Reimplement random-forging chain

Minor fixes.

benchmarks/build.gradle.kts

@@ -15,8 +15,6 @@ repositories {
     mavenCentral()
 }
 
-val multikVersion: String by rootProject.extra
-
 kotlin {
     jvm()
 
@@ -45,7 +43,7 @@ kotlin {
                 implementation(project(":kmath-for-real"))
                 implementation(project(":kmath-tensors"))
                 implementation(project(":kmath-multik"))
-                implementation("org.jetbrains.kotlinx:multik-default:$multikVersion")
+                implementation(libs.multik.default)
                 implementation(spclibs.kotlinx.benchmark.runtime)
             }
         }

build.gradle.kts

@@ -3,7 +3,7 @@ import space.kscience.gradle.useSPCTeam
 
 plugins {
     id("space.kscience.gradle.project")
-    id("org.jetbrains.kotlinx.kover") version "0.7.6"
+    alias(spclibs.plugins.kotlinx.kover)
 }
 
 val attributesVersion by extra("0.2.0")
@@ -70,5 +70,3 @@ ksciencePublish {
 }
 
 apiValidation.nonPublicMarkers.add("space.kscience.kmath.UnstableKMathAPI")
-
-val multikVersion by extra("0.2.3")

examples/build.gradle.kts

@@ -10,8 +10,6 @@ repositories {
     maven("https://maven.pkg.jetbrains.space/kotlin/p/kotlin/kotlin-js-wrappers")
 }
 
-val multikVersion: String by rootProject.extra
-
 dependencies {
     implementation(project(":kmath-ast"))
     implementation(project(":kmath-kotlingrad"))
@@ -33,7 +31,7 @@ dependencies {
     implementation(project(":kmath-jafama"))
     //multik
     implementation(project(":kmath-multik"))
-    implementation("org.jetbrains.kotlinx:multik-default:$multikVersion")
+    implementation(libs.multik.default)
 
     //datetime
     implementation("org.jetbrains.kotlinx:kotlinx-datetime:0.4.0")

gradle/libs.versions.toml

@@ -1,9 +1,12 @@
 [versions]
 
 commons-rng = "1.6"
-
+multik = "0.2.3"
 
 [libraries]
 
-commons-rng-simple = {module ="org.apache.commons:commons-rng-simple", version.ref = "commons-rng"}
+commons-rng-simple = { module = "org.apache.commons:commons-rng-simple", version.ref = "commons-rng" }
-commons-rng-sampling = {module ="org.apache.commons:commons-rng-sampling", version.ref = "commons-rng"}
+commons-rng-sampling = { module = "org.apache.commons:commons-rng-sampling", version.ref = "commons-rng" }
+
+multik-core = { module = "org.jetbrains.kotlinx:multik-core", version.ref = "multik" }
+multik-default = { module = "org.jetbrains.kotlinx:multik-default", version.ref = "multik" }

kmath-multik/build.gradle.kts

@@ -4,8 +4,6 @@ plugins {
 
 description = "JetBrains Multik connector"
 
-val multikVersion: String by rootProject.extra
-
 kscience {
     jvm()
     js()
@@ -16,12 +14,12 @@ kotlin {
         commonMain {
             dependencies {
                 api(projects.kmathTensors)
-                api("org.jetbrains.kotlinx:multik-core:$multikVersion")
+                api(libs.multik.core)
             }
         }
         commonTest {
             dependencies {
-                api("org.jetbrains.kotlinx:multik-default:$multikVersion")
+                api(libs.multik.default)
             }
         }
     }

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

@@ -0,0 +1,71 @@
+/*
+ * Copyright 2018-2024 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+@file:OptIn(ExperimentalCoroutinesApi::class)
+
+package space.kscience.kmath.samplers
+
+import kotlinx.coroutines.CoroutineScope
+import kotlinx.coroutines.ExperimentalCoroutinesApi
+import kotlinx.coroutines.channels.Channel
+import kotlinx.coroutines.isActive
+import kotlinx.coroutines.launch
+import space.kscience.kmath.chains.Chain
+import space.kscience.kmath.random.RandomGenerator
+import space.kscience.kmath.stat.Sampler
+import kotlin.coroutines.coroutineContext
+
+
+/**
+ * A sampler that creates a chain that could be split at each computation
+ */
+public class RandomForkingSampler<T: Any>(
+    private val scope: CoroutineScope,
+    private val initialValue: T,
+    private val makeStep: suspend RandomGenerator.(T) -> List<T>
+) : Sampler<T?> {
+
+    override fun sample(generator: RandomGenerator): Chain<T?> = buildChain(scope, initialValue) { generator.makeStep(it) }
+
+    public companion object {
+        private suspend fun <T> Channel<T>.receiveEvents(
+            initial: T,
+            makeStep: suspend (T) -> List<T>
+        ) {
+            send(initial)
+            //inner dispatch queue
+            val innerChannel = Channel<T>(50)
+            innerChannel.send(initial)
+            while (coroutineContext.isActive && !innerChannel.isEmpty) {
+                val current = innerChannel.receive()
+                //add event immediately, but it does not mean that the value is computed immediately as well
+                makeStep(current).forEach {
+                    innerChannel.send(it)
+                    send(it)
+                }
+            }
+            innerChannel.close()
+            close()
+        }
+
+
+        public fun <T: Any> buildChain(
+            scope: CoroutineScope,
+            initial: T,
+            makeStep: suspend (T) -> List<T>
+        ): Chain<T?> {
+            val channel = Channel<T>(Channel.RENDEZVOUS)
+            scope.launch {
+                channel.receiveEvents(initial, makeStep)
+            }
+
+            return object : Chain<T?> {
+                override suspend fun next(): T? = channel.receiveCatching().getOrNull()
+
+                override suspend fun fork(): Chain<T?> = buildChain(scope, channel.receive(), makeStep)
+            }
+        }
+    }
+}

kmath-stat/src/commonTest/kotlin/space/kscience/kmath/samplers/TestMetropolisHastingsSampler.kt

@@ -24,8 +24,8 @@ class TestMetropolisHastingsSampler {
 
     data class TestSetup(val mean: Double, val startPoint: Double, val sigma: Double = 0.5)
 
-    val sample = 1e6.toInt()
+    private val sample = 1e6.toInt()
-    val burnIn = sample / 5
+    private val burnIn = sample / 5
 
     @Test
     fun samplingNormalTest() = runTest {
@@ -66,6 +66,7 @@ class TestMetropolisHastingsSampler {
     @Test
     fun samplingRayleighTest() = runTest {
         val generator = RandomGenerator.default(1)
+
         fun rayleighDist(sigma: Double, arg: Double): Double = if (arg < 0.0) {
             0.0
         } else {
@@ -83,17 +84,5 @@ class TestMetropolisHastingsSampler {
 
             assertEquals(setup.mean * sqrt(PI / 2), Float64Field.mean(sampledValues), 1e-2)
         }
-//
-//        fun rayleighDist1(arg: Double) = rayleighDist(arg, 1.0)
-//        var sampler = MetropolisHastingsSampler(::rayleighDist1, initialPoint = 2.0, proposalStd = 1.0)
-//        var sampledValues = sampler.sample(DefaultGenerator()).nextBufferBlocking(1_000_000)
-//
-//        assertEquals(1.25, Float64Field.mean(sampledValues), 1e-2)
-//
-//        fun rayleighDist2(arg: Double) = rayleighDist(arg, 2.0)
-//        sampler = MetropolisHastingsSampler(::rayleighDist2, proposalStd = 1.0)
-//        sampledValues = sampler.sample(DefaultGenerator()).nextBufferBlocking(10_000_000)
-//
-//        assertEquals(2.5, Float64Field.mean(sampledValues), 1e-2)
     }
 }