KSLCore/ksl.utilities.random.rvariable/KSLRandom

KSLRandom

The purpose of this class is to facilitate random variate generation from various distributions without having to create object instances.

Each function marked rXXXX will generate random variates from the named distribution. The user has the option of supplying a RNStreamIfc as the source of the randomness. Functions that do not have a RNStreamIfc parameter use, defaultRNStream() as the default source of randomness. That is, they all share the same stream, which is the default stream from the default random number stream provider. The user has the option of supplying a stream number to identify the stream from the underlying stream provider. By default, stream 1 is the default stream for the default provider. Stream 2 refers to the 2nd stream, etc.

Also provides a number of methods for sampling with and without replacement from arrays and lists as well as creating permutations of arrays and lists.

Author

rossetti

Types

AlgoType

enum AlgoType : Enum<KSLRandom.AlgoType>

Properties

DefaultRNStreamProvider

var DefaultRNStreamProvider: RNStreamProviderIfc

defaultStreamNumber

val defaultStreamNumber: Int

The default stream number for the underlying provider

Functions

defaultRNStream

fun defaultRNStream(): RNStreamIfc

discreteInverseCDF

fun discreteInverseCDF(array: DoubleArray, cdf: DoubleArray, stream: RNStreamIfc): Double

fun discreteInverseCDF(array: IntArray, cdf: DoubleArray, stream: RNStreamIfc): Int

Randomly selects from the array using the supplied cdf, NO checking of arrays

isValidCDF

fun isValidCDF(cdf: DoubleArray): Boolean

isValidPMF

fun isValidPMF(prob: DoubleArray): Boolean

Each element must be in (0,1) and sum of elements must be less than or equal to 1.0.

makeCDF

fun makeCDF(prob: DoubleArray): DoubleArray

There must be at least two elements in the supplied array. All elements must be probabilities within 0.0, 1.0. The sum of the probabilities must be 1.0

makePMF

fun makePMF(cdf: DoubleArray): DoubleArray

Makes an array that holds the probability mass function associated with the supplied discrete cumulative distribution function.

nextRNStream

fun nextRNStream(): RNStreamIfc

permute

fun <T> permute(x: Array<T>, streamNum: Int)

fun permute(x: DoubleArray, streamNum: Int)

fun permute(x: DoubleArray, stream: RNStreamIfc = defaultRNStream())

Randomly permutes the supplied array using the supplied stream number, the array is changed

fun <T> permute(x: Array<T>, rng: RNStreamIfc = defaultRNStream())

Randomly permutes the supplied array using the supplied random number generator, the array is changed

fun permute(x: BooleanArray, streamNum: Int)

fun permute(x: IntArray, streamNum: Int)

Randomly permutes the supplied array using the supplied stream number, the array is changed.

fun permute(x: BooleanArray, rng: RNStreamIfc = defaultRNStream())

fun permute(x: IntArray, rng: RNStreamIfc = defaultRNStream())

Randomly permutes the supplied array using the supplied random number generator, the array is changed.

fun <T> permute(x: MutableList<T>, streamNum: Int)

Randomly permutes the supplied List using the supplied stream number, the list is changed

fun <T> permute(x: MutableList<T>, stream: RNStreamIfc = defaultRNStream())

Randomly permutes the supplied List using the supplied random number generator, the list is changed

randomlySelect

fun randomlySelect(array: DoubleArray, streamNum: Int): Double

fun randomlySelect(array: DoubleArray, stream: RNStreamIfc = defaultRNStream()): Double

fun randomlySelect(array: IntArray, streamNum: Int): Int

fun randomlySelect(array: IntArray, stream: RNStreamIfc = defaultRNStream()): Int

Randomly select an element from the array

fun <T> randomlySelect(list: List<T>, streamNum: Int): T

fun <T> randomlySelect(list: List<T>, stream: RNStreamIfc = defaultRNStream()): T

Randomly select from the list

fun randomlySelect(array: DoubleArray, cdf: DoubleArray, streamNum: Int): Double

fun randomlySelect(array: DoubleArray, cdf: DoubleArray, stream: RNStreamIfc = defaultRNStream()): Double

fun randomlySelect(array: IntArray, cdf: DoubleArray, streamNum: Int): Int

fun randomlySelect(array: IntArray, cdf: DoubleArray, stream: RNStreamIfc = defaultRNStream()): Int

Randomly selects from the array using the supplied cdf

fun <T> randomlySelect(list: List<T>, cdf: DoubleArray, streamNum: Int): T

fun <T> randomlySelect(list: List<T>, cdf: DoubleArray, stream: RNStreamIfc = defaultRNStream()): T

Randomly selects from the list using the supplied cdf

rBernoulli

fun rBernoulli(pSuccess: Double, streamNum: Int): Double

fun rBernoulli(pSuccess: Double, stream: RNStreamIfc = defaultRNStream()): Double

rBernoulliBoolean

fun rBernoulliBoolean(pSuccess: Double, stream: RNStreamIfc = defaultRNStream()): Boolean

rBeta

fun rBeta(alpha: Double, beta: Double, streamNum: Int): Double

fun rBeta(alpha: Double, beta: Double, rng: RNStreamIfc = defaultRNStream()): Double

This beta is restricted to the range of (0,1)

rBetaG

fun rBetaG(alpha: Double, beta: Double, minimum: Double, maximum: Double, streamNum: Int): Double

fun rBetaG(alpha: Double, beta: Double, minimum: Double, maximum: Double, rng: RNStreamIfc = defaultRNStream()): Double

This beta is restricted to the range of (minimum,maximum)

rBinomial

fun rBinomial(pSuccess: Double, nTrials: Int, streamNum: Int): Int

fun rBinomial(pSuccess: Double, nTrials: Int, stream: RNStreamIfc = defaultRNStream()): Int

rChiSquared

fun rChiSquared(dof: Double, streamNum: Int): Double

fun rChiSquared(dof: Double, rng: RNStreamIfc = defaultRNStream()): Double

rDUniform

fun rDUniform(range: IntRange, streamNum: Int): Int

fun rDUniform(range: IntRange, stream: RNStreamIfc = defaultRNStream()): Int

fun rDUniform(minimum: Int, maximum: Int, streamNum: Int): Int

fun rDUniform(minimum: Int, maximum: Int, stream: RNStreamIfc = defaultRNStream()): Int

Generates a discrete uniform over the range

rExponential

fun rExponential(mean: Double, streamNum: Int): Double

fun rExponential(mean: Double, stream: RNStreamIfc = defaultRNStream()): Double

rGamma

fun rGamma(shape: Double, scale: Double, streamNum: Int, type: KSLRandom.AlgoType = AlgoType.Inverse): Double

fun rGamma(shape: Double, scale: Double, rng: RNStreamIfc = defaultRNStream(), type: KSLRandom.AlgoType = AlgoType.Inverse): Double

rGeometric

fun rGeometric(pSuccess: Double, streamNum: Int): Int

fun rGeometric(pSuccess: Double, stream: RNStreamIfc = defaultRNStream()): Int

rJohnsonB

fun rJohnsonB(alpha1: Double, alpha2: Double, min: Double, max: Double, streamNum: Int): Double

fun rJohnsonB(alpha1: Double, alpha2: Double, min: Double, max: Double, stream: RNStreamIfc = defaultRNStream()): Double

rLaplace

fun rLaplace(location: Double, scale: Double, streamNum: Int): Double

fun rLaplace(location: Double, scale: Double, rng: RNStreamIfc = defaultRNStream()): Double

Generates according to a Laplace(location, scale)

rLogistic

fun rLogistic(location: Double, scale: Double, streamNum: Int): Double

fun rLogistic(location: Double, scale: Double, rng: RNStreamIfc = defaultRNStream()): Double

rLogLogistic

fun rLogLogistic(shape: Double, scale: Double, streamNum: Int): Double

fun rLogLogistic(shape: Double, scale: Double, rng: RNStreamIfc = defaultRNStream()): Double

rLogNormal

fun rLogNormal(mean: Double, variance: Double, streamNum: Int): Double

fun rLogNormal(mean: Double, variance: Double, stream: RNStreamIfc = defaultRNStream()): Double

rNegBinomial

fun rNegBinomial(pSuccess: Double, rSuccesses: Double, streamNum: Int): Int

fun rNegBinomial(pSuccess: Double, rSuccesses: Double, stream: RNStreamIfc = defaultRNStream()): Int

rNormal

fun rNormal(streamNum: Int): Double

Generates a N(0,1) random value using the supplied stream number

fun rNormal(stream: RNStreamIfc): Double

Generates a N(0,1) random value using the supplied stream

fun rNormal(mean: Double, variance: Double, streamNum: Int): Double

fun rNormal(mean: Double = 0.0, variance: Double = 1.0, stream: RNStreamIfc = defaultRNStream()): Double

rNormalBoxMuller

fun rNormalBoxMuller(mean: Double = 0.0, variance: Double = 1.0, streamNum: Int): Pair<Double, Double>

fun rNormalBoxMuller(mean: Double = 0.0, variance: Double = 1.0, stream: RNStreamIfc = defaultRNStream()): Pair<Double, Double>

Generated a pair of normal random variates via the Box-Muller transform method The user can use destructuring to access the individual values

rNormalPolar

fun rNormalPolar(mean: Double = 0.0, variance: Double = 1.0, streamNum: Int): Pair<Double, Double>

fun rNormalPolar(mean: Double = 0.0, variance: Double = 1.0, stream: RNStreamIfc = defaultRNStream()): Pair<Double, Double>

Generated a pair of normal random variates via the Box-Muller transform method The user can use destructuring to access the individual values

rnStream

fun rnStream(streamNum: Int): RNStreamIfc

rPearsonType5

fun rPearsonType5(shape: Double, scale: Double, streamNum: Int): Double

fun rPearsonType5(shape: Double, scale: Double, rng: RNStreamIfc = defaultRNStream()): Double

rPearsonType6

fun rPearsonType6(alpha1: Double, alpha2: Double, beta: Double, streamNum: Int): Double

fun rPearsonType6(alpha1: Double, alpha2: Double, beta: Double, rng: RNStreamIfc = defaultRNStream()): Double

Pearson Type 6

rPoisson

fun rPoisson(mean: Double, streamNum: Int): Int

fun rPoisson(mean: Double, stream: RNStreamIfc = defaultRNStream()): Int

rTriangular

fun rTriangular(min: Double, mode: Double, max: Double, streamNum: Int): Double

fun rTriangular(min: Double, mode: Double, max: Double, stream: RNStreamIfc = defaultRNStream()): Double

rUniform

fun rUniform(streamNum: Int): Double

Generates a continuous U(0,1) using the supplied stream number

fun rUniform(stream: RNStreamIfc = defaultRNStream()): Double

Generates a continuous U(0,1) using the supplied stream

fun rUniform(minimum: Double, maximum: Double, streamNum: Int): Double

fun rUniform(minimum: Double = 0.0, maximum: Double = 1.0, stream: RNStreamIfc = defaultRNStream()): Double

Generates a continuous uniform over the range

rWeibull

fun rWeibull(shape: Double, scale: Double, streamNum: Int): Double

fun rWeibull(shape: Double, scale: Double, stream: RNStreamIfc = defaultRNStream()): Double

sampleWithoutReplacement

fun <T> sampleWithoutReplacement(x: Array<T>, sampleSize: Int, streamNum: Int)

fun <T> sampleWithoutReplacement(x: Array<T>, sampleSize: Int, stream: RNStreamIfc = defaultRNStream())

The array x is changed, such that the first sampleSize elements contain the generated sample. That is, x0, x1, ... , xsampleSize-1 is the randomly sampled values without replacement

fun sampleWithoutReplacement(x: BooleanArray, sampleSize: Int, streamNum: Int)

fun sampleWithoutReplacement(x: BooleanArray, sampleSize: Int, rng: RNStreamIfc = defaultRNStream())

fun sampleWithoutReplacement(x: IntArray, sampleSize: Int, streamNum: Int)

fun sampleWithoutReplacement(x: IntArray, sampleSize: Int, rng: RNStreamIfc = defaultRNStream())

The array x is changed, such that the first sampleSize elements contain the generated sample. That is, x0, x1, ... , xsampleSize-1 is the random sample without replacement

fun sampleWithoutReplacement(x: DoubleArray, sampleSize: Int, streamNum: Int)

fun sampleWithoutReplacement(x: DoubleArray, sampleSize: Int, rng: RNStreamIfc = defaultRNStream())

The array x is changed, such that the first sampleSize elements contain the sample. That is, x0, x1, ... , xsampleSize-1 is the random sample without replacement

fun <T> sampleWithoutReplacement(x: MutableList<T>, sampleSize: Int, streamNum: Int)

The List x is changed, such that the first sampleSize elements contain the generate. That is, x.get(0), x.get(1), ... , x.get(sampleSize-1) is the random sample without replacement

fun <T> sampleWithoutReplacement(x: MutableList<T>, sampleSize: Int, stream: RNStreamIfc = defaultRNStream())

The List x is changed, such that the first sampleSize elements contain the sampled values. That is, x.get(0), x.get(1), ... , x.get(sampleSize-1) is the random sample without replacement

streamNumber

fun streamNumber(stream: RNStreamIfc): Int