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.
-
-
Field Summary
-
Fields inherited from class org.apache.commons.math3.distribution.AbstractIntegerDistribution
random, randomData
-
-
Constructor Summary
Constructors Constructor Description HypergeometricDistribution(int populationSize, int numberOfSuccesses, int sampleSize)
Construct a new hypergeometric distribution with the specified population size, number of successes in the population, and sample size.HypergeometricDistribution(RandomGenerator rng, int populationSize, int numberOfSuccesses, int sampleSize)
Creates a new hypergeometric distribution.
-
Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description protected double
calculateNumericalVariance()
Used bygetNumericalVariance()
.double
cumulativeProbability(int x)
For a random variableX
whose values are distributed according to this distribution, this method returnsP(X <= x)
.int
getNumberOfSuccesses()
Access the number of successes.double
getNumericalMean()
Use this method to get the numerical value of the mean of this distribution.double
getNumericalVariance()
Use this method to get the numerical value of the variance of this distribution.int
getPopulationSize()
Access the population size.int
getSampleSize()
Access the sample size.int
getSupportLowerBound()
Access the lower bound of the support.int
getSupportUpperBound()
Access the upper bound of the support.boolean
isSupportConnected()
Use this method to get information about whether the support is connected, i.e.double
logProbability(int x)
For a random variableX
whose values are distributed according to this distribution, this method returnslog(P(X = x))
, wherelog
is the natural logarithm.double
probability(int x)
For a random variableX
whose values are distributed according to this distribution, this method returnsP(X = x)
.double
upperCumulativeProbability(int x)
For this distribution,X
, this method returnsP(X >= x)
.-
Methods inherited from class org.apache.commons.math3.distribution.AbstractIntegerDistribution
cumulativeProbability, inverseCumulativeProbability, reseedRandomGenerator, sample, sample, solveInverseCumulativeProbability
-
-
-
-
Constructor Detail
-
HypergeometricDistribution
public HypergeometricDistribution(int populationSize, int numberOfSuccesses, int sampleSize) throws NotPositiveException, NotStrictlyPositiveException, NumberIsTooLargeException
Construct a new hypergeometric distribution with the specified population size, number of successes in the population, and sample size.Note: this constructor will implicitly create an instance of
Well19937c
as random generator to be used for sampling only (seeAbstractIntegerDistribution.sample()
andAbstractIntegerDistribution.sample(int)
). In case no sampling is needed for the created distribution, it is advised to passnull
as random generator via the appropriate constructors to avoid the additional initialisation overhead.- Parameters:
populationSize
- Population size.numberOfSuccesses
- Number of successes in the population.sampleSize
- Sample size.- Throws:
NotPositiveException
- ifnumberOfSuccesses < 0
.NotStrictlyPositiveException
- ifpopulationSize <= 0
.NumberIsTooLargeException
- ifnumberOfSuccesses > populationSize
, orsampleSize > populationSize
.
-
HypergeometricDistribution
public HypergeometricDistribution(RandomGenerator rng, int populationSize, int numberOfSuccesses, int sampleSize) throws NotPositiveException, NotStrictlyPositiveException, NumberIsTooLargeException
Creates a new hypergeometric distribution.- Parameters:
rng
- Random number generator.populationSize
- Population size.numberOfSuccesses
- Number of successes in the population.sampleSize
- Sample size.- Throws:
NotPositiveException
- ifnumberOfSuccesses < 0
.NotStrictlyPositiveException
- ifpopulationSize <= 0
.NumberIsTooLargeException
- ifnumberOfSuccesses > populationSize
, orsampleSize > populationSize
.- Since:
- 3.1
-
-
Method Detail
-
cumulativeProbability
public double cumulativeProbability(int x)
For a random variableX
whose values are distributed according to this distribution, this method returnsP(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
-
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 variableX
whose values are distributed according to this distribution, this method returnsP(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 variableX
whose values are distributed according to this distribution, this method returnslog(P(X = x))
, wherelog
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 ofIntegerDistribution.probability(int)
.The default implementation simply computes the logarithm of
probability(x)
.- Overrides:
logProbability
in classAbstractIntegerDistribution
- 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 returnsP(X >= x)
.- Parameters:
x
- Value at which the CDF is evaluated.- Returns:
- the upper tail CDF for this distribution.
- Since:
- 1.1
-
getNumericalMean
public double getNumericalMean()
Use this method to get the numerical value of the mean of this distribution. For population sizeN
, number of successesm
, and sample sizen
, the mean isn * 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 sizeN
, number of successesm
, and sample sizen
, the variance is[n * m * (N - n) * (N - m)] / [N^2 * (N - 1)]
.- Returns:
- the variance (possibly
Double.POSITIVE_INFINITY
orDouble.NaN
if it is not defined)
-
calculateNumericalVariance
protected double calculateNumericalVariance()
Used bygetNumericalVariance()
.- Returns:
- the variance of this distribution
-
getSupportLowerBound
public int getSupportLowerBound()
Access the lower bound of the support. This method must return the same value asinverseCumulativeProbability(0)
. In other words, this method must return
For population sizeinf {x in Z | P(X <= x) > 0}
.N
, number of successesm
, and sample sizen
, the lower bound of the support ismax(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 asinverseCumulativeProbability(1)
. In other words, this method must return
For number of successesinf {x in R | P(X <= x) = 1}
.m
and sample sizen
, the upper bound of the support ismin(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
-
-