kmath-stat/space.kscience.kmath.samplers/SmallMeanPoissonSampler

SmallMeanPoissonSampler

class SmallMeanPoissonSampler(val mean: Double) : Sampler<Int> (source)

Sampler for the Poisson distribution.

For small means, a Poisson process is simulated using uniform deviates, as described in Knuth (1969). Seminumerical Algorithms. The Art of Computer Programming, Volume 2. Chapter 3.4.1.F.3 Important integer-valued distributions: The Poisson distribution. Addison Wesley.
The Poisson process (and hence, the returned value) is bounded by 1000 * mean. This sampler is suitable for mean < 40. For large means, LargeMeanPoissonSampler should be used instead.

Based on Commons RNG implementation.

See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/SmallMeanPoissonSampler.html.

Constructors

SmallMeanPoissonSampler

constructor(mean: Double)

Functions

suspend fun <T : Any> Sampler<T>.next(generator: RandomGenerator): T

Samples one value from this Sampler.

suspend fun Sampler<Double>.nextBuffer(generator: RandomGenerator, size: Int): Buffer<Double>

Samples a Buffer of values from this Sampler.

open override fun sample(generator: RandomGenerator): BlockingIntChain

Generates a chain of samples.

@JvmName(name = "sampleRealBuffer")

fun Sampler<Double>.sampleBuffer(generator: RandomGenerator, size: Int): Chain<Buffer<Double>>

Generates size real samples and chunks them into some buffers.

@JvmName(name = "sampleIntBuffer")

fun Sampler<Int>.sampleBuffer(generator: RandomGenerator, size: Int): Chain<Buffer<Int>>

Generates size integer samples and chunks them into some buffers.

fun <T : Any> Sampler<T>.sampleBuffer(generator: RandomGenerator, size: Int, bufferFactory: BufferFactory<T> = BufferFactory.boxing()): Chain<Buffer<T>>

Sample a bunch of values

open override fun toString(): String

Properties

val mean: Double