  Parameters:
    `size` - the new sample size.
  Throws:
    `IllegalArgumentException` - if `size` is negative.

### upperCumulativeProbability

    public double upperCumulativeProbability(int x)

  For this distribution, X, this method returns P(X ≥ x).

  Parameters:
    `x` - the value at which the CDF is evaluated.
  Returns:
    upper tail CDF for this distribution.
  Since:
    1.1

### innerCumulativeProbability

    private double innerCumulativeProbability(int x0,
                                              int x1,
                                              int dx,
                                              int n,
                                              int m,
                                              int k)

  For this distribution, X, this method returns P(x0 ≤ X ≤ x1). This probability is computed by summing the point probabilities for the values x0, x0 + 1, x0 + 2, ..., x1, in the order directed by dx.

  Parameters:
    `x0` - the inclusive, lower bound
    `x1` - the inclusive, upper bound
    `dx` - the direction of summation. 1 indicates summing from x0 to x1. 0 indicates summing from x1 to x0.
    `n` - the population size.
    `m` - number of successes in the population.
    `k` - the sample size.
  Returns:
    P(x0 ≤ X ≤ x1).

### getSupportLowerBound

    public int getSupportLowerBound()

  Returns the lower bound for the support for the distribution. For population size `N`, number of successes `m`, and sample size `n`, the lower bound of the support is `max(0, n + m - N)`

  Returns:
    lower bound of the support
  Since:
    2.2

### getSupportUpperBound

    public int getSupportUpperBound()

  Returns the upper bound for the support of the distribution. For number of successes `m` and sample size `n`, the upper bound of the support is `min(m, n)`

  Returns:
    upper bound of the support
  Since:
    2.2

### getNumericalMean

    protected double getNumericalMean()

  Returns the mean. For population size `N`, number of successes `m`, and sample size `n`, the mean is `n * m / N`

  Returns:
    the mean
  Since:
    2.2

### getNumericalVariance

    public double getNumericalVariance()

  Returns the variance. For population size `N`, number of successes `m`, and sample size `n`, the variance is `[ n * m * (N - n) * (N - m) ] / [ N^2 * (N - 1) ]`

  Returns:
    the variance
  Since:
    2.2

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