forked from kscience/kmath
Simplify BlockingIntChain and BlockingRealChain; add blocking extension function for RandomChain; copy general documentation to samplers created with Apache Commons RNG
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@ -4,9 +4,5 @@ package scientifik.kmath.chains
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* Performance optimized chain for integer values
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*/
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abstract class BlockingIntChain : Chain<Int> {
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abstract fun nextInt(): Int
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override suspend fun next(): Int = nextInt()
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fun nextBlock(size: Int): IntArray = IntArray(size) { nextInt() }
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}
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suspend fun nextBlock(size: Int): IntArray = IntArray(size) { next() }
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}
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@ -4,9 +4,5 @@ package scientifik.kmath.chains
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* Performance optimized chain for real values
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*/
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abstract class BlockingRealChain : Chain<Double> {
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abstract fun nextDouble(): Double
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override suspend fun next(): Double = nextDouble()
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fun nextBlock(size: Int): DoubleArray = DoubleArray(size) { nextDouble() }
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}
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suspend fun nextBlock(size: Int): DoubleArray = DoubleArray(size) { next() }
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}
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@ -1,5 +1,7 @@
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package scientifik.kmath.prob
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import scientifik.kmath.chains.BlockingIntChain
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import scientifik.kmath.chains.BlockingRealChain
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import scientifik.kmath.chains.Chain
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/**
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@ -12,3 +14,17 @@ class RandomChain<out R>(val generator: RandomGenerator, private val gen: suspen
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}
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fun <R> RandomGenerator.chain(gen: suspend RandomGenerator.() -> R): RandomChain<R> = RandomChain(this, gen)
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fun RandomChain<Double>.blocking(): BlockingRealChain = let {
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object : BlockingRealChain() {
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override suspend fun next(): Double = it.next()
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override fun fork(): Chain<Double> = it.fork()
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}
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}
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fun RandomChain<Int>.blocking(): BlockingIntChain = let {
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object : BlockingIntChain() {
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override suspend fun next(): Int = it.next()
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override fun fork(): Chain<Int> = it.fork()
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}
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}
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@ -8,8 +8,9 @@ import kotlin.math.ln
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import kotlin.math.pow
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/**
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* Based on commons-rng implementation.
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* Sampling from an [exponential distribution](http://mathworld.wolfram.com/ExponentialDistribution.html).
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*
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* Based on Commons RNG implementation.
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* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AhrensDieterExponentialSampler.html
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*/
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class AhrensDieterExponentialSampler private constructor(val mean: Double) : Sampler<Double> {
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@ -7,6 +7,16 @@ import scientifik.kmath.prob.chain
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import scientifik.kmath.prob.next
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import kotlin.math.*
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/**
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* Sampling from the [gamma distribution](http://mathworld.wolfram.com/GammaDistribution.html).
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* - For 0 < alpha < 1:
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* Ahrens, J. H. and Dieter, U., Computer methods for sampling from gamma, beta, Poisson and binomial distributions, Computing, 12, 223-246, 1974.
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* - For alpha >= 1:
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* Marsaglia and Tsang, A Simple Method for Generating Gamma Variables. ACM Transactions on Mathematical Software, Volume 26 Issue 3, September, 2000.
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*
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* Based on Commons RNG implementation.
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* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AhrensDieterMarsagliaTsangGammaSampler.html
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*/
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class AhrensDieterMarsagliaTsangGammaSampler private constructor(
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alpha: Double,
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theta: Double
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@ -9,6 +9,33 @@ import kotlin.math.ceil
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import kotlin.math.max
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import kotlin.math.min
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/**
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* Distribution sampler that uses the Alias method. It can be used to sample from n values each with an associated
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* probability. This implementation is based on the detailed explanation of the alias method by Keith Schartz and
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* implements Vose's algorithm.
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*
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* Vose, M.D., A linear algorithm for generating random numbers with a given distribution, IEEE Transactions on
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* Software Engineering, 17, 972-975, 1991. he algorithm will sample values in O(1) time after a pre-processing step
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* of O(n) time.
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*
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* The alias tables are constructed using fraction probabilities with an assumed denominator of 253. In the generic
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* case sampling uses UniformRandomProvider.nextInt(int) and the upper 53-bits from UniformRandomProvider.nextLong().
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*
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* Zero padding the input probabilities can be used to make more sampling more efficient. Any zero entry will always be
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* aliased removing the requirement to compute a long. Increased sampling speed comes at the cost of increased storage
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* space. The algorithm requires approximately 12 bytes of storage per input probability, that is n * 12 for size n.
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* Zero-padding only requires 4 bytes of storage per padded value as the probability is known to be zero.
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*
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* An optimisation is performed for small table sizes that are a power of 2. In this case the sampling uses 1 or 2
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* calls from UniformRandomProvider.nextInt() to generate up to 64-bits for creation of an 11-bit index and 53-bits
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* for the long. This optimisation requires a generator with a high cycle length for the lower order bits.
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*
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* Larger table sizes that are a power of 2 will benefit from fast algorithms for UniformRandomProvider.nextInt(int)
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* that exploit the power of 2.
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*
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* Based on Commons RNG implementation.
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* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AliasMethodDiscreteSampler.html
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*/
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open class AliasMethodDiscreteSampler private constructor(
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// Deliberate direct storage of input arrays
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protected val probability: LongArray,
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@ -7,8 +7,10 @@ import scientifik.kmath.prob.chain
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import kotlin.math.*
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/**
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* Based on commons-rng implementation.
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* [Box-Muller algorithm](https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform) for sampling from a Gaussian
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* distribution.
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*
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* Based on Commons RNG implementation.
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* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/BoxMullerNormalizedGaussianSampler.html
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*/
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class BoxMullerNormalizedGaussianSampler private constructor() : NormalizedGaussianSampler, Sampler<Double> {
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@ -6,8 +6,9 @@ import scientifik.kmath.prob.RandomGenerator
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import scientifik.kmath.prob.Sampler
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/**
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* Based on commons-rng implementation.
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* Sampling from a Gaussian distribution with given mean and standard deviation.
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*
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* Based on Commons RNG implementation.
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* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/GaussianSampler.html
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*/
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class GaussianSampler private constructor(
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@ -7,8 +7,15 @@ import scientifik.kmath.prob.chain
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import kotlin.math.exp
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/**
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* Based on commons-rng implementation.
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* Sampler for the Poisson distribution.
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* - Kemp, A, W, (1981) Efficient Generation of Logarithmically Distributed Pseudo-Random Variables. Journal of the Royal Statistical Society. Vol. 30, No. 3, pp. 249-253.
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* This sampler is suitable for mean < 40. For large means, LargeMeanPoissonSampler should be used instead.
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*
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* Note: The algorithm uses a recurrence relation to compute the Poisson probability and a rolling summation for the cumulative probability. When the mean is large the initial probability (Math.exp(-mean)) is zero and an exception is raised by the constructor.
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*
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* Sampling uses 1 call to UniformRandomProvider.nextDouble(). This method provides an alternative to the SmallMeanPoissonSampler for slow generators of double.
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*
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* Based on Commons RNG implementation.
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* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/KempSmallMeanPoissonSampler.html
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*/
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class KempSmallMeanPoissonSampler private constructor(
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@ -9,8 +9,14 @@ import scientifik.kmath.prob.next
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import kotlin.math.*
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/**
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* Based on commons-rng implementation.
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* Sampler for the Poisson distribution.
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* - For large means, we use the rejection algorithm described in
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* Devroye, Luc. (1981).The Computer Generation of Poisson Random Variables
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* Computing vol. 26 pp. 197-207.
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*
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* This sampler is suitable for mean >= 40.
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*
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* Based on Commons RNG implementation.
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* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/LargeMeanPoissonSampler.html
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*/
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class LargeMeanPoissonSampler private constructor(val mean: Double) : Sampler<Int> {
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@ -112,7 +118,7 @@ class LargeMeanPoissonSampler private constructor(val mean: Double) : Sampler<In
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private const val MAX_MEAN: Double = 0.5 * Int.MAX_VALUE
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private val NO_CACHE_FACTORIAL_LOG: InternalUtils.FactorialLog = InternalUtils.FactorialLog.create()
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private val NO_SMALL_MEAN_POISSON_SAMPLER = object : Sampler<Int> {
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private val NO_SMALL_MEAN_POISSON_SAMPLER: Sampler<Int> = object : Sampler<Int> {
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override fun sample(generator: RandomGenerator): Chain<Int> = ConstantChain(0)
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}
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@ -8,11 +8,14 @@ import kotlin.math.ln
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import kotlin.math.sqrt
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/**
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* Based on commons-rng implementation.
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* [Marsaglia polar method](https://en.wikipedia.org/wiki/Marsaglia_polar_method) for sampling from a Gaussian
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* distribution with mean 0 and standard deviation 1. This is a variation of the algorithm implemented in
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* [BoxMullerNormalizedGaussianSampler].
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*
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* Based on Commons RNG implementation.
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* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/MarsagliaNormalizedGaussianSampler.html
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*/
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class MarsagliaNormalizedGaussianSampler private constructor(): NormalizedGaussianSampler, Sampler<Double> {
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class MarsagliaNormalizedGaussianSampler private constructor() : NormalizedGaussianSampler, Sampler<Double> {
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private var nextGaussian = Double.NaN
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override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {
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@ -2,4 +2,8 @@ package scientifik.kmath.prob.samplers
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import scientifik.kmath.prob.Sampler
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/**
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* Marker interface for a sampler that generates values from an N(0,1)
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* [Gaussian distribution](https://en.wikipedia.org/wiki/Normal_distribution).
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*/
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interface NormalizedGaussianSampler : Sampler<Double>
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@ -5,9 +5,16 @@ import scientifik.kmath.prob.RandomGenerator
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import scientifik.kmath.prob.Sampler
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/**
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* Based on commons-rng implementation.
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* Sampler for the Poisson distribution.
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* - For small means, a Poisson process is simulated using uniform deviates, as described in
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* Knuth (1969). Seminumerical Algorithms. The Art of Computer Programming, Volume 2. Chapter 3.4.1.F.3
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* Important integer-valued distributions: The Poisson distribution. Addison Wesley.
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* The Poisson process (and hence, the returned value) is bounded by 1000 * mean.
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* - For large means, we use the rejection algorithm described in
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* Devroye, Luc. (1981). The Computer Generation of Poisson Random Variables Computing vol. 26 pp. 197-207.
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*
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* https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/PoissonSampler.html
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* Based on Commons RNG implementation.
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* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/PoissonSampler.html
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*/
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class PoissonSampler private constructor(
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mean: Double
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@ -8,7 +8,14 @@ import kotlin.math.ceil
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import kotlin.math.exp
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/**
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* Based on commons-rng implementation.
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* Sampler for the Poisson distribution.
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* - For small means, a Poisson process is simulated using uniform deviates, as described in
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* Knuth (1969). Seminumerical Algorithms. The Art of Computer Programming, Volume 2. Chapter 3.4.1.F.3 Important
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* integer-valued distributions: The Poisson distribution. Addison Wesley.
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* - The Poisson process (and hence, the returned value) is bounded by 1000 * mean.
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* This sampler is suitable for mean < 40. For large means, [LargeMeanPoissonSampler] should be used instead.
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*
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* Based on Commons RNG implementation.
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*
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* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/SmallMeanPoissonSampler.html
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*/
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import kotlin.math.*
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/**
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* Based on commons-rng implementation.
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* [Marsaglia and Tsang "Ziggurat"](https://en.wikipedia.org/wiki/Ziggurat_algorithm) method for sampling from a
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* Gaussian distribution with mean 0 and standard deviation 1. The algorithm is explained in this paper and this
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* implementation has been adapted from the C code provided therein.
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*
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* Based on Commons RNG implementation.
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* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSampler.html
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*/
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class ZigguratNormalizedGaussianSampler private constructor() :
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