forked from kscience/kmath
Add Apache Javadocs references
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@ -7,6 +7,11 @@ import scientifik.kmath.prob.chain
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import kotlin.math.ln
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import kotlin.math.ln
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import kotlin.math.pow
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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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*
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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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class AhrensDieterExponentialSampler private constructor(val mean: Double) : Sampler<Double> {
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override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {
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override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {
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// Step 1:
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// Step 1:
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@ -6,6 +6,11 @@ import scientifik.kmath.prob.Sampler
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import scientifik.kmath.prob.chain
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import scientifik.kmath.prob.chain
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import kotlin.math.*
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import kotlin.math.*
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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/BoxMullerNormalizedGaussianSampler.html
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*/
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class BoxMullerNormalizedGaussianSampler private constructor() : NormalizedGaussianSampler, Sampler<Double> {
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class BoxMullerNormalizedGaussianSampler private constructor() : NormalizedGaussianSampler, Sampler<Double> {
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private var nextGaussian: Double = Double.NaN
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private var nextGaussian: Double = Double.NaN
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@ -5,6 +5,11 @@ import scientifik.kmath.chains.map
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import scientifik.kmath.prob.RandomGenerator
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import scientifik.kmath.prob.RandomGenerator
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import scientifik.kmath.prob.Sampler
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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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*
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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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class GaussianSampler private constructor(
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private val mean: Double,
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private val mean: Double,
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private val standardDeviation: Double,
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private val standardDeviation: Double,
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@ -6,6 +6,11 @@ import scientifik.kmath.prob.Sampler
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import scientifik.kmath.prob.chain
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import scientifik.kmath.prob.chain
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import kotlin.math.exp
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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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*
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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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class KempSmallMeanPoissonSampler private constructor(
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private val p0: Double,
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private val p0: Double,
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private val mean: Double
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private val mean: Double
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@ -8,9 +8,14 @@ import scientifik.kmath.prob.Sampler
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import scientifik.kmath.prob.next
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import scientifik.kmath.prob.next
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import kotlin.math.*
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import kotlin.math.*
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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/LargeMeanPoissonSampler.html
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*/
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class LargeMeanPoissonSampler private constructor(val mean: Double) : Sampler<Int> {
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class LargeMeanPoissonSampler private constructor(val mean: Double) : Sampler<Int> {
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private val exponential: Sampler<Double> = AhrensDieterExponentialSampler.of(1.0)
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private val exponential: Sampler<Double> = AhrensDieterExponentialSampler.of(1.0)
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private val gaussian: Sampler<Double> = ZigguratNormalizedGaussianSampler()
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private val gaussian: Sampler<Double> = ZigguratNormalizedGaussianSampler.of()
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private val factorialLog: InternalUtils.FactorialLog = NO_CACHE_FACTORIAL_LOG
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private val factorialLog: InternalUtils.FactorialLog = NO_CACHE_FACTORIAL_LOG
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private val lambda: Double = floor(mean)
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private val lambda: Double = floor(mean)
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private val logLambda: Double = ln(lambda)
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private val logLambda: Double = ln(lambda)
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@ -7,6 +7,11 @@ import scientifik.kmath.prob.chain
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import kotlin.math.ln
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import kotlin.math.ln
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import kotlin.math.sqrt
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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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*
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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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private var nextGaussian = Double.NaN
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@ -4,7 +4,11 @@ import scientifik.kmath.chains.Chain
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import scientifik.kmath.prob.RandomGenerator
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import scientifik.kmath.prob.RandomGenerator
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import scientifik.kmath.prob.Sampler
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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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*
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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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*/
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class PoissonSampler private constructor(
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class PoissonSampler private constructor(
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mean: Double
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mean: Double
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) : Sampler<Int> {
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) : Sampler<Int> {
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@ -8,6 +8,11 @@ import scientifik.kmath.prob.chain
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import kotlin.math.ceil
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import kotlin.math.ceil
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import kotlin.math.exp
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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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*
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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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class SmallMeanPoissonSampler private constructor(mean: Double) : Sampler<Int> {
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class SmallMeanPoissonSampler private constructor(mean: Double) : Sampler<Int> {
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private val p0: Double = exp(-mean)
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private val p0: Double = exp(-mean)
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@ -7,6 +7,11 @@ import scientifik.kmath.prob.Sampler
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import scientifik.kmath.prob.chain
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import scientifik.kmath.prob.chain
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import kotlin.math.*
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import kotlin.math.*
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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/ZigguratNormalizedGaussianSampler.html
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*/
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class ZigguratNormalizedGaussianSampler private constructor() :
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class ZigguratNormalizedGaussianSampler private constructor() :
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NormalizedGaussianSampler, Sampler<Double> {
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NormalizedGaussianSampler, Sampler<Double> {
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