Merge pull request #164 from mipt-npm/feature/mp-samplers

Implement Commons RNG-like samplers in kmath-prob module for Multiplatform

examples/build.gradle.kts

@@ -106,6 +106,7 @@ kotlin.sourceSets.all {
     with(languageSettings) {
         useExperimentalAnnotation("kotlin.contracts.ExperimentalContracts")
         useExperimentalAnnotation("kotlin.ExperimentalUnsignedTypes")
+        useExperimentalAnnotation("space.kscience.kmath.misc.UnstableKMathAPI")
     }
 }
 

examples/src/main/kotlin/space/kscience/kmath/commons/fit/fitWithAutoDiff.kt

@@ -13,9 +13,8 @@ import space.kscience.kmath.optimization.OptimizationResult
 import space.kscience.kmath.real.DoubleVector
 import space.kscience.kmath.real.map
 import space.kscience.kmath.real.step
-import space.kscience.kmath.stat.Distribution
 import space.kscience.kmath.stat.RandomGenerator
-import space.kscience.kmath.stat.normal
+import space.kscience.kmath.stat.distributions.NormalDistribution
 import space.kscience.kmath.structures.asIterable
 import space.kscience.kmath.structures.toList
 import kotlin.math.pow
@@ -37,10 +36,9 @@ operator fun TraceValues.invoke(vector: DoubleVector) {
 /**
  * Least squares fie with auto-differentiation. Uses `kmath-commons` and `kmath-for-real` modules.
  */
-fun main() {
+suspend fun main() {
-
     //A generator for a normally distributed values
-    val generator = Distribution.normal()
+    val generator = NormalDistribution(2.0, 7.0)
 
     //A chain/flow of random values with the given seed
     val chain = generator.sample(RandomGenerator.default(112667))
@@ -53,7 +51,7 @@ fun main() {
     //Perform an operation on each x value (much more effective, than numpy)
     val y = x.map {
         val value = it.pow(2) + it + 1
-        value + chain.nextDouble() * sqrt(value)
+        value + chain.next() * sqrt(value)
     }
     // this will also work, but less effective:
     // val y = x.pow(2)+ x + 1 + chain.nextDouble()

examples/src/main/kotlin/space/kscience/kmath/stat/DistributionBenchmark.kt

@@ -1,23 +1,24 @@
-package kscience.kmath.commons.prob
+package space.kscience.kmath.stat
 
 import kotlinx.coroutines.Dispatchers
 import kotlinx.coroutines.async
 import kotlinx.coroutines.runBlocking
-import org.apache.commons.rng.sampling.distribution.ZigguratNormalizedGaussianSampler
+import space.kscience.kmath.stat.samplers.GaussianSampler
 import org.apache.commons.rng.simple.RandomSource
-import space.kscience.kmath.stat.*
 import java.time.Duration
 import java.time.Instant
+import org.apache.commons.rng.sampling.distribution.GaussianSampler as CMGaussianSampler
+import org.apache.commons.rng.sampling.distribution.ZigguratNormalizedGaussianSampler as CMZigguratNormalizedGaussianSampler
 
-private fun runChain(): Duration {
+private suspend fun runKMathChained(): Duration {
     val generator = RandomGenerator.fromSource(RandomSource.MT, 123L)
-    val normal = Distribution.normal(NormalSamplerMethod.Ziggurat)
+    val normal = GaussianSampler.of(7.0, 2.0)
-    val chain = normal.sample(generator)
+    val chain = normal.sample(generator).blocking()
     val startTime = Instant.now()
     var sum = 0.0
 
     repeat(10000001) { counter ->
-        sum += chain.nextDouble()
+        sum += chain.next()
 
         if (counter % 100000 == 0) {
             val duration = Duration.between(startTime, Instant.now())
@@ -29,9 +30,15 @@ private fun runChain(): Duration {
     return Duration.between(startTime, Instant.now())
 }
 
-private fun runDirect(): Duration {
+private fun runApacheDirect(): Duration {
-    val provider = RandomSource.create(RandomSource.MT, 123L)
+    val rng = RandomSource.create(RandomSource.MT, 123L)
-    val sampler = ZigguratNormalizedGaussianSampler(provider)
+
+    val sampler = CMGaussianSampler.of(
+        CMZigguratNormalizedGaussianSampler.of(rng),
+        7.0,
+        2.0
+    )
+
     val startTime = Instant.now()
     var sum = 0.0
 
@@ -51,11 +58,9 @@ private fun runDirect(): Duration {
 /**
  * Comparing chain sampling performance with direct sampling performance
  */
-fun main() {
+fun main(): Unit = runBlocking(Dispatchers.Default) {
-    runBlocking(Dispatchers.Default) {
+    val chainJob = async { runKMathChained() }
-        val chainJob = async { runChain() }
+    val directJob = async { runApacheDirect() }
-        val directJob = async { runDirect() }
+    println("KMath Chained: ${chainJob.await()}")
-        println("Chain: ${chainJob.await()}")
+    println("Apache Direct: ${directJob.await()}")
-        println("Direct: ${directJob.await()}")
-    }
 }

examples/src/main/kotlin/space/kscience/kmath/stat/DistributionDemo.kt

@@ -3,14 +3,15 @@ package space.kscience.kmath.stat
 import kotlinx.coroutines.runBlocking
 import space.kscience.kmath.chains.Chain
 import space.kscience.kmath.chains.collectWithState
+import space.kscience.kmath.stat.distributions.NormalDistribution
 
 /**
- * The state of distribution averager
+ * The state of distribution averager.
  */
 private data class AveragingChainState(var num: Int = 0, var value: Double = 0.0)
 
 /**
- * Averaging
+ * Averaging.
  */
 private fun Chain<Double>.mean(): Chain<Double> = collectWithState(AveragingChainState(), { it.copy() }) { chain ->
     val next = chain.next()
@@ -21,7 +22,7 @@ private fun Chain<Double>.mean(): Chain<Double> = collectWithState(AveragingChai
 
 
 fun main() {
-    val normal = Distribution.normal()
+    val normal = NormalDistribution(0.0, 2.0)
     val chain = normal.sample(RandomGenerator.default).mean()
 
     runBlocking {

kmath-commons/src/test/kotlin/space/kscience/kmath/commons/optimization/OptimizeTest.kt

@@ -1,13 +1,13 @@
 package space.kscience.kmath.commons.optimization
 
-import org.junit.jupiter.api.Test
+import kotlinx.coroutines.runBlocking
 import space.kscience.kmath.commons.expressions.DerivativeStructureExpression
 import space.kscience.kmath.misc.symbol
 import space.kscience.kmath.optimization.FunctionOptimization
-import space.kscience.kmath.stat.Distribution
 import space.kscience.kmath.stat.RandomGenerator
-import space.kscience.kmath.stat.normal
+import space.kscience.kmath.stat.distributions.NormalDistribution
 import kotlin.math.pow
+import kotlin.test.Test
 
 internal class OptimizeTest {
     val x by symbol
@@ -34,23 +34,24 @@ internal class OptimizeTest {
             simplexSteps(x to 2.0, y to 0.5)
             //this sets simplex optimizer
         }
+
         println(result.point)
         println(result.value)
     }
 
     @Test
-    fun testCmFit() {
+    fun testCmFit() = runBlocking {
         val a by symbol
         val b by symbol
         val c by symbol
 
         val sigma = 1.0
-        val generator = Distribution.normal(0.0, sigma)
+        val generator = NormalDistribution(0.0, sigma)
         val chain = generator.sample(RandomGenerator.default(112667))
         val x = (1..100).map(Int::toDouble)
 
         val y = x.map {
-            it.pow(2) + it + 1 + chain.nextDouble()
+            it.pow(2) + it + 1 + chain.next()
         }
 
         val yErr = List(x.size) { sigma }
@@ -64,5 +65,4 @@ internal class OptimizeTest {
         println(result)
         println("Chi2/dof = ${result.value / (x.size - 3)}")
     }
-
 }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/BigInt.kt

@@ -241,18 +241,18 @@ public class BigInt internal constructor(
         )
 
         private fun compareMagnitudes(mag1: Magnitude, mag2: Magnitude): Int {
-            when {
+            return when {
-                mag1.size > mag2.size -> return 1
+                mag1.size > mag2.size -> 1
-                mag1.size < mag2.size -> return -1
+                mag1.size < mag2.size -> -1
+
                 else -> {
-                    for (i in mag1.size - 1 downTo 0) {
+                    for (i in mag1.size - 1 downTo 0) return when {
-                        if (mag1[i] > mag2[i]) {
+                        mag1[i] > mag2[i] -> 1
-                            return 1
+                        mag1[i] < mag2[i] -> -1
-                        } else if (mag1[i] < mag2[i]) {
+                        else -> continue
-                            return -1
                     }
-                    }
+
-                    return 0
+                    0
                 }
             }
         }
@@ -302,10 +302,11 @@ public class BigInt internal constructor(
             var carry = 0uL
 
             for (i in mag.indices) {
-                val cur: ULong = carry + mag[i].toULong() * x.toULong()
+                val cur = carry + mag[i].toULong() * x.toULong()
                 result[i] = (cur and BASE).toUInt()
                 carry = cur shr BASE_SIZE
             }
+
             result[resultLength - 1] = (carry and BASE).toUInt()
 
             return stripLeadingZeros(result)

kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt

@@ -40,7 +40,6 @@ public interface Buffer<out T> {
     public operator fun iterator(): Iterator<T>
 
     public companion object {
-
         /**
          * Check the element-by-element match of content of two buffers.
          */
@@ -110,7 +109,6 @@ public interface MutableBuffer<T> : Buffer<T> {
     public fun copy(): MutableBuffer<T>
 
     public companion object {
-
         /**
          * Creates a [DoubleBuffer] with the specified [size], where each element is calculated by calling the specified
          * [initializer] function.

kmath-core/src/commonTest/kotlin/space/kscience/kmath/structures/NumberNDFieldTest.kt

@@ -38,13 +38,12 @@ class NumberNDFieldTest {
             (i * 10 + j).toDouble()
         }
 
-        for (i in 0..2) {
+        for (i in 0..2)
             for (j in 0..2) {
                 val expected = (i * 10 + j).toDouble()
                 assertEquals(expected, array[i, j], "Error at index [$i, $j]")
             }
     }
-    }
 
     @Test
     fun testExternalFunction() {

kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/BlockingDoubleChain.kt

@@ -1,12 +1,13 @@
 package space.kscience.kmath.chains
 
 /**
- * Performance optimized chain for real values
+ * Chunked, specialized chain for real values.
  */
-public abstract class BlockingDoubleChain : Chain<Double> {
+public interface BlockingDoubleChain : Chain<Double> {
-    public abstract fun nextDouble(): Double
+    public override suspend fun next(): Double
 
-    override suspend fun next(): Double = nextDouble()
+    /**
-
+     * Returns an [DoubleArray] chunk of [size] values of [next].
-    public open fun nextBlock(size: Int): DoubleArray = DoubleArray(size) { nextDouble() }
+     */
+    public suspend fun nextBlock(size: Int): DoubleArray = DoubleArray(size) { next() }
 }

kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/BlockingIntChain.kt

@@ -3,10 +3,7 @@ package space.kscience.kmath.chains
 /**
  * Performance optimized chain for integer values
  */
-public abstract class BlockingIntChain : Chain<Int> {
+public interface BlockingIntChain : Chain<Int> {
-    public abstract fun nextInt(): Int
+    public override suspend fun next(): Int
-
+    public suspend fun nextBlock(size: Int): IntArray = IntArray(size) { next() }
-    override suspend fun next(): Int = nextInt()
-
-    public fun nextBlock(size: Int): IntArray = IntArray(size) { nextInt() }
 }

kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/Chain.kt

@@ -63,12 +63,10 @@ public class MarkovChain<out R : Any>(private val seed: suspend () -> R, private
 
     public fun value(): R? = value
 
-    public override suspend fun next(): R {
+    public override suspend fun next(): R = mutex.withLock {
-        mutex.withLock {
         val newValue = gen(value ?: seed())
         value = newValue
-            return newValue
+        newValue
-        }
     }
 
     public override fun fork(): Chain<R> = MarkovChain(seed = { value ?: seed() }, gen = gen)
@@ -90,12 +88,10 @@ public class StatefulChain<S, out R>(
 
     public fun value(): R? = value
 
-    public override suspend fun next(): R {
+    public override suspend fun next(): R = mutex.withLock {
-        mutex.withLock {
         val newValue = state.gen(value ?: state.seed())
         value = newValue
-            return newValue
+        newValue
-        }
     }
 
     public override fun fork(): Chain<R> = StatefulChain(forkState(state), seed, forkState, gen)

kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/streaming/BufferFlow.kt

@@ -28,7 +28,7 @@ public fun <T> Flow<T>.chunked(bufferSize: Int, bufferFactory: BufferFactory<T>)
     var counter = 0
 
     this@chunked.collect { element ->
-        list.add(element)
+        list += element
         counter++
 
         if (counter == bufferSize) {

kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/streaming/RingBuffer.kt

@@ -48,11 +48,9 @@ public class RingBuffer<T>(
     /**
      * A safe snapshot operation
      */
-    public suspend fun snapshot(): Buffer<T> {
+    public suspend fun snapshot(): Buffer<T> = mutex.withLock {
-        mutex.withLock {
         val copy = buffer.copy()
-            return VirtualBuffer(size) { i -> copy[startIndex.forward(i)] as T }
+        VirtualBuffer(size) { i -> copy[startIndex.forward(i)] as T }
-        }
     }
 
     public suspend fun push(element: T) {

kmath-dimensions/src/commonTest/kotlin/kscience/dimensions/DMatrixContextTest.kt

@@ -1,4 +1,4 @@
-package kscience.dimensions
+package space.kscience.dimensions
 
 import space.kscience.kmath.dimensions.D2
 import space.kscience.kmath.dimensions.D3

kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/Piecewise.kt

@@ -38,8 +38,8 @@ public class OrderedPiecewisePolynomial<T : Comparable<T>>(delimiter: T) :
      */
     public fun putRight(right: T, piece: Polynomial<T>) {
         require(right > delimiters.last()) { "New delimiter should be to the right of old one" }
-        delimiters.add(right)
+        delimiters += right
-        pieces.add(piece)
+        pieces += piece
     }
 
     /**

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Distribution.kt

@@ -1,17 +1,29 @@
 package space.kscience.kmath.stat
 
+import kotlinx.coroutines.flow.first
 import space.kscience.kmath.chains.Chain
 import space.kscience.kmath.chains.collect
 import space.kscience.kmath.structures.Buffer
 import space.kscience.kmath.structures.BufferFactory
-import space.kscience.kmath.structures.DoubleBuffer
+import space.kscience.kmath.structures.IntBuffer
+import space.kscience.kmath.structures.MutableBuffer
+import kotlin.jvm.JvmName
 
-public interface Sampler<T : Any> {
+/**
+ * Sampler that generates chains of values of type [T].
+ */
+public fun interface Sampler<T : Any> {
+    /**
+     * Generates a chain of samples.
+     *
+     * @param generator the randomness provider.
+     * @return the new chain.
+     */
     public fun sample(generator: RandomGenerator): Chain<T>
 }
 
 /**
- * A distribution of typed objects
+ * A distribution of typed objects.
  */
 public interface Distribution<T : Any> : Sampler<T> {
     /**
@@ -20,11 +32,7 @@ public interface Distribution<T : Any> : Sampler<T> {
      */
     public fun probability(arg: T): Double
 
-    /**
+    public override fun sample(generator: RandomGenerator): Chain<T>
-     * Create a chain of samples from this distribution.
-     * The chain is not guaranteed to be stateless, but different sample chains should be independent.
-     */
-    override fun sample(generator: RandomGenerator): Chain<T>
 
     /**
      * An empty companion. Distribution factories should be written as its extensions
@@ -63,16 +71,27 @@ public fun <T : Any> Sampler<T>.sampleBuffer(
         //clear list from previous run
         tmp.clear()
         //Fill list
-        repeat(size) {
+        repeat(size) { tmp += chain.next() }
-            tmp.add(chain.next())
-        }
         //return new buffer with elements from tmp
         bufferFactory(size) { tmp[it] }
     }
 }
 
 /**
- * Generate a bunch of samples from real distributions
+ * Samples one value from this [Sampler].
  */
+public suspend fun <T : Any> Sampler<T>.next(generator: RandomGenerator): T = sample(generator).first()
+
+/**
+ * Generates [size] real samples and chunks them into some buffers.
+ */
+@JvmName("sampleRealBuffer")
 public fun Sampler<Double>.sampleBuffer(generator: RandomGenerator, size: Int): Chain<Buffer<Double>> =
-    sampleBuffer(generator, size, ::DoubleBuffer)
+    sampleBuffer(generator, size, MutableBuffer.Companion::double)
+
+/**
+ * Generates [size] integer samples and chunks them into some buffers.
+ */
+@JvmName("sampleIntBuffer")
+public fun Sampler<Int>.sampleBuffer(generator: RandomGenerator, size: Int): Chain<Buffer<Int>> =
+    sampleBuffer(generator, size, ::IntBuffer)

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/FactorizedDistribution.kt

@@ -14,7 +14,7 @@ public interface NamedDistribution<T> : Distribution<Map<String, T>>
 public class FactorizedDistribution<T>(public val distributions: Collection<NamedDistribution<T>>) :
     NamedDistribution<T> {
     override fun probability(arg: Map<String, T>): Double =
-        distributions.fold(1.0) { acc, distr -> acc * distr.probability(arg) }
+        distributions.fold(1.0) { acc, dist -> acc * dist.probability(arg) }
 
     override fun sample(generator: RandomGenerator): Chain<Map<String, T>> {
         val chains = distributions.map { it.sample(generator) }
@@ -38,6 +38,6 @@ public class DistributionBuilder<T : Any> {
     private val distributions = ArrayList<NamedDistribution<T>>()
 
     public infix fun String.to(distribution: Distribution<T>) {
-        distributions.add(NamedDistributionWrapper(this, distribution))
+        distributions += NamedDistributionWrapper(this, distribution)
     }
 }

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/RandomChain.kt

@@ -1,17 +1,22 @@
 package space.kscience.kmath.stat
 
+import space.kscience.kmath.chains.BlockingDoubleChain
+import space.kscience.kmath.chains.BlockingIntChain
 import space.kscience.kmath.chains.Chain
 
 /**
  * A possibly stateful chain producing random values.
+ *
+ * @property generator the underlying [RandomGenerator] instance.
  */
 public class RandomChain<out R>(
     public val generator: RandomGenerator,
-    private val gen: suspend RandomGenerator.() -> R,
+    private val gen: suspend RandomGenerator.() -> R
 ) : Chain<R> {
     override suspend fun next(): R = generator.gen()
-
     override fun fork(): Chain<R> = RandomChain(generator.fork(), gen)
 }
 
 public fun <R> RandomGenerator.chain(gen: suspend RandomGenerator.() -> R): RandomChain<R> = RandomChain(this, gen)
+public fun Chain<Double>.blocking(): BlockingDoubleChain = object : Chain<Double> by this, BlockingDoubleChain {}
+public fun Chain<Int>.blocking(): BlockingIntChain = object : Chain<Int> by this, BlockingIntChain {}

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/RandomGenerator.kt

@@ -82,6 +82,8 @@ public interface RandomGenerator {
 
 /**
  * Implements [RandomGenerator] by delegating all operations to [Random].
+ *
+ * @property random the underlying [Random] object.
  */
 public class DefaultGenerator(public val random: Random = Random) : RandomGenerator {
     public override fun nextBoolean(): Boolean = random.nextBoolean()

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/SamplerAlgebra.kt

@@ -8,16 +8,28 @@ import space.kscience.kmath.operations.Group
 import space.kscience.kmath.operations.ScaleOperations
 import space.kscience.kmath.operations.invoke
 
-public class BasicSampler<T : Any>(public val chainBuilder: (RandomGenerator) -> Chain<T>) : Sampler<T> {
+/**
-    public override fun sample(generator: RandomGenerator): Chain<T> = chainBuilder(generator)
+ * Implements [Sampler] by sampling only certain [value].
-}
+ *
-
+ * @property value the value to sample.
+ */
 public class ConstantSampler<T : Any>(public val value: T) : Sampler<T> {
     public override fun sample(generator: RandomGenerator): Chain<T> = ConstantChain(value)
 }
 
 /**
- * A space for samplers. Allows to perform simple operations on distributions
+ * Implements [Sampler] by delegating sampling to value of [chainBuilder].
+ *
+ * @property chainBuilder the provider of [Chain].
+ */
+public class BasicSampler<T : Any>(public val chainBuilder: (RandomGenerator) -> Chain<T>) : Sampler<T> {
+    public override fun sample(generator: RandomGenerator): Chain<T> = chainBuilder(generator)
+}
+
+/**
+ * A space of samplers. Allows to perform simple operations on distributions.
+ *
+ * @property algebra the space to provide addition and scalar multiplication for [T].
  */
 public class SamplerSpace<T : Any, S>(public val algebra: S) : Group<Sampler<T>>,
     ScaleOperations<Sampler<T>> where S : Group<T>, S : ScaleOperations<T> {
@@ -29,8 +41,10 @@ public class SamplerSpace<T : Any, S>(public val algebra: S) : Group<Sampler<T>>
     }
 
     public override fun scale(a: Sampler<T>, value: Double): Sampler<T> = BasicSampler { generator ->
-        a.sample(generator).map { algebra { it * value } }
+        a.sample(generator).map { a ->
+            algebra { a * value }
+        }
     }
 
-    override fun Sampler<T>.unaryMinus(): Sampler<T> = scale(this, -1.0)
+    public override fun Sampler<T>.unaryMinus(): Sampler<T> = scale(this, -1.0)
 }

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/distributions/NormalDistribution.kt

@@ -0,0 +1,41 @@
+package space.kscience.kmath.stat.distributions
+
+import space.kscience.kmath.chains.Chain
+import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.kmath.stat.UnivariateDistribution
+import space.kscience.kmath.stat.internal.InternalErf
+import space.kscience.kmath.stat.samplers.GaussianSampler
+import space.kscience.kmath.stat.samplers.NormalizedGaussianSampler
+import space.kscience.kmath.stat.samplers.ZigguratNormalizedGaussianSampler
+import kotlin.math.*
+
+/**
+ * Implements [UnivariateDistribution] for the normal (gaussian) distribution.
+ */
+public inline class NormalDistribution(public val sampler: GaussianSampler) : UnivariateDistribution<Double> {
+    public constructor(
+        mean: Double,
+        standardDeviation: Double,
+        normalized: NormalizedGaussianSampler = ZigguratNormalizedGaussianSampler.of(),
+    ) : this(GaussianSampler.of(mean, standardDeviation, normalized))
+
+    public override fun probability(arg: Double): Double {
+        val x1 = (arg - sampler.mean) / sampler.standardDeviation
+        return exp(-0.5 * x1 * x1 - (ln(sampler.standardDeviation) + 0.5 * ln(2 * PI)))
+    }
+
+    public override fun sample(generator: RandomGenerator): Chain<Double> = sampler.sample(generator)
+
+    public override fun cumulative(arg: Double): Double {
+        val dev = arg - sampler.mean
+
+        return when {
+            abs(dev) > 40 * sampler.standardDeviation -> if (dev < 0) 0.0 else 1.0
+            else -> 0.5 * InternalErf.erfc(-dev / (sampler.standardDeviation * SQRT2))
+        }
+    }
+
+    private companion object {
+        private val SQRT2 = sqrt(2.0)
+    }
+}

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/internal/InternalErf.kt

@@ -0,0 +1,15 @@
+package space.kscience.kmath.stat.internal
+
+import kotlin.math.abs
+
+/**
+ * Based on Commons Math implementation.
+ * See [https://commons.apache.org/proper/commons-math/javadocs/api-3.3/org/apache/commons/math3/special/Erf.html].
+ */
+internal object InternalErf {
+    fun erfc(x: Double): Double {
+        if (abs(x) > 40) return if (x > 0) 0.0 else 2.0
+        val ret = InternalGamma.regularizedGammaQ(0.5, x * x, 10000)
+        return if (x < 0) 2 - ret else ret
+    }
+}

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/internal/InternalGamma.kt

@@ -0,0 +1,238 @@
+package space.kscience.kmath.stat.internal
+
+import kotlin.math.*
+
+private abstract class ContinuedFraction protected constructor() {
+    protected abstract fun getA(n: Int, x: Double): Double
+    protected abstract fun getB(n: Int, x: Double): Double
+
+    fun evaluate(x: Double, maxIterations: Int): Double {
+        val small = 1e-50
+        var hPrev = getA(0, x)
+        if (hPrev == 0.0 || abs(0.0 - hPrev) <= small) hPrev = small
+        var n = 1
+        var dPrev = 0.0
+        var cPrev = hPrev
+        var hN = hPrev
+
+        while (n < maxIterations) {
+            val a = getA(n, x)
+            val b = getB(n, x)
+            var dN = a + b * dPrev
+            if (dN == 0.0 || abs(0.0 - dN) <= small) dN = small
+            var cN = a + b / cPrev
+            if (cN == 0.0 || abs(0.0 - cN) <= small) cN = small
+            dN = 1 / dN
+            val deltaN = cN * dN
+            hN = hPrev * deltaN
+            check(!hN.isInfinite()) { "hN is infinite" }
+            check(!hN.isNaN()) { "hN is NaN" }
+            if (abs(deltaN - 1.0) < 10e-9) break
+            dPrev = dN
+            cPrev = cN
+            hPrev = hN
+            n++
+        }
+
+        check(n < maxIterations) { "n is more than maxIterations" }
+        return hN
+    }
+}
+
+internal object InternalGamma {
+    const val LANCZOS_G = 607.0 / 128.0
+
+    private val LANCZOS = doubleArrayOf(
+        0.99999999999999709182,
+        57.156235665862923517,
+        -59.597960355475491248,
+        14.136097974741747174,
+        -0.49191381609762019978,
+        .33994649984811888699e-4,
+        .46523628927048575665e-4,
+        -.98374475304879564677e-4,
+        .15808870322491248884e-3,
+        -.21026444172410488319e-3,
+        .21743961811521264320e-3,
+        -.16431810653676389022e-3,
+        .84418223983852743293e-4,
+        -.26190838401581408670e-4,
+        .36899182659531622704e-5
+    )
+
+    private val HALF_LOG_2_PI = 0.5 * ln(2.0 * PI)
+    private const val INV_GAMMA1P_M1_A0 = .611609510448141581788E-08
+    private const val INV_GAMMA1P_M1_A1 = .624730830116465516210E-08
+    private const val INV_GAMMA1P_M1_B1 = .203610414066806987300E+00
+    private const val INV_GAMMA1P_M1_B2 = .266205348428949217746E-01
+    private const val INV_GAMMA1P_M1_B3 = .493944979382446875238E-03
+    private const val INV_GAMMA1P_M1_B4 = -.851419432440314906588E-05
+    private const val INV_GAMMA1P_M1_B5 = -.643045481779353022248E-05
+    private const val INV_GAMMA1P_M1_B6 = .992641840672773722196E-06
+    private const val INV_GAMMA1P_M1_B7 = -.607761895722825260739E-07
+    private const val INV_GAMMA1P_M1_B8 = .195755836614639731882E-09
+    private const val INV_GAMMA1P_M1_P0 = .6116095104481415817861E-08
+    private const val INV_GAMMA1P_M1_P1 = .6871674113067198736152E-08
+    private const val INV_GAMMA1P_M1_P2 = .6820161668496170657918E-09
+    private const val INV_GAMMA1P_M1_P3 = .4686843322948848031080E-10
+    private const val INV_GAMMA1P_M1_P4 = .1572833027710446286995E-11
+    private const val INV_GAMMA1P_M1_P5 = -.1249441572276366213222E-12
+    private const val INV_GAMMA1P_M1_P6 = .4343529937408594255178E-14
+    private const val INV_GAMMA1P_M1_Q1 = .3056961078365221025009E+00
+    private const val INV_GAMMA1P_M1_Q2 = .5464213086042296536016E-01
+    private const val INV_GAMMA1P_M1_Q3 = .4956830093825887312020E-02
+    private const val INV_GAMMA1P_M1_Q4 = .2692369466186361192876E-03
+    private const val INV_GAMMA1P_M1_C = -.422784335098467139393487909917598E+00
+    private const val INV_GAMMA1P_M1_C0 = .577215664901532860606512090082402E+00
+    private const val INV_GAMMA1P_M1_C1 = -.655878071520253881077019515145390E+00
+    private const val INV_GAMMA1P_M1_C2 = -.420026350340952355290039348754298E-01
+    private const val INV_GAMMA1P_M1_C3 = .166538611382291489501700795102105E+00
+    private const val INV_GAMMA1P_M1_C4 = -.421977345555443367482083012891874E-01
+    private const val INV_GAMMA1P_M1_C5 = -.962197152787697356211492167234820E-02
+    private const val INV_GAMMA1P_M1_C6 = .721894324666309954239501034044657E-02
+    private const val INV_GAMMA1P_M1_C7 = -.116516759185906511211397108401839E-02
+    private const val INV_GAMMA1P_M1_C8 = -.215241674114950972815729963053648E-03
+    private const val INV_GAMMA1P_M1_C9 = .128050282388116186153198626328164E-03
+    private const val INV_GAMMA1P_M1_C10 = -.201348547807882386556893914210218E-04
+    private const val INV_GAMMA1P_M1_C11 = -.125049348214267065734535947383309E-05
+    private const val INV_GAMMA1P_M1_C12 = .113302723198169588237412962033074E-05
+    private const val INV_GAMMA1P_M1_C13 = -.205633841697760710345015413002057E-06
+
+    fun logGamma(x: Double): Double = when {
+        x.isNaN() || x <= 0.0 -> Double.NaN
+        x < 0.5 -> logGamma1p(x) - ln(x)
+        x <= 2.5 -> logGamma1p(x - 0.5 - 0.5)
+
+        x <= 8.0 -> {
+            val n = floor(x - 1.5).toInt()
+            val prod = (1..n).fold(1.0, { prod, i -> prod * (x - i) })
+            logGamma1p(x - (n + 1)) + ln(prod)
+        }
+
+        else -> {
+            val tmp = x + LANCZOS_G + .5
+            (x + .5) * ln(tmp) - tmp + HALF_LOG_2_PI + ln(lanczos(x) / x)
+        }
+    }
+
+    private fun regularizedGammaP(
+        a: Double,
+        x: Double,
+        maxIterations: Int = Int.MAX_VALUE
+    ): Double = when {
+        a.isNaN() || x.isNaN() || a <= 0.0 || x < 0.0 -> Double.NaN
+        x == 0.0 -> 0.0
+        x >= a + 1 -> 1.0 - regularizedGammaQ(a, x, maxIterations)
+
+        else -> {
+            // calculate series
+            var n = 0.0 // current element index
+            var an = 1.0 / a // n-th element in the series
+            var sum = an // partial sum
+
+            while (abs(an / sum) > 10e-15 && n < maxIterations && sum < Double.POSITIVE_INFINITY) {
+                // compute next element in the series
+                n += 1.0
+                an *= x / (a + n)
+
+                // update partial sum
+                sum += an
+            }
+
+            when {
+                n >= maxIterations -> throw error("Maximal iterations is exceeded $maxIterations")
+                sum.isInfinite() -> 1.0
+                else -> exp(-x + a * ln(x) - logGamma(a)) * sum
+            }
+        }
+    }
+
+    fun regularizedGammaQ(
+        a: Double,
+        x: Double,
+        maxIterations: Int = Int.MAX_VALUE
+    ): Double = when {
+        a.isNaN() || x.isNaN() || a <= 0.0 || x < 0.0 -> Double.NaN
+        x == 0.0 -> 1.0
+        x < a + 1.0 -> 1.0 - regularizedGammaP(a, x, maxIterations)
+
+        else -> 1.0 / object : ContinuedFraction() {
+            override fun getA(n: Int, x: Double): Double = 2.0 * n + 1.0 - a + x
+            override fun getB(n: Int, x: Double): Double = n * (a - n)
+        }.evaluate(x, maxIterations) * exp(-x + a * ln(x) - logGamma(a))
+    }
+
+    private fun lanczos(x: Double): Double =
+        (LANCZOS.size - 1 downTo 1).sumByDouble { LANCZOS[it] / (x + it) } + LANCZOS[0]
+
+    private fun invGamma1pm1(x: Double): Double {
+        require(x >= -0.5)
+        require(x <= 1.5)
+        val ret: Double
+        val t = if (x <= 0.5) x else x - 0.5 - 0.5
+
+        if (t < 0.0) {
+            val a = INV_GAMMA1P_M1_A0 + t * INV_GAMMA1P_M1_A1
+            var b = INV_GAMMA1P_M1_B8
+            b = INV_GAMMA1P_M1_B7 + t * b
+            b = INV_GAMMA1P_M1_B6 + t * b
+            b = INV_GAMMA1P_M1_B5 + t * b
+            b = INV_GAMMA1P_M1_B4 + t * b
+            b = INV_GAMMA1P_M1_B3 + t * b
+            b = INV_GAMMA1P_M1_B2 + t * b
+            b = INV_GAMMA1P_M1_B1 + t * b
+            b = 1.0 + t * b
+            var c = INV_GAMMA1P_M1_C13 + t * (a / b)
+            c = INV_GAMMA1P_M1_C12 + t * c
+            c = INV_GAMMA1P_M1_C11 + t * c
+            c = INV_GAMMA1P_M1_C10 + t * c
+            c = INV_GAMMA1P_M1_C9 + t * c
+            c = INV_GAMMA1P_M1_C8 + t * c
+            c = INV_GAMMA1P_M1_C7 + t * c
+            c = INV_GAMMA1P_M1_C6 + t * c
+            c = INV_GAMMA1P_M1_C5 + t * c
+            c = INV_GAMMA1P_M1_C4 + t * c
+            c = INV_GAMMA1P_M1_C3 + t * c
+            c = INV_GAMMA1P_M1_C2 + t * c
+            c = INV_GAMMA1P_M1_C1 + t * c
+            c = INV_GAMMA1P_M1_C + t * c
+            ret = (if (x > 0.5) t * c / x else x * (c + 0.5 + 0.5))
+        } else {
+            var p = INV_GAMMA1P_M1_P6
+            p = INV_GAMMA1P_M1_P5 + t * p
+            p = INV_GAMMA1P_M1_P4 + t * p
+            p = INV_GAMMA1P_M1_P3 + t * p
+            p = INV_GAMMA1P_M1_P2 + t * p
+            p = INV_GAMMA1P_M1_P1 + t * p
+            p = INV_GAMMA1P_M1_P0 + t * p
+            var q = INV_GAMMA1P_M1_Q4
+            q = INV_GAMMA1P_M1_Q3 + t * q
+            q = INV_GAMMA1P_M1_Q2 + t * q
+            q = INV_GAMMA1P_M1_Q1 + t * q
+            q = 1.0 + t * q
+            var c = INV_GAMMA1P_M1_C13 + p / q * t
+            c = INV_GAMMA1P_M1_C12 + t * c
+            c = INV_GAMMA1P_M1_C11 + t * c
+            c = INV_GAMMA1P_M1_C10 + t * c
+            c = INV_GAMMA1P_M1_C9 + t * c
+            c = INV_GAMMA1P_M1_C8 + t * c
+            c = INV_GAMMA1P_M1_C7 + t * c
+            c = INV_GAMMA1P_M1_C6 + t * c
+            c = INV_GAMMA1P_M1_C5 + t * c
+            c = INV_GAMMA1P_M1_C4 + t * c
+            c = INV_GAMMA1P_M1_C3 + t * c
+            c = INV_GAMMA1P_M1_C2 + t * c
+            c = INV_GAMMA1P_M1_C1 + t * c
+            c = INV_GAMMA1P_M1_C0 + t * c
+            ret = (if (x > 0.5) t / x * (c - 0.5 - 0.5) else x * c)
+        }
+
+        return ret
+    }
+
+    private fun logGamma1p(x: Double): Double {
+        require(x >= -0.5)
+        require(x <= 1.5)
+        return -ln1p(invGamma1pm1(x))
+    }
+}

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/internal/InternalUtils.kt

@@ -0,0 +1,70 @@
+package space.kscience.kmath.stat.internal
+
+import kotlin.math.ln
+import kotlin.math.min
+
+internal object InternalUtils {
+    private val FACTORIALS = longArrayOf(
+        1L, 1L, 2L,
+        6L, 24L, 120L,
+        720L, 5040L, 40320L,
+        362880L, 3628800L, 39916800L,
+        479001600L, 6227020800L, 87178291200L,
+        1307674368000L, 20922789888000L, 355687428096000L,
+        6402373705728000L, 121645100408832000L, 2432902008176640000L
+    )
+
+    private const val BEGIN_LOG_FACTORIALS = 2
+
+    fun factorial(n: Int): Long = FACTORIALS[n]
+
+    fun validateProbabilities(probabilities: DoubleArray?): Double {
+        require(!(probabilities == null || probabilities.isEmpty())) { "Probabilities must not be empty." }
+
+        val sumProb = probabilities.sumByDouble { prob ->
+            require(!(prob < 0 || prob.isInfinite() || prob.isNaN())) { "Invalid probability: $prob" }
+            prob
+        }
+
+        require(!(sumProb.isInfinite() || sumProb <= 0)) { "Invalid sum of probabilities: $sumProb" }
+        return sumProb
+    }
+
+    class FactorialLog private constructor(numValues: Int, cache: DoubleArray?) {
+        private val logFactorials: DoubleArray = DoubleArray(numValues)
+
+        init {
+            val endCopy: Int
+
+            if (cache != null && cache.size > BEGIN_LOG_FACTORIALS) {
+                // Copy available values.
+                endCopy = min(cache.size, numValues)
+
+                cache.copyInto(
+                    logFactorials,
+                    BEGIN_LOG_FACTORIALS,
+                    BEGIN_LOG_FACTORIALS, endCopy
+                )
+            } else
+            // All values to be computed
+                endCopy = BEGIN_LOG_FACTORIALS
+
+            // Compute remaining values.
+            (endCopy until numValues).forEach { i ->
+                if (i < FACTORIALS.size)
+                    logFactorials[i] = ln(FACTORIALS[i].toDouble())
+                else
+                    logFactorials[i] = logFactorials[i - 1] + ln(i.toDouble())
+            }
+        }
+
+        fun value(n: Int): Double {
+            if (n < logFactorials.size) return logFactorials[n]
+            return if (n < FACTORIALS.size) ln(FACTORIALS[n].toDouble()) else InternalGamma.logGamma(n + 1.0)
+        }
+
+        companion object {
+            fun create(): FactorialLog = FactorialLog(0, null)
+        }
+    }
+}

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/AhrensDieterExponentialSampler.kt

@@ -0,0 +1,73 @@
+package space.kscience.kmath.stat.samplers
+
+import space.kscience.kmath.chains.Chain
+import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.kmath.stat.Sampler
+import space.kscience.kmath.stat.chain
+import space.kscience.kmath.stat.internal.InternalUtils
+import kotlin.math.ln
+import kotlin.math.pow
+
+/**
+ * Sampling from an [exponential distribution](http://mathworld.wolfram.com/ExponentialDistribution.html).
+ *
+ * Based on Commons RNG implementation.
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AhrensDieterExponentialSampler.html].
+ */
+public class AhrensDieterExponentialSampler private constructor(public val mean: Double) : Sampler<Double> {
+    public override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {
+        // Step 1:
+        var a = 0.0
+        var u = nextDouble()
+
+        // Step 2 and 3:
+        while (u < 0.5) {
+            a += EXPONENTIAL_SA_QI[0]
+            u *= 2.0
+        }
+
+        // Step 4 (now u >= 0.5):
+        u += u - 1
+        // Step 5:
+        if (u <= EXPONENTIAL_SA_QI[0]) return@chain mean * (a + u)
+        // Step 6:
+        var i = 0 // Should be 1, be we iterate before it in while using 0.
+        var u2 = nextDouble()
+        var umin = u2
+
+        // Step 7 and 8:
+        do {
+            ++i
+            u2 = nextDouble()
+            if (u2 < umin) umin = u2
+            // Step 8:
+        } while (u > EXPONENTIAL_SA_QI[i]) // Ensured to exit since EXPONENTIAL_SA_QI[MAX] = 1.
+
+        mean * (a + umin * EXPONENTIAL_SA_QI[0])
+    }
+
+    override fun toString(): String = "Ahrens-Dieter Exponential deviate"
+
+    public companion object {
+        private val EXPONENTIAL_SA_QI by lazy { DoubleArray(16) }
+
+        init {
+            /**
+             * Filling EXPONENTIAL_SA_QI table.
+             * Note that we don't want qi = 0 in the table.
+             */
+            val ln2 = ln(2.0)
+            var qi = 0.0
+
+            EXPONENTIAL_SA_QI.indices.forEach { i ->
+                qi += ln2.pow(i + 1.0) / InternalUtils.factorial(i + 1)
+                EXPONENTIAL_SA_QI[i] = qi
+            }
+        }
+
+        public fun of(mean: Double): AhrensDieterExponentialSampler {
+            require(mean > 0) { "mean is not strictly positive: $mean" }
+            return AhrensDieterExponentialSampler(mean)
+        }
+    }
+}

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/AhrensDieterMarsagliaTsangGammaSampler.kt

@@ -0,0 +1,120 @@
+package space.kscience.kmath.stat.samplers
+
+import space.kscience.kmath.chains.Chain
+import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.kmath.stat.Sampler
+import space.kscience.kmath.stat.chain
+import space.kscience.kmath.stat.next
+import kotlin.math.*
+
+/**
+ * Sampling from the [gamma distribution](http://mathworld.wolfram.com/GammaDistribution.html).
+ *  - For 0 < alpha < 1:
+ *    Ahrens, J. H. and Dieter, U., Computer methods for sampling from gamma, beta, Poisson and binomial distributions, Computing, 12, 223-246, 1974.
+ *  - For alpha >= 1:
+ *    Marsaglia and Tsang, A Simple Method for Generating Gamma Variables. ACM Transactions on Mathematical Software, Volume 26 Issue 3, September, 2000.
+ *
+ * Based on Commons RNG implementation.
+ *
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AhrensDieterMarsagliaTsangGammaSampler.html].
+ */
+public class AhrensDieterMarsagliaTsangGammaSampler private constructor(
+    alpha: Double,
+    theta: Double
+) : Sampler<Double> {
+    private val delegate: BaseGammaSampler =
+        if (alpha < 1) AhrensDieterGammaSampler(alpha, theta) else MarsagliaTsangGammaSampler(alpha, theta)
+
+    private abstract class BaseGammaSampler internal constructor(
+        protected val alpha: Double,
+        protected val theta: Double
+    ) : Sampler<Double> {
+        init {
+            require(alpha > 0) { "alpha is not strictly positive: $alpha" }
+            require(theta > 0) { "theta is not strictly positive: $theta" }
+        }
+
+        override fun toString(): String = "Ahrens-Dieter-Marsaglia-Tsang Gamma deviate"
+    }
+
+    private class AhrensDieterGammaSampler(alpha: Double, theta: Double) :
+        BaseGammaSampler(alpha, theta) {
+        private val oneOverAlpha: Double = 1.0 / alpha
+        private val bGSOptim: Double = 1.0 + alpha / E
+
+        override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {
+            var x: Double
+
+            // [1]: p. 228, Algorithm GS.
+            while (true) {
+                // Step 1:
+                val u = generator.nextDouble()
+                val p = bGSOptim * u
+
+                if (p <= 1) {
+                    // Step 2:
+                    x = p.pow(oneOverAlpha)
+                    val u2 = generator.nextDouble()
+
+                    if (u2 > exp(-x)) // Reject.
+                        continue
+
+                    break
+                }
+
+                // Step 3:
+                x = -ln((bGSOptim - p) * oneOverAlpha)
+                val u2: Double = generator.nextDouble()
+                if (u2 <= x.pow(alpha - 1.0)) break
+                // Reject and continue.
+            }
+
+            x * theta
+        }
+    }
+
+    private class MarsagliaTsangGammaSampler(alpha: Double, theta: Double) :
+        BaseGammaSampler(alpha, theta) {
+        private val dOptim: Double
+        private val cOptim: Double
+        private val gaussian: NormalizedGaussianSampler
+
+        init {
+            gaussian = ZigguratNormalizedGaussianSampler.of()
+            dOptim = alpha - ONE_THIRD
+            cOptim = ONE_THIRD / sqrt(dOptim)
+        }
+
+        override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {
+            var v: Double
+
+            while (true) {
+                val x = gaussian.next(generator)
+                val oPcTx = 1 + cOptim * x
+                v = oPcTx * oPcTx * oPcTx
+                if (v <= 0) continue
+                val x2 = x * x
+                val u = generator.nextDouble()
+                // Squeeze.
+                if (u < 1 - 0.0331 * x2 * x2) break
+                if (ln(u) < 0.5 * x2 + dOptim * (1 - v + ln(v))) break
+            }
+
+            theta * dOptim * v
+        }
+
+        companion object {
+            private const val ONE_THIRD = 1.0 / 3.0
+        }
+    }
+
+    public override fun sample(generator: RandomGenerator): Chain<Double> = delegate.sample(generator)
+    public override fun toString(): String = delegate.toString()
+
+    public companion object {
+        public fun of(
+            alpha: Double,
+            theta: Double
+        ): Sampler<Double> = AhrensDieterMarsagliaTsangGammaSampler(alpha, theta)
+    }
+}

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/AliasMethodDiscreteSampler.kt

@@ -0,0 +1,286 @@
+package space.kscience.kmath.stat.samplers
+
+import space.kscience.kmath.chains.Chain
+import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.kmath.stat.Sampler
+import space.kscience.kmath.stat.chain
+import space.kscience.kmath.stat.internal.InternalUtils
+import kotlin.math.ceil
+import kotlin.math.max
+import kotlin.math.min
+
+/**
+ * Distribution sampler that uses the Alias method. It can be used to sample from n values each with an associated
+ * probability. This implementation is based on the detailed explanation of the alias method by Keith Schartz and
+ * implements Vose's algorithm.
+ *
+ * Vose, M.D., A linear algorithm for generating random numbers with a given distribution, IEEE Transactions on
+ * Software Engineering, 17, 972-975, 1991. he algorithm will sample values in O(1) time after a pre-processing step
+ * of O(n) time.
+ *
+ * The alias tables are constructed using fraction probabilities with an assumed denominator of 253. In the generic
+ * case sampling uses UniformRandomProvider.nextInt(int) and the upper 53-bits from UniformRandomProvider.nextLong().
+ *
+ * Zero padding the input probabilities can be used to make more sampling more efficient. Any zero entry will always be
+ * aliased removing the requirement to compute a long. Increased sampling speed comes at the cost of increased storage
+ * space. The algorithm requires approximately 12 bytes of storage per input probability, that is n * 12 for size n.
+ * Zero-padding only requires 4 bytes of storage per padded value as the probability is known to be zero.
+ *
+ * An optimisation is performed for small table sizes that are a power of 2. In this case the sampling uses 1 or 2
+ * calls from UniformRandomProvider.nextInt() to generate up to 64-bits for creation of an 11-bit index and 53-bits
+ * for the long. This optimisation requires a generator with a high cycle length for the lower order bits.
+ *
+ * Larger table sizes that are a power of 2 will benefit from fast algorithms for UniformRandomProvider.nextInt(int)
+ * that exploit the power of 2.
+ *
+ * Based on Commons RNG implementation.
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AliasMethodDiscreteSampler.html].
+ */
+public open class AliasMethodDiscreteSampler private constructor(
+    // Deliberate direct storage of input arrays
+    protected val probability: LongArray,
+    protected val alias: IntArray
+) : Sampler<Int> {
+
+    private class SmallTableAliasMethodDiscreteSampler(
+        probability: LongArray,
+        alias: IntArray
+    ) : AliasMethodDiscreteSampler(probability, alias) {
+        // Assume the table size is a power of 2 and create the mask
+        private val mask: Int = alias.size - 1
+
+        override fun sample(generator: RandomGenerator): Chain<Int> = generator.chain {
+            val bits = generator.nextInt()
+            // Isolate lower bits
+            val j = bits and mask
+
+            // Optimisation for zero-padded input tables
+            if (j >= probability.size)
+            // No probability must use the alias
+                return@chain alias[j]
+
+            // Create a uniform random deviate as a long.
+            // This replicates functionality from the o.a.c.rng.core.utils.NumberFactory.makeLong
+            val longBits = generator.nextInt().toLong() shl 32 or (bits.toLong() and hex_ffffffff)
+            // Choose between the two. Use a 53-bit long for the probability.
+            if (longBits ushr 11 < probability[j]) j else alias[j]
+        }
+
+        private companion object {
+            private const val hex_ffffffff = 4294967295L
+        }
+    }
+
+    public override fun sample(generator: RandomGenerator): Chain<Int> = generator.chain {
+        // This implements the algorithm as per Vose (1991):
+        // v = uniform()  in [0, 1)
+        // j = uniform(n) in [0, n)
+        // if v < prob[j] then
+        //   return j
+        // else
+        //   return alias[j]
+        val j = generator.nextInt(alias.size)
+
+        // Optimisation for zero-padded input tables
+        // No probability must use the alias
+        if (j >= probability.size) return@chain alias[j]
+
+        // Note: We could check the probability before computing a deviate.
+        // p(j) == 0  => alias[j]
+        // p(j) == 1  => j
+        // However it is assumed these edge cases are rare:
+        //
+        // The probability table will be 1 for approximately 1/n samples, i.e. only the
+        // last unpaired probability. This is only worth checking for when the table size (n)
+        // is small. But in that case the user should zero-pad the table for performance.
+        //
+        // The probability table will be 0 when an input probability was zero. We
+        // will assume this is also rare if modelling a discrete distribution where
+        // all samples are possible. The edge case for zero-padded tables is handled above.
+
+        // Choose between the two. Use a 53-bit long for the probability.
+        if (generator.nextLong() ushr 11 < probability[j]) j else alias[j]
+    }
+
+    public override fun toString(): String = "Alias method"
+
+    public companion object {
+        private const val DEFAULT_ALPHA = 0
+        private const val ZERO = 0.0
+        private const val ONE_AS_NUMERATOR = 1L shl 53
+        private const val CONVERT_TO_NUMERATOR: Double = ONE_AS_NUMERATOR.toDouble()
+        private const val MAX_SMALL_POWER_2_SIZE = 1 shl 11
+
+        public fun of(
+            probabilities: DoubleArray,
+            alpha: Int = DEFAULT_ALPHA
+        ): Sampler<Int> {
+            // The Alias method balances N categories with counts around the mean into N sections,
+            // each allocated 'mean' observations.
+            //
+            // Consider 4 categories with counts 6,3,2,1. The histogram can be balanced into a
+            // 2D array as 4 sections with a height of the mean:
+            //
+            // 6
+            // 6
+            // 6
+            // 63   => 6366   --
+            // 632     6326    |-- mean
+            // 6321    6321   --
+            //
+            // section abcd
+            //
+            // Each section is divided as:
+            // a: 6=1/1
+            // b: 3=1/1
+            // c: 2=2/3; 6=1/3   (6 is the alias)
+            // d: 1=1/3; 6=2/3   (6 is the alias)
+            //
+            // The sample is obtained by randomly selecting a section, then choosing which category
+            // from the pair based on a uniform random deviate.
+            val sumProb = InternalUtils.validateProbabilities(probabilities)
+            // Allow zero-padding
+            val n = computeSize(probabilities.size, alpha)
+            // Partition into small and large by splitting on the average.
+            val mean = sumProb / n
+            // The cardinality of smallSize + largeSize = n.
+            // So fill the same array from either end.
+            val indices = IntArray(n)
+            var large = n
+            var small = 0
+
+            probabilities.indices.forEach { i ->
+                if (probabilities[i] >= mean) indices[--large] = i else indices[small++] = i
+            }
+
+            small = fillRemainingIndices(probabilities.size, indices, small)
+            // This may be smaller than the input length if the probabilities were already padded.
+            val nonZeroIndex = findLastNonZeroIndex(probabilities)
+            // The probabilities are modified so use a copy.
+            // Note: probabilities are required only up to last nonZeroIndex
+            val remainingProbabilities = probabilities.copyOf(nonZeroIndex + 1)
+            // Allocate the final tables.
+            // Probability table may be truncated (when zero padded).
+            // The alias table is full length.
+            val probability = LongArray(remainingProbabilities.size)
+            val alias = IntArray(n)
+
+            // This loop uses each large in turn to fill the alias table for small probabilities that
+            // do not reach the requirement to fill an entire section alone (i.e. p < mean).
+            // Since the sum of the small should be less than the sum of the large it should use up
+            // all the small first. However floating point round-off can result in
+            // misclassification of items as small or large. The Vose algorithm handles this using
+            // a while loop conditioned on the size of both sets and a subsequent loop to use
+            // unpaired items.
+            while (large != n && small != 0) {
+                // Index of the small and the large probabilities.
+                val j = indices[--small]
+                val k = indices[large++]
+
+                // Optimisation for zero-padded input:
+                // p(j) = 0 above the last nonZeroIndex
+                if (j > nonZeroIndex)
+                // The entire amount for the section is taken from the alias.
+                    remainingProbabilities[k] -= mean
+                else {
+                    val pj = remainingProbabilities[j]
+                    // Item j is a small probability that is below the mean.
+                    // Compute the weight of the section for item j: pj / mean.
+                    // This is scaled by 2^53 and the ceiling function used to round-up
+                    // the probability to a numerator of a fraction in the range [1,2^53].
+                    // Ceiling ensures non-zero values.
+                    probability[j] = ceil(CONVERT_TO_NUMERATOR * (pj / mean)).toLong()
+                    // The remaining amount for the section is taken from the alias.
+                    // Effectively: probabilities[k] -= (mean - pj)
+                    remainingProbabilities[k] += pj - mean
+                }
+
+                // If not j then the alias is k
+                alias[j] = k
+
+                // Add the remaining probability from large to the appropriate list.
+                if (remainingProbabilities[k] >= mean) indices[--large] = k else indices[small++] = k
+            }
+
+            // Final loop conditions to consume unpaired items.
+            // Note: The large set should never be non-empty but this can occur due to round-off
+            // error so consume from both.
+            fillTable(probability, alias, indices, 0, small)
+            fillTable(probability, alias, indices, large, n)
+
+            // Change the algorithm for small power of 2 sized tables
+            return if (isSmallPowerOf2(n))
+                SmallTableAliasMethodDiscreteSampler(probability, alias)
+            else
+                AliasMethodDiscreteSampler(probability, alias)
+        }
+
+        private fun fillRemainingIndices(length: Int, indices: IntArray, small: Int): Int {
+            var updatedSmall = small
+            (length until indices.size).forEach { i -> indices[updatedSmall++] = i }
+            return updatedSmall
+        }
+
+        private fun findLastNonZeroIndex(probabilities: DoubleArray): Int {
+            // No bounds check is performed when decrementing as the array contains at least one
+            // value above zero.
+            var nonZeroIndex = probabilities.size - 1
+            while (probabilities[nonZeroIndex] == ZERO) nonZeroIndex--
+            return nonZeroIndex
+        }
+
+        private fun computeSize(length: Int, alpha: Int): Int {
+            // If No padding
+            if (alpha < 0) return length
+            // Use the number of leading zeros function to find the next power of 2,
+            // i.e. ceil(log2(x))
+            var pow2 = 32 - numberOfLeadingZeros(length - 1)
+            // Increase by the alpha. Clip this to limit to a positive integer (2^30)
+            pow2 = min(30, pow2 + alpha)
+            // Use max to handle a length above the highest possible power of 2
+            return max(length, 1 shl pow2)
+        }
+
+        private fun fillTable(
+            probability: LongArray,
+            alias: IntArray,
+            indices: IntArray,
+            start: Int,
+            end: Int
+        ) = (start until end).forEach { i ->
+            val index = indices[i]
+            probability[index] = ONE_AS_NUMERATOR
+            alias[index] = index
+        }
+
+        private fun isSmallPowerOf2(n: Int): Boolean = n <= MAX_SMALL_POWER_2_SIZE && n and n - 1 == 0
+
+        private fun numberOfLeadingZeros(i: Int): Int {
+            var mutI = i
+            if (mutI <= 0) return if (mutI == 0) 32 else 0
+            var n = 31
+
+            if (mutI >= 1 shl 16) {
+                n -= 16
+                mutI = mutI ushr 16
+            }
+
+            if (mutI >= 1 shl 8) {
+                n -= 8
+                mutI = mutI ushr 8
+            }
+
+            if (mutI >= 1 shl 4) {
+                n -= 4
+                mutI = mutI ushr 4
+            }
+
+            if (mutI >= 1 shl 2) {
+                n -= 2
+                mutI = mutI ushr 2
+            }
+
+            return n - (mutI ushr 1)
+        }
+    }
+}

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/BoxMullerNormalizedGaussianSampler.kt

@@ -0,0 +1,48 @@
+package space.kscience.kmath.stat.samplers
+
+import space.kscience.kmath.chains.Chain
+import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.kmath.stat.Sampler
+import space.kscience.kmath.stat.chain
+import kotlin.math.*
+
+/**
+ * [Box-Muller algorithm](https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform) for sampling from a Gaussian
+ * distribution.
+ *
+ * Based on Commons RNG implementation.
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/BoxMullerNormalizedGaussianSampler.html].
+ */
+public class BoxMullerNormalizedGaussianSampler private constructor() : NormalizedGaussianSampler, Sampler<Double> {
+    private var nextGaussian: Double = Double.NaN
+
+    public override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {
+        val random: Double
+
+        if (nextGaussian.isNaN()) {
+            // Generate a pair of Gaussian numbers.
+            val x = nextDouble()
+            val y = nextDouble()
+            val alpha = 2 * PI * x
+            val r = sqrt(-2 * ln(y))
+            // Return the first element of the generated pair.
+            random = r * cos(alpha)
+            // Keep second element of the pair for next invocation.
+            nextGaussian = r * sin(alpha)
+        } else {
+            // Use the second element of the pair (generated at the
+            // previous invocation).
+            random = nextGaussian
+            // Both elements of the pair have been used.
+            nextGaussian = Double.NaN
+        }
+
+        random
+    }
+
+    public override fun toString(): String = "Box-Muller normalized Gaussian deviate"
+
+    public companion object {
+        public fun of(): BoxMullerNormalizedGaussianSampler = BoxMullerNormalizedGaussianSampler()
+    }
+}

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/GaussianSampler.kt

@@ -0,0 +1,43 @@
+package space.kscience.kmath.stat.samplers
+
+import space.kscience.kmath.chains.Chain
+import space.kscience.kmath.chains.map
+import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.kmath.stat.Sampler
+
+/**
+ * Sampling from a Gaussian distribution with given mean and standard deviation.
+ *
+ * Based on Commons RNG implementation.
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/GaussianSampler.html].
+ *
+ * @property mean the mean of the distribution.
+ * @property standardDeviation the variance of the distribution.
+ */
+public class GaussianSampler private constructor(
+    public val mean: Double,
+    public val standardDeviation: Double,
+    private val normalized: NormalizedGaussianSampler
+) : Sampler<Double> {
+    public override fun sample(generator: RandomGenerator): Chain<Double> = normalized
+        .sample(generator)
+        .map { standardDeviation * it + mean }
+
+    override fun toString(): String = "Gaussian deviate [$normalized]"
+
+    public companion object {
+        public fun of(
+            mean: Double,
+            standardDeviation: Double,
+            normalized: NormalizedGaussianSampler = ZigguratNormalizedGaussianSampler.of()
+        ): GaussianSampler {
+            require(standardDeviation > 0.0) { "standard deviation is not strictly positive: $standardDeviation" }
+
+            return GaussianSampler(
+                mean,
+                standardDeviation,
+                normalized
+            )
+        }
+    }
+}

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/KempSmallMeanPoissonSampler.kt

@@ -0,0 +1,63 @@
+package space.kscience.kmath.stat.samplers
+
+import space.kscience.kmath.chains.Chain
+import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.kmath.stat.Sampler
+import space.kscience.kmath.stat.chain
+import kotlin.math.exp
+
+/**
+ * Sampler for the Poisson distribution.
+ * - 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.
+ * This sampler is suitable for mean < 40. For large means, LargeMeanPoissonSampler should be used instead.
+ *
+ * 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.
+ *
+ * Sampling uses 1 call to UniformRandomProvider.nextDouble(). This method provides an alternative to the SmallMeanPoissonSampler for slow generators of double.
+ *
+ * Based on Commons RNG implementation.
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/KempSmallMeanPoissonSampler.html].
+ */
+public class KempSmallMeanPoissonSampler private constructor(
+    private val p0: Double,
+    private val mean: Double
+) : Sampler<Int> {
+    public override fun sample(generator: RandomGenerator): Chain<Int> = generator.chain {
+        // Note on the algorithm:
+        // - X is the unknown sample deviate (the output of the algorithm)
+        // - x is the current value from the distribution
+        // - p is the probability of the current value x, p(X=x)
+        // - u is effectively the cumulative probability that the sample X
+        //   is equal or above the current value x, p(X>=x)
+        // So if p(X>=x) > p(X=x) the sample must be above x, otherwise it is x
+        var u = nextDouble()
+        var x = 0
+        var p = p0
+
+        while (u > p) {
+            u -= p
+            // Compute the next probability using a recurrence relation.
+            // p(x+1) = p(x) * mean / (x+1)
+            p *= mean / ++x
+            // The algorithm listed in Kemp (1981) does not check that the rolling probability
+            // is positive. This check is added to ensure no errors when the limit of the summation
+            // 1 - sum(p(x)) is above 0 due to cumulative error in floating point arithmetic.
+            if (p == 0.0) return@chain x
+        }
+
+        x
+    }
+
+    public override fun toString(): String = "Kemp Small Mean Poisson deviate"
+
+    public companion object {
+        public fun of(mean: Double): KempSmallMeanPoissonSampler {
+            require(mean > 0) { "Mean is not strictly positive: $mean" }
+            val p0 = exp(-mean)
+            // Probability must be positive. As mean increases then p(0) decreases.
+            require(p0 > 0) { "No probability for mean: $mean" }
+            return KempSmallMeanPoissonSampler(p0, mean)
+        }
+    }
+}
+

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/LargeMeanPoissonSampler.kt

@@ -0,0 +1,130 @@
+package space.kscience.kmath.stat.samplers
+
+import space.kscience.kmath.chains.Chain
+import space.kscience.kmath.chains.ConstantChain
+import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.kmath.stat.Sampler
+import space.kscience.kmath.stat.chain
+import space.kscience.kmath.stat.internal.InternalUtils
+import space.kscience.kmath.stat.next
+import kotlin.math.*
+
+/**
+ * Sampler for the Poisson distribution.
+ * - For large means, we use the rejection algorithm described in
+ *   Devroye, Luc. (1981).The Computer Generation of Poisson Random Variables
+ *   Computing vol. 26 pp. 197-207.
+ *
+ * This sampler is suitable for mean >= 40.
+ *
+ * Based on Commons RNG implementation.
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/LargeMeanPoissonSampler.html].
+ */
+public class LargeMeanPoissonSampler private constructor(public val mean: Double) : Sampler<Int> {
+    private val exponential: Sampler<Double> = AhrensDieterExponentialSampler.of(1.0)
+    private val gaussian: Sampler<Double> = ZigguratNormalizedGaussianSampler.of()
+    private val factorialLog: InternalUtils.FactorialLog = NO_CACHE_FACTORIAL_LOG
+    private val lambda: Double = floor(mean)
+    private val logLambda: Double = ln(lambda)
+    private val logLambdaFactorial: Double = getFactorialLog(lambda.toInt())
+    private val delta: Double = sqrt(lambda * ln(32 * lambda / PI + 1))
+    private val halfDelta: Double = delta / 2
+    private val twolpd: Double = 2 * lambda + delta
+    private val c1: Double = 1 / (8 * lambda)
+    private val a1: Double = sqrt(PI * twolpd) * exp(c1)
+    private val a2: Double = twolpd / delta * exp(-delta * (1 + delta) / twolpd)
+    private val aSum: Double = a1 + a2 + 1
+    private val p1: Double = a1 / aSum
+    private val p2: Double = a2 / aSum
+
+    private val smallMeanPoissonSampler: Sampler<Int> = if (mean - lambda < Double.MIN_VALUE)
+        NO_SMALL_MEAN_POISSON_SAMPLER
+    else  // Not used.
+        KempSmallMeanPoissonSampler.of(mean - lambda)
+
+    public override fun sample(generator: RandomGenerator): Chain<Int> = generator.chain {
+        // This will never be null. It may be a no-op delegate that returns zero.
+        val y2 = smallMeanPoissonSampler.next(generator)
+        var x: Double
+        var y: Double
+        var v: Double
+        var a: Int
+        var t: Double
+        var qr: Double
+        var qa: Double
+
+        while (true) {
+            // Step 1:
+            val u = generator.nextDouble()
+
+            if (u <= p1) {
+                // Step 2:
+                val n = gaussian.next(generator)
+                x = n * sqrt(lambda + halfDelta) - 0.5
+                if (x > delta || x < -lambda) continue
+                y = if (x < 0) floor(x) else ceil(x)
+                val e = exponential.next(generator)
+                v = -e - 0.5 * n * n + c1
+            } else {
+                // Step 3:
+                if (u > p1 + p2) {
+                    y = lambda
+                    break
+                }
+
+                x = delta + twolpd / delta * exponential.next(generator)
+                y = ceil(x)
+                v = -exponential.next(generator) - delta * (x + 1) / twolpd
+            }
+
+            // The Squeeze Principle
+            // Step 4.1:
+            a = if (x < 0) 1 else 0
+            t = y * (y + 1) / (2 * lambda)
+
+            // Step 4.2
+            if (v < -t && a == 0) {
+                y += lambda
+                break
+            }
+
+            // Step 4.3:
+            qr = t * ((2 * y + 1) / (6 * lambda) - 1)
+            qa = qr - t * t / (3 * (lambda + a * (y + 1)))
+
+            // Step 4.4:
+            if (v < qa) {
+                y += lambda
+                break
+            }
+
+            // Step 4.5:
+            if (v > qr) continue
+
+            // Step 4.6:
+            if (v < y * logLambda - getFactorialLog((y + lambda).toInt()) + logLambdaFactorial) {
+                y += lambda
+                break
+            }
+        }
+
+        min(y2 + y.toLong(), Int.MAX_VALUE.toLong()).toInt()
+    }
+
+    private fun getFactorialLog(n: Int): Double = factorialLog.value(n)
+    public override fun toString(): String = "Large Mean Poisson deviate"
+
+    public companion object {
+        private const val MAX_MEAN: Double = 0.5 * Int.MAX_VALUE
+        private val NO_CACHE_FACTORIAL_LOG: InternalUtils.FactorialLog = InternalUtils.FactorialLog.create()
+
+        private val NO_SMALL_MEAN_POISSON_SAMPLER: Sampler<Int> = Sampler { ConstantChain(0) }
+
+        public fun of(mean: Double): LargeMeanPoissonSampler {
+            require(mean >= 1) { "mean is not >= 1: $mean" }
+            // The algorithm is not valid if Math.floor(mean) is not an integer.
+            require(mean <= MAX_MEAN) { "mean $mean > $MAX_MEAN" }
+            return LargeMeanPoissonSampler(mean)
+        }
+    }
+}

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/MarsagliaNormalizedGaussianSampler.kt

@@ -0,0 +1,61 @@
+package space.kscience.kmath.stat.samplers
+
+import space.kscience.kmath.chains.Chain
+import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.kmath.stat.Sampler
+import space.kscience.kmath.stat.chain
+import kotlin.math.ln
+import kotlin.math.sqrt
+
+/**
+ * [Marsaglia polar method](https://en.wikipedia.org/wiki/Marsaglia_polar_method) for sampling from a Gaussian
+ * distribution with mean 0 and standard deviation 1. This is a variation of the algorithm implemented in
+ * [BoxMullerNormalizedGaussianSampler].
+ *
+ * Based on Commons RNG implementation.
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/MarsagliaNormalizedGaussianSampler.html]
+ */
+public class MarsagliaNormalizedGaussianSampler private constructor() : NormalizedGaussianSampler, Sampler<Double> {
+    private var nextGaussian = Double.NaN
+
+    public override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {
+        if (nextGaussian.isNaN()) {
+            val alpha: Double
+            var x: Double
+
+            // Rejection scheme for selecting a pair that lies within the unit circle.
+            while (true) {
+                // Generate a pair of numbers within [-1 , 1).
+                x = 2.0 * generator.nextDouble() - 1.0
+                val y = 2.0 * generator.nextDouble() - 1.0
+                val r2 = x * x + y * y
+
+                if (r2 < 1 && r2 > 0) {
+                    // Pair (x, y) is within unit circle.
+                    alpha = sqrt(-2 * ln(r2) / r2)
+                    // Keep second element of the pair for next invocation.
+                    nextGaussian = alpha * y
+                    // Return the first element of the generated pair.
+                    break
+                }
+                // Pair is not within the unit circle: Generate another one.
+            }
+
+            // Return the first element of the generated pair.
+            alpha * x
+        } else {
+            // Use the second element of the pair (generated at the
+            // previous invocation).
+            val r = nextGaussian
+            // Both elements of the pair have been used.
+            nextGaussian = Double.NaN
+            r
+        }
+    }
+
+    public override fun toString(): String = "Box-Muller (with rejection) normalized Gaussian deviate"
+
+    public companion object {
+        public fun of(): MarsagliaNormalizedGaussianSampler = MarsagliaNormalizedGaussianSampler()
+    }
+}

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/NormalizedGaussianSampler.kt

@@ -0,0 +1,9 @@
+package space.kscience.kmath.stat.samplers
+
+import space.kscience.kmath.stat.Sampler
+
+/**
+ * Marker interface for a sampler that generates values from an N(0,1)
+ * [Gaussian distribution](https://en.wikipedia.org/wiki/Normal_distribution).
+ */
+public interface NormalizedGaussianSampler : Sampler<Double>

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/PoissonSampler.kt

@@ -0,0 +1,30 @@
+package space.kscience.kmath.stat.samplers
+
+import space.kscience.kmath.chains.Chain
+import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.kmath.stat.Sampler
+
+/**
+ * Sampler for the Poisson distribution.
+ * - For small means, a Poisson process is simulated using uniform deviates, as described in
+ *   Knuth (1969). Seminumerical Algorithms. The Art of Computer Programming, Volume 2. Chapter 3.4.1.F.3
+ *   Important integer-valued distributions: The Poisson distribution. Addison Wesley.
+ * The Poisson process (and hence, the returned value) is bounded by 1000 * mean.
+ * - For large means, we use the rejection algorithm described in
+ *   Devroye, Luc. (1981). The Computer Generation of Poisson Random Variables Computing vol. 26 pp. 197-207.
+ *
+ * Based on Commons RNG implementation.
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/PoissonSampler.html].
+ */
+public class PoissonSampler private constructor(mean: Double) : Sampler<Int> {
+    private val poissonSamplerDelegate: Sampler<Int> = of(mean)
+    public override fun sample(generator: RandomGenerator): Chain<Int> = poissonSamplerDelegate.sample(generator)
+    public override fun toString(): String = poissonSamplerDelegate.toString()
+
+    public companion object {
+        private const val PIVOT = 40.0
+
+        public fun of(mean: Double): Sampler<Int> =// Each sampler should check the input arguments.
+            if (mean < PIVOT) SmallMeanPoissonSampler.of(mean) else LargeMeanPoissonSampler.of(mean)
+    }
+}

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/SmallMeanPoissonSampler.kt

@@ -0,0 +1,50 @@
+package space.kscience.kmath.stat.samplers
+
+import space.kscience.kmath.chains.Chain
+import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.kmath.stat.Sampler
+import space.kscience.kmath.stat.chain
+import kotlin.math.ceil
+import kotlin.math.exp
+
+/**
+ * Sampler for the Poisson distribution.
+ * - For small means, a Poisson process is simulated using uniform deviates, as described in
+ *   Knuth (1969). Seminumerical Algorithms. The Art of Computer Programming, Volume 2. Chapter 3.4.1.F.3 Important
+ *   integer-valued distributions: The Poisson distribution. Addison Wesley.
+ * - The Poisson process (and hence, the returned value) is bounded by 1000 * mean.
+ *   This sampler is suitable for mean < 40. For large means, [LargeMeanPoissonSampler] should be used instead.
+ *
+ * Based on Commons RNG implementation.
+ *
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/SmallMeanPoissonSampler.html].
+ */
+public class SmallMeanPoissonSampler private constructor(mean: Double) : Sampler<Int> {
+    private val p0: Double = exp(-mean)
+
+    private val limit: Int = (if (p0 > 0)
+        ceil(1000 * mean)
+    else
+        throw IllegalArgumentException("No p(x=0) probability for mean: $mean")).toInt()
+
+    public override fun sample(generator: RandomGenerator): Chain<Int> = generator.chain {
+        var n = 0
+        var r = 1.0
+
+        while (n < limit) {
+            r *= nextDouble()
+            if (r >= p0) n++ else break
+        }
+
+        n
+    }
+
+    public override fun toString(): String = "Small Mean Poisson deviate"
+
+    public companion object {
+        public fun of(mean: Double): SmallMeanPoissonSampler {
+            require(mean > 0) { "mean is not strictly positive: $mean" }
+            return SmallMeanPoissonSampler(mean)
+        }
+    }
+}

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/ZigguratNormalizedGaussianSampler.kt

@@ -0,0 +1,88 @@
+package space.kscience.kmath.stat.samplers
+
+import space.kscience.kmath.chains.Chain
+import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.kmath.stat.Sampler
+import space.kscience.kmath.stat.chain
+import kotlin.math.*
+
+/**
+ * [Marsaglia and Tsang "Ziggurat"](https://en.wikipedia.org/wiki/Ziggurat_algorithm) method for sampling from a
+ * Gaussian distribution with mean 0 and standard deviation 1. The algorithm is explained in this paper and this
+ * implementation has been adapted from the C code provided therein.
+ *
+ * Based on Commons RNG implementation.
+ * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSampler.html].
+ */
+public class ZigguratNormalizedGaussianSampler private constructor() :
+    NormalizedGaussianSampler, Sampler<Double> {
+
+    private fun sampleOne(generator: RandomGenerator): Double {
+        val j = generator.nextLong()
+        val i = (j and LAST.toLong()).toInt()
+        return if (abs(j) < K[i]) j * W[i] else fix(generator, j, i)
+    }
+
+    public override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain { sampleOne(this) }
+    public override fun toString(): String = "Ziggurat normalized Gaussian deviate"
+
+    private fun fix(generator: RandomGenerator, hz: Long, iz: Int): Double {
+        var x = hz * W[iz]
+
+        return when {
+            iz == 0 -> {
+                var y: Double
+
+                do {
+                    y = -ln(generator.nextDouble())
+                    x = -ln(generator.nextDouble()) * ONE_OVER_R
+                } while (y + y < x * x)
+
+                val out = R + x
+                if (hz > 0) out else -out
+            }
+
+            F[iz] + generator.nextDouble() * (F[iz - 1] - F[iz]) < gauss(x) -> x
+            else -> sampleOne(generator)
+        }
+    }
+
+    public companion object {
+        private const val R: Double = 3.442619855899
+        private const val ONE_OVER_R: Double = 1 / R
+        private const val V: Double = 9.91256303526217e-3
+        private val MAX: Double = 2.0.pow(63.0)
+        private val ONE_OVER_MAX: Double = 1.0 / MAX
+        private const val LEN: Int = 128
+        private const val LAST: Int = LEN - 1
+        private val K: LongArray = LongArray(LEN)
+        private val W: DoubleArray = DoubleArray(LEN)
+        private val F: DoubleArray = DoubleArray(LEN)
+
+        init {
+            // Filling the tables.
+            var d = R
+            var t = d
+            var fd = gauss(d)
+            val q = V / fd
+            K[0] = (d / q * MAX).toLong()
+            K[1] = 0
+            W[0] = q * ONE_OVER_MAX
+            W[LAST] = d * ONE_OVER_MAX
+            F[0] = 1.0
+            F[LAST] = fd
+
+            (LAST - 1 downTo 1).forEach { i ->
+                d = sqrt(-2 * ln(V / d + fd))
+                fd = gauss(d)
+                K[i + 1] = (d / t * MAX).toLong()
+                t = d
+                F[i] = fd
+                W[i] = d * ONE_OVER_MAX
+            }
+        }
+
+        public fun of(): ZigguratNormalizedGaussianSampler = ZigguratNormalizedGaussianSampler()
+        private fun gauss(x: Double): Double = exp(-0.5 * x * x)
+    }
+}

kmath-stat/src/jvmMain/kotlin/space/kscience/kmath/stat/RandomSourceGenerator.kt

@@ -3,10 +3,14 @@ package space.kscience.kmath.stat
 import org.apache.commons.rng.UniformRandomProvider
 import org.apache.commons.rng.simple.RandomSource
 
-public class RandomSourceGenerator(public val source: RandomSource, seed: Long?) : RandomGenerator {
+/**
-    internal val random: UniformRandomProvider = seed?.let {
+ * Implements [RandomGenerator] by delegating all operations to [RandomSource].
-        RandomSource.create(source, seed)
+ *
-    } ?: RandomSource.create(source)
+ * @property source the underlying [RandomSource] object.
+ */
+public class RandomSourceGenerator internal constructor(public val source: RandomSource, seed: Long?) : RandomGenerator {
+    internal val random: UniformRandomProvider = seed?.let { RandomSource.create(source, seed) }
+        ?: RandomSource.create(source)
 
     public override fun nextBoolean(): Boolean = random.nextBoolean()
     public override fun nextDouble(): Double = random.nextDouble()
@@ -23,22 +27,84 @@ public class RandomSourceGenerator(public val source: RandomSource, seed: Long?)
     public override fun fork(): RandomGenerator = RandomSourceGenerator(source, nextLong())
 }
 
+/**
+ * Implements [UniformRandomProvider] by delegating all operations to [RandomGenerator].
+ *
+ * @property generator the underlying [RandomGenerator] object.
+ */
 public inline class RandomGeneratorProvider(public val generator: RandomGenerator) : UniformRandomProvider {
+    /**
+     * Generates a [Boolean] value.
+     *
+     * @return the next random value.
+     */
     public override fun nextBoolean(): Boolean = generator.nextBoolean()
+
+    /**
+     * Generates a [Float] value between 0 and 1.
+     *
+     * @return the next random value between 0 and 1.
+     */
     public override fun nextFloat(): Float = generator.nextDouble().toFloat()
 
-    public override fun nextBytes(bytes: ByteArray) {
+    /**
-        generator.fillBytes(bytes)
+     * Generates [Byte] values and places them into a user-supplied array.
-    }
+     *
+     * The number of random bytes produced is equal to the length of the the byte array.
+     *
+     * @param bytes byte array in which to put the random bytes.
+     */
+    public override fun nextBytes(bytes: ByteArray): Unit = generator.fillBytes(bytes)
 
+    /**
+     * Generates [Byte] values and places them into a user-supplied array.
+     *
+     * The array is filled with bytes extracted from random integers. This implies that the number of random bytes
+     * generated may be larger than the length of the byte array.
+     *
+     * @param bytes the array in which to put the generated bytes.
+     * @param start the index at which to start inserting the generated bytes.
+     * @param len the number of bytes to insert.
+     */
     public override fun nextBytes(bytes: ByteArray, start: Int, len: Int) {
         generator.fillBytes(bytes, start, start + len)
     }
 
+    /**
+     * Generates an [Int] value.
+     *
+     * @return the next random value.
+     */
     public override fun nextInt(): Int = generator.nextInt()
+
+    /**
+     * Generates an [Int] value between 0 (inclusive) and the specified value (exclusive).
+     *
+     * @param n the bound on the random number to be returned. Must be positive.
+     * @return a random integer between 0 (inclusive) and [n] (exclusive).
+     */
     public override fun nextInt(n: Int): Int = generator.nextInt(n)
+
+    /**
+     * Generates a [Double] value between 0 and 1.
+     *
+     * @return the next random value between 0 and 1.
+     */
     public override fun nextDouble(): Double = generator.nextDouble()
+
+    /**
+     * Generates a [Long] value.
+     *
+     * @return the next random value.
+     */
     public override fun nextLong(): Long = generator.nextLong()
+
+    /**
+     * Generates a [Long] value between 0 (inclusive) and the specified value (exclusive).
+     *
+     * @param n Bound on the random number to be returned.  Must be positive.
+     * @return a random long value between 0 (inclusive) and [n] (exclusive).
+     */
     public override fun nextLong(n: Long): Long = generator.nextLong(n)
 }
 
@@ -51,8 +117,14 @@ public fun RandomGenerator.asUniformRandomProvider(): UniformRandomProvider = if
 else
     RandomGeneratorProvider(this)
 
+/**
+ * Returns [RandomSourceGenerator] with given [RandomSource] and [seed].
+ */
 public fun RandomGenerator.Companion.fromSource(source: RandomSource, seed: Long? = null): RandomSourceGenerator =
     RandomSourceGenerator(source, seed)
 
+/**
+ * Returns [RandomSourceGenerator] with [RandomSource.MT] algorithm and given [seed].
+ */
 public fun RandomGenerator.Companion.mersenneTwister(seed: Long? = null): RandomSourceGenerator =
     fromSource(RandomSource.MT, seed)

kmath-stat/src/jvmMain/kotlin/space/kscience/kmath/stat/distributions.kt

@@ -1,99 +0,0 @@
-package space.kscience.kmath.stat
-
-import org.apache.commons.rng.UniformRandomProvider
-import org.apache.commons.rng.sampling.distribution.*
-import space.kscience.kmath.chains.BlockingDoubleChain
-import space.kscience.kmath.chains.BlockingIntChain
-import space.kscience.kmath.chains.Chain
-import kotlin.math.PI
-import kotlin.math.exp
-import kotlin.math.pow
-import kotlin.math.sqrt
-
-public abstract class ContinuousSamplerDistribution : Distribution<Double> {
-    private inner class ContinuousSamplerChain(val generator: RandomGenerator) : BlockingDoubleChain() {
-        private val sampler = buildCMSampler(generator)
-
-        override fun nextDouble(): Double = sampler.sample()
-        override fun fork(): Chain<Double> = ContinuousSamplerChain(generator.fork())
-    }
-
-    protected abstract fun buildCMSampler(generator: RandomGenerator): ContinuousSampler
-
-    public override fun sample(generator: RandomGenerator): BlockingDoubleChain = ContinuousSamplerChain(generator)
-}
-
-public abstract class DiscreteSamplerDistribution : Distribution<Int> {
-    private inner class ContinuousSamplerChain(val generator: RandomGenerator) : BlockingIntChain() {
-        private val sampler = buildSampler(generator)
-
-        override fun nextInt(): Int = sampler.sample()
-        override fun fork(): Chain<Int> = ContinuousSamplerChain(generator.fork())
-    }
-
-    protected abstract fun buildSampler(generator: RandomGenerator): DiscreteSampler
-
-    public override fun sample(generator: RandomGenerator): BlockingIntChain = ContinuousSamplerChain(generator)
-}
-
-public enum class NormalSamplerMethod {
-    BoxMuller,
-    Marsaglia,
-    Ziggurat
-}
-
-private fun normalSampler(method: NormalSamplerMethod, provider: UniformRandomProvider): NormalizedGaussianSampler =
-    when (method) {
-        NormalSamplerMethod.BoxMuller -> BoxMullerNormalizedGaussianSampler(provider)
-        NormalSamplerMethod.Marsaglia -> MarsagliaNormalizedGaussianSampler(provider)
-        NormalSamplerMethod.Ziggurat -> ZigguratNormalizedGaussianSampler(provider)
-    }
-
-public fun Distribution.Companion.normal(
-    method: NormalSamplerMethod = NormalSamplerMethod.Ziggurat,
-): ContinuousSamplerDistribution = object : ContinuousSamplerDistribution() {
-    override fun buildCMSampler(generator: RandomGenerator): ContinuousSampler {
-        val provider = generator.asUniformRandomProvider()
-        return normalSampler(method, provider)
-    }
-
-    override fun probability(arg: Double): Double = exp(-arg.pow(2) / 2) / sqrt(PI * 2)
-}
-
-/**
- * A univariate normal distribution with given [mean] and [sigma]. [method] defines commons-rng generation method
- */
-public fun Distribution.Companion.normal(
-    mean: Double,
-    sigma: Double,
-    method: NormalSamplerMethod = NormalSamplerMethod.Ziggurat,
-): ContinuousSamplerDistribution = object : ContinuousSamplerDistribution() {
-    private val sigma2 = sigma.pow(2)
-    private val norm = sigma * sqrt(PI * 2)
-
-    override fun buildCMSampler(generator: RandomGenerator): ContinuousSampler {
-        val provider = generator.asUniformRandomProvider()
-        val normalizedSampler = normalSampler(method, provider)
-        return GaussianSampler(normalizedSampler, mean, sigma)
-    }
-
-    override fun probability(arg: Double): Double = exp(-(arg - mean).pow(2) / 2 / sigma2) / norm
-}
-
-public fun Distribution.Companion.poisson(
-    lambda: Double,
-): DiscreteSamplerDistribution = object : DiscreteSamplerDistribution() {
-    private val computedProb: HashMap<Int, Double> = hashMapOf(0 to exp(-lambda))
-
-    override fun buildSampler(generator: RandomGenerator): DiscreteSampler =
-        PoissonSampler.of(generator.asUniformRandomProvider(), lambda)
-
-    override fun probability(arg: Int): Double {
-        require(arg >= 0) { "The argument must be >= 0" }
-
-        return if (arg > 40)
-            exp(-(arg - lambda).pow(2) / 2 / lambda) / sqrt(2 * PI * lambda)
-        else
-            computedProb.getOrPut(arg) { probability(arg - 1) * lambda / arg }
-    }
-}

kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/CommonsDistributionsTest.kt

@@ -5,23 +5,22 @@ import kotlinx.coroutines.flow.toList
 import kotlinx.coroutines.runBlocking
 import org.junit.jupiter.api.Assertions
 import org.junit.jupiter.api.Test
+import space.kscience.kmath.stat.samplers.GaussianSampler
 
 internal class CommonsDistributionsTest {
     @Test
     fun testNormalDistributionSuspend() {
-        val distribution = Distribution.normal(7.0, 2.0)
+        val distribution = GaussianSampler.of(7.0, 2.0)
         val generator = RandomGenerator.default(1)
-        val sample = runBlocking {
+        val sample = runBlocking { distribution.sample(generator).take(1000).toList() }
-            distribution.sample(generator).take(1000).toList()
-        }
         Assertions.assertEquals(7.0, sample.average(), 0.1)
     }
 
     @Test
     fun testNormalDistributionBlocking() {
-        val distribution = Distribution.normal(7.0, 2.0)
+        val distribution = GaussianSampler.of(7.0, 2.0)
         val generator = RandomGenerator.default(1)
-        val sample = distribution.sample(generator).nextBlock(1000)
+        val sample = runBlocking { distribution.sample(generator).blocking().nextBlock(1000) }
         Assertions.assertEquals(7.0, sample.average(), 0.1)
     }
 }

kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/SamplerTest.kt

@@ -7,11 +7,8 @@ class SamplerTest {
 
     @Test
     fun bufferSamplerTest() {
-        val sampler: Sampler<Double> =
+        val sampler = Sampler { it.chain { nextDouble() } }
-            BasicSampler { it.chain { nextDouble() } }
         val data = sampler.sampleBuffer(RandomGenerator.default, 100)
-        runBlocking {
+        runBlocking { println(data.next()) }
-            println(data.next())
-        }
     }
 }