Package space.kscience.kmath.stat

Types

BasicSampler

common

class BasicSampler<T : Any>(chainBuilder: (RandomGenerator) -> Chain<T>) : Sampler<T>

Implements Sampler by delegating sampling to value of chainBuilder.

BlockingStatistic

common

interface BlockingStatistic<T, R> : Statistic<T, R>

ComposableStatistic

common

interface ComposableStatistic<T, I, R> : Statistic<T, R>

A statistic tha could be computed separately on different blocks of data and then composed

ConstantSampler

common

class ConstantSampler<T : Any>(value: T) : Sampler<T>

Implements Sampler by sampling only certain value.

DefaultGenerator

common

class DefaultGenerator(random: Random) : RandomGenerator

Implements RandomGenerator by delegating all operations to Random.

MCScope

common

class MCScope(coroutineContext: CoroutineContext, random: RandomGenerator)

A scope for a Monte-Carlo computations or multi-coroutine random number generation.

Mean

common

class Mean<T>(group: Ring<T>, division: (T, count: Int) -> T) : ComposableStatistic<T, Pair<T, Int>, T> , BlockingStatistic<T, T>

Arithmetic mean

Median

common

class Median<T>(comparator: Comparator<T>) : BlockingStatistic<T, T>

Non-composable median

RandomChain

common

class RandomChain<out R>(generator: RandomGenerator, gen: suspend RandomGenerator.() -> R) : Chain<R>

A possibly stateful chain producing random values.

RandomGenerator

common

interface RandomGenerator

An interface that is implemented by random number generator algorithms.

RandomGeneratorProvider

jvm

class RandomGeneratorProvider(generator: RandomGenerator) : UniformRandomProvider

Implements UniformRandomProvider by delegating all operations to RandomGenerator.

RandomSourceGenerator

jvm

class RandomSourceGenerator : RandomGenerator

Implements RandomGenerator by delegating all operations to RandomSource.

Sampler

common

fun fun interface Sampler<out T : Any>

Sampler that generates chains of values of type T in a chain of type C.

SamplerSpace

common

class SamplerSpace<T : Any, S : Group<T>, ScaleOperations<T>>(algebra: S) : Group<Sampler<T>> , ScaleOperations<Sampler<T>>

A space of samplers.

Statistic

common

interface Statistic<T, R>

A function, that transforms a buffer of random quantities to some resulting value

UniformDistribution

common

class UniformDistribution(range: ClosedFloatingPointRange<Double>) : UnivariateDistribution<Double>

UniformDoubleChain

common

class UniformDoubleChain(generator: RandomGenerator) : BlockingDoubleChain

A type-specific double chunk random chain

Functions

asUniformRandomProvider

jvm

fun RandomGenerator.asUniformRandomProvider(): UniformRandomProvider

Represent this RandomGenerator as commons-rng UniformRandomProvider preserving and mirroring its current state.

async

common

inline fun <T> MCScope.async(context: CoroutineContext = EmptyCoroutineContext, start: CoroutineStart = CoroutineStart.DEFAULT, crossinline block: suspend MCScope.() -> T): Deferred<T>

Specialized async for MCScope.

chain

common

fun <R> RandomGenerator.chain(generator: suspend RandomGenerator.() -> R): RandomChain<R>

Create a generic random chain with provided generator

flow

common

fun <T, I, R> ComposableStatistic<T, I, R>.flow(flow: Flow<Buffer<T>>, dispatcher: CoroutineDispatcher = Dispatchers.Default): Flow<R>

Perform a streaming statistical analysis on a chunked data.

fromSource

jvm

fun RandomGenerator.Companion.fromSource(source: RandomSource, seed: Long? = null): RandomSourceGenerator

Returns RandomSourceGenerator with given RandomSource and seed.

launch

common

inline fun MCScope.launch(context: CoroutineContext = EmptyCoroutineContext, start: CoroutineStart = CoroutineStart.DEFAULT, crossinline block: suspend MCScope.() -> Unit): Job

Specialized launch for MCScope.

mcScope

common

inline suspend fun <T> mcScope(seed: Long, block: MCScope.() -> T): T

Launch mc scope with a given seed

inline suspend fun <T> mcScope(generator: RandomGenerator, block: MCScope.() -> T): T

Launches a supervised Monte-Carlo scope

mersenneTwister

jvm

fun RandomGenerator.Companion.mersenneTwister(seed: Long? = null): RandomSourceGenerator

Returns RandomSourceGenerator with RandomSource.MT algorithm and given seed.

common

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

Samples one value from this Sampler.

sampleBuffer

common

@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> = Buffer.Companion::boxing): Chain<Buffer<T>>

Sample a bunch of values

uniform

common

fun Distribution.Companion.uniform(range: ClosedFloatingPointRange<Double>): UniformDistribution