kmath-stat/space.kscience.kmath.stat

Package-level declarations

Types

BlockingStatistic

fun interface BlockingStatistic<in T, out R> : Statistic<T, R>

A statistic that is computed in a synchronous blocking mode

ComposableStatistic

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

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

KMComparisonResult

@UnstableKMathAPI

data class KMComparisonResult<T : Comparable<T>>(val n: Int, val m: Int, val value: T)

Resulting value of kolmogorov-smirnov two-sample statistic

Mean

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

Arithmetic mean

Median

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

Non-composable median

RandomGeneratorProvider

class RandomGeneratorProvider(val generator: RandomGenerator) : UniformRandomProvider

Implements UniformRandomProvider by delegating all operations to RandomGenerator.

RandomSourceGenerator

class RandomSourceGenerator : RandomGenerator

Implements RandomGenerator by delegating all operations to RandomSource.

Rank

class Rank<T : Comparable<T>> : BlockingStatistic<T, IntArray>

Rank statistics

Sampler

fun interface Sampler<out T : Any>

Sampler that generates chains of values of type T.

Statistic

fun interface Statistic<in T, out R>

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

StatisticalAlgebra

interface StatisticalAlgebra<T, out A : Algebra<T>, out BA : BufferAlgebra<T, A>> : Algebra<Buffer<T>>

ValueAndError

data class ValueAndError(val value: Double, val dispersion: Double)

A combination of a random value and its dispersion.

ValueAndErrorField

object ValueAndErrorField : Field<ValueAndError>

An algebra for double value + its error combination. The multiplication assumes linear error propagation

Functions

asUniformRandomProvider

fun RandomGenerator.asUniformRandomProvider(): UniformRandomProvider

Represent this RandomGenerator as commons-rng UniformRandomProvider preserving and mirroring its current state. Getting new value from one of those changes the state of another.

chiSquaredExpression

@JvmName(name = "genericChiSquaredExpression")

fun <T : Comparable<T>, I : Any, A : ExtendedField<I>, ExpressionAlgebra<T, I>> AutoDiffProcessor<T, I, A>.chiSquaredExpression(x: Buffer<T>, y: Buffer<T>, yErr: Buffer<T>, model: A.(I) -> I): DifferentiableExpression<T>

Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation.

fun <I : Any, A : ExtendedField<I>, ExpressionAlgebra<Double, I>> AutoDiffProcessor<Double, I, A>.chiSquaredExpression(x: Buffer<Double>, y: Buffer<Double>, yErr: Buffer<Double>, model: A.(I) -> I): DifferentiableExpression<Double>

ecdf

fun <T : Comparable<T>> StatisticalAlgebra<T, *, *>.ecdf(buffer: Buffer<T>): (T) -> Double

Compute empirical CDF function

flow

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. The computation of inner representation is done in parallel if dispatcher allows it.

fromSource

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

Returns RandomSourceGenerator with given RandomSource and seed.

invoke

operator fun <T, R> BlockingStatistic<T, R>.invoke(data: Buffer<T>): R

suspend operator fun <T, R> Statistic<T, R>.invoke(data: Buffer<T>): R

ksComparisonStatistic

@UnstableKMathAPI

fun <T : Comparable<T>, A : Group<T>, NumericAlgebra<T>, BA : BufferAlgebra<T, A>> StatisticalAlgebra<T, A, BA>.ksComparisonStatistic(x: Buffer<T>, y: Buffer<T>): KMComparisonResult<T>

Kolmogorov-Smirnov sample comparison test Implementation copied from https://commons.apache.org/proper/commons-math/javadocs/api-3.6.1/index.html?org/apache/commons/math3/stat/inference/KolmogorovSmirnovTest.html

mersenneTwister