org.apache.commons.statistics.distribution

## Class HypergeometricDistribution

• java.lang.Object
• org.apache.commons.statistics.distribution.HypergeometricDistribution

• ### Nested classes/interfaces inherited from interface org.apache.commons.statistics.distribution.DiscreteDistribution

DiscreteDistribution.Sampler
• ### Constructor Summary

Constructors
Constructor and Description
HypergeometricDistribution(int populationSize, int numberOfSuccesses, int sampleSize)
Creates a new hypergeometric distribution.
• ### Method Summary

All Methods
Modifier and Type Method and Description
DiscreteDistribution.Sampler createSampler(org.apache.commons.rng.UniformRandomProvider rng)
Creates a sampler.
double cumulativeProbability(int x)
For a random variable X whose values are distributed according to this distribution, this method returns P(X <= x).
double getMean()
Gets the mean of this distribution.
int getNumberOfSuccesses()
Access the number of successes.
int getPopulationSize()
Access the population size.
int getSampleSize()
Access the sample size.
int getSupportLowerBound()
Gets the lower bound of the support.
int getSupportUpperBound()
Gets the upper bound of the support.
double getVariance()
Gets the variance of this distribution.
int inverseCumulativeProbability(double p)
Computes the quantile function of this distribution.
boolean isSupportConnected()
Indicates whether the support is connected, i.e.
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.
double probability(int x)
For a random variable X whose values are distributed according to this distribution, this method returns P(X = x).
double probability(int x0, int x1)
For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1).
static int[] sample(int n, DiscreteDistribution.Sampler sampler)
Utility function for allocating an array and filling it with n samples generated by the given sampler.
double upperCumulativeProbability(int x)
For this distribution, X, this method returns P(X >= x).
• ### Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
• ### Constructor Detail

• #### HypergeometricDistribution

public HypergeometricDistribution(int populationSize,
int numberOfSuccesses,
int sampleSize)
Creates a new hypergeometric distribution.
Parameters:
populationSize - Population size.
numberOfSuccesses - Number of successes in the population.
sampleSize - Sample size.
Throws:
IllegalArgumentException - if numberOfSuccesses < 0, or populationSize <= 0 or numberOfSuccesses > populationSize, or sampleSize > populationSize.
• ### Method Detail

• #### 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 - 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.
• #### 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.
• #### 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 - 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.
Parameters:
x - 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.
• #### getMean

public double getMean()
Gets 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.
• #### getVariance

public double getVariance()
Gets 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, or Double.NaN if it is not defined.
• #### getSupportLowerBound

public int getSupportLowerBound()
Gets the lower bound of the support. This method must return the same value as inverseCumulativeProbability(0), i.e. inf {x in Z | P(X <= x) > 0}. By convention, Integer.MIN_VALUE should be substituted for negative infinity. 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()
Gets the upper bound of the support. This method must return the same value as inverseCumulativeProbability(1), i.e. inf {x in R | P(X <= x) = 1}. By convention, Integer.MAX_VALUE should be substituted for positive infinity. 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()
Indicates 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
• #### probability

public double probability(int x0,
int x1)
For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1). The default implementation uses the identity P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)
Specified by:
probability in interface DiscreteDistribution
Parameters:
x0 - Lower bound (exclusive).
x1 - Upper bound (inclusive).
Returns:
the probability that a random variable with this distribution will take a value between x0 and x1, excluding the lower and including the upper endpoint.
• #### inverseCumulativeProbability

public int inverseCumulativeProbability(double p)
Computes the quantile function of this distribution. For a random variable X distributed according to this distribution, the returned value is
• inf{x in Z | P(X<=x) >= p} for 0 < p <= 1,
• inf{x in Z | P(X<=x) > 0} for p = 0.
If the result exceeds the range of the data type int, then Integer.MIN_VALUE or Integer.MAX_VALUE is returned. The default implementation returns
Specified by:
inverseCumulativeProbability in interface DiscreteDistribution
Parameters:
p - Cumulative probability.
Returns:
the smallest p-quantile of this distribution (largest 0-quantile for p = 0).
• #### sample

public static int[] sample(int n,
DiscreteDistribution.Sampler sampler)
Utility function for allocating an array and filling it with n samples generated by the given sampler.
Parameters:
n - Number of samples.
sampler - Sampler.
Returns:
an array of size n.
• #### createSampler

public DiscreteDistribution.Sampler createSampler(org.apache.commons.rng.UniformRandomProvider rng)
Creates a sampler.
Specified by:
createSampler in interface DiscreteDistribution
Parameters:
rng - Generator of uniformly distributed numbers.
Returns:
a sampler that produces random numbers according this distribution.