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

GaussianSampler

class GaussianSampler(val mean: Double, val standardDeviation: Double, normalized: NormalizedGaussianSampler = BoxMullerSampler) : BlockingDoubleSampler(source)

Sampling from a Gaussian distribution with given mean and standard deviation.

Based on Commons RNG implementation. See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/GaussianSampler.html.

Constructors

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constructor(mean: Double, standardDeviation: Double, normalized: NormalizedGaussianSampler = BoxMullerSampler)

Types

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object Companion

Functions

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

Samples one value from this Sampler.

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

Samples a Buffer of values from this Sampler.

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open override fun sample(generator: RandomGenerator): BlockingDoubleChain

Generates a chain of samples.

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@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

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open override fun toString(): String

Properties

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val mean: Double

the mean of the distribution.

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val standardDeviation: Double

the variance of the distribution.