Class HypergeometricDistribution

java.lang.Object
org.apache.commons.math3.distribution.AbstractIntegerDistribution
org.apache.commons.math3.distribution.HypergeometricDistribution
All Implemented Interfaces:
Serializable, IntegerDistribution

public class HypergeometricDistribution extends AbstractIntegerDistribution
Implementation of the hypergeometric distribution.
See Also:
  • Field Details

    • serialVersionUID

      private static final long serialVersionUID
      Serializable version identifier.
      See Also:
    • numberOfSuccesses

      private final int numberOfSuccesses
      The number of successes in the population.
    • populationSize

      private final int populationSize
      The population size.
    • sampleSize

      private final int sampleSize
      The sample size.
    • numericalVariance

      private double numericalVariance
      Cached numerical variance
    • numericalVarianceIsCalculated

      private boolean numericalVarianceIsCalculated
      Whether or not the numerical variance has been calculated
  • Constructor Details

  • Method Details

    • cumulativeProbability

      public double cumulativeProbability(int x)
      For a random variable X whose values are distributed according to this distribution, this method returns P(X <= x). In other words, this method represents the (cumulative) distribution function (CDF) for this distribution.
      Parameters:
      x - the point at which the CDF is evaluated
      Returns:
      the probability that a random variable with this distribution takes a value less than or equal to x
    • getDomain

      private int[] getDomain(int n, int m, int k)
      Return the domain for the given hypergeometric distribution parameters.
      Parameters:
      n - Population size.
      m - Number of successes in the population.
      k - Sample size.
      Returns:
      a two element array containing the lower and upper bounds of the hypergeometric distribution.
    • getLowerDomain

      private int getLowerDomain(int n, int m, int k)
      Return the lowest domain value for the given hypergeometric distribution parameters.
      Parameters:
      n - Population size.
      m - Number of successes in the population.
      k - Sample size.
      Returns:
      the lowest domain value of the hypergeometric distribution.
    • getNumberOfSuccesses

      public int getNumberOfSuccesses()
      Access the number of successes.
      Returns:
      the number of successes.
    • getPopulationSize

      public int getPopulationSize()
      Access the population size.
      Returns:
      the population size.
    • getSampleSize

      public int getSampleSize()
      Access the sample size.
      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 - Sample size.
      Returns:
      the highest domain value of the hypergeometric distribution.
    • probability

      public double probability(int x)
      For a random variable X whose values are distributed according to this distribution, this method returns P(X = x). In other words, this method represents the probability mass function (PMF) for the distribution.
      Parameters:
      x - the point at which the PMF is evaluated
      Returns:
      the value of the probability mass function at x
    • logProbability

      public double logProbability(int x)
      For a random variable X whose values are distributed according to this distribution, this method returns log(P(X = x)), where log is the natural logarithm. In other words, this method represents the logarithm of the probability mass function (PMF) for the distribution. Note that due to the floating point precision and under/overflow issues, this method will for some distributions be more precise and faster than computing the logarithm of IntegerDistribution.probability(int).

      The default implementation simply computes the logarithm of probability(x).

      Overrides:
      logProbability in class AbstractIntegerDistribution
      Parameters:
      x - the point at which the PMF is evaluated
      Returns:
      the logarithm of the value of the probability mass function at x
    • upperCumulativeProbability

      public double upperCumulativeProbability(int x)
      For this distribution, X, this method returns P(X >= x).
      Parameters:
      x - Value at which the CDF is evaluated.
      Returns:
      the upper tail CDF for this distribution.
      Since:
      1.1
    • innerCumulativeProbability

      private double innerCumulativeProbability(int x0, int x1, int dx)
      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 - Inclusive lower bound.
      x1 - Inclusive upper bound.
      dx - Direction of summation (1 indicates summing from x0 to x1, and 0 indicates summing from x1 to x0).
      Returns:
      P(x0 <= X <= x1).
    • getNumericalMean

      public double getNumericalMean()
      Use this method to get the numerical value of the mean of this distribution. For population size N, number of successes m, and sample size n, the mean is n * m / N.
      Returns:
      the mean or Double.NaN if it is not defined
    • getNumericalVariance

      public double getNumericalVariance()
      Use this method to get the numerical value of the variance of this distribution. 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 (possibly Double.POSITIVE_INFINITY or Double.NaN if it is not defined)
    • calculateNumericalVariance

      protected double calculateNumericalVariance()
      Returns:
      the variance of this distribution
    • getSupportLowerBound

      public int getSupportLowerBound()
      Access the lower bound of the support. This method must return the same value as inverseCumulativeProbability(0). In other words, this method must return

      inf {x in Z | P(X invalid input: '<'= x) > 0}.

      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
    • getSupportUpperBound

      public int getSupportUpperBound()
      Access the upper bound of the support. This method must return the same value as inverseCumulativeProbability(1). In other words, this method must return

      inf {x in R | P(X invalid input: '<'= x) = 1}.

      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
    • isSupportConnected

      public boolean isSupportConnected()
      Use this method to get information about whether the support is connected, i.e. whether all integers between the lower and upper bound of the support are included in the support. The support of this distribution is connected.
      Returns:
      true