HypergeometricDistribution.Sample Method

Definition

Namespace: Extreme.Statistics.Distributions
Assembly: Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.23

Overload List

Sample() Returns a random sample from the distribution.
Sample(Int32) Returns a vector of random samples from the distribution.
Sample(Random) Returns a random sample from the distribution.
Sample(Int32, Random) Returns a vector of random samples from the distribution.
Sample(Random, Int32[]) Fills an Int32 array with random numbers.
Sample(Random, Int32[], Int32, Int32) Fills an Int32 array with random numbers from this DiscreteDistribution.
Sample(Random, Int32, Int32, Int32) Returns a single random sample from a binomial distribution with the specified parameters.

Sample(Random)

Returns a random sample from the distribution.
C#
public override int Sample(
	Random random
)

Parameters

random  Random
The Random derived random number generator used to generate the sample.

Return Value

Int32
A signed 32-bit integer.

Remarks

This method uses the random number generator specified by random to generate a random sample from the distribution. The return values of successive calls to this method follow the statistical distribution represented by this distribution.

Exceptions

ArgumentNullExceptionrandom is null.

Sample(Random, Int32, Int32, Int32)

Returns a single random sample from a binomial distribution with the specified parameters.
C#
public static int Sample(
	Random random,
	int taggedPopulation,
	int untaggedPopulation,
	int samples
)

Parameters

random  Random
The Random derived random number generator used to generate the sample.
taggedPopulation  Int32
The number of tagged members of the population.
untaggedPopulation  Int32
The number of untagged members of the population.
samples  Int32
The number of samples.

Return Value

Int32
A 32-bit signed integer sample from the binomial distribution with the specified parameters.

Remarks

This method is useful when only a single random sample is required, or if the parameters of the distribution change often. To obtain a large number of samples from a distribution with identical parameters, create an instance of the class and call the Sample() method repeatedly.

Exceptions

ArgumentNullExceptionrandom is null.

See Also