Merge pull request #72 from mipt-npm/dev

Dev

README.md

@@ -1,6 +1,7 @@
 Bintray: [ ![Download](https://api.bintray.com/packages/mipt-npm/scientifik/kmath-core/images/download.svg) ](https://bintray.com/mipt-npm/scientifik/kmath-core/_latestVersion)
 
 # KMath
+Could be pronounced as `key-math`.
 The Kotlin MATHematics library is intended as a Kotlin-based analog to Python's `numpy` library. In contrast to `numpy` and `scipy` it is modular and has a lightweight core.
 
 ## Features
@@ -13,7 +14,7 @@ Actual feature list is [here](doc/features.md)
     * Complex numbers backed by the `Field` API (meaning that they will be usable in any structure like vectors and N-dimensional arrays).
     * Advanced linear algebra operations like matrix inversion and LU decomposition.
 
-* **Array-like structures** Full support of many-dimenstional array-like structures 
+* **Array-like structures** Full support of many-dimensional array-like structures 
 including mixed arithmetic operations and function operations over arrays and numbers (with the added benefit of static type checking).
 
 * **Expressions** By writing a single mathematical expression
@@ -22,13 +23,13 @@ can be used for a wide variety of purposes from high performance calculations to
 
 * **Histograms** Fast multi-dimensional histograms.
 
-* **Streaming** Streaming operations on mathematica objects and objects buffers.
+* **Streaming** Streaming operations on mathematical objects and objects buffers.
 
 * **Commons-math wrapper** It is planned to gradually wrap most parts of [Apache commons-math](http://commons.apache.org/proper/commons-math/)
                            library in Kotlin code and maybe rewrite some parts to better suit the Kotlin programming paradigm, however there is no fixed roadmap for that. Feel free
                            to submit a feature request if you want something to be done first.
                            
-* **Koma wrapper** [Koma](https://github.com/kyonifer/koma) is a well established numerics library in kotlin, specifically linear algebra.
+* **Koma wrapper** [Koma](https://github.com/kyonifer/koma) is a well established numerics library in Kotlin, specifically linear algebra.
 The plan is to have wrappers for koma implementations for compatibility with kmath API.
 
 ## Planned features
@@ -110,4 +111,4 @@ dependencies{
 
 ## Contributing
 
-The project requires a lot of additional work. Please fill free to contribute in any way and propose new features.
+The project requires a lot of additional work. Please feel free to contribute in any way and propose new features.

benchmarks/build.gradle

@@ -1,39 +0,0 @@
-plugins {
-    id "java"
-    id "me.champeau.gradle.jmh" version "0.4.8"
-    id 'org.jetbrains.kotlin.jvm'
-}
-
-repositories {
-    maven { url 'https://dl.bintray.com/kotlin/kotlin-eap' }
-    maven{ url "http://dl.bintray.com/kyonifer/maven"}
-    mavenCentral()
-}
-
-dependencies {
-    implementation project(":kmath-core")
-    implementation project(":kmath-coroutines")
-    implementation project(":kmath-commons")
-    implementation project(":kmath-koma")
-    implementation group: "com.kyonifer", name:"koma-core-ejml", version: "0.12"
-    implementation "org.jetbrains.kotlinx:kotlinx-io-jvm:0.1.5"
-    //compile "org.jetbrains.kotlin:kotlin-stdlib-jdk8"
-    //jmh project(':kmath-core')
-}
-
-jmh {
-    warmupIterations = 1
-}
-
-jmhClasses.dependsOn(compileKotlin)
-
-compileKotlin {
-    kotlinOptions {
-        jvmTarget = "1.8"
-    }
-}
-compileTestKotlin {
-    kotlinOptions {
-        jvmTarget = "1.8"
-    }
-}

build.gradle.kts

@@ -1,4 +1,4 @@
-val kmathVersion by extra("0.1.2")
+val kmathVersion by extra("0.1.3")
 
 allprojects {
     repositories {

doc/algebra.md

@@ -32,12 +32,12 @@ Typical case of `Field` is the `RealField` which works on doubles. And typical c
 
 In some cases algebra context could hold additional operation like `exp` or `sin`, in this case it inherits appropriate
 interface. Also a context could have an operation which produces an element outside of its context. For example
-`Matrix` `dot` operation produces a matrix with new dimensions which could not be compatible with initial matrix in
+`Matrix` `dot` operation produces a matrix with new dimensions which can be incompatible with initial matrix in
 terms of linear operations.
 
 ## Algebra element
 
-In order to achieve more familiar behavior (where you apply operations directly to mathematica objects), without involving contexts
+In order to achieve more familiar behavior (where you apply operations directly to mathematical objects), without involving contexts
 `kmath` introduces special type objects called `MathElement`. A `MathElement` is basically some object coupled to
 a mathematical context. For example `Complex` is the pair of real numbers representing real and imaginary parts,
 but it also holds reference to the `ComplexField` singleton which allows to perform direct operations on `Complex`

doc/nd-structure.md

@@ -9,17 +9,12 @@ structures. In `kmath` performance depends on which particular context was used
 
 Let us consider following contexts:
 ```kotlin
-    // specialized nd-field for Double. It works as generic Double field as well
-    val specializedField = NDField.real(intArrayOf(dim, dim))
-
     // automatically build context most suited for given type.
-    val autoField = NDField.auto(intArrayOf(dim, dim), RealField)
+    val autoField = NDField.auto(RealField, dim, dim)
-
+    // specialized nd-field for Double. It works as generic Double field as well
-    //A field implementing lazy computations. All elements are computed on-demand
+    val specializedField = NDField.real(dim, dim)
-    val lazyField = NDField.lazy(intArrayOf(dim, dim), RealField)
-
     //A generic boxing field. It should be used for objects, not primitives.
-    val genericField = NDField.buffered(intArrayOf(dim, dim), RealField)
+    val genericField = NDField.buffered(RealField, dim, dim)
 ```
 Now let us perform several tests and see which implementation is best suited for each case:
 
@@ -32,7 +27,7 @@ to it `n = 1000` times.
 The code to run this looks like:
 ```kotlin
     specializedField.run {
-        var res = one
+        var res: NDBuffer<Double> = one
         repeat(n) {
             res += 1.0
         }
@@ -93,7 +88,7 @@ In this case it completes in about `4x-5x` time due to boxing.
 The boxing field produced by
 ```kotlin
     genericField.run {
-        var res = one
+        var res: NDBuffer<Double> = one
         repeat(n) {
             res += 1.0
         }

examples/build.gradle.kts

@@ -0,0 +1,67 @@
+import org.jetbrains.gradle.benchmarks.JvmBenchmarkTarget
+import org.jetbrains.kotlin.allopen.gradle.AllOpenExtension
+import org.jetbrains.kotlin.gradle.tasks.KotlinCompile
+
+plugins {
+    java
+    kotlin("jvm")
+    kotlin("plugin.allopen") version "1.3.31"
+    id("org.jetbrains.gradle.benchmarks.plugin") version "0.1.7-dev-24"
+}
+
+configure<AllOpenExtension> {
+    annotation("org.openjdk.jmh.annotations.State")
+}
+
+repositories {
+    maven("https://dl.bintray.com/kotlin/kotlin-eap")
+    maven("http://dl.bintray.com/kyonifer/maven")
+    maven("https://dl.bintray.com/orangy/maven")
+    mavenCentral()
+}
+
+sourceSets {
+    register("benchmarks")
+}
+
+dependencies {
+    implementation(project(":kmath-core"))
+    implementation(project(":kmath-coroutines"))
+    implementation(project(":kmath-commons"))
+    implementation(project(":kmath-koma"))
+    implementation("com.kyonifer:koma-core-ejml:0.12")
+    implementation("org.jetbrains.kotlinx:kotlinx-io-jvm:0.1.5")
+
+    implementation("org.jetbrains.gradle.benchmarks:runtime:0.1.7-dev-24")
+
+
+    "benchmarksCompile"(sourceSets.main.get().compileClasspath)
+}
+
+// Configure benchmark
+benchmark {
+    // Setup configurations
+    targets {
+        // This one matches sourceSet name above
+        register("benchmarks") {
+            this as JvmBenchmarkTarget
+            jmhVersion = "1.21"
+        }
+    }
+
+    configurations {
+        register("fast") {
+            warmups = 5 // number of warmup iterations
+            iterations = 3 // number of iterations
+            iterationTime = 500 // time in seconds per iteration
+            iterationTimeUnit = "ms" // time unity for iterationTime, default is seconds
+        }
+    }
+}
+
+
+tasks.withType<KotlinCompile> {
+    kotlinOptions {
+        jvmTarget = "1.8"
+    }
+}

examples/src/benchmarks/kotlin/scientifik/kmath/structures/ArrayBenchmark.kt

@@ -7,7 +7,7 @@ import java.nio.IntBuffer
 
 
 @State(Scope.Benchmark)
-open class ArrayBenchmark {
+class ArrayBenchmark {
 
     @Benchmark
     fun benchmarkArrayRead() {

examples/src/benchmarks/kotlin/scientifik/kmath/structures/BufferBenchmark.kt

@@ -7,7 +7,7 @@ import scientifik.kmath.operations.Complex
 import scientifik.kmath.operations.complex
 
 @State(Scope.Benchmark)
-open class BufferBenchmark {
+class BufferBenchmark {
 
     @Benchmark
     fun genericDoubleBufferReadWrite() {

examples/src/benchmarks/kotlin/scientifik/kmath/structures/NDFieldBenchmark.kt

@@ -1,9 +1,12 @@
 package scientifik.kmath.structures
 
 import org.openjdk.jmh.annotations.Benchmark
+import org.openjdk.jmh.annotations.Scope
+import org.openjdk.jmh.annotations.State
 import scientifik.kmath.operations.RealField
 
-open class NDFieldBenchmark {
+@State(Scope.Benchmark)
+class NDFieldBenchmark {
 
     @Benchmark
     fun autoFieldAdd() {
@@ -50,6 +53,6 @@ open class NDFieldBenchmark {
 
         val bufferedField = NDField.auto(RealField, dim, dim)
         val specializedField = NDField.real(dim, dim)
-        val genericField = NDField.buffered(intArrayOf(dim, dim), RealField)
+        val genericField = NDField.boxing(RealField, dim, dim)
     }
 }

examples/src/main/kotlin/scientifik/kmath/commons/prob/DistributionDemo.kt

@@ -0,0 +1,31 @@
+package scientifik.kmath.commons.prob
+
+import kotlinx.coroutines.runBlocking
+import scientifik.kmath.chains.Chain
+import scientifik.kmath.chains.mapWithState
+import scientifik.kmath.prob.Distribution
+import scientifik.kmath.prob.RandomGenerator
+
+data class AveragingChainState(var num: Int = 0, var value: Double = 0.0)
+
+fun Chain<Double>.mean(): Chain<Double> = mapWithState(AveragingChainState(),{it.copy()}){chain->
+    val next = chain.next()
+    num++
+    value += next
+    return@mapWithState value / num
+}
+
+
+fun main() {
+    val normal = Distribution.normal()
+    val chain = normal.sample(RandomGenerator.default).mean()
+
+    runBlocking {
+        repeat(10001) { counter ->
+            val mean = chain.next()
+            if (counter % 1000 == 0) {
+                println("[$counter] Average value is $mean")
+            }
+        }
+    }
+}

examples/src/main/kotlin/scientifik/kmath/linear/LinearAlgebraBenchmark.kt

@@ -1,6 +1,9 @@
 package scientifik.kmath.linear
 
 import koma.matrix.ejml.EJMLMatrixFactory
+import scientifik.kmath.commons.linear.CMMatrixContext
+import scientifik.kmath.commons.linear.inverse
+import scientifik.kmath.commons.linear.toCM
 import scientifik.kmath.operations.RealField
 import scientifik.kmath.structures.Matrix
 import kotlin.contracts.ExperimentalContracts

examples/src/main/kotlin/scientifik/kmath/linear/MultiplicationBenchmark.kt

@@ -1,6 +1,8 @@
 package scientifik.kmath.linear
 
 import koma.matrix.ejml.EJMLMatrixFactory
+import scientifik.kmath.commons.linear.CMMatrixContext
+import scientifik.kmath.commons.linear.toCM
 import scientifik.kmath.operations.RealField
 import scientifik.kmath.structures.Matrix
 import kotlin.random.Random

examples/src/main/kotlin/scientifik/kmath/operations/ComplexDemo.kt

@@ -0,0 +1,10 @@
+package scientifik.kmath.operations
+
+import scientifik.kmath.structures.NDElement
+import scientifik.kmath.structures.complex
+
+fun main() {
+    val element = NDElement.complex(2, 2) { index: IntArray ->
+        Complex(index[0].toDouble() - index[1].toDouble(), index[0].toDouble() + index[1].toDouble())
+    }
+}

examples/src/main/kotlin/scientifik/kmath/structures/ComplexND.kt

@@ -1,5 +1,8 @@
 package scientifik.kmath.structures
 
+import scientifik.kmath.linear.transpose
+import scientifik.kmath.operations.Complex
+import scientifik.kmath.operations.toComplex
 import kotlin.system.measureTimeMillis
 
 fun main() {
@@ -32,3 +35,23 @@ fun main() {
 
     println("Complex addition completed in $complexTime millis")
 }
+
+
+fun complexExample() {
+    //Create a context for 2-d structure with complex values
+    NDField.complex(4, 8).run {
+        //a constant real-valued structure
+        val x = one * 2.5
+        operator fun Number.plus(other: Complex) = Complex(this.toDouble() + other.re, other.im)
+        //a structure generator specific to this context
+        val matrix = produce { (k, l) ->
+            k + l*i
+        }
+
+        //Perform sum
+        val sum = matrix + x + 1.0
+
+        //Represent the sum as 2d-structure and transpose
+        sum.as2D().transpose()
+    }
+}

examples/src/main/kotlin/scientifik/kmath/structures/NDField.kt

@@ -13,7 +13,7 @@ fun main(args: Array<String>) {
     // specialized nd-field for Double. It works as generic Double field as well
     val specializedField = NDField.real(dim, dim)
     //A generic boxing field. It should be used for objects, not primitives.
-    val genericField = NDField.buffered(intArrayOf(dim, dim), RealField)
+    val genericField = NDField.boxing(RealField, dim, dim)
 
 
     val autoTime = measureTimeMillis {

kmath-commons/build.gradle.kts

@@ -8,6 +8,7 @@ description = "Commons math binding for kmath"
 dependencies {
     api(project(":kmath-core"))
     api(project(":kmath-coroutines"))
+    api(project(":kmath-prob"))
     api("org.apache.commons:commons-math3:3.6.1")
     testImplementation("org.jetbrains.kotlin:kotlin-test")
     testImplementation("org.jetbrains.kotlin:kotlin-test-junit")

kmath-commons/src/main/kotlin/scientifik/kmath/commons/expressions/DiffExpression.kt

@@ -1,6 +1,8 @@
-package scientifik.kmath.expressions
+package scientifik.kmath.commons.expressions
 
 import org.apache.commons.math3.analysis.differentiation.DerivativeStructure
+import scientifik.kmath.expressions.Expression
+import scientifik.kmath.expressions.ExpressionContext
 import scientifik.kmath.operations.ExtendedField
 import scientifik.kmath.operations.Field
 import kotlin.properties.ReadOnlyProperty
@@ -82,8 +84,11 @@ class DerivativeStructureField(
  * A constructs that creates a derivative structure with required order on-demand
  */
 class DiffExpression(val function: DerivativeStructureField.() -> DerivativeStructure) : Expression<Double> {
-    override fun invoke(arguments: Map<String, Double>): Double = DerivativeStructureField(0, arguments)
+
-        .run(function).value
+    override fun invoke(arguments: Map<String, Double>): Double = DerivativeStructureField(
+        0,
+        arguments
+    ).run(function).value
 
     /**
      * Get the derivative expression with given orders
@@ -109,21 +114,27 @@ fun DiffExpression.derivative(name: String) = derivative(name to 1)
  * A context for [DiffExpression] (not to be confused with [DerivativeStructure])
  */
 object DiffExpressionContext : ExpressionContext<Double>, Field<DiffExpression> {
-    override fun variable(name: String, default: Double?) = DiffExpression { variable(name, default?.const()) }
+    override fun variable(name: String, default: Double?) =
+        DiffExpression { variable(name, default?.const()) }
 
-    override fun const(value: Double): DiffExpression = DiffExpression { value.const() }
+    override fun const(value: Double): DiffExpression =
+        DiffExpression { value.const() }
 
-    override fun add(a: DiffExpression, b: DiffExpression) = DiffExpression { a.function(this) + b.function(this) }
+    override fun add(a: DiffExpression, b: DiffExpression) =
+        DiffExpression { a.function(this) + b.function(this) }
 
     override val zero = DiffExpression { 0.0.const() }
 
-    override fun multiply(a: DiffExpression, k: Number) = DiffExpression { a.function(this) * k }
+    override fun multiply(a: DiffExpression, k: Number) =
+        DiffExpression { a.function(this) * k }
 
     override val one = DiffExpression { 1.0.const() }
 
-    override fun multiply(a: DiffExpression, b: DiffExpression) = DiffExpression { a.function(this) * b.function(this) }
+    override fun multiply(a: DiffExpression, b: DiffExpression) =
+        DiffExpression { a.function(this) * b.function(this) }
 
-    override fun divide(a: DiffExpression, b: DiffExpression) = DiffExpression { a.function(this) / b.function(this) }
+    override fun divide(a: DiffExpression, b: DiffExpression) =
+        DiffExpression { a.function(this) / b.function(this) }
 }
 
 

kmath-commons/src/main/kotlin/scientifik/kmath/commons/linear/CMMatrix.kt

@@ -1,11 +1,13 @@
-package scientifik.kmath.linear
+package scientifik.kmath.commons.linear
 
 import org.apache.commons.math3.linear.*
 import org.apache.commons.math3.linear.RealMatrix
 import org.apache.commons.math3.linear.RealVector
+import scientifik.kmath.linear.*
 import scientifik.kmath.structures.Matrix
 
-class CMMatrix(val origin: RealMatrix, features: Set<MatrixFeature>? = null) : FeaturedMatrix<Double> {
+class CMMatrix(val origin: RealMatrix, features: Set<MatrixFeature>? = null) :
+    FeaturedMatrix<Double> {
     override val rowNum: Int get() = origin.rowDimension
     override val colNum: Int get() = origin.columnDimension
 
@@ -70,10 +72,14 @@ object CMMatrixContext : MatrixContext<Double> {
     override fun multiply(a: Matrix<Double>, k: Number) =
         CMMatrix(a.toCM().origin.scalarMultiply(k.toDouble()))
 
-    override fun Matrix<Double>.times(value: Double): Matrix<Double>  = produce(rowNum,colNum){i,j-> get(i,j)*value}
+    override fun Matrix<Double>.times(value: Double): Matrix<Double>  =
+        produce(rowNum, colNum) { i, j -> get(i, j) * value }
 }
 
-operator fun CMMatrix.plus(other: CMMatrix): CMMatrix = CMMatrix(this.origin.add(other.origin))
+operator fun CMMatrix.plus(other: CMMatrix): CMMatrix =
-operator fun CMMatrix.minus(other: CMMatrix): CMMatrix = CMMatrix(this.origin.subtract(other.origin))
+    CMMatrix(this.origin.add(other.origin))
+operator fun CMMatrix.minus(other: CMMatrix): CMMatrix =
+    CMMatrix(this.origin.subtract(other.origin))
 
-infix fun CMMatrix.dot(other: CMMatrix): CMMatrix = CMMatrix(this.origin.multiply(other.origin))
+infix fun CMMatrix.dot(other: CMMatrix): CMMatrix =
+    CMMatrix(this.origin.multiply(other.origin))

kmath-commons/src/main/kotlin/scientifik/kmath/commons/linear/CMSolver.kt

@@ -1,6 +1,7 @@
-package scientifik.kmath.linear
+package scientifik.kmath.commons.linear
 
 import org.apache.commons.math3.linear.*
+import scientifik.kmath.linear.Point
 import scientifik.kmath.structures.Matrix
 
 enum class CMDecomposition {

kmath-commons/src/main/kotlin/scientifik/kmath/commons/prob/CMRandomGeneratorWrapper.kt

@@ -0,0 +1,32 @@
+package scientifik.kmath.commons.prob
+
+import org.apache.commons.math3.random.JDKRandomGenerator
+import scientifik.kmath.prob.RandomGenerator
+import org.apache.commons.math3.random.RandomGenerator as CMRandom
+
+inline class CMRandomGeneratorWrapper(val generator: CMRandom) : RandomGenerator {
+    override fun nextDouble(): Double = generator.nextDouble()
+
+    override fun nextInt(): Int = generator.nextInt()
+
+    override fun nextLong(): Long = generator.nextLong()
+
+    override fun nextBlock(size: Int): ByteArray = ByteArray(size).apply { generator.nextBytes(this) }
+
+    override fun fork(): RandomGenerator {
+        TODO("not implemented") //To change body of created functions use File | Settings | File Templates.
+    }
+}
+
+fun CMRandom.asKmathGenerator(): RandomGenerator = CMRandomGeneratorWrapper(this)
+
+fun RandomGenerator.asCMGenerator(): CMRandom =
+    (this as? CMRandomGeneratorWrapper)?.generator ?: TODO("Implement reverse CM wrapper")
+
+val RandomGenerator.Companion.default: RandomGenerator by lazy { JDKRandomGenerator().asKmathGenerator() }
+
+fun RandomGenerator.Companion.jdk(seed: Int? = null): RandomGenerator = if (seed == null) {
+    JDKRandomGenerator()
+} else {
+    JDKRandomGenerator(seed)
+}.asKmathGenerator()

kmath-commons/src/main/kotlin/scientifik/kmath/commons/prob/CommonsDistribution.kt

@@ -0,0 +1,82 @@
+package scientifik.kmath.commons.prob
+
+import org.apache.commons.math3.distribution.*
+import scientifik.kmath.prob.Distribution
+import scientifik.kmath.prob.RandomChain
+import scientifik.kmath.prob.RandomGenerator
+import scientifik.kmath.prob.UnivariateDistribution
+import org.apache.commons.math3.random.RandomGenerator as CMRandom
+
+class CMRealDistributionWrapper(val builder: (CMRandom?) -> RealDistribution) : UnivariateDistribution<Double> {
+
+    private val defaultDistribution by lazy { builder(null) }
+
+    override fun probability(arg: Double): Double = defaultDistribution.probability(arg)
+
+    override fun cumulative(arg: Double): Double = defaultDistribution.cumulativeProbability(arg)
+
+    override fun sample(generator: RandomGenerator): RandomChain<Double> {
+        val distribution = builder(generator.asCMGenerator())
+        return RandomChain(generator) { distribution.sample() }
+    }
+}
+
+class CMIntDistributionWrapper(val builder: (CMRandom?) -> IntegerDistribution) : UnivariateDistribution<Int> {
+
+    private val defaultDistribution by lazy { builder(null) }
+
+    override fun probability(arg: Int): Double = defaultDistribution.probability(arg)
+
+    override fun cumulative(arg: Int): Double = defaultDistribution.cumulativeProbability(arg)
+
+    override fun sample(generator: RandomGenerator): RandomChain<Int> {
+        val distribution = builder(generator.asCMGenerator())
+        return RandomChain(generator) { distribution.sample() }
+    }
+}
+
+
+fun Distribution.Companion.normal(mean: Double = 0.0, sigma: Double = 1.0): UnivariateDistribution<Double> =
+    CMRealDistributionWrapper { generator -> NormalDistribution(generator, mean, sigma) }
+
+fun Distribution.Companion.poisson(mean: Double): UnivariateDistribution<Int> = CMIntDistributionWrapper { generator ->
+    PoissonDistribution(
+        generator,
+        mean,
+        PoissonDistribution.DEFAULT_EPSILON,
+        PoissonDistribution.DEFAULT_MAX_ITERATIONS
+    )
+}
+
+fun Distribution.Companion.binomial(trials: Int, p: Double): UnivariateDistribution<Int> =
+    CMIntDistributionWrapper { generator ->
+        BinomialDistribution(generator, trials, p)
+    }
+
+fun Distribution.Companion.student(degreesOfFreedom: Double): UnivariateDistribution<Double> =
+    CMRealDistributionWrapper { generator ->
+        TDistribution(generator, degreesOfFreedom, TDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY)
+    }
+
+fun Distribution.Companion.chi2(degreesOfFreedom: Double): UnivariateDistribution<Double> =
+    CMRealDistributionWrapper { generator ->
+        ChiSquaredDistribution(generator, degreesOfFreedom)
+    }
+
+fun Distribution.Companion.fisher(
+    numeratorDegreesOfFreedom: Double,
+    denominatorDegreesOfFreedom: Double
+): UnivariateDistribution<Double> =
+    CMRealDistributionWrapper { generator ->
+        FDistribution(generator, numeratorDegreesOfFreedom, denominatorDegreesOfFreedom)
+    }
+
+fun Distribution.Companion.exponential(mean: Double): UnivariateDistribution<Double> =
+    CMRealDistributionWrapper { generator ->
+        ExponentialDistribution(generator, mean)
+    }
+
+fun Distribution.Companion.uniform(a: Double, b: Double): UnivariateDistribution<Double> =
+    CMRealDistributionWrapper { generator ->
+        UniformRealDistribution(generator, a, b)
+    }

kmath-commons/src/main/kotlin/scientifik/kmath/commons/transform/Transformations.kt

@@ -1,4 +1,4 @@
-package scientifik.kmath.transform
+package scientifik.kmath.commons.transform
 
 import kotlinx.coroutines.FlowPreview
 import kotlinx.coroutines.flow.Flow

kmath-commons/src/test/kotlin/scientifik/kmath/commons/expressions/AutoDiffTest.kt

@@ -1,6 +1,7 @@
-package scientifik.kmath.expressions
+package scientifik.kmath.commons.expressions
 
 import org.junit.Test
+import scientifik.kmath.expressions.invoke
 import kotlin.test.assertEquals
 
 inline fun <R> diff(order: Int, vararg parameters: Pair<String, Double>, block: DerivativeStructureField.() -> R) =

kmath-core/src/commonMain/kotlin/scientifik/kmath/expressions/Expression.kt

@@ -58,32 +58,35 @@ internal class DivExpession<T>(val context: Field<T>, val expr: Expression<T>, v
     override fun invoke(arguments: Map<String, T>): T = context.divide(expr.invoke(arguments), second.invoke(arguments))
 }
 
-class ExpressionField<T>(val field: Field<T>) : Field<Expression<T>>, ExpressionContext<T> {
+open class ExpressionSpace<T>(val space: Space<T>) : Space<Expression<T>>, ExpressionContext<T> {
-
+    override val zero: Expression<T> = ConstantExpression(space.zero)
-    override val zero: Expression<T> = ConstantExpression(field.zero)
-
-    override val one: Expression<T> = ConstantExpression(field.one)
 
     override fun const(value: T): Expression<T> = ConstantExpression(value)
 
     override fun variable(name: String, default: T?): Expression<T> = VariableExpression(name, default)
 
-    override fun add(a: Expression<T>, b: Expression<T>): Expression<T> = SumExpression(field, a, b)
+    override fun add(a: Expression<T>, b: Expression<T>): Expression<T> = SumExpression(space, a, b)
 
-    override fun multiply(a: Expression<T>, k: Number): Expression<T> = ConstProductExpession(field, a, k)
+    override fun multiply(a: Expression<T>, k: Number): Expression<T> = ConstProductExpession(space, a, k)
-
-    override fun multiply(a: Expression<T>, b: Expression<T>): Expression<T> = ProductExpression(field, a, b)
-
-    override fun divide(a: Expression<T>, b: Expression<T>): Expression<T> = DivExpession(field, a, b)
 
 
     operator fun Expression<T>.plus(arg: T) = this + const(arg)
     operator fun Expression<T>.minus(arg: T) = this - const(arg)
-    operator fun Expression<T>.times(arg: T) = this * const(arg)
-    operator fun Expression<T>.div(arg: T) = this / const(arg)
 
     operator fun T.plus(arg: Expression<T>) = arg + this
     operator fun T.minus(arg: Expression<T>) = arg - this
+}
+
+
+class ExpressionField<T>(val field: Field<T>) : Field<Expression<T>>, ExpressionSpace<T>(field) {
+    override val one: Expression<T> = ConstantExpression(field.one)
+    override fun multiply(a: Expression<T>, b: Expression<T>): Expression<T> = ProductExpression(field, a, b)
+
+    override fun divide(a: Expression<T>, b: Expression<T>): Expression<T> = DivExpession(field, a, b)
+
+    operator fun Expression<T>.times(arg: T) = this * const(arg)
+    operator fun Expression<T>.div(arg: T) = this / const(arg)
+
     operator fun T.times(arg: Expression<T>) = arg * this
     operator fun T.div(arg: Expression<T>) = arg / this
 }

kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/BufferMatrix.kt

@@ -24,6 +24,7 @@ class BufferMatrixContext<T : Any, R : Ring<T>>(
     }
 }
 
+@Suppress("OVERRIDE_BY_INLINE")
 object RealMatrixContext : GenericMatrixContext<Double, RealField> {
 
     override val elementContext = RealField

kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/BoxingNDRing.kt

@@ -0,0 +1,72 @@
+package scientifik.kmath.structures
+
+import scientifik.kmath.operations.Ring
+import scientifik.kmath.operations.RingElement
+
+class BoxingNDRing<T, R : Ring<T>>(
+    override val shape: IntArray,
+    override val elementContext: R,
+    val bufferFactory: BufferFactory<T>
+) : BufferedNDRing<T, R> {
+
+    override val strides: Strides = DefaultStrides(shape)
+
+    fun buildBuffer(size: Int, initializer: (Int) -> T): Buffer<T> =
+        bufferFactory(size, initializer)
+
+    override fun check(vararg elements: NDBuffer<T>) {
+        if (!elements.all { it.strides == this.strides }) error("Element strides are not the same as context strides")
+    }
+
+    override val zero by lazy { produce { zero } }
+    override val one by lazy { produce { one } }
+
+    override fun produce(initializer: R.(IntArray) -> T) =
+        BufferedNDRingElement(
+            this,
+            buildBuffer(strides.linearSize) { offset -> elementContext.initializer(strides.index(offset)) })
+
+    override fun map(arg: NDBuffer<T>, transform: R.(T) -> T): BufferedNDRingElement<T, R> {
+        check(arg)
+        return BufferedNDRingElement(
+            this,
+            buildBuffer(arg.strides.linearSize) { offset -> elementContext.transform(arg.buffer[offset]) })
+
+//        val buffer = arg.buffer.transform { _, value -> elementContext.transform(value) }
+//        return BufferedNDFieldElement(this, buffer)
+
+    }
+
+    override fun mapIndexed(
+        arg: NDBuffer<T>,
+        transform: R.(index: IntArray, T) -> T
+    ): BufferedNDRingElement<T, R> {
+        check(arg)
+        return BufferedNDRingElement(
+            this,
+            buildBuffer(arg.strides.linearSize) { offset ->
+                elementContext.transform(
+                    arg.strides.index(offset),
+                    arg.buffer[offset]
+                )
+            })
+
+//        val buffer =
+//            arg.buffer.transform { offset, value -> elementContext.transform(arg.strides.index(offset), value) }
+//        return BufferedNDFieldElement(this, buffer)
+    }
+
+    override fun combine(
+        a: NDBuffer<T>,
+        b: NDBuffer<T>,
+        transform: R.(T, T) -> T
+    ): BufferedNDRingElement<T, R> {
+        check(a, b)
+        return BufferedNDRingElement(
+            this,
+            buildBuffer(strides.linearSize) { offset -> elementContext.transform(a.buffer[offset], b.buffer[offset]) })
+    }
+
+    override fun NDBuffer<T>.toElement(): RingElement<NDBuffer<T>, *, out BufferedNDRing<T, R>> =
+        BufferedNDRingElement(this@BoxingNDRing, buffer)
+}

kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/ComplexNDField.kt

@@ -131,4 +131,7 @@ operator fun ComplexNDElement.plus(arg: Double) =
 operator fun ComplexNDElement.minus(arg: Double) =
     map { it - arg }
 
-fun NDField.Companion.complex(vararg shape: Int) = ComplexNDField(shape)
+fun NDField.Companion.complex(vararg shape: Int): ComplexNDField = ComplexNDField(shape)
+
+fun NDElement.Companion.complex(vararg shape: Int, initializer: ComplexField.(IntArray) -> Complex): ComplexNDElement =
+    NDField.complex(*shape).produce(initializer)

kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/NDAlgebra.kt

@@ -1,9 +1,9 @@
 package scientifik.kmath.structures
 
+import scientifik.kmath.operations.Complex
 import scientifik.kmath.operations.Field
 import scientifik.kmath.operations.Ring
 import scientifik.kmath.operations.Space
-import kotlin.jvm.JvmName
 
 
 /**
@@ -57,6 +57,8 @@ interface NDAlgebra<T, C, N : NDStructure<T>> {
      * element-by-element invoke a function working on [T] on a [NDStructure]
      */
     operator fun Function1<T, T>.invoke(structure: N) = map(structure) { value -> this@invoke(value) }
+
+    companion object
 }
 
 /**
@@ -75,10 +77,13 @@ interface NDSpace<T, S : Space<T>, N : NDStructure<T>> : Space<N>, NDAlgebra<T,
 
     //TODO move to extensions after KEEP-176
     operator fun N.plus(arg: T) = map(this) { value -> add(arg, value) }
+
     operator fun N.minus(arg: T) = map(this) { value -> add(arg, -value) }
 
     operator fun T.plus(arg: N) = map(arg) { value -> add(this@plus, value) }
     operator fun T.minus(arg: N) = map(arg) { value -> add(-this@minus, value) }
+
+    companion object
 }
 
 /**
@@ -93,7 +98,10 @@ interface NDRing<T, R : Ring<T>, N : NDStructure<T>> : Ring<N>, NDSpace<T, R, N>
 
     //TODO move to extensions after KEEP-176
     operator fun N.times(arg: T) = map(this) { value -> multiply(arg, value) }
+
     operator fun T.times(arg: N) = map(arg) { value -> multiply(this@times, value) }
+
+    companion object
 }
 
 /**
@@ -113,6 +121,7 @@ interface NDField<T, F : Field<T>, N : NDStructure<T>> : Field<N>, NDRing<T, F,
 
     //TODO move to extensions after KEEP-176
     operator fun N.div(arg: T) = map(this) { value -> divide(arg, value) }
+
     operator fun T.div(arg: N) = map(arg) { divide(it, this@div) }
 
     companion object {
@@ -127,12 +136,11 @@ interface NDField<T, F : Field<T>, N : NDStructure<T>> : Field<N>, NDRing<T, F,
         /**
          * Create a nd-field with boxing generic buffer
          */
-        fun <T : Any, F : Field<T>> buffered(
+        fun <T : Any, F : Field<T>> boxing(
-            shape: IntArray,
             field: F,
+            vararg shape: Int,
             bufferFactory: BufferFactory<T> = Buffer.Companion::boxing
-        ) =
+        ) = BoxingNDField(shape, field, bufferFactory)
-            BoxingNDField(shape, field, bufferFactory)
 
         /**
          * Create a most suitable implementation for nd-field using reified class.
@@ -141,6 +149,7 @@ interface NDField<T, F : Field<T>, N : NDStructure<T>> : Field<N>, NDRing<T, F,
         inline fun <reified T : Any, F : Field<T>> auto(field: F, vararg shape: Int): BufferedNDField<T, F> =
             when {
                 T::class == Double::class -> real(*shape) as BufferedNDField<T, F>
+                T::class == Complex::class -> complex(*shape) as BufferedNDField<T, F>
                 else -> BoxingNDField(shape, field, Buffer.Companion::auto)
             }
     }

kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/NDElement.kt

@@ -41,7 +41,7 @@ interface NDElement<T, C, N : NDStructure<T>> : NDStructure<T> {
         /**
          * Simple boxing NDArray
          */
-        fun <T : Any, F : Field<T>> buffered(
+        fun <T : Any, F : Field<T>> boxing(
             shape: IntArray,
             field: F,
             initializer: F.(IntArray) -> T

kmath-core/src/jvmMain/kotlin/scientifik/kmath/operations/BigNumers.kt

@@ -0,0 +1,45 @@
+package scientifik.kmath.operations
+
+import scientifik.kmath.structures.*
+import java.math.BigDecimal
+import java.math.BigInteger
+import java.math.MathContext
+
+object BigIntegerRing : Ring<BigInteger> {
+    override val zero: BigInteger = BigInteger.ZERO
+    override val one: BigInteger = BigInteger.ONE
+
+    override fun add(a: BigInteger, b: BigInteger): BigInteger = a.add(b)
+
+    override fun multiply(a: BigInteger, k: Number): BigInteger = a.multiply(k.toInt().toBigInteger())
+
+    override fun multiply(a: BigInteger, b: BigInteger): BigInteger = a.multiply(b)
+}
+
+class BigDecimalField(val mathContext: MathContext = MathContext.DECIMAL64) : Field<BigDecimal> {
+    override val zero: BigDecimal = BigDecimal.ZERO
+    override val one: BigDecimal = BigDecimal.ONE
+
+    override fun add(a: BigDecimal, b: BigDecimal): BigDecimal = a.add(b)
+
+    override fun multiply(a: BigDecimal, k: Number): BigDecimal =
+        a.multiply(k.toDouble().toBigDecimal(mathContext), mathContext)
+
+    override fun multiply(a: BigDecimal, b: BigDecimal): BigDecimal = a.multiply(b, mathContext)
+    override fun divide(a: BigDecimal, b: BigDecimal): BigDecimal = a.divide(b, mathContext)
+}
+
+inline fun Buffer.Companion.bigInt(size: Int, initializer: (Int) -> BigInteger): Buffer<BigInteger> =
+    boxing(size, initializer)
+
+inline fun MutableBuffer.Companion.bigInt(size: Int, initializer: (Int) -> BigInteger): MutableBuffer<BigInteger> =
+    boxing(size, initializer)
+
+fun NDAlgebra.Companion.bigInt(vararg shape: Int): BoxingNDRing<BigInteger, BigIntegerRing> =
+    BoxingNDRing(shape, BigIntegerRing, Buffer.Companion::bigInt)
+
+fun NDElement.Companion.bigInt(
+    vararg shape: Int,
+    initializer: BigIntegerRing.(IntArray) -> BigInteger
+): BufferedNDRingElement<BigInteger, BigIntegerRing> =
+    NDAlgebra.bigInt(*shape).produce(initializer)

kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/chains/Chain.kt

@@ -17,7 +17,9 @@
 package scientifik.kmath.chains
 
 import kotlinx.atomicfu.atomic
+import kotlinx.atomicfu.updateAndGet
 import kotlinx.coroutines.FlowPreview
+import kotlinx.coroutines.flow.Flow
 
 
 /**
@@ -25,11 +27,6 @@ import kotlinx.coroutines.FlowPreview
  * @param R - the chain element type
  */
 interface Chain<out R> {
-    /**
-     * Last cached value of the chain. Returns null if [next] was not called
-     */
-    val value: R?
-
     /**
      * Generate next value, changing state if needed
      */
@@ -40,109 +37,115 @@ interface Chain<out R> {
      */
     fun fork(): Chain<R>
 
+    companion object
+
 }
 
 /**
  * Chain as a coroutine flow. The flow emit affects chain state and vice versa
  */
 @FlowPreview
-val <R> Chain<R>.flow
+val <R> Chain<R>.flow: Flow<R>
     get() = kotlinx.coroutines.flow.flow { while (true) emit(next()) }
 
 fun <T> Iterator<T>.asChain(): Chain<T> = SimpleChain { next() }
 fun <T> Sequence<T>.asChain(): Chain<T> = iterator().asChain()
 
-
-/**
- * Map the chain result using suspended transformation. Initial chain result can no longer be safely consumed
- * since mapped chain consumes tokens. Accepts regular transformation function
- */
-fun <T, R> Chain<T>.map(func: (T) -> R): Chain<R> {
-    val parent = this;
-    return object : Chain<R> {
-        override val value: R? get() = parent.value?.let(func)
-
-        override suspend fun next(): R {
-            return func(parent.next())
-        }
-
-        override fun fork(): Chain<R> {
-            return parent.fork().map(func)
-        }
-    }
-}
-
 /**
  * A simple chain of independent tokens
  */
 class SimpleChain<out R>(private val gen: suspend () -> R) : Chain<R> {
-    private val atomicValue = atomic<R?>(null)
+    override suspend fun next(): R = gen()
-    override val value: R? get() = atomicValue.value
-
-    override suspend fun next(): R = gen().also { atomicValue.lazySet(it) }
-
     override fun fork(): Chain<R> = this
 }
 
-//TODO force forks on mapping operations?
-
 /**
  * A stateless Markov chain
  */
-class MarkovChain<out R : Any>(private val seed: () -> R, private val gen: suspend (R) -> R) :
+class MarkovChain<out R : Any>(private val seed: suspend () -> R, private val gen: suspend (R) -> R) : Chain<R> {
-    Chain<R> {
 
-    constructor(seed: R, gen: suspend (R) -> R) : this({ seed }, gen)
+    constructor(seedValue: R, gen: suspend (R) -> R) : this({ seedValue }, gen)
 
-    private val atomicValue = atomic<R?>(null)
+    private val value = atomic<R?>(null)
-    override val value: R get() = atomicValue.value ?: seed()
 
     override suspend fun next(): R {
-        val newValue = gen(value)
+        return value.updateAndGet { prev -> gen(prev ?: seed()) }!!
-        atomicValue.lazySet(newValue)
-        return value
     }
 
     override fun fork(): Chain<R> {
-        return MarkovChain(value, gen)
+        return MarkovChain(seed = { value.value ?: seed() }, gen = gen)
     }
 }
 
 /**
  * A chain with possibly mutable state. The state must not be changed outside the chain. Two chins should never share the state
  * @param S - the state of the chain
+ * @param forkState - the function to copy current state without modifying it
  */
 class StatefulChain<S, out R>(
     private val state: S,
     private val seed: S.() -> R,
+    private val forkState: ((S) -> S),
     private val gen: suspend S.(R) -> R
 ) : Chain<R> {
 
-    constructor(state: S, seed: R, gen: suspend S.(R) -> R) : this(state, { seed }, gen)
+    constructor(state: S, seedValue: R, forkState: ((S) -> S), gen: suspend S.(R) -> R) : this(
+        state,
+        { seedValue },
+        forkState,
+        gen
+    )
 
     private val atomicValue = atomic<R?>(null)
-    override val value: R get() = atomicValue.value ?: seed(state)
 
     override suspend fun next(): R {
-        val newValue = gen(state, value)
+        return atomicValue.updateAndGet { prev -> state.gen(prev ?: state.seed()) }!!
-        atomicValue.lazySet(newValue)
-        return value
     }
 
     override fun fork(): Chain<R> {
-        throw RuntimeException("Fork not supported for stateful chain")
+        return StatefulChain(forkState(state), seed, forkState, gen)
     }
 }
 
 /**
  * A chain that repeats the same value
  */
-class ConstantChain<out T>(override val value: T) : Chain<T> {
+class ConstantChain<out T>(val value: T) : Chain<T> {
-    override suspend fun next(): T {
+    override suspend fun next(): T = value
-        return value
-    }
 
     override fun fork(): Chain<T> {
         return this
     }
 }
+
+/**
+ * Map the chain result using suspended transformation. Initial chain result can no longer be safely consumed
+ * since mapped chain consumes tokens. Accepts regular transformation function
+ */
+fun <T, R> Chain<T>.pipe(func: suspend (T) -> R): Chain<R> = object : Chain<R> {
+    override suspend fun next(): R = func(this@pipe.next())
+    override fun fork(): Chain<R> = this@pipe.fork().pipe(func)
+}
+
+/**
+ * Map the whole chain
+ */
+fun <T, R> Chain<T>.map(mapper: suspend (Chain<T>) -> R): Chain<R> = object : Chain<R> {
+    override suspend fun next(): R = mapper(this@map)
+    override fun fork(): Chain<R> = this@map.fork().map(mapper)
+}
+
+fun <T, S, R> Chain<T>.mapWithState(state: S, stateFork: (S) -> S, mapper: suspend S.(Chain<T>) -> R): Chain<R> =
+    object : Chain<R> {
+        override suspend fun next(): R = state.mapper(this@mapWithState)
+        override fun fork(): Chain<R> = this@mapWithState.fork().mapWithState(stateFork(state), stateFork, mapper)
+    }
+
+/**
+ * Zip two chains together using given transformation
+ */
+fun <T, U, R> Chain<T>.zip(other: Chain<U>, block: suspend (T, U) -> R): Chain<R> = object : Chain<R> {
+    override suspend fun next(): R = block(this@zip.next(), other.next())
+
+    override fun fork(): Chain<R> = this@zip.fork().zip(other.fork(), block)
+}

kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/coroutines/CoroutinesExtra.kt

@@ -1,4 +1,4 @@
-package scientifik.kmath
+package scientifik.kmath.coroutines
 
 import kotlinx.coroutines.*
 import kotlinx.coroutines.channels.produce
@@ -42,13 +42,14 @@ fun <T, R> Flow<T>.async(
 }
 
 @FlowPreview
-fun <T, R> AsyncFlow<T>.map(action: (T) -> R) = AsyncFlow(deferredFlow.map { input ->
+fun <T, R> AsyncFlow<T>.map(action: (T) -> R) =
-    //TODO add function composition
+    AsyncFlow(deferredFlow.map { input ->
-    LazyDeferred(input.dispatcher) {
+        //TODO add function composition
-        input.start(this)
+        LazyDeferred(input.dispatcher) {
-        action(input.await())
+            input.start(this)
-    }
+            action(input.await())
-})
+        }
+    })
 
 @ExperimentalCoroutinesApi
 @FlowPreview

kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/streaming/BufferFlow.kt

@@ -22,7 +22,7 @@ fun <T> Flow<Buffer<out T>>.spread(): Flow<T> = flatMapConcat { it.asFlow() }
  * Collect incoming flow into fixed size chunks
  */
 @FlowPreview
-fun <T> Flow<T>.chunked(bufferSize: Int, bufferFactory: BufferFactory<T>) = flow {
+fun <T> Flow<T>.chunked(bufferSize: Int, bufferFactory: BufferFactory<T>): Flow<Buffer<T>> = flow {
     require(bufferSize > 0) { "Resulting chunk size must be more than zero" }
     val list = ArrayList<T>(bufferSize)
     var counter = 0
@@ -46,7 +46,7 @@ fun <T> Flow<T>.chunked(bufferSize: Int, bufferFactory: BufferFactory<T>) = flow
  * Specialized flow chunker for real buffer
  */
 @FlowPreview
-fun Flow<Double>.chunked(bufferSize: Int) = flow {
+fun Flow<Double>.chunked(bufferSize: Int): Flow<DoubleBuffer> = flow {
     require(bufferSize > 0) { "Resulting chunk size must be more than zero" }
     val array = DoubleArray(bufferSize)
     var counter = 0

kmath-coroutines/src/jvmMain/kotlin/scientifik/kmath/chains/ChainExt.kt

@@ -18,23 +18,3 @@ operator fun <R> Chain<R>.iterator() = object : Iterator<R> {
 fun <R> Chain<R>.asSequence(): Sequence<R> = object : Sequence<R> {
     override fun iterator(): Iterator<R> = this@asSequence.iterator()
 }
-
-
-/**
- * Map the chain result using suspended transformation. Initial chain result can no longer be safely consumed
- * since mapped chain consumes tokens. Accepts suspending transformation function.
- */
-fun <T, R> Chain<T>.map(func: suspend (T) -> R): Chain<R> {
-    val parent = this;
-    return object : Chain<R> {
-        override val value: R? get() = runBlocking { parent.value?.let { func(it) } }
-
-        override suspend fun next(): R {
-            return func(parent.next())
-        }
-
-        override fun fork(): Chain<R> {
-            return parent.fork().map(func)
-        }
-    }
-}

kmath-coroutines/src/jvmMain/kotlin/scientifik/kmath/structures/LazyNDStructure.kt

@@ -1,7 +1,7 @@
 package scientifik.kmath.structures
 
 import kotlinx.coroutines.*
-import scientifik.kmath.Math
+import scientifik.kmath.coroutines.Math
 
 class LazyNDStructure<T>(
     val scope: CoroutineScope,

kmath-coroutines/src/jvmTest/kotlin/scientifik/kmath/streaming/BufferFlowTest.kt

@@ -4,9 +4,9 @@ import kotlinx.coroutines.*
 import kotlinx.coroutines.flow.asFlow
 import kotlinx.coroutines.flow.collect
 import org.junit.Test
-import scientifik.kmath.async
+import scientifik.kmath.coroutines.async
-import scientifik.kmath.collect
+import scientifik.kmath.coroutines.collect
-import scientifik.kmath.map
+import scientifik.kmath.coroutines.map
 import java.util.concurrent.Executors
 
 

kmath-histograms/build.gradle.kts

@@ -2,7 +2,6 @@ plugins {
     `npm-multiplatform`
 }
 
-// Just an example how we can collapse nested DSL for simple declarations
 kotlin.sourceSets.commonMain {
     dependencies {
         api(project(":kmath-core"))

kmath-prob/build.gradle.kts

@@ -6,13 +6,14 @@ plugins {
 kotlin.sourceSets {
     commonMain {
         dependencies {
-            api(project(":kmath-core"))
             api(project(":kmath-coroutines"))
             compileOnly("org.jetbrains.kotlinx:atomicfu-common:${Versions.atomicfuVersion}")
         }
     }
     jvmMain {
         dependencies {
+            // https://mvnrepository.com/artifact/org.apache.commons/commons-rng-simple
+            //api("org.apache.commons:commons-rng-sampling:1.2")
             compileOnly("org.jetbrains.kotlinx:atomicfu:${Versions.atomicfuVersion}")
         }
     }

kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/Distribution.kt

@@ -0,0 +1,68 @@
+package scientifik.kmath.prob
+
+import scientifik.kmath.chains.Chain
+import scientifik.kmath.chains.map
+import kotlin.jvm.JvmName
+
+interface Sampler<T : Any> {
+    fun sample(generator: RandomGenerator): Chain<T>
+}
+
+/**
+ * A distribution of typed objects
+ */
+interface Distribution<T : Any> : Sampler<T> {
+    /**
+     * A probability value for given argument [arg].
+     * For continuous distributions returns PDF
+     */
+    fun probability(arg: T): Double
+
+    /**
+     * Create a chain of samples from this distribution.
+     * The chain is not guaranteed to be stateless.
+     */
+    override fun sample(generator: RandomGenerator): Chain<T>
+
+    /**
+     * An empty companion. Distribution factories should be written as its extensions
+     */
+    companion object
+}
+
+interface UnivariateDistribution<T : Comparable<T>> : Distribution<T> {
+    /**
+     * Cumulative distribution for ordered parameter
+     */
+    fun cumulative(arg: T): Double
+}
+
+/**
+ * Compute probability integral in an interval
+ */
+fun <T : Comparable<T>> UnivariateDistribution<T>.integral(from: T, to: T): Double {
+    require(to > from)
+    return cumulative(to) - cumulative(from)
+}
+
+
+/**
+ * Sample a bunch of values
+ */
+fun <T : Any> Sampler<T>.sampleBunch(generator: RandomGenerator, size: Int): Chain<List<T>> {
+    require(size > 1)
+    return sample(generator).map{chain ->
+        List(size){chain.next()}
+    }
+}
+
+/**
+ * Generate a bunch of samples from real distributions
+ */
+@JvmName("realSampleBunch")
+fun Sampler<Double>.sampleBunch(generator: RandomGenerator, size: Int): Chain<DoubleArray> {
+    require(size > 1)
+    return sample(generator).map{chain ->
+        DoubleArray(size){chain.next()}
+    }
+}

kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/RandomChain.kt

@@ -0,0 +1,13 @@
+package scientifik.kmath.prob
+
+import kotlinx.atomicfu.atomic
+import scientifik.kmath.chains.Chain
+
+/**
+ * A possibly stateful chain producing random values.
+ */
+class RandomChain<out R>(val generator: RandomGenerator, private val gen: suspend RandomGenerator.() -> R) : Chain<R> {
+    override suspend fun next(): R = generator.gen()
+
+    override fun fork(): Chain<R> = RandomChain(generator.fork(), gen)
+}

kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/RandomGenerator.kt

@@ -0,0 +1,41 @@
+package scientifik.kmath.prob
+
+import kotlin.random.Random
+
+/**
+ * A basic generator
+ */
+interface RandomGenerator {
+    fun nextDouble(): Double
+    fun nextInt(): Int
+    fun nextLong(): Long
+    fun nextBlock(size: Int): ByteArray
+
+    /**
+     * Create a new generator which is independent from current generator (operations on new generator do not affect this one
+     * and vise versa). The statistical properties of new generator should be the same as for this one.
+     * For pseudo-random generator, the fork is keeping the same sequence of numbers for given call order for each run.
+     *
+     * The thread safety of this operation is not guaranteed since it could affect the state of the generator.
+     */
+    fun fork(): RandomGenerator
+
+    companion object {
+        val default by lazy { DefaultGenerator(Random.nextLong()) }
+    }
+}
+
+class DefaultGenerator(seed: Long?) : RandomGenerator {
+    private val random = seed?.let { Random(it) } ?: Random
+
+    override fun nextDouble(): Double = random.nextDouble()
+
+    override fun nextInt(): Int = random.nextInt()
+
+    override fun nextLong(): Long = random.nextLong()
+
+    override fun nextBlock(size: Int): ByteArray = random.nextBytes(size)
+
+    override fun fork(): RandomGenerator = DefaultGenerator(nextLong())
+
+}

kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/SamplerAlgebra.kt

@@ -0,0 +1,31 @@
+package scientifik.kmath.prob
+
+import scientifik.kmath.chains.Chain
+import scientifik.kmath.chains.ConstantChain
+import scientifik.kmath.chains.pipe
+import scientifik.kmath.chains.zip
+import scientifik.kmath.operations.Space
+
+class BasicSampler<T : Any>(val chainBuilder: (RandomGenerator) -> Chain<T>) : Sampler<T> {
+    override fun sample(generator: RandomGenerator): Chain<T> = chainBuilder(generator)
+}
+
+class ConstantSampler<T : Any>(val value: T) : Sampler<T> {
+    override fun sample(generator: RandomGenerator): Chain<T> = ConstantChain(value)
+}
+
+/**
+ * A space for samplers. Allows to perform simple operations on distributions
+ */
+class SamplerSpace<T : Any>(val space: Space<T>) : Space<Sampler<T>> {
+
+    override val zero: Sampler<T> = ConstantSampler(space.zero)
+
+    override fun add(a: Sampler<T>, b: Sampler<T>): Sampler<T> = BasicSampler { generator ->
+        a.sample(generator).zip(b.sample(generator)) { aValue, bValue -> space.run { aValue + bValue } }
+    }
+
+    override fun multiply(a: Sampler<T>, k: Number): Sampler<T> = BasicSampler { generator ->
+        a.sample(generator).pipe { space.run { it * k.toDouble() } }
+    }
+}

settings.gradle.kts

@@ -3,6 +3,7 @@ pluginManagement {
         jcenter()
         gradlePluginPortal()
         maven("https://dl.bintray.com/kotlin/kotlin-eap")
+        maven("https://dl.bintray.com/orangy/maven")
     }
     resolutionStrategy {
         eachPlugin {
@@ -28,5 +29,5 @@ include(
     ":kmath-commons",
     ":kmath-koma",
     ":kmath-prob",
-    ":benchmarks"
+    ":examples"
 )