Merge pull request #59 from mipt-npm/dev

Merge dev into master

.gitignore

@@ -1,6 +1,7 @@
 .gradle
-**/build/
+build/
-/.idea/
+out/
+.idea/
 
 # Avoid ignoring Gradle wrapper jar file (.jar files are usually ignored)
 !gradle-wrapper.jar

benchmarks/build.gradle

@@ -21,7 +21,7 @@ dependencies {
     //jmh project(':kmath-core')
 }
 
-jmh{
+jmh {
     warmupIterations = 1
 }
 

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

@@ -4,16 +4,14 @@ import org.openjdk.jmh.annotations.Benchmark
 import org.openjdk.jmh.annotations.Scope
 import org.openjdk.jmh.annotations.State
 import scientifik.kmath.operations.Complex
+import scientifik.kmath.operations.complex
 
 @State(Scope.Benchmark)
 open class BufferBenchmark {
 
     @Benchmark
     fun genericDoubleBufferReadWrite() {
-        val buffer = Double.createBuffer(size)
+        val buffer = DoubleBuffer(size){it.toDouble()}
-        (0 until size).forEach {
-            buffer[it] = it.toDouble()
-        }
 
         (0 until size).forEach {
             buffer[it]
@@ -22,10 +20,7 @@ open class BufferBenchmark {
 
     @Benchmark
     fun complexBufferReadWrite() {
-        val buffer = MutableBuffer.complex(size / 2)
+        val buffer = MutableBuffer.complex(size / 2){Complex(it.toDouble(), -it.toDouble())}
-        (0 until size / 2).forEach {
-            buffer[it] = Complex(it.toDouble(), -it.toDouble())
-        }
 
         (0 until size / 2).forEach {
             buffer[it]

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

@@ -34,19 +34,6 @@ open class NDFieldBenchmark {
     }
 
 
-    @Benchmark
-    fun lazyFieldAdd() {
-        lazyNDField.run {
-            var res = one
-            repeat(n) {
-                res += one
-            }
-
-            res.elements().sumByDouble { it.second }
-        }
-    }
-
-
     @Benchmark
     fun boxingFieldAdd() {
         genericField.run {
@@ -61,9 +48,8 @@ open class NDFieldBenchmark {
         val dim = 1000
         val n = 100
 
-        val bufferedField = NDField.auto(RealField, intArrayOf(dim, dim))
+        val bufferedField = NDField.auto(RealField, dim, dim)
-        val specializedField = NDField.real(intArrayOf(dim, dim))
+        val specializedField = NDField.real(dim, dim)
         val genericField = NDField.buffered(intArrayOf(dim, dim), RealField)
-        val lazyNDField = NDField.lazy(intArrayOf(dim, dim), RealField)
     }
 }

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

@@ -1,39 +1,42 @@
 package scientifik.kmath.linear
 
 import koma.matrix.ejml.EJMLMatrixFactory
+import scientifik.kmath.operations.RealField
+import scientifik.kmath.structures.Matrix
+import kotlin.contracts.ExperimentalContracts
 import kotlin.random.Random
 import kotlin.system.measureTimeMillis
 
+@ExperimentalContracts
 fun main() {
-    val random = Random(12224)
+    val random = Random(1224)
     val dim = 100
     //creating invertible matrix
     val u = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
     val l = Matrix.real(dim, dim) { i, j -> if (i >= j) random.nextDouble() else 0.0 }
     val matrix = l dot u
 
-    val n = 500 // iterations
+    val n = 5000 // iterations
 
-    val solver = LUSolver.real
+    MatrixContext.real.run {
 
         repeat(50) {
-        val res = solver.inverse(matrix)
+            val res = inverse(matrix)
         }
 
         val inverseTime = measureTimeMillis {
             repeat(n) {
-            val res = solver.inverse(matrix)
+                val res = inverse(matrix)
             }
         }
 
         println("[kmath] Inversion of $n matrices $dim x $dim finished in $inverseTime millis")
+    }
 
     //commons-math
 
-    val cmContext = CMMatrixContext
-
     val commonsTime = measureTimeMillis {
-        cmContext.run {
+        CMMatrixContext.run {
             val cm = matrix.toCM()             //avoid overhead on conversion
             repeat(n) {
                 val res = inverse(cm)
@@ -46,10 +49,8 @@ fun main() {
 
     //koma-ejml
 
-    val komaContext = KomaMatrixContext(EJMLMatrixFactory())
-
     val komaTime = measureTimeMillis {
-        komaContext.run {
+        KomaMatrixContext(EJMLMatrixFactory(), RealField).run {
             val km = matrix.toKoma()      //avoid overhead on conversion
             repeat(n) {
                 val res = inverse(km)

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

@@ -1,6 +1,8 @@
 package scientifik.kmath.linear
 
 import koma.matrix.ejml.EJMLMatrixFactory
+import scientifik.kmath.operations.RealField
+import scientifik.kmath.structures.Matrix
 import kotlin.random.Random
 import kotlin.system.measureTimeMillis
 
@@ -26,7 +28,7 @@ fun main() {
     }
 
 
-    KomaMatrixContext(EJMLMatrixFactory()).run {
+    KomaMatrixContext(EJMLMatrixFactory(), RealField).run {
         val komaMatrix1 = matrix1.toKoma()
         val komaMatrix2 = matrix2.toKoma()
 

benchmarks/src/main/kotlin/scientifik/kmath/structures/ComplexNDBenchmark.kt

@@ -23,7 +23,7 @@ fun main() {
 
     val complexTime = measureTimeMillis {
         complexField.run {
-            var res: ComplexNDElement = one
+            var res = one
             repeat(n) {
                 res += 1.0
             }

build.gradle.kts

@@ -1,110 +1,29 @@
-import org.jetbrains.kotlin.gradle.dsl.KotlinMultiplatformExtension
+val kmathVersion by extra("0.1.2-dev-4")
-
-buildscript {
-    val kotlinVersion: String by rootProject.extra("1.3.21")
-    val ioVersion: String by rootProject.extra("0.1.5")
-    val coroutinesVersion: String by rootProject.extra("1.1.1")
-    val atomicfuVersion: String by rootProject.extra("0.12.1")
-    val dokkaVersion: String by rootProject.extra("0.9.17")
-
-    repositories {
-        //maven("https://dl.bintray.com/kotlin/kotlin-eap")
-        jcenter()
-    }
-
-    dependencies {
-        classpath("org.jetbrains.kotlin:kotlin-gradle-plugin:$kotlinVersion")
-        classpath("org.jfrog.buildinfo:build-info-extractor-gradle:4+")
-        classpath("com.jfrog.bintray.gradle:gradle-bintray-plugin:1.8.4")
-        classpath("org.jetbrains.dokka:dokka-gradle-plugin:$dokkaVersion")
-    }
-}
-
-plugins {
-    id("com.jfrog.artifactory") version "4.9.1" apply false
-}
-
-val kmathVersion by extra("0.1.0")
 
 allprojects {
-    group = "scientifik"
-    version = kmathVersion
-
     repositories {
-        //maven("https://dl.bintray.com/kotlin/kotlin-eap")
         jcenter()
+        maven("https://kotlin.bintray.com/kotlinx")
     }
     
-    apply(plugin = "maven")
+    group = "scientifik"
-    apply(plugin = "maven-publish")
+    version = kmathVersion
-
-    // apply bintray configuration
-    apply(from = "${rootProject.rootDir}/gradle/bintray.gradle")
-
-    //apply artifactory configuration
-    apply(from = "${rootProject.rootDir}/gradle/artifactory.gradle")
 }
 
 subprojects {
-    if (!name.startsWith("kmath")) return@subprojects
+    // Actually, probably we should apply it to plugins explicitly
-
+    // We also can merge them to single kmath-publish plugin
-
+    if (name.startsWith("kmath")) {
-    extensions.findByType<KotlinMultiplatformExtension>()?.apply {
+        apply(plugin = "bintray-config")
-        jvm {
+        apply(plugin = "artifactory-config")
-            compilations.all {
-                kotlinOptions {
-                    jvmTarget = "1.8"
     }
-            }
+    //    dokka {
-        }
+//        outputFormat = "html"
-        targets.all {
+//        outputDirectory = javadoc.destinationDir
-            sourceSets.all {
-                languageSettings.progressiveMode = true
-            }
-        }
-
-
-        extensions.findByType<PublishingExtension>()?.apply {
-            publications.filterIsInstance<MavenPublication>().forEach { publication ->
-                if (publication.name == "kotlinMultiplatform") {
-                    // for our root metadata publication, set artifactId with a package and project name
-                    publication.artifactId = project.name
-                } else {
-                    // for targets, set artifactId with a package, project name and target name (e.g. iosX64)
-                    publication.artifactId = "${project.name}-${publication.name}"
-                }
-            }
-
-            // Create empty jar for sources classifier to satisfy maven requirements
-            val stubSources by tasks.registering(Jar::class) {
-                archiveClassifier.set("sources")
-                //from(sourceSets.main.get().allSource)
-            }
-
-            // Create empty jar for javadoc classifier to satisfy maven requirements
-            val stubJavadoc by tasks.registering(Jar::class) {
-                archiveClassifier.set("javadoc")
-            }
-
-            extensions.findByType<KotlinMultiplatformExtension>()?.apply {
-
-                targets.forEach { target ->
-                    val publication = publications.findByName(target.name) as MavenPublication
-
-                    // Patch publications with fake javadoc
-                    publication.artifact(stubJavadoc)
-
-                    // Remove gradle metadata publishing from all targets which are not native
-//                if (target.platformType.name != "native") {
-//                    publication.gradleModuleMetadataFile = null
-//                    tasks.matching { it.name == "generateMetadataFileFor${name.capitalize()}Publication" }.all {
-//                        onlyIf { false }
 //    }
+//
+//    task dokkaJar (type: Jar, dependsOn: dokka) {
+//           from javadoc . destinationDir
+//            classifier = "javadoc"
 //    }
-                }
-            }
-        }
-    }
 }
-
-

buildSrc/build.gradle.kts

@@ -0,0 +1,18 @@
+plugins {
+    `kotlin-dsl`
+}
+
+repositories {
+    gradlePluginPortal()
+    jcenter()
+}
+
+val kotlinVersion = "1.3.31"
+
+// Add plugins used in buildSrc as dependencies, also we should specify version only here
+dependencies {
+    implementation("org.jetbrains.kotlin:kotlin-gradle-plugin:$kotlinVersion")
+    implementation("org.jfrog.buildinfo:build-info-extractor-gradle:4.9.5")
+    implementation("com.jfrog.bintray.gradle:gradle-bintray-plugin:1.8.4")
+    implementation("com.moowork.gradle:gradle-node-plugin:1.3.1")
+}

buildSrc/src/main/kotlin/Versions.kt

@@ -0,0 +1,10 @@
+// Instead of defining runtime properties and use them dynamically
+// define version in buildSrc and have autocompletion and compile-time check
+// Also dependencies itself can be moved here
+object Versions {
+    val ioVersion = "0.1.8"
+    val coroutinesVersion = "1.2.1"
+    val atomicfuVersion = "0.12.6"
+    // This version is not used and IDEA shows this property as unused
+    val dokkaVersion = "0.9.18"
+}

buildSrc/src/main/kotlin/artifactory-config.gradle.kts

@@ -0,0 +1,38 @@
+import groovy.lang.GroovyObject
+import org.jfrog.gradle.plugin.artifactory.dsl.PublisherConfig
+import org.jfrog.gradle.plugin.artifactory.dsl.ResolverConfig
+
+plugins {
+    id("com.jfrog.artifactory")
+}
+
+artifactory {
+    val artifactoryUser: String? by project
+    val artifactoryPassword: String? by project
+    val artifactoryContextUrl = "http://npm.mipt.ru:8081/artifactory"
+
+    setContextUrl(artifactoryContextUrl)//The base Artifactory URL if not overridden by the publisher/resolver
+    publish(delegateClosureOf<PublisherConfig> {
+        repository(delegateClosureOf<GroovyObject> {
+            setProperty("repoKey", "gradle-dev-local")
+            setProperty("username", artifactoryUser)
+            setProperty("password", artifactoryPassword)
+        })
+
+        defaults(delegateClosureOf<GroovyObject>{
+            invokeMethod("publications", arrayOf("jvm", "js", "kotlinMultiplatform", "metadata"))
+            //TODO: This property is not available for ArtifactoryTask
+            //setProperty("publishBuildInfo", false)
+            setProperty("publishArtifacts", true)
+            setProperty("publishPom", true)
+            setProperty("publishIvy", false)
+        })
+    })
+    resolve(delegateClosureOf<ResolverConfig> {
+        repository(delegateClosureOf<GroovyObject> {
+            setProperty("repoKey", "gradle-dev")
+            setProperty("username", artifactoryUser)
+            setProperty("password", artifactoryPassword)
+        })
+    })
+}

buildSrc/src/main/kotlin/bintray-config.gradle.kts

@@ -0,0 +1,97 @@
+@file:Suppress("UnstableApiUsage")
+
+import com.jfrog.bintray.gradle.BintrayExtension.PackageConfig
+import com.jfrog.bintray.gradle.BintrayExtension.VersionConfig
+
+// Old bintray.gradle script converted to real Gradle plugin (precompiled script plugin)
+// It now has own dependencies and support type safe accessors
+// Syntax is pretty close to what we had in Groovy
+// (excluding Property.set and bintray dynamic configs)
+
+plugins {
+    id("com.jfrog.bintray")
+    `maven-publish`
+}
+
+val vcs = "https://github.com/mipt-npm/kmath"
+
+// Configure publishing
+publishing {
+    repositories {
+        maven("https://bintray.com/mipt-npm/scientifik")
+    }
+
+    // Process each publication we have in this project
+    publications.filterIsInstance<MavenPublication>().forEach { publication ->
+
+        // use type safe pom config GSL insterad of old dynamic
+        publication.pom {
+            name.set(project.name)
+            description.set(project.description)
+            url.set(vcs)
+
+            licenses {
+                license {
+                    name.set("The Apache Software License, Version 2.0")
+                    url.set("http://www.apache.org/licenses/LICENSE-2.0.txt")
+                    distribution.set("repo")
+                }
+            }
+            developers {
+                developer {
+                    id.set("MIPT-NPM")
+                    name.set("MIPT nuclear physics methods laboratory")
+                    organization.set("MIPT")
+                    organizationUrl.set("http://npm.mipt.ru")
+                }
+
+            }
+            scm {
+                url.set(vcs)
+            }
+        }
+
+    }
+}
+
+bintray {
+    // delegates for runtime properties
+    val bintrayUser: String? by project
+    val bintrayApiKey: String? by project
+    user = bintrayUser ?: System.getenv("BINTRAY_USER")
+    key = bintrayApiKey ?: System.getenv("BINTRAY_API_KEY")
+    publish = true
+    override = true // for multi-platform Kotlin/Native publishing
+
+    // We have to use delegateClosureOf because bintray supports only dynamic groovy syntax
+    // this is a problem of this plugin
+    pkg(delegateClosureOf<PackageConfig> {
+        userOrg = "mipt-npm"
+        repo = "scientifik"
+        name = "scientifik.kmath"
+        issueTrackerUrl = "https://github.com/mipt-npm/kmath/issues"
+        setLicenses("Apache-2.0")
+        vcsUrl = vcs
+        version(delegateClosureOf<VersionConfig> {
+            name = project.version.toString()
+            vcsTag = project.version.toString()
+            released = java.util.Date().toString()
+        })
+    })
+
+    tasks {
+        bintrayUpload {
+            dependsOn(publishToMavenLocal)
+            doFirst {
+                setPublications(project.publishing.publications
+                    .filterIsInstance<MavenPublication>()
+                    .filter { !it.name.contains("-test") && it.name != "kotlinMultiplatform" }
+                    .map {
+                        println("""Uploading artifact "${it.groupId}:${it.artifactId}:${it.version}" from publication "${it.name}""")
+                        it.name //https://github.com/bintray/gradle-bintray-plugin/issues/256
+                    })
+            }
+        }
+
+    }
+}

buildSrc/src/main/kotlin/js-test.gradle.kts

@@ -0,0 +1,44 @@
+import com.moowork.gradle.node.npm.NpmTask
+import com.moowork.gradle.node.task.NodeTask
+import org.gradle.kotlin.dsl.*
+import org.jetbrains.kotlin.gradle.tasks.Kotlin2JsCompile
+
+plugins {
+    id("com.moowork.node")
+    kotlin("multiplatform")
+}
+
+node {
+    nodeModulesDir = file("$buildDir/node_modules")
+}
+
+val compileKotlinJs by tasks.getting(Kotlin2JsCompile::class)
+val compileTestKotlinJs by tasks.getting(Kotlin2JsCompile::class)
+
+val populateNodeModules by tasks.registering(Copy::class) {
+    dependsOn(compileKotlinJs)
+    from(compileKotlinJs.destinationDir)
+
+    kotlin.js().compilations["test"].runtimeDependencyFiles.forEach {
+        if (it.exists() && !it.isDirectory) {
+            from(zipTree(it.absolutePath).matching { include("*.js") })
+        }
+    }
+
+    into("$buildDir/node_modules")
+}
+
+val installMocha by tasks.registering(NpmTask::class) {
+    setWorkingDir(buildDir)
+    setArgs(listOf("install", "mocha"))
+}
+
+val runMocha by tasks.registering(NodeTask::class) {
+    dependsOn(compileTestKotlinJs, populateNodeModules, installMocha)
+    setScript(file("$buildDir/node_modules/mocha/bin/mocha"))
+    setArgs(listOf(compileTestKotlinJs.outputFile))
+}
+
+tasks["jsTest"].dependsOn(runMocha)
+
+

buildSrc/src/main/kotlin/multiplatform-config.gradle.kts

@@ -0,0 +1,118 @@
+import org.gradle.kotlin.dsl.*
+
+plugins {
+    kotlin("multiplatform")
+    `maven-publish`
+}
+
+
+kotlin {
+    jvm {
+        compilations.all {
+            kotlinOptions {
+                jvmTarget = "1.8"
+                // This was used in kmath-koma, but probably if we need it better to apply it for all modules
+                freeCompilerArgs = listOf("-progressive")
+            }
+        }
+    }
+
+    js {
+        compilations.all {
+            kotlinOptions {
+                metaInfo = true
+                sourceMap = true
+                sourceMapEmbedSources = "always"
+                moduleKind = "commonjs"
+            }
+        }
+
+        compilations.named("main") {
+            kotlinOptions {
+                main = "call"
+            }
+        }
+    }
+
+    sourceSets.invoke {
+        commonMain {
+            dependencies {
+                api(kotlin("stdlib"))
+            }
+        }
+        commonTest {
+            dependencies {
+                implementation(kotlin("test-common"))
+                implementation(kotlin("test-annotations-common"))
+            }
+        }
+        "jvmMain" {
+            dependencies {
+                api(kotlin("stdlib-jdk8"))
+            }
+        }
+        "jvmTest" {
+            dependencies {
+                implementation(kotlin("test"))
+                implementation(kotlin("test-junit"))
+            }
+        }
+        "jsMain" {
+            dependencies {
+                api(kotlin("stdlib-js"))
+            }
+        }
+        "jsTest" {
+            dependencies {
+                implementation(kotlin("test-js"))
+            }
+        }
+    }
+
+    targets.all {
+        sourceSets.all {
+            languageSettings.progressiveMode = true
+            languageSettings.enableLanguageFeature("InlineClasses")
+        }
+    }
+
+    // Create empty jar for sources classifier to satisfy maven requirements
+    val stubSources by tasks.registering(Jar::class){
+        archiveClassifier.set("sources")
+        //from(sourceSets.main.get().allSource)
+    }
+
+    // Create empty jar for javadoc classifier to satisfy maven requirements
+    val stubJavadoc by tasks.registering(Jar::class) {
+        archiveClassifier.set("javadoc")
+    }
+
+
+    publishing {
+
+        publications.filterIsInstance<MavenPublication>().forEach { publication ->
+            if (publication.name == "kotlinMultiplatform") {
+                // for our root metadata publication, set artifactId with a package and project name
+                publication.artifactId = project.name
+            } else {
+                // for targets, set artifactId with a package, project name and target name (e.g. iosX64)
+                publication.artifactId = "${project.name}-${publication.name}"
+            }
+        }
+
+        targets.all {
+            val publication = publications.findByName(name) as MavenPublication
+
+            // Patch publications with fake javadoc
+            publication.artifact(stubJavadoc.get())
+        }
+    }
+
+    // Apply JS test configuration
+    val runJsTests by ext(false)
+
+    if (runJsTests) {
+        apply(plugin = "js-test")
+    }
+
+}

doc/linear.md

@@ -1 +1,19 @@
-**TODO**
+## Basic linear algebra layout
+
+Kmath support for linear algebra organized in a context-oriented way. Meaning that operations are in most cases declared 
+in context classes, and are not the members of classes that store data. This allows more flexible approach to maintain multiple 
+back-ends. The new operations added as extensions to contexts instead of being member functions of data structures.
+
+Two major contexts used for linear algebra and hyper-geometry:
+
+* `VectorSpace` forms a mathematical space on top of array-like structure (`Buffer` and its typealias `Point` used for geometry). 
+
+* `MatrixContext` forms a space-like context for 2d-structures. It does not store matrix size and therefore does not implement
+`Space` interface (it is not possible to create zero element without knowing the matrix size). 
+
+## Vector spaces
+
+
+## Matrix operations
+
+## Back-end overview

gradle/wrapper/gradle-wrapper.properties

@@ -1,5 +1,5 @@
 distributionBase=GRADLE_USER_HOME
 distributionPath=wrapper/dists
-distributionUrl=https\://services.gradle.org/distributions/gradle-5.0-bin.zip
+distributionUrl=https\://services.gradle.org/distributions/gradle-5.4-bin.zip
 zipStoreBase=GRADLE_USER_HOME
 zipStorePath=wrapper/dists

gradlew

@@ -1,5 +1,21 @@
 #!/usr/bin/env sh
 
+#
+# Copyright 2015 the original author or authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
 ##############################################################################
 ##
 ##  Gradle start up script for UN*X
@@ -28,7 +44,7 @@ APP_NAME="Gradle"
 APP_BASE_NAME=`basename "$0"`
 
 # Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
-DEFAULT_JVM_OPTS='"-Xmx64m"'
+DEFAULT_JVM_OPTS='"-Xmx64m" "-Xms64m"'
 
 # Use the maximum available, or set MAX_FD != -1 to use that value.
 MAX_FD="maximum"

gradlew.bat

@@ -1,3 +1,19 @@
+@rem
+@rem Copyright 2015 the original author or authors.
+@rem
+@rem Licensed under the Apache License, Version 2.0 (the "License");
+@rem you may not use this file except in compliance with the License.
+@rem You may obtain a copy of the License at
+@rem
+@rem      http://www.apache.org/licenses/LICENSE-2.0
+@rem
+@rem Unless required by applicable law or agreed to in writing, software
+@rem distributed under the License is distributed on an "AS IS" BASIS,
+@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+@rem See the License for the specific language governing permissions and
+@rem limitations under the License.
+@rem
+
 @if "%DEBUG%" == "" @echo off
 @rem ##########################################################################
 @rem
@@ -14,7 +30,7 @@ set APP_BASE_NAME=%~n0
 set APP_HOME=%DIRNAME%
 
 @rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
-set DEFAULT_JVM_OPTS="-Xmx64m"
+set DEFAULT_JVM_OPTS="-Xmx64m" "-Xms64m"
 
 @rem Find java.exe
 if defined JAVA_HOME goto findJavaFromJavaHome

kmath-commons/build.gradle.kts

@@ -7,7 +7,7 @@ description = "Commons math binding for kmath"
 
 dependencies {
     api(project(":kmath-core"))
-    api(project(":kmath-sequential"))
+    api(project(":kmath-coroutines"))
     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/linear/CMMatrix.kt

@@ -3,13 +3,14 @@ package scientifik.kmath.linear
 import org.apache.commons.math3.linear.*
 import org.apache.commons.math3.linear.RealMatrix
 import org.apache.commons.math3.linear.RealVector
+import scientifik.kmath.structures.Matrix
 
-class CMMatrix(val origin: RealMatrix, features: Set<MatrixFeature>? = null) : Matrix<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
 
     override val features: Set<MatrixFeature> = features ?: sequence<MatrixFeature> {
-        if(origin is DiagonalMatrix) yield(DiagonalFeature)
+        if (origin is DiagonalMatrix) yield(DiagonalFeature)
     }.toSet()
 
     override fun suggestFeature(vararg features: MatrixFeature) =
@@ -26,7 +27,7 @@ fun Matrix<Double>.toCM(): CMMatrix = if (this is CMMatrix) {
     CMMatrix(Array2DRowRealMatrix(array))
 }
 
-fun RealMatrix.toMatrix() = CMMatrix(this)
+fun RealMatrix.asMatrix() = CMMatrix(this)
 
 class CMVector(val origin: RealVector) : Point<Double> {
     override val size: Int get() = origin.dimension
@@ -45,46 +46,31 @@ fun Point<Double>.toCM(): CMVector = if (this is CMVector) {
 
 fun RealVector.toPoint() = CMVector(this)
 
-object CMMatrixContext : MatrixContext<Double>, LinearSolver<Double> {
+object CMMatrixContext : MatrixContext<Double> {
-
     override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> Double): CMMatrix {
         val array = Array(rows) { i -> DoubleArray(columns) { j -> initializer(i, j) } }
         return CMMatrix(Array2DRowRealMatrix(array))
     }
 
-    override fun solve(a: Matrix<Double>, b: Matrix<Double>): CMMatrix {
-        val decomposition = LUDecomposition(a.toCM().origin)
-        return decomposition.solver.solve(b.toCM().origin).toMatrix()
-    }
-
-    override fun solve(a: Matrix<Double>, b: Point<Double>): CMVector {
-        val decomposition = LUDecomposition(a.toCM().origin)
-        return decomposition.solver.solve(b.toCM().origin).toPoint()
-    }
-
-    override fun inverse(a: Matrix<Double>): CMMatrix {
-        val decomposition = LUDecomposition(a.toCM().origin)
-        return decomposition.solver.inverse.toMatrix()
-    }
-
     override fun Matrix<Double>.dot(other: Matrix<Double>) =
         CMMatrix(this.toCM().origin.multiply(other.toCM().origin))
 
     override fun Matrix<Double>.dot(vector: Point<Double>): CMVector =
         CMVector(this.toCM().origin.preMultiply(vector.toCM().origin))
 
-
     override fun Matrix<Double>.unaryMinus(): CMMatrix =
         produce(rowNum, colNum) { i, j -> -get(i, j) }
 
-    override fun Matrix<Double>.plus(b: Matrix<Double>) =
+    override fun add(a: Matrix<Double>, b: Matrix<Double>) =
-        CMMatrix(this.toCM().origin.multiply(b.toCM().origin))
+        CMMatrix(a.toCM().origin.multiply(b.toCM().origin))
 
     override fun Matrix<Double>.minus(b: Matrix<Double>) =
         CMMatrix(this.toCM().origin.subtract(b.toCM().origin))
 
-    override fun Matrix<Double>.times(value: Double) =
+    override fun multiply(a: Matrix<Double>, k: Number) =
-        CMMatrix(this.toCM().origin.scalarMultiply(value.toDouble()))
+        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}
 }
 
 operator fun CMMatrix.plus(other: CMMatrix): CMMatrix = CMMatrix(this.origin.add(other.origin))

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

@@ -0,0 +1,39 @@
+package scientifik.kmath.linear
+
+import org.apache.commons.math3.linear.*
+import scientifik.kmath.structures.Matrix
+
+enum class CMDecomposition {
+    LUP,
+    QR,
+    RRQR,
+    EIGEN,
+    CHOLESKY
+}
+
+
+fun CMMatrixContext.solver(a: Matrix<Double>, decomposition: CMDecomposition = CMDecomposition.LUP) =
+    when (decomposition) {
+        CMDecomposition.LUP -> LUDecomposition(a.toCM().origin).solver
+        CMDecomposition.RRQR -> RRQRDecomposition(a.toCM().origin).solver
+        CMDecomposition.QR -> QRDecomposition(a.toCM().origin).solver
+        CMDecomposition.EIGEN -> EigenDecomposition(a.toCM().origin).solver
+        CMDecomposition.CHOLESKY -> CholeskyDecomposition(a.toCM().origin).solver
+    }
+
+fun CMMatrixContext.solve(
+    a: Matrix<Double>,
+    b: Matrix<Double>,
+    decomposition: CMDecomposition = CMDecomposition.LUP
+) = solver(a, decomposition).solve(b.toCM().origin).asMatrix()
+
+fun CMMatrixContext.solve(
+    a: Matrix<Double>,
+    b: Point<Double>,
+    decomposition: CMDecomposition = CMDecomposition.LUP
+) = solver(a, decomposition).solve(b.toCM().origin).toPoint()
+
+fun CMMatrixContext.inverse(
+    a: Matrix<Double>,
+    decomposition: CMDecomposition = CMDecomposition.LUP
+) = solver(a, decomposition).inverse.asMatrix()

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

@@ -1,10 +1,12 @@
 package scientifik.kmath.transform
 
+import kotlinx.coroutines.FlowPreview
+import kotlinx.coroutines.flow.Flow
+import kotlinx.coroutines.flow.map
 import org.apache.commons.math3.transform.*
 import scientifik.kmath.operations.Complex
-import scientifik.kmath.sequential.Processor
+import scientifik.kmath.streaming.chunked
-import scientifik.kmath.sequential.Producer
+import scientifik.kmath.streaming.spread
-import scientifik.kmath.sequential.map
 import scientifik.kmath.structures.*
 
 
@@ -33,54 +35,75 @@ object Transformations {
     fun fourier(
         normalization: DftNormalization = DftNormalization.STANDARD,
         direction: TransformType = TransformType.FORWARD
-    ): BufferTransform<Complex, Complex> = {
+    ): SuspendBufferTransform<Complex, Complex> = {
         FastFourierTransformer(normalization).transform(it.toArray(), direction).asBuffer()
     }
 
     fun realFourier(
         normalization: DftNormalization = DftNormalization.STANDARD,
         direction: TransformType = TransformType.FORWARD
-    ): BufferTransform<Double, Complex> = {
+    ): SuspendBufferTransform<Double, Complex> = {
         FastFourierTransformer(normalization).transform(it.asArray(), direction).asBuffer()
     }
 
     fun sine(
         normalization: DstNormalization = DstNormalization.STANDARD_DST_I,
         direction: TransformType = TransformType.FORWARD
-    ): BufferTransform<Double, Double> = {
+    ): SuspendBufferTransform<Double, Double> = {
         FastSineTransformer(normalization).transform(it.asArray(), direction).asBuffer()
     }
 
     fun cosine(
         normalization: DctNormalization = DctNormalization.STANDARD_DCT_I,
         direction: TransformType = TransformType.FORWARD
-    ): BufferTransform<Double, Double> = {
+    ): SuspendBufferTransform<Double, Double> = {
         FastCosineTransformer(normalization).transform(it.asArray(), direction).asBuffer()
     }
 
     fun hadamard(
         direction: TransformType = TransformType.FORWARD
-    ): BufferTransform<Double, Double> = {
+    ): SuspendBufferTransform<Double, Double> = {
         FastHadamardTransformer().transform(it.asArray(), direction).asBuffer()
     }
 }
 
 /**
- * Process given [Producer] with commons-math fft transformation
+ * Process given [Flow] with commons-math fft transformation
  */
-fun Producer<Buffer<Complex>>.FFT(
+@FlowPreview
+fun Flow<Buffer<Complex>>.FFT(
     normalization: DftNormalization = DftNormalization.STANDARD,
     direction: TransformType = TransformType.FORWARD
-): Processor<Buffer<Complex>, Buffer<Complex>> {
+): Flow<Buffer<Complex>> {
     val transform = Transformations.fourier(normalization, direction)
     return map { transform(it) }
 }
 
+@FlowPreview
 @JvmName("realFFT")
-fun Producer<Buffer<Double>>.FFT(
+fun Flow<Buffer<Double>>.FFT(
     normalization: DftNormalization = DftNormalization.STANDARD,
     direction: TransformType = TransformType.FORWARD
-): Processor<Buffer<Double>, Buffer<Complex>> {
+): Flow<Buffer<Complex>> {
     val transform = Transformations.realFourier(normalization, direction)
-    return map { transform(it) }
+    return map(transform)
 }
+
+/**
+ * Process a continous flow of real numbers in FFT splitting it in chunks of [bufferSize].
+ */
+@FlowPreview
+@JvmName("realFFT")
+fun Flow<Double>.FFT(
+    bufferSize: Int = Int.MAX_VALUE,
+    normalization: DftNormalization = DftNormalization.STANDARD,
+    direction: TransformType = TransformType.FORWARD
+): Flow<Complex> {
+    return chunked(bufferSize).FFT(normalization,direction).spread()
+}
+
+/**
+ * Map a complex flow into real flow by taking real part of each number
+ */
+@FlowPreview
+fun Flow<Complex>.real(): Flow<Double> = map{it.re}

kmath-core/build.gradle.kts

@@ -1,51 +1,13 @@
 plugins {
-    kotlin("multiplatform")
+    `multiplatform-config`
 }
 
-val ioVersion: String by rootProject.extra
+kotlin.sourceSets {
-
+    commonMain {
-
-kotlin {
-    jvm()
-    js()
-
-    sourceSets {
-        val commonMain by getting {
         dependencies {
             api(project(":kmath-memory"))
-                api(kotlin("stdlib"))
         }
     }
-        val commonTest by getting {
+    //mingwMain {}
-            dependencies {
+    //mingwTest {}
-                implementation(kotlin("test-common"))
-                implementation(kotlin("test-annotations-common"))
-            }
-        }
-        val jvmMain by getting {
-            dependencies {
-                api(kotlin("stdlib-jdk8"))
-            }
-        }
-        val jvmTest by getting {
-            dependencies {
-                implementation(kotlin("test"))
-                implementation(kotlin("test-junit"))
-            }
-        }
-        val jsMain by getting {
-            dependencies {
-                api(kotlin("stdlib-js"))
-            }
-        }
-        val jsTest by getting {
-            dependencies {
-                implementation(kotlin("test-js"))
-            }
-        }
-//        mingwMain {
-//        }
-//        mingwTest {
-//        }
-    }
 }

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

@@ -1,8 +1,8 @@
 package scientifik.kmath.linear
 
+import scientifik.kmath.operations.RealField
 import scientifik.kmath.operations.Ring
 import scientifik.kmath.structures.*
-import kotlin.jvm.JvmSynthetic
 
 /**
  * Basic implementation of Matrix space based on [NDStructure]
@@ -18,6 +18,22 @@ class BufferMatrixContext<T : Any, R : Ring<T>>(
     }
 
     override fun point(size: Int, initializer: (Int) -> T): Point<T> = bufferFactory(size, initializer)
+
+    companion object {
+
+    }
+}
+
+object RealMatrixContext : GenericMatrixContext<Double, RealField> {
+
+    override val elementContext = RealField
+
+    override inline fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> Double): Matrix<Double> {
+        val buffer = DoubleBuffer(rows * columns) { offset -> initializer(offset / columns, offset % columns) }
+        return BufferMatrix(rows, columns, buffer)
+    }
+
+    override inline fun point(size: Int, initializer: (Int) -> Double): Point<Double> = DoubleBuffer(size,initializer)
 }
 
 class BufferMatrix<T : Any>(
@@ -25,7 +41,7 @@ class BufferMatrix<T : Any>(
     override val colNum: Int,
     val buffer: Buffer<out T>,
     override val features: Set<MatrixFeature> = emptySet()
-) : Matrix<T> {
+) : FeaturedMatrix<T> {
 
     init {
         if (buffer.size != rowNum * colNum) {

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

@@ -0,0 +1,96 @@
+package scientifik.kmath.linear
+
+import scientifik.kmath.operations.Ring
+import scientifik.kmath.structures.Matrix
+import scientifik.kmath.structures.Structure2D
+import scientifik.kmath.structures.asBuffer
+import kotlin.math.sqrt
+
+/**
+ * A 2d structure plus optional matrix-specific features
+ */
+interface FeaturedMatrix<T : Any> : Matrix<T> {
+
+    override val shape: IntArray get() = intArrayOf(rowNum, colNum)
+
+    val features: Set<MatrixFeature>
+
+    /**
+     * Suggest new feature for this matrix. The result is the new matrix that may or may not reuse existing data structure.
+     *
+     * The implementation does not guarantee to check that matrix actually have the feature, so one should be careful to
+     * add only those features that are valid.
+     */
+    fun suggestFeature(vararg features: MatrixFeature): FeaturedMatrix<T>
+
+    companion object {
+
+    }
+}
+
+fun Structure2D.Companion.real(rows: Int, columns: Int, initializer: (Int, Int) -> Double) =
+    MatrixContext.real.produce(rows, columns, initializer)
+
+/**
+ * Build a square matrix from given elements.
+ */
+fun <T : Any> Structure2D.Companion.square(vararg elements: T): FeaturedMatrix<T> {
+    val size: Int = sqrt(elements.size.toDouble()).toInt()
+    if (size * size != elements.size) error("The number of elements ${elements.size} is not a full square")
+    val buffer = elements.asBuffer()
+    return BufferMatrix(size, size, buffer)
+}
+
+fun <T : Any> Structure2D.Companion.build(rows: Int, columns: Int): MatrixBuilder<T> = MatrixBuilder(rows, columns)
+
+class MatrixBuilder<T : Any>(val rows: Int, val columns: Int) {
+    operator fun invoke(vararg elements: T): FeaturedMatrix<T> {
+        if (rows * columns != elements.size) error("The number of elements ${elements.size} is not equal $rows * $columns")
+        val buffer = elements.asBuffer()
+        return BufferMatrix(rows, columns, buffer)
+    }
+}
+
+val Matrix<*>.features get() = (this as? FeaturedMatrix)?.features?: emptySet()
+
+/**
+ * Check if matrix has the given feature class
+ */
+inline fun <reified T : Any> Matrix<*>.hasFeature(): Boolean =
+    features.find { it is T } != null
+
+/**
+ * Get the first feature matching given class. Does not guarantee that matrix has only one feature matching the criteria
+ */
+inline fun <reified T : Any> Matrix<*>.getFeature(): T? =
+    features.filterIsInstance<T>().firstOrNull()
+
+/**
+ * Diagonal matrix of ones. The matrix is virtual no actual matrix is created
+ */
+fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R>.one(rows: Int, columns: Int): FeaturedMatrix<T> =
+    VirtualMatrix(rows, columns, DiagonalFeature) { i, j ->
+        if (i == j) elementContext.one else elementContext.zero
+    }
+
+
+/**
+ * A virtual matrix of zeroes
+ */
+fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R>.zero(rows: Int, columns: Int): FeaturedMatrix<T> =
+    VirtualMatrix<T>(rows, columns) { _, _ -> elementContext.zero }
+
+class TransposedFeature<T : Any>(val original: Matrix<T>) : MatrixFeature
+
+/**
+ * Create a virtual transposed matrix without copying anything. `A.transpose().transpose() === A`
+ */
+fun <T : Any> Matrix<T>.transpose(): Matrix<T> {
+    return this.getFeature<TransposedFeature<T>>()?.original ?: VirtualMatrix(
+        this.colNum,
+        this.rowNum,
+        setOf(TransposedFeature(this))
+    ) { i, j -> get(j, i) }
+}
+
+infix fun Matrix<Double>.dot(other: Matrix<Double>): Matrix<Double> = with(MatrixContext.real) { dot(other) }

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

@@ -1,26 +1,31 @@
 package scientifik.kmath.linear
 
 import scientifik.kmath.operations.Field
+import scientifik.kmath.operations.RealField
 import scientifik.kmath.operations.Ring
-import scientifik.kmath.structures.MutableBuffer
+import scientifik.kmath.structures.BufferAccessor2D
-import scientifik.kmath.structures.MutableBuffer.Companion.boxing
+import scientifik.kmath.structures.Matrix
-import scientifik.kmath.structures.MutableBufferFactory
+import scientifik.kmath.structures.Structure2D
-import scientifik.kmath.structures.NDStructure
+import kotlin.reflect.KClass
-import scientifik.kmath.structures.get
 
-class LUPDecomposition<T : Comparable<T>>(
+/**
-    private val elementContext: Ring<T>,
+ * Common implementation of [LUPDecompositionFeature]
-    internal val lu: NDStructure<T>,
+ */
+class LUPDecomposition<T : Any>(
+    val context: GenericMatrixContext<T, out Field<T>>,
+    val lu: Structure2D<T>,
     val pivot: IntArray,
     private val even: Boolean
 ) : LUPDecompositionFeature<T>, DeterminantFeature<T> {
 
+    val elementContext get() = context.elementContext
+
     /**
      * Returns the matrix L of the decomposition.
      *
      * L is a lower-triangular matrix with [Ring.one] in diagonal
      */
-    override val l: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1], setOf(LFeature)) { i, j ->
+    override val l: FeaturedMatrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1], setOf(LFeature)) { i, j ->
         when {
             j < i -> lu[i, j]
             j == i -> elementContext.one
@@ -34,7 +39,7 @@ class LUPDecomposition<T : Comparable<T>>(
      *
      * U is an upper-triangular matrix including the diagonal
      */
-    override val u: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1], setOf(UFeature)) { i, j ->
+    override val u: FeaturedMatrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1], setOf(UFeature)) { i, j ->
         if (j >= i) lu[i, j] else elementContext.zero
     }
 
@@ -45,7 +50,7 @@ class LUPDecomposition<T : Comparable<T>>(
      * P is a sparse matrix with exactly one element set to [Ring.one] in
      * each row and each column, all other elements being set to [Ring.zero].
      */
-    override val p: Matrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
+    override val p: FeaturedMatrix<T> = VirtualMatrix(lu.shape[0], lu.shape[1]) { i, j ->
         if (j == pivot[i]) elementContext.one else elementContext.zero
     }
 
@@ -62,18 +67,18 @@ class LUPDecomposition<T : Comparable<T>>(
 
 }
 
-
+fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.abs(value: T) =
-class LUSolver<T : Comparable<T>, F : Field<T>>(
+    if (value > elementContext.zero) value else with(elementContext) { -value }
-    val context: GenericMatrixContext<T, F>,
-    val bufferFactory: MutableBufferFactory<T> = ::boxing,
-    val singularityCheck: (T) -> Boolean
-) : LinearSolver<T> {
 
 
-    private fun abs(value: T) =
+/**
-        if (value > context.elementContext.zero) value else with(context.elementContext) { -value }
+ * Create a lup decomposition of generic matrix
-
+ */
-    fun buildDecomposition(matrix: Matrix<T>): LUPDecomposition<T> {
+fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.lup(
+    type: KClass<T>,
+    matrix: Matrix<T>,
+    checkSingular: (T) -> Boolean
+): LUPDecomposition<T> {
     if (matrix.rowNum != matrix.colNum) {
         error("LU decomposition supports only square matrices")
     }
@@ -81,51 +86,67 @@ class LUSolver<T : Comparable<T>, F : Field<T>>(
     val m = matrix.colNum
     val pivot = IntArray(matrix.rowNum)
 
-        val lu = Mutable2DStructure.create(matrix.rowNum, matrix.colNum, bufferFactory) { i, j ->
+    //TODO just waits for KEEP-176
-            matrix[i, j]
+    BufferAccessor2D(type, matrix.rowNum, matrix.colNum).run {
-        }
+        elementContext.run {
 
+            val lu = create(matrix)
 
-        with(context.elementContext) {
             // Initialize permutation array and parity
             for (row in 0 until m) {
                 pivot[row] = row
             }
             var even = true
 
+            // Initialize permutation array and parity
+            for (row in 0 until m) {
+                pivot[row] = row
+            }
+
             // Loop over columns
             for (col in 0 until m) {
 
                 // upper
                 for (row in 0 until col) {
-                    var sum = lu[row, col]
+                    val luRow = lu.row(row)
+                    var sum = luRow[col]
                     for (i in 0 until row) {
-                        sum -= lu[row, i] * lu[i, col]
+                        sum -= luRow[i] * lu[i, col]
                     }
-                    lu[row, col] = sum
+                    luRow[col] = sum
                 }
 
                 // lower
-                val max = (col until m).maxBy { row ->
+                var max = col // permutation row
-                    var sum = lu[row, col]
+                var largest = -one
+                for (row in col until m) {
+                    val luRow = lu.row(row)
+                    var sum = luRow[col]
                     for (i in 0 until col) {
-                        sum -= lu[row, i] * lu[i, col]
+                        sum -= luRow[i] * lu[i, col]
                     }
-                    lu[row, col] = sum
+                    luRow[col] = sum
 
-                    abs(sum)
+                    // maintain best permutation choice
-                } ?: col
+                    if (abs(sum) > largest) {
+                        largest = abs(sum)
+                        max = row
+                    }
+                }
 
                 // Singularity check
-                if (singularityCheck(lu[max, col])) {
+                if (checkSingular(abs(lu[max, col]))) {
-                    error("Singular matrix")
+                    error("The matrix is singular")
                 }
 
                 // Pivot if necessary
                 if (max != col) {
+                    val luMax = lu.row(max)
+                    val luCol = lu.row(col)
                     for (i in 0 until m) {
-                        lu[max, i] = lu[col, i]
+                        val tmp = luMax[i]
-                        lu[col, i] = lu[max, i]
+                        luMax[i] = luCol[i]
+                        luCol[i] = tmp
                     }
                     val temp = pivot[max]
                     pivot[max] = pivot[col]
@@ -136,70 +157,96 @@ class LUSolver<T : Comparable<T>, F : Field<T>>(
                 // Divide the lower elements by the "winning" diagonal elt.
                 val luDiag = lu[col, col]
                 for (row in col + 1 until m) {
-                    lu[row, col] = lu[row, col] / luDiag
+                    lu[row, col] /= luDiag
-                }
-            }
-            return LUPDecomposition(context.elementContext, lu, pivot, even)
                 }
             }
 
-    /**
+            return LUPDecomposition(this@lup, lu.collect(), pivot, even)
-     * Produce a matrix with added decomposition feature
-     */
-    fun decompose(matrix: Matrix<T>): Matrix<T> {
-        if (matrix.hasFeature<LUPDecomposition<*>>()) {
-            return matrix
-        } else {
-            val decomposition = buildDecomposition(matrix)
-            return VirtualMatrix.wrap(matrix, decomposition)
         }
     }
+}
 
+inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.lup(
+    matrix: Matrix<T>,
+    noinline checkSingular: (T) -> Boolean
+) = lup(T::class, matrix, checkSingular)
 
-    override fun solve(a: Matrix<T>, b: Matrix<T>): Matrix<T> {
+fun GenericMatrixContext<Double, RealField>.lup(matrix: Matrix<Double>) = lup(Double::class, matrix) { it < 1e-11 }
-        if (b.rowNum != a.colNum) {
+
-            error("Matrix dimension mismatch expected ${a.rowNum}, but got ${b.colNum}")
+fun <T : Any> LUPDecomposition<T>.solve(type: KClass<T>, matrix: Matrix<T>): Matrix<T> {
+
+    if (matrix.rowNum != pivot.size) {
+        error("Matrix dimension mismatch. Expected ${pivot.size}, but got ${matrix.colNum}")
     }
 
-        // Use existing decomposition if it is provided by matrix
+    BufferAccessor2D(type, matrix.rowNum, matrix.colNum).run {
-        val decomposition = a.getFeature() ?: buildDecomposition(a)
+        elementContext.run {
 
-        with(decomposition) {
-            with(context.elementContext) {
             // Apply permutations to b
-                val bp = Mutable2DStructure.create(a.rowNum, a.colNum, bufferFactory) { i, j ->
+            val bp = create { i, j -> zero }
-                    b[pivot[i], j]
+
+            for (row in 0 until pivot.size) {
+                val bpRow = bp.row(row)
+                val pRow = pivot[row]
+                for (col in 0 until matrix.colNum) {
+                    bpRow[col] = matrix[pRow, col]
+                }
             }
 
             // Solve LY = b
-                for (col in 0 until a.rowNum) {
+            for (col in 0 until pivot.size) {
-                    for (i in col + 1 until a.rowNum) {
+                val bpCol = bp.row(col)
-                        for (j in 0 until b.colNum) {
+                for (i in col + 1 until pivot.size) {
-                            bp[i, j] -= bp[col, j] * lu[i, col]
+                    val bpI = bp.row(i)
+                    val luICol = lu[i, col]
+                    for (j in 0 until matrix.colNum) {
+                        bpI[j] -= bpCol[j] * luICol
                     }
                 }
             }
 
             // Solve UX = Y
-                for (col in a.rowNum - 1 downTo 0) {
+            for (col in pivot.size - 1 downTo 0) {
-                    for (j in 0 until b.colNum) {
+                val bpCol = bp.row(col)
-                        bp[col, j] /= lu[col, col]
+                val luDiag = lu[col, col]
+                for (j in 0 until matrix.colNum) {
+                    bpCol[j] /= luDiag
                 }
                 for (i in 0 until col) {
-                        for (j in 0 until b.colNum) {
+                    val bpI = bp.row(i)
-                            bp[i, j] -= bp[col, j] * lu[i, col]
+                    val luICol = lu[i, col]
+                    for (j in 0 until matrix.colNum) {
+                        bpI[j] -= bpCol[j] * luICol
                     }
                 }
             }
-
+            return context.produce(pivot.size, matrix.colNum) { i, j -> bp[i, j] }
-                return context.produce(a.rowNum, a.colNum) { i, j -> bp[i, j] }
         }
     }
-    }
-
-    override fun inverse(a: Matrix<T>): Matrix<T> = solve(a, context.one(a.rowNum, a.colNum))
-
-    companion object {
-        val real = LUSolver(MatrixContext.real, MutableBuffer.Companion::auto) { it < 1e-11 }
-    }
 }
+
+inline fun <reified T : Any> LUPDecomposition<T>.solve(matrix: Matrix<T>) = solve(T::class, matrix)
+
+/**
+ * Solve a linear equation **a*x = b**
+ */
+inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.solve(
+    a: Matrix<T>,
+    b: Matrix<T>,
+    noinline checkSingular: (T) -> Boolean
+): Matrix<T> {
+    // Use existing decomposition if it is provided by matrix
+    val decomposition = a.getFeature() ?: lup(T::class, a, checkSingular)
+    return decomposition.solve(T::class, b)
+}
+
+fun RealMatrixContext.solve(a: Matrix<Double>, b: Matrix<Double>) =
+    solve(a, b) { it < 1e-11 }
+
+inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.inverse(
+    matrix: Matrix<T>,
+    noinline checkSingular: (T) -> Boolean
+) = solve(matrix, one(matrix.rowNum, matrix.colNum), checkSingular)
+
+fun RealMatrixContext.inverse(matrix: Matrix<Double>) =
+    solve(matrix, one(matrix.rowNum, matrix.colNum)) { it < 1e-11 }

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

@@ -3,6 +3,7 @@ package scientifik.kmath.linear
 import scientifik.kmath.operations.Field
 import scientifik.kmath.operations.Norm
 import scientifik.kmath.operations.RealField
+import scientifik.kmath.structures.Matrix
 import scientifik.kmath.structures.VirtualBuffer
 import scientifik.kmath.structures.asSequence
 
@@ -12,7 +13,7 @@ import scientifik.kmath.structures.asSequence
  */
 interface LinearSolver<T : Any> {
     fun solve(a: Matrix<T>, b: Matrix<T>): Matrix<T>
-    fun solve(a: Matrix<T>, b: Point<T>): Point<T> = solve(a, b.toMatrix()).toVector()
+    fun solve(a: Matrix<T>, b: Point<T>): Point<T> = solve(a, b.asMatrix()).asPoint()
     fun inverse(a: Matrix<T>): Matrix<T>
 }
 
@@ -32,14 +33,14 @@ object VectorL2Norm : Norm<Point<out Number>, Double> {
 typealias RealVector = Vector<Double, RealField>
 typealias RealMatrix = Matrix<Double>
 
-
-
 /**
  * Convert matrix to vector if it is possible
  */
-fun <T: Any> Matrix<T>.toVector(): Point<T> =
+fun <T : Any> Matrix<T>.asPoint(): Point<T> =
     if (this.colNum == 1) {
-        VirtualBuffer(rowNum){ get(it, 0) }
+        VirtualBuffer(rowNum) { get(it, 0) }
-    } else error("Can't convert matrix with more than one column to vector")
+    } else {
+        error("Can't convert matrix with more than one column to vector")
+    }
 
-fun <T: Any> Point<T>.toMatrix(): Matrix<T> = VirtualMatrix(size, 1) { i, _ -> get(i) }
+fun <T : Any> Point<T>.asMatrix() = VirtualMatrix(size, 1) { i, _ -> get(i) }

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

@@ -1,213 +0,0 @@
-package scientifik.kmath.linear
-
-import scientifik.kmath.operations.RealField
-import scientifik.kmath.operations.Ring
-import scientifik.kmath.operations.sum
-import scientifik.kmath.structures.*
-import scientifik.kmath.structures.Buffer.Companion.boxing
-import kotlin.math.sqrt
-
-
-interface MatrixContext<T : Any> {
-    /**
-     * Produce a matrix with this context and given dimensions
-     */
-    fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T): Matrix<T>
-
-    infix fun Matrix<T>.dot(other: Matrix<T>): Matrix<T>
-
-    infix fun Matrix<T>.dot(vector: Point<T>): Point<T>
-
-    operator fun Matrix<T>.unaryMinus(): Matrix<T>
-
-    operator fun Matrix<T>.plus(b: Matrix<T>): Matrix<T>
-
-    operator fun Matrix<T>.minus(b: Matrix<T>): Matrix<T>
-
-    operator fun Matrix<T>.times(value: T): Matrix<T>
-
-    operator fun T.times(m: Matrix<T>): Matrix<T> = m * this
-
-    companion object {
-        /**
-         * Non-boxing double matrix
-         */
-        val real = BufferMatrixContext(RealField, Buffer.Companion::auto)
-
-        /**
-         * A structured matrix with custom buffer
-         */
-        fun <T : Any, R : Ring<T>> buffered(
-            ring: R,
-            bufferFactory: BufferFactory<T> = ::boxing
-        ): GenericMatrixContext<T, R> =
-            BufferMatrixContext(ring, bufferFactory)
-
-        /**
-         * Automatic buffered matrix, unboxed if it is possible
-         */
-        inline fun <reified T : Any, R : Ring<T>> auto(ring: R): GenericMatrixContext<T, R> =
-            buffered(ring, Buffer.Companion::auto)
-    }
-}
-
-interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
-    /**
-     * The ring context for matrix elements
-     */
-    val elementContext: R
-
-    /**
-     * Produce a point compatible with matrix space
-     */
-    fun point(size: Int, initializer: (Int) -> T): Point<T>
-
-    override infix fun Matrix<T>.dot(other: Matrix<T>): Matrix<T> {
-        //TODO add typed error
-        if (this.colNum != other.rowNum) error("Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})")
-        return produce(rowNum, other.colNum) { i, j ->
-            val row = rows[i]
-            val column = other.columns[j]
-            with(elementContext) {
-                sum(row.asSequence().zip(column.asSequence(), ::multiply))
-            }
-        }
-    }
-
-    override infix fun Matrix<T>.dot(vector: Point<T>): Point<T> {
-        //TODO add typed error
-        if (this.colNum != vector.size) error("Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})")
-        return point(rowNum) { i ->
-            val row = rows[i]
-            with(elementContext) {
-                sum(row.asSequence().zip(vector.asSequence(), ::multiply))
-            }
-        }
-    }
-
-    override operator fun Matrix<T>.unaryMinus() =
-        produce(rowNum, colNum) { i, j -> elementContext.run { -get(i, j) } }
-
-    override operator fun Matrix<T>.plus(b: Matrix<T>): Matrix<T> {
-        if (rowNum != b.rowNum || colNum != b.colNum) error("Matrix operation dimension mismatch. [$rowNum,$colNum] + [${b.rowNum},${b.colNum}]")
-        return produce(rowNum, colNum) { i, j -> elementContext.run { get(i, j) + b[i, j] } }
-    }
-
-    override operator fun Matrix<T>.minus(b: Matrix<T>): Matrix<T> {
-        if (rowNum != b.rowNum || colNum != b.colNum) error("Matrix operation dimension mismatch. [$rowNum,$colNum] - [${b.rowNum},${b.colNum}]")
-        return produce(rowNum, colNum) { i, j -> elementContext.run { get(i, j) + b[i, j] } }
-    }
-
-    operator fun Matrix<T>.times(number: Number): Matrix<T> =
-        produce(rowNum, colNum) { i, j -> elementContext.run { get(i, j) * number } }
-
-    operator fun Number.times(matrix: Matrix<T>): Matrix<T> = matrix * this
-
-    override fun Matrix<T>.times(value: T): Matrix<T> =
-        produce(rowNum, colNum) { i, j -> elementContext.run { get(i, j) * value } }
-}
-
-/**
- * Specialized 2-d structure
- */
-interface Matrix<T : Any> : Structure2D<T> {
-    val rowNum: Int
-    val colNum: Int
-
-    val features: Set<MatrixFeature>
-
-    /**
-     * Suggest new feature for this matrix. The result is the new matrix that may or may not reuse existing data structure.
-     *
-     * The implementation does not guarantee to check that matrix actually have the feature, so one should be careful to
-     * add only those features that are valid.
-     */
-    fun suggestFeature(vararg features: MatrixFeature): Matrix<T>
-
-    override fun get(index: IntArray): T = get(index[0], index[1])
-
-    override val shape: IntArray get() = intArrayOf(rowNum, colNum)
-
-    val rows: Point<Point<T>>
-        get() = VirtualBuffer(rowNum) { i ->
-            VirtualBuffer(colNum) { j -> get(i, j) }
-        }
-
-    val columns: Point<Point<T>>
-        get() = VirtualBuffer(colNum) { j ->
-            VirtualBuffer(rowNum) { i -> get(i, j) }
-        }
-
-    override fun elements(): Sequence<Pair<IntArray, T>> = sequence {
-        for (i in (0 until rowNum)) {
-            for (j in (0 until colNum)) {
-                yield(intArrayOf(i, j) to get(i, j))
-            }
-        }
-    }
-
-    companion object {
-        fun real(rows: Int, columns: Int, initializer: (Int, Int) -> Double) =
-            MatrixContext.real.produce(rows, columns, initializer)
-
-        /**
-         * Build a square matrix from given elements.
-         */
-        fun <T : Any> square(vararg elements: T): Matrix<T> {
-            val size: Int = sqrt(elements.size.toDouble()).toInt()
-            if (size * size != elements.size) error("The number of elements ${elements.size} is not a full square")
-            val buffer = elements.asBuffer()
-            return BufferMatrix(size, size, buffer)
-        }
-
-        fun <T : Any> build(rows: Int, columns: Int): MatrixBuilder<T> = MatrixBuilder(rows, columns)
-    }
-}
-
-class MatrixBuilder<T : Any>(val rows: Int, val columns: Int) {
-    operator fun invoke(vararg elements: T): Matrix<T> {
-        if (rows * columns != elements.size) error("The number of elements ${elements.size} is not equal $rows * $columns")
-        val buffer = elements.asBuffer()
-        return BufferMatrix(rows, columns, buffer)
-    }
-}
-
-/**
- * Check if matrix has the given feature class
- */
-inline fun <reified T : Any> Matrix<*>.hasFeature(): Boolean = features.find { it is T } != null
-
-/**
- * Get the first feature matching given class. Does not guarantee that matrix has only one feature matching the criteria
- */
-inline fun <reified T : Any> Matrix<*>.getFeature(): T? = features.filterIsInstance<T>().firstOrNull()
-
-/**
- * Diagonal matrix of ones. The matrix is virtual no actual matrix is created
- */
-fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R>.one(rows: Int, columns: Int): Matrix<T> =
-    VirtualMatrix<T>(rows, columns) { i, j ->
-        if (i == j) elementContext.one else elementContext.zero
-    }
-
-
-/**
- * A virtual matrix of zeroes
- */
-fun <T : Any, R : Ring<T>> GenericMatrixContext<T, R>.zero(rows: Int, columns: Int): Matrix<T> =
-    VirtualMatrix<T>(rows, columns) { _, _ -> elementContext.zero }
-
-class TransposedFeature<T : Any>(val original: Matrix<T>) : MatrixFeature
-
-/**
- * Create a virtual transposed matrix without copying anything. `A.transpose().transpose() === A`
- */
-fun <T : Any, R : Ring<T>> Matrix<T>.transpose(): Matrix<T> {
-    return this.getFeature<TransposedFeature<T>>()?.original ?: VirtualMatrix(
-        this.colNum,
-        this.rowNum,
-        setOf(TransposedFeature(this))
-    ) { i, j -> get(j, i) }
-}
-
-infix fun Matrix<Double>.dot(other: Matrix<Double>): Matrix<Double> = with(MatrixContext.real) { dot(other) }

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

@@ -0,0 +1,105 @@
+package scientifik.kmath.linear
+
+import scientifik.kmath.operations.Ring
+import scientifik.kmath.operations.SpaceOperations
+import scientifik.kmath.operations.sum
+import scientifik.kmath.structures.Buffer
+import scientifik.kmath.structures.BufferFactory
+import scientifik.kmath.structures.Matrix
+import scientifik.kmath.structures.asSequence
+
+/**
+ * Basic operations on matrices. Operates on [Matrix]
+ */
+interface MatrixContext<T : Any> : SpaceOperations<Matrix<T>> {
+    /**
+     * Produce a matrix with this context and given dimensions
+     */
+    fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T): Matrix<T>
+
+    infix fun Matrix<T>.dot(other: Matrix<T>): Matrix<T>
+
+    infix fun Matrix<T>.dot(vector: Point<T>): Point<T>
+
+    operator fun Matrix<T>.times(value: T): Matrix<T>
+
+    operator fun T.times(m: Matrix<T>): Matrix<T> = m * this
+
+    companion object {
+        /**
+         * Non-boxing double matrix
+         */
+        val real = RealMatrixContext
+
+        /**
+         * A structured matrix with custom buffer
+         */
+        fun <T : Any, R : Ring<T>> buffered(
+            ring: R,
+            bufferFactory: BufferFactory<T> = Buffer.Companion::boxing
+        ): GenericMatrixContext<T, R> =
+            BufferMatrixContext(ring, bufferFactory)
+
+        /**
+         * Automatic buffered matrix, unboxed if it is possible
+         */
+        inline fun <reified T : Any, R : Ring<T>> auto(ring: R): GenericMatrixContext<T, R> =
+            buffered(ring, Buffer.Companion::auto)
+    }
+}
+
+interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
+    /**
+     * The ring context for matrix elements
+     */
+    val elementContext: R
+
+    /**
+     * Produce a point compatible with matrix space
+     */
+    fun point(size: Int, initializer: (Int) -> T): Point<T>
+
+    override infix fun Matrix<T>.dot(other: Matrix<T>): Matrix<T> {
+        //TODO add typed error
+        if (this.colNum != other.rowNum) error("Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})")
+        return produce(rowNum, other.colNum) { i, j ->
+            val row = rows[i]
+            val column = other.columns[j]
+            with(elementContext) {
+                sum(row.asSequence().zip(column.asSequence(), ::multiply))
+            }
+        }
+    }
+
+    override infix fun Matrix<T>.dot(vector: Point<T>): Point<T> {
+        //TODO add typed error
+        if (this.colNum != vector.size) error("Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})")
+        return point(rowNum) { i ->
+            val row = rows[i]
+            with(elementContext) {
+                sum(row.asSequence().zip(vector.asSequence(), ::multiply))
+            }
+        }
+    }
+
+    override operator fun Matrix<T>.unaryMinus() =
+        produce(rowNum, colNum) { i, j -> elementContext.run { -get(i, j) } }
+
+    override fun add(a: Matrix<T>, b: Matrix<T>): Matrix<T> {
+        if (a.rowNum != b.rowNum || a.colNum != b.colNum) error("Matrix operation dimension mismatch. [${a.rowNum},${a.colNum}] + [${b.rowNum},${b.colNum}]")
+        return produce(a.rowNum, a.colNum) { i, j -> elementContext.run { a.get(i, j) + b[i, j] } }
+    }
+
+    override operator fun Matrix<T>.minus(b: Matrix<T>): Matrix<T> {
+        if (rowNum != b.rowNum || colNum != b.colNum) error("Matrix operation dimension mismatch. [$rowNum,$colNum] - [${b.rowNum},${b.colNum}]")
+        return produce(rowNum, colNum) { i, j -> elementContext.run { get(i, j) + b[i, j] } }
+    }
+
+    override fun multiply(a: Matrix<T>, k: Number): Matrix<T> =
+        produce(a.rowNum, a.colNum) { i, j -> elementContext.run { a.get(i, j) * k } }
+
+    operator fun Number.times(matrix: FeaturedMatrix<T>): Matrix<T> = matrix * this
+
+    override fun Matrix<T>.times(value: T): Matrix<T> =
+        produce(rowNum, colNum) { i, j -> elementContext.run { get(i, j) * value } }
+}

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

@@ -25,7 +25,7 @@ object UnitFeature : MatrixFeature
  * Inverted matrix feature
  */
 interface InverseMatrixFeature<T : Any> : MatrixFeature {
-    val inverse: Matrix<T>
+    val inverse: FeaturedMatrix<T>
 }
 
 /**
@@ -54,9 +54,9 @@ object UFeature: MatrixFeature
  * TODO add documentation
  */
 interface LUPDecompositionFeature<T : Any> : MatrixFeature {
-    val l: Matrix<T>
+    val l: FeaturedMatrix<T>
-    val u: Matrix<T>
+    val u: FeaturedMatrix<T>
-    val p: Matrix<T>
+    val p: FeaturedMatrix<T>
 }
 
 //TODO add sparse matrix feature

kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/Mutable2DStructure.kt

@@ -1,40 +0,0 @@
-package scientifik.kmath.linear
-
-import scientifik.kmath.structures.MutableBuffer
-import scientifik.kmath.structures.MutableBufferFactory
-import scientifik.kmath.structures.MutableNDStructure
-
-class Mutable2DStructure<T>(val rowNum: Int, val colNum: Int, val buffer: MutableBuffer<T>) : MutableNDStructure<T> {
-    override val shape: IntArray
-        get() = intArrayOf(rowNum, colNum)
-
-    operator fun get(i: Int, j: Int): T = buffer[i * colNum + j]
-
-    override fun get(index: IntArray): T = get(index[0], index[1])
-
-    override fun elements(): Sequence<Pair<IntArray, T>> = sequence {
-        for (i in 0 until rowNum) {
-            for (j in 0 until colNum) {
-                yield(intArrayOf(i, j) to get(i, j))
-            }
-        }
-    }
-
-    operator fun set(i: Int, j: Int, value: T) {
-        buffer[i * colNum + j] = value
-    }
-
-    override fun set(index: IntArray, value: T) = set(index[0], index[1], value)
-
-    companion object {
-        fun <T> create(
-            rowNum: Int,
-            colNum: Int,
-            bufferFactory: MutableBufferFactory<T>,
-            init: (i: Int, j: Int) -> T
-        ): Mutable2DStructure<T> {
-            val buffer = bufferFactory(rowNum * colNum) { offset -> init(offset / colNum, offset % colNum) }
-            return Mutable2DStructure(rowNum, colNum, buffer)
-        }
-    }
-}

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

@@ -4,68 +4,23 @@ import scientifik.kmath.operations.RealField
 import scientifik.kmath.operations.Space
 import scientifik.kmath.operations.SpaceElement
 import scientifik.kmath.structures.Buffer
-import scientifik.kmath.structures.BufferFactory
 import scientifik.kmath.structures.asSequence
+import kotlin.jvm.JvmName
 
 typealias Point<T> = Buffer<T>
 
-/**
- * A linear space for vectors.
- * Could be used on any point-like structure
- */
-interface VectorSpace<T : Any, S : Space<T>> : Space<Point<T>> {
 
-    val size: Int
+fun <T : Any, S : Space<T>> BufferVectorSpace<T, S>.produceElement(initializer: (Int) -> T): Vector<T, S> =
-
+    BufferVector(this, produce(initializer))
-    val space: S
-
-    fun produce(initializer: (Int) -> T): Point<T>
-
-    /**
-     * Produce a space-element of this vector space for expressions
-     */
-    fun produceElement(initializer: (Int) -> T): Vector<T, S>
-
-    override val zero: Point<T> get() = produce { space.zero }
-
-    override fun add(a: Point<T>, b: Point<T>): Point<T> = produce { with(space) { a[it] + b[it] } }
-
-    override fun multiply(a: Point<T>, k: Number): Point<T> = produce { with(space) { a[it] * k } }
-
-    //TODO add basis
-
-    companion object {
-
-        private val realSpaceCache = HashMap<Int, BufferVectorSpace<Double, RealField>>()
-
-        /**
-         * Non-boxing double vector space
-         */
-        fun real(size: Int): BufferVectorSpace<Double, RealField> {
-            return realSpaceCache.getOrPut(size) { BufferVectorSpace(size, RealField, Buffer.Companion::auto) }
-        }
-
-        /**
-         * A structured vector space with custom buffer
-         */
-        fun <T : Any, S : Space<T>> buffered(
-            size: Int,
-            space: S,
-            bufferFactory: BufferFactory<T> = Buffer.Companion::boxing
-        ): VectorSpace<T, S> = BufferVectorSpace(size, space, bufferFactory)
-
-        /**
-         * Automatic buffered vector, unboxed if it is possible
-         */
-        inline fun <reified T : Any, S : Space<T>> smart(size: Int, space: S): VectorSpace<T, S> =
-            buffered(size, space, Buffer.Companion::auto)
-    }
-}
 
+@JvmName("produceRealElement")
+fun BufferVectorSpace<Double, RealField>.produceElement(initializer: (Int) -> Double): Vector<Double, RealField> =
+    BufferVector(this, produce(initializer))
 
 /**
  * A point coupled to the linear space
  */
+@Deprecated("Use VectorContext instead")
 interface Vector<T : Any, S : Space<T>> : SpaceElement<Point<T>, Vector<T, S>, VectorSpace<T, S>>, Point<T> {
     override val size: Int get() = context.size
 
@@ -90,16 +45,7 @@ interface Vector<T : Any, S : Space<T>> : SpaceElement<Point<T>, Vector<T, S>, V
     }
 }
 
-data class BufferVectorSpace<T : Any, S : Space<T>>(
+@Deprecated("Use VectorContext instead")
-    override val size: Int,
-    override val space: S,
-    val bufferFactory: BufferFactory<T>
-) : VectorSpace<T, S> {
-    override fun produce(initializer: (Int) -> T) = bufferFactory(size, initializer)
-    override fun produceElement(initializer: (Int) -> T): Vector<T, S> = BufferVector(this, produce(initializer))
-}
-
-
 data class BufferVector<T : Any, S : Space<T>>(override val context: VectorSpace<T, S>, val buffer: Buffer<T>) :
     Vector<T, S> {
 

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

@@ -0,0 +1,75 @@
+package scientifik.kmath.linear
+
+import scientifik.kmath.operations.RealField
+import scientifik.kmath.operations.Space
+import scientifik.kmath.structures.Buffer
+import scientifik.kmath.structures.BufferFactory
+
+/**
+ * A linear space for vectors.
+ * Could be used on any point-like structure
+ */
+interface VectorSpace<T : Any, S : Space<T>> : Space<Point<T>> {
+
+    val size: Int
+
+    val space: S
+
+    fun produce(initializer: (Int) -> T): Point<T>
+
+    /**
+     * Produce a space-element of this vector space for expressions
+     */
+    //fun produceElement(initializer: (Int) -> T): Vector<T, S>
+
+    override val zero: Point<T> get() = produce { space.zero }
+
+    override fun add(a: Point<T>, b: Point<T>): Point<T> = produce { with(space) { a[it] + b[it] } }
+
+    override fun multiply(a: Point<T>, k: Number): Point<T> = produce { with(space) { a[it] * k } }
+
+    //TODO add basis
+
+    companion object {
+
+        private val realSpaceCache = HashMap<Int, BufferVectorSpace<Double, RealField>>()
+
+        /**
+         * Non-boxing double vector space
+         */
+        fun real(size: Int): BufferVectorSpace<Double, RealField> {
+            return realSpaceCache.getOrPut(size) {
+                BufferVectorSpace(
+                    size,
+                    RealField,
+                    Buffer.Companion::auto
+                )
+            }
+        }
+
+        /**
+         * A structured vector space with custom buffer
+         */
+        fun <T : Any, S : Space<T>> buffered(
+            size: Int,
+            space: S,
+            bufferFactory: BufferFactory<T> = Buffer.Companion::boxing
+        ) = BufferVectorSpace(size, space, bufferFactory)
+
+        /**
+         * Automatic buffered vector, unboxed if it is possible
+         */
+        inline fun <reified T : Any, S : Space<T>> auto(size: Int, space: S): VectorSpace<T, S> =
+            buffered(size, space, Buffer.Companion::auto)
+    }
+}
+
+
+class BufferVectorSpace<T : Any, S : Space<T>>(
+    override val size: Int,
+    override val space: S,
+    val bufferFactory: BufferFactory<T>
+) : VectorSpace<T, S> {
+    override fun produce(initializer: (Int) -> T) = bufferFactory(size, initializer)
+    //override fun produceElement(initializer: (Int) -> T): Vector<T, S> = BufferVector(this, produce(initializer))
+}

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

@@ -1,11 +1,23 @@
 package scientifik.kmath.linear
 
+import scientifik.kmath.structures.Matrix
+
 class VirtualMatrix<T : Any>(
     override val rowNum: Int,
     override val colNum: Int,
     override val features: Set<MatrixFeature> = emptySet(),
     val generator: (i: Int, j: Int) -> T
-) : Matrix<T> {
+) : FeaturedMatrix<T> {
+
+    constructor(rowNum: Int, colNum: Int, vararg features: MatrixFeature, generator: (i: Int, j: Int) -> T) : this(
+        rowNum,
+        colNum,
+        setOf(*features),
+        generator
+    )
+
+    override val shape: IntArray get() = intArrayOf(rowNum, colNum)
+
     override fun get(i: Int, j: Int): T = generator(i, j)
 
     override fun suggestFeature(vararg features: MatrixFeature) =
@@ -13,7 +25,7 @@ class VirtualMatrix<T : Any>(
 
     override fun equals(other: Any?): Boolean {
         if (this === other) return true
-        if (other !is Matrix<*>) return false
+        if (other !is FeaturedMatrix<*>) return false
 
         if (rowNum != other.rowNum) return false
         if (colNum != other.colNum) return false
@@ -34,7 +46,7 @@ class VirtualMatrix<T : Any>(
         /**
          * Wrap a matrix adding additional features to it
          */
-        fun <T : Any> wrap(matrix: Matrix<T>, vararg features: MatrixFeature): Matrix<T> {
+        fun <T : Any> wrap(matrix: Matrix<T>, vararg features: MatrixFeature): FeaturedMatrix<T> {
             return if (matrix is VirtualMatrix) {
                 VirtualMatrix(matrix.rowNum, matrix.colNum, matrix.features + features, matrix.generator)
             } else {

kmath-core/src/commonMain/kotlin/scientifik/kmath/misc/Cumulative.kt

@@ -1,4 +1,4 @@
-package scientifik.kmath.sequential
+package scientifik.kmath.misc
 
 import scientifik.kmath.operations.Space
 import kotlin.jvm.JvmName

kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/AutoDiff.kt

@@ -0,0 +1,218 @@
+package scientifik.kmath.operations
+
+import scientifik.kmath.linear.Point
+import scientifik.kmath.structures.asBuffer
+import kotlin.math.pow
+import kotlin.math.sqrt
+
+/*
+ * Implementation of backward-mode automatic differentiation.
+ * Initial gist by Roman Elizarov: https://gist.github.com/elizarov/1ad3a8583e88cb6ea7a0ad09bb591d3d
+ */
+
+/**
+ * Differentiable variable with value and derivative of differentiation ([deriv]) result
+ * with respect to this variable.
+ */
+open class Variable(val value: Double) {
+    constructor(x: Number) : this(x.toDouble())
+}
+
+class DerivationResult(value: Double, val deriv: Map<Variable, Double>) : Variable(value) {
+    fun deriv(variable: Variable) = deriv[variable] ?: 0.0
+
+    /**
+     * compute divergence
+     */
+    fun div() = deriv.values.sum()
+
+    /**
+     * Compute a gradient for variables in given order
+     */
+    fun grad(vararg variables: Variable): Point<Double> = if (variables.isEmpty()) {
+        error("Variable order is not provided for gradient construction")
+    } else {
+        variables.map(::deriv).toDoubleArray().asBuffer()
+    }
+}
+
+/**
+ * Runs differentiation and establishes [AutoDiffField] context inside the block of code.
+ *
+ * The partial derivatives are placed in argument `d` variable
+ *
+ * Example:
+ * ```
+ * val x = Variable(2) // define variable(s) and their values
+ * val y = deriv { sqr(x) + 5 * x + 3 } // write formulate in deriv context
+ * assertEquals(17.0, y.x) // the value of result (y)
+ * assertEquals(9.0, x.d)  // dy/dx
+ * ```
+ */
+fun deriv(body: AutoDiffField.() -> Variable): DerivationResult =
+    AutoDiffContext().run {
+        val result = body()
+        result.d = 1.0 // computing derivative w.r.t result
+        runBackwardPass()
+        DerivationResult(result.value, derivatives)
+    }
+
+
+abstract class AutoDiffField : Field<Variable> {
+    /**
+     * Performs update of derivative after the rest of the formula in the back-pass.
+     *
+     * For example, implementation of `sin` function is:
+     *
+     * ```
+     * fun AD.sin(x: Variable): Variable = derive(Variable(sin(x.x)) { z -> // call derive with function result
+     *     x.d += z.d * cos(x.x) // update derivative using chain rule and derivative of the function
+     * }
+     * ```
+     */
+    abstract fun <R> derive(value: R, block: (R) -> Unit): R
+
+    /**
+     * A variable accessing inner state of derivatives.
+     * Use this function in inner builders to avoid creating additional derivative bindings
+     */
+    abstract var Variable.d: Double
+
+    abstract fun variable(value: Double): Variable
+
+    // Overloads for Double constants
+
+    operator fun Number.plus(that: Variable): Variable = derive(variable(this.toDouble() + that.value)) { z ->
+        that.d += z.d
+    }
+
+    operator fun Variable.plus(b: Number): Variable = b.plus(this)
+
+    operator fun Number.minus(that: Variable): Variable = derive(variable(this.toDouble() - that.value)) { z ->
+        that.d -= z.d
+    }
+
+    operator fun Variable.minus(that: Number): Variable = derive(variable(this.value - that.toDouble())) { z ->
+        this.d += z.d
+    }
+}
+
+/**
+ * Automatic Differentiation context class.
+ */
+private class AutoDiffContext : AutoDiffField() {
+
+    // this stack contains pairs of blocks and values to apply them to
+    private var stack = arrayOfNulls<Any?>(8)
+    private var sp = 0
+
+    internal val derivatives = HashMap<Variable, Double>()
+
+
+    /**
+     * A variable coupled with its derivative. For internal use only
+     */
+    class VariableWithDeriv(x: Double, var d: Double = 0.0) : Variable(x)
+
+
+    override fun variable(value: Double): Variable = VariableWithDeriv(value)
+
+    override var Variable.d: Double
+        get() = (this as? VariableWithDeriv)?.d ?: derivatives[this] ?: 0.0
+        set(value) {
+            if (this is VariableWithDeriv) {
+                d = value
+            } else {
+                derivatives[this] = value
+            }
+        }
+
+    @Suppress("UNCHECKED_CAST")
+    override fun <R> derive(value: R, block: (R) -> Unit): R {
+        // save block to stack for backward pass
+        if (sp >= stack.size) stack = stack.copyOf(stack.size * 2)
+        stack[sp++] = block
+        stack[sp++] = value
+        return value
+    }
+
+    @Suppress("UNCHECKED_CAST")
+    fun runBackwardPass() {
+        while (sp > 0) {
+            val value = stack[--sp]
+            val block = stack[--sp] as (Any?) -> Unit
+            block(value)
+        }
+    }
+
+    // Basic math (+, -, *, /)
+
+
+    override fun add(a: Variable, b: Variable): Variable =
+        derive(variable(a.value + b.value)) { z ->
+            a.d += z.d
+            b.d += z.d
+        }
+
+    override fun multiply(a: Variable, b: Variable): Variable =
+        derive(variable(a.value * b.value)) { z ->
+            a.d += z.d * b.value
+            b.d += z.d * a.value
+        }
+
+    override fun divide(a: Variable, b: Variable): Variable =
+        derive(Variable(a.value / b.value)) { z ->
+            a.d += z.d / b.value
+            b.d -= z.d * a.value / (b.value * b.value)
+        }
+
+    override fun multiply(a: Variable, k: Number): Variable =
+        derive(variable(k.toDouble() * a.value)) { z ->
+            a.d += z.d * k.toDouble()
+        }
+
+    override val zero: Variable get() = Variable(0.0)
+    override val one: Variable get() = Variable(1.0)
+}
+
+// Extensions for differentiation of various basic mathematical functions
+
+// x ^ 2
+fun AutoDiffField.sqr(x: Variable): Variable = derive(variable(x.value * x.value)) { z ->
+    x.d += z.d * 2 * x.value
+}
+
+// x ^ 1/2
+fun AutoDiffField.sqrt(x: Variable): Variable = derive(variable(sqrt(x.value))) { z ->
+    x.d += z.d * 0.5 / z.value
+}
+
+// x ^ y (const)
+fun AutoDiffField.pow(x: Variable, y: Double): Variable = derive(variable(x.value.pow(y))) { z ->
+    x.d += z.d * y * x.value.pow(y - 1)
+}
+
+fun AutoDiffField.pow(x: Variable, y: Int): Variable = pow(x, y.toDouble())
+
+// exp(x)
+fun AutoDiffField.exp(x: Variable): Variable = derive(variable(kotlin.math.exp(x.value))) { z ->
+    x.d += z.d * z.value
+}
+
+// ln(x)
+fun AutoDiffField.ln(x: Variable): Variable = derive(Variable(kotlin.math.ln(x.value))) { z ->
+    x.d += z.d / x.value
+}
+
+// x ^ y (any)
+fun AutoDiffField.pow(x: Variable, y: Variable): Variable = exp(y * ln(x))
+
+// sin(x)
+fun AutoDiffField.sin(x: Variable): Variable = derive(variable(kotlin.math.sin(x.value))) { z ->
+    x.d += z.d * kotlin.math.cos(x.value)
+}
+
+// cos(x)
+fun AutoDiffField.cos(x: Variable): Variable = derive(variable(kotlin.math.cos(x.value))) { z ->
+    x.d -= z.d * kotlin.math.sin(x.value)
+}

kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/Complex.kt

@@ -55,23 +55,14 @@ object ComplexField : ExtendedFieldOperations<Complex>, Field<Complex> {
 /**
  * Complex number class
  */
-data class Complex(val re: Double, val im: Double) : FieldElement<Complex, Complex, ComplexField> {
+data class Complex(val re: Double, val im: Double) : FieldElement<Complex, Complex, ComplexField>, Comparable<Complex> {
     override fun unwrap(): Complex = this
 
     override fun Complex.wrap(): Complex = this
 
     override val context: ComplexField get() = ComplexField
 
-    /**
+    override fun compareTo(other: Complex): Int = abs.compareTo(other.abs)
-     * A complex conjugate
-     */
-    val conjugate: Complex get() = Complex(re, -im)
-
-    val square: Double get() = re * re + im * im
-
-    val abs: Double get() = sqrt(square)
-
-    val theta: Double get() = atan(im / re)
 
     companion object : MemorySpec<Complex> {
         override val objectSize: Int = 16
@@ -86,6 +77,26 @@ data class Complex(val re: Double, val im: Double) : FieldElement<Complex, Compl
     }
 }
 
+/**
+ * A complex conjugate
+ */
+val Complex.conjugate: Complex get() = Complex(re, -im)
+
+/**
+ * Square of the complex number
+ */
+val Complex.square: Double get() = re * re + im * im
+
+/**
+ * Absolute value of complex number
+ */
+val Complex.abs: Double get() = sqrt(square)
+
+/**
+ * An angle between vector represented by complex number and X axis
+ */
+val Complex.theta: Double get() = atan(im / re)
+
 fun Double.toComplex() = Complex(this, 0.0)
 
 inline fun Buffer.Companion.complex(size: Int, crossinline init: (Int) -> Complex): Buffer<Complex> {

kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/NumberAlgebra.kt

@@ -1,5 +1,6 @@
 package scientifik.kmath.operations
 
+import kotlin.math.abs
 import kotlin.math.pow
 
 /**
@@ -31,79 +32,152 @@ inline class Real(val value: Double) : FieldElement<Double, Real, RealField> {
 /**
  * A field for double without boxing. Does not produce appropriate field element
  */
-@Suppress("EXTENSION_SHADOWED_BY_MEMBER")
+@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
 object RealField : Field<Double>, ExtendedFieldOperations<Double>, Norm<Double, Double> {
     override val zero: Double = 0.0
-    override fun add(a: Double, b: Double): Double = a + b
+    override inline fun add(a: Double, b: Double) = a + b
-    override fun multiply(a: Double, b: Double): Double = a * b
+    override inline fun multiply(a: Double, b: Double) = a * b
-    override fun multiply(a: Double, k: Number): Double = a * k.toDouble()
+    override inline fun multiply(a: Double, k: Number) = a * k.toDouble()
 
     override val one: Double = 1.0
-    override fun divide(a: Double, b: Double): Double = a / b
+    override inline fun divide(a: Double, b: Double) = a / b
 
-    override fun sin(arg: Double): Double = kotlin.math.sin(arg)
+    override inline fun sin(arg: Double) = kotlin.math.sin(arg)
-    override fun cos(arg: Double): Double = kotlin.math.cos(arg)
+    override inline fun cos(arg: Double) = kotlin.math.cos(arg)
 
-    override fun power(arg: Double, pow: Number): Double = arg.pow(pow.toDouble())
+    override inline fun power(arg: Double, pow: Number) = arg.pow(pow.toDouble())
 
-    override fun exp(arg: Double): Double = kotlin.math.exp(arg)
+    override inline fun exp(arg: Double) = kotlin.math.exp(arg)
-    override fun ln(arg: Double): Double = kotlin.math.ln(arg)
+    override inline fun ln(arg: Double) = kotlin.math.ln(arg)
 
-    override fun norm(arg: Double): Double = kotlin.math.abs(arg)
+    override inline fun norm(arg: Double) = abs(arg)
 
-    override fun Double.unaryMinus(): Double = -this
+    override inline fun Double.unaryMinus() = -this
 
-    override fun Double.minus(b: Double): Double = this - b
+    override inline fun Double.plus(b: Double) = this + b
+
+    override inline fun Double.minus(b: Double) = this - b
+
+    override inline fun Double.times(b: Double) = this * b
+
+    override inline fun Double.div(b: Double) = this / b
+}
+
+@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
+object FloatField : Field<Float>, ExtendedFieldOperations<Float>, Norm<Float, Float> {
+    override val zero: Float = 0f
+    override inline fun add(a: Float, b: Float) = a + b
+    override inline fun multiply(a: Float, b: Float) = a * b
+    override inline fun multiply(a: Float, k: Number) = a * k.toFloat()
+
+    override val one: Float = 1f
+    override inline fun divide(a: Float, b: Float) = a / b
+
+    override inline fun sin(arg: Float) = kotlin.math.sin(arg)
+    override inline fun cos(arg: Float) = kotlin.math.cos(arg)
+
+    override inline fun power(arg: Float, pow: Number) = arg.pow(pow.toFloat())
+
+    override inline fun exp(arg: Float) = kotlin.math.exp(arg)
+    override inline fun ln(arg: Float) = kotlin.math.ln(arg)
+
+    override inline fun norm(arg: Float) = abs(arg)
+
+    override inline fun Float.unaryMinus() = -this
+
+    override inline fun Float.plus(b: Float) = this + b
+
+    override inline fun Float.minus(b: Float) = this - b
+
+    override inline fun Float.times(b: Float) = this * b
+
+    override inline fun Float.div(b: Float) = this / b
 }
 
 /**
  * A field for [Int] without boxing. Does not produce corresponding field element
  */
+@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
 object IntRing : Ring<Int>, Norm<Int, Int> {
     override val zero: Int = 0
-    override fun add(a: Int, b: Int): Int = a + b
+    override inline fun add(a: Int, b: Int) = a + b
-    override fun multiply(a: Int, b: Int): Int = a * b
+    override inline fun multiply(a: Int, b: Int) = a * b
-    override fun multiply(a: Int, k: Number): Int = (k * a)
+    override inline fun multiply(a: Int, k: Number) = (k * a)
     override val one: Int = 1
 
-    override fun norm(arg: Int): Int = arg
+    override inline fun norm(arg: Int) = abs(arg)
+
+    override inline fun Int.unaryMinus() = -this
+
+    override inline fun Int.plus(b: Int): Int = this + b
+
+    override inline fun Int.minus(b: Int): Int = this - b
+
+    override inline fun Int.times(b: Int): Int = this * b
 }
 
 /**
  * A field for [Short] without boxing. Does not produce appropriate field element
  */
+@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
 object ShortRing : Ring<Short>, Norm<Short, Short> {
     override val zero: Short = 0
-    override fun add(a: Short, b: Short): Short = (a + b).toShort()
+    override inline fun add(a: Short, b: Short) = (a + b).toShort()
-    override fun multiply(a: Short, b: Short): Short = (a * b).toShort()
+    override inline fun multiply(a: Short, b: Short) = (a * b).toShort()
-    override fun multiply(a: Short, k: Number): Short = (a * k)
+    override inline fun multiply(a: Short, k: Number) = (a * k)
     override val one: Short = 1
 
-    override fun norm(arg: Short): Short = arg
+    override fun norm(arg: Short): Short = if (arg > 0) arg else (-arg).toShort()
+
+    override inline fun Short.unaryMinus() = (-this).toShort()
+
+    override inline fun Short.plus(b: Short) = (this + b).toShort()
+
+    override inline fun Short.minus(b: Short) = (this - b).toShort()
+
+    override inline fun Short.times(b: Short) = (this * b).toShort()
 }
 
 /**
  * A field for [Byte] values
  */
+@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
 object ByteRing : Ring<Byte>, Norm<Byte, Byte> {
     override val zero: Byte = 0
-    override fun add(a: Byte, b: Byte): Byte = (a + b).toByte()
+    override inline fun add(a: Byte, b: Byte) = (a + b).toByte()
-    override fun multiply(a: Byte, b: Byte): Byte = (a * b).toByte()
+    override inline fun multiply(a: Byte, b: Byte) = (a * b).toByte()
-    override fun multiply(a: Byte, k: Number): Byte = (a * k)
+    override inline fun multiply(a: Byte, k: Number) = (a * k)
     override val one: Byte = 1
 
-    override fun norm(arg: Byte): Byte = arg
+    override fun norm(arg: Byte): Byte = if (arg > 0) arg else (-arg).toByte()
+
+    override inline fun Byte.unaryMinus() = (-this).toByte()
+
+    override inline fun Byte.plus(b: Byte) = (this + b).toByte()
+
+    override inline fun Byte.minus(b: Byte) = (this - b).toByte()
+
+    override inline fun Byte.times(b: Byte) = (this * b).toByte()
 }
 
 /**
  * A field for [Long] values
  */
+@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
 object LongRing : Ring<Long>, Norm<Long, Long> {
     override val zero: Long = 0
-    override fun add(a: Long, b: Long): Long = (a + b)
+    override inline fun add(a: Long, b: Long) = (a + b)
-    override fun multiply(a: Long, b: Long): Long = (a * b)
+    override inline fun multiply(a: Long, b: Long) = (a * b)
-    override fun multiply(a: Long, k: Number): Long = (a * k)
+    override inline fun multiply(a: Long, k: Number) = (a * k)
     override val one: Long = 1
 
-    override fun norm(arg: Long): Long = arg
+    override fun norm(arg: Long): Long = abs(arg)
+
+    override inline fun Long.unaryMinus() = (-this)
+
+    override inline fun Long.plus(b: Long) = (this + b)
+
+    override inline fun Long.minus(b: Long) = (this - b)
+
+    override inline fun Long.times(b: Long) = (this * b)
 }

kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/BufferAccessor2D.kt

@@ -0,0 +1,45 @@
+package scientifik.kmath.structures
+
+import kotlin.reflect.KClass
+
+/**
+ * A context that allows to operate on a [MutableBuffer] as on 2d array
+ */
+class BufferAccessor2D<T : Any>(val type: KClass<T>, val rowNum: Int, val colNum: Int) {
+
+    operator fun Buffer<T>.get(i: Int, j: Int) = get(i + colNum * j)
+
+    operator fun MutableBuffer<T>.set(i: Int, j: Int, value: T) {
+        set(i + colNum * j, value)
+    }
+
+    inline fun create(init: (i: Int, j: Int) -> T) =
+        MutableBuffer.auto(type, rowNum * colNum) { offset -> init(offset / colNum, offset % colNum) }
+
+    fun create(mat: Structure2D<T>) = create { i, j -> mat[i, j] }
+
+    //TODO optimize wrapper
+    fun MutableBuffer<T>.collect(): Structure2D<T> =
+        NDStructure.auto(type, rowNum, colNum) { (i, j) -> get(i, j) }.as2D()
+
+
+    inner class Row(val buffer: MutableBuffer<T>, val rowIndex: Int) : MutableBuffer<T> {
+        override val size: Int get() = colNum
+
+        override fun get(index: Int): T = buffer[rowIndex, index]
+
+        override fun set(index: Int, value: T) {
+            buffer[rowIndex, index] = value
+        }
+
+        override fun copy(): MutableBuffer<T> = MutableBuffer.auto(type, colNum) { get(it) }
+
+        override fun iterator(): Iterator<T> = (0 until colNum).map(::get).iterator()
+
+    }
+
+    /**
+     * Get row
+     */
+    fun MutableBuffer<T>.row(i: Int) = Row(this, i)
+}

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

@@ -2,6 +2,7 @@ package scientifik.kmath.structures
 
 import scientifik.kmath.operations.Complex
 import scientifik.kmath.operations.complex
+import kotlin.reflect.KClass
 
 
 typealias BufferFactory<T> = (Int, (Int) -> T) -> Buffer<T>
@@ -36,18 +37,20 @@ interface Buffer<T> {
 
     companion object {
 
+        inline fun real(size: Int, initializer: (Int) -> Double): DoubleBuffer {
+            val array = DoubleArray(size) { initializer(it) }
+            return DoubleBuffer(array)
+        }
+
         /**
          * Create a boxing buffer of given type
          */
         inline fun <T> boxing(size: Int, initializer: (Int) -> T): Buffer<T> = ListBuffer(List(size, initializer))
 
-        /**
-         * Create most appropriate immutable buffer for given type avoiding boxing wherever possible
-         */
         @Suppress("UNCHECKED_CAST")
-        inline fun <reified T : Any> auto(size: Int, crossinline initializer: (Int) -> T): Buffer<T> {
+        inline fun <T : Any> auto(type: KClass<T>, size: Int, crossinline initializer: (Int) -> T): Buffer<T> {
             //TODO add resolution based on Annotation or companion resolution
-            return when (T::class) {
+            return when (type) {
                 Double::class -> DoubleBuffer(DoubleArray(size) { initializer(it) as Double }) as Buffer<T>
                 Short::class -> ShortBuffer(ShortArray(size) { initializer(it) as Short }) as Buffer<T>
                 Int::class -> IntBuffer(IntArray(size) { initializer(it) as Int }) as Buffer<T>
@@ -56,6 +59,13 @@ interface Buffer<T> {
                 else -> boxing(size, initializer)
             }
         }
+
+        /**
+         * Create most appropriate immutable buffer for given type avoiding boxing wherever possible
+         */
+        @Suppress("UNCHECKED_CAST")
+        inline fun <reified T : Any> auto(size: Int, crossinline initializer: (Int) -> T): Buffer<T> =
+            auto(T::class, size, initializer)
     }
 }
 
@@ -78,12 +88,9 @@ interface MutableBuffer<T> : Buffer<T> {
         inline fun <T> boxing(size: Int, initializer: (Int) -> T): MutableBuffer<T> =
             MutableListBuffer(MutableList(size, initializer))
 
-        /**
-         * Create most appropriate mutable buffer for given type avoiding boxing wherever possible
-         */
         @Suppress("UNCHECKED_CAST")
-        inline fun <reified T : Any> auto(size: Int, initializer: (Int) -> T): MutableBuffer<T> {
+        inline fun <T : Any> auto(type: KClass<out T>, size: Int, initializer: (Int) -> T): MutableBuffer<T> {
-            return when (T::class) {
+            return when (type) {
                 Double::class -> DoubleBuffer(DoubleArray(size) { initializer(it) as Double }) as MutableBuffer<T>
                 Short::class -> ShortBuffer(ShortArray(size) { initializer(it) as Short }) as MutableBuffer<T>
                 Int::class -> IntBuffer(IntArray(size) { initializer(it) as Int }) as MutableBuffer<T>
@@ -91,6 +98,17 @@ interface MutableBuffer<T> : Buffer<T> {
                 else -> boxing(size, initializer)
             }
         }
+
+        /**
+         * Create most appropriate mutable buffer for given type avoiding boxing wherever possible
+         */
+        @Suppress("UNCHECKED_CAST")
+        inline fun <reified T : Any> auto(size: Int, initializer: (Int) -> T): MutableBuffer<T> =
+            auto(T::class, size, initializer)
+
+        val real: MutableBufferFactory<Double> = { size: Int, initializer: (Int) -> Double ->
+            DoubleBuffer(DoubleArray(size) { initializer(it) })
+        }
     }
 }
 
@@ -236,7 +254,10 @@ inline class ReadOnlyBuffer<T>(val buffer: MutableBuffer<T>) : Buffer<T> {
  * Useful when one needs single element from the buffer.
  */
 class VirtualBuffer<T>(override val size: Int, private val generator: (Int) -> T) : Buffer<T> {
-    override fun get(index: Int): T = generator(index)
+    override fun get(index: Int): T {
+        if (index < 0 || index >= size) throw IndexOutOfBoundsException("Expected index from 0 to ${size - 1}, but found $index")
+        return generator(index)
+    }
 
     override fun iterator(): Iterator<T> = (0 until size).asSequence().map(generator).iterator()
 
@@ -262,3 +283,5 @@ fun <T> Buffer<T>.asReadOnly(): Buffer<T> = if (this is MutableBuffer) {
  * Typealias for buffer transformations
  */
 typealias BufferTransform<T, R> = (Buffer<T>) -> Buffer<R>
+
+typealias SuspendBufferTransform<T, R> = suspend (Buffer<T>) -> Buffer<R>

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

@@ -73,6 +73,7 @@ interface NDSpace<T, S : Space<T>, N : NDStructure<T>> : Space<N>, NDAlgebra<T,
      */
     override fun multiply(a: N, k: Number): N = map(a) { multiply(it, k) }
 
+    //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) }
 
@@ -90,6 +91,7 @@ interface NDRing<T, R : Ring<T>, N : NDStructure<T>> : Ring<N>, NDSpace<T, R, N>
      */
     override fun multiply(a: N, b: N): N = combine(a, b) { aValue, bValue -> multiply(aValue, bValue) }
 
+    //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) }
 }
@@ -109,6 +111,7 @@ interface NDField<T, F : Field<T>, N : NDStructure<T>> : Field<N>, NDRing<T, F,
      */
     override fun divide(a: N, b: N): N = combine(a, b) { aValue, bValue -> divide(aValue, bValue) }
 
+    //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) }
 

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

@@ -1,12 +1,14 @@
 package scientifik.kmath.structures
 
+import kotlin.jvm.JvmName
+import kotlin.reflect.KClass
+
 
 interface NDStructure<T> {
 
     val shape: IntArray
 
-    val dimension
+    val dimension get() = shape.size
-        get() = shape.size
 
     operator fun get(index: IntArray): T
 
@@ -41,6 +43,9 @@ interface NDStructure<T> {
         inline fun <reified T : Any> auto(strides: Strides, crossinline initializer: (IntArray) -> T) =
             BufferNDStructure(strides, Buffer.auto(strides.linearSize) { i -> initializer(strides.index(i)) })
 
+        inline fun <T : Any> auto(type: KClass<T>, strides: Strides, crossinline initializer: (IntArray) -> T) =
+            BufferNDStructure(strides, Buffer.auto(type, strides.linearSize) { i -> initializer(strides.index(i)) })
+
         fun <T> build(
             shape: IntArray,
             bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
@@ -49,6 +54,13 @@ interface NDStructure<T> {
 
         inline fun <reified T : Any> auto(shape: IntArray, crossinline initializer: (IntArray) -> T) =
             auto(DefaultStrides(shape), initializer)
+
+        @JvmName("autoVarArg")
+        inline fun <reified T : Any> auto(vararg shape: Int, crossinline initializer: (IntArray) -> T) =
+            auto(DefaultStrides(shape), initializer)
+
+        inline fun <T : Any> auto(type: KClass<T>, vararg shape: Int, crossinline initializer: (IntArray) -> T) =
+            auto(type, DefaultStrides(shape), initializer)
     }
 }
 
@@ -58,7 +70,7 @@ interface MutableNDStructure<T> : NDStructure<T> {
     operator fun set(index: IntArray, value: T)
 }
 
-fun <T> MutableNDStructure<T>.mapInPlace(action: (IntArray, T) -> T) {
+inline fun <T> MutableNDStructure<T>.mapInPlace(action: (IntArray, T) -> T) {
     elements().forEach { (index, oldValue) ->
         this[index] = action(index, oldValue)
     }

kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Structure1D.kt

@@ -27,20 +27,6 @@ private inline class Structure1DWrapper<T>(val structure: NDStructure<T>) : Stru
     override fun elements(): Sequence<Pair<IntArray, T>> = structure.elements()
 }
 
-/**
- * Represent a [NDStructure] as [Structure1D]. Throw error in case of dimension mismatch
- */
-fun <T> NDStructure<T>.as1D(): Structure1D<T> = if (shape.size == 1) {
-    Structure1DWrapper(this)
-} else {
-    error("Can't create 1d-structure from ${shape.size}d-structure")
-}
-
-fun <T> NDBuffer<T>.as1D(): Structure1D<T> = if (shape.size == 1) {
-    Buffer1DWrapper(this.buffer)
-} else {
-    error("Can't create 1d-structure from ${shape.size}d-structure")
-}
 
 /**
  * A structure wrapper for buffer
@@ -56,39 +42,21 @@ private inline class Buffer1DWrapper<T>(val buffer: Buffer<T>) : Structure1D<T>
     override fun get(index: Int): T = buffer.get(index)
 }
 
+/**
+ * Represent a [NDStructure] as [Structure1D]. Throw error in case of dimension mismatch
+ */
+fun <T> NDStructure<T>.as1D(): Structure1D<T> = if (shape.size == 1) {
+    if( this is NDBuffer){
+        Buffer1DWrapper(this.buffer)
+    } else {
+        Structure1DWrapper(this)
+    }
+} else {
+    error("Can't create 1d-structure from ${shape.size}d-structure")
+}
+
+
 /**
  * Represent this buffer as 1D structure
  */
 fun <T> Buffer<T>.asND(): Structure1D<T> = Buffer1DWrapper(this)
-
-/**
- * A structure that is guaranteed to be two-dimensional
- */
-interface Structure2D<T> : NDStructure<T> {
-    operator fun get(i: Int, j: Int): T
-
-    override fun get(index: IntArray): T {
-        if (index.size != 2) error("Index dimension mismatch. Expected 2 but found ${index.size}")
-        return get(index[0], index[1])
-    }
-}
-
-/**
- * A 2D wrapper for nd-structure
- */
-private inline class Structure2DWrapper<T>(val structure: NDStructure<T>) : Structure2D<T> {
-    override fun get(i: Int, j: Int): T = structure[i, j]
-
-    override val shape: IntArray get() = structure.shape
-
-    override fun elements(): Sequence<Pair<IntArray, T>> = structure.elements()
-}
-
-/**
- * Represent a [NDStructure] as [Structure1D]. Throw error in case of dimension mismatch
- */
-fun <T> NDStructure<T>.as2D(): Structure2D<T> = if (shape.size == 2) {
-    Structure2DWrapper(this)
-} else {
-    error("Can't create 2d-structure from ${shape.size}d-structure")
-}

kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Structure2D.kt

@@ -0,0 +1,79 @@
+package scientifik.kmath.structures
+
+/**
+ * A structure that is guaranteed to be two-dimensional
+ */
+interface Structure2D<T> : NDStructure<T> {
+    val rowNum: Int get() = shape[0]
+    val colNum: Int get() = shape[1]
+
+    operator fun get(i: Int, j: Int): T
+
+    override fun get(index: IntArray): T {
+        if (index.size != 2) error("Index dimension mismatch. Expected 2 but found ${index.size}")
+        return get(index[0], index[1])
+    }
+
+
+    val rows: Buffer<Buffer<T>>
+        get() = VirtualBuffer(rowNum) { i ->
+            VirtualBuffer(colNum) { j -> get(i, j) }
+        }
+
+    val columns: Buffer<Buffer<T>>
+        get() = VirtualBuffer(colNum) { j ->
+            VirtualBuffer(rowNum) { i -> get(i, j) }
+        }
+
+    override fun elements(): Sequence<Pair<IntArray, T>> = sequence {
+        for (i in (0 until rowNum)) {
+            for (j in (0 until colNum)) {
+                yield(intArrayOf(i, j) to get(i, j))
+            }
+        }
+    }
+
+    companion object {
+
+    }
+}
+
+/**
+ * A 2D wrapper for nd-structure
+ */
+private inline class Structure2DWrapper<T>(val structure: NDStructure<T>) : Structure2D<T> {
+    override fun get(i: Int, j: Int): T = structure[i, j]
+
+    override val shape: IntArray get() = structure.shape
+
+    override fun elements(): Sequence<Pair<IntArray, T>> = structure.elements()
+}
+
+/**
+ * Represent a [NDStructure] as [Structure1D]. Throw error in case of dimension mismatch
+ */
+fun <T> NDStructure<T>.as2D(): Structure2D<T> = if (shape.size == 2) {
+    Structure2DWrapper(this)
+} else {
+    error("Can't create 2d-structure from ${shape.size}d-structure")
+}
+
+/**
+ * Represent this 2D structure as 1D if it has exactly one column. Throw error otherwise.
+ */
+fun <T> Structure2D<T>.as1D() = if (colNum == 1) {
+    object : Structure1D<T> {
+        override fun get(index: Int): T = get(index, 0)
+
+        override val shape: IntArray get() = intArrayOf(rowNum)
+
+        override fun elements(): Sequence<Pair<IntArray, T>> = elements()
+
+        override val size: Int get() = rowNum
+    }
+} else {
+    error("Can't convert matrix with more than one column to vector")
+}
+
+
+typealias Matrix<T> = Structure2D<T>

kmath-core/src/commonTest/kotlin/scientifik/kmath/linear/MatrixTest.kt

@@ -1,5 +1,6 @@
 package scientifik.kmath.linear
 
+import scientifik.kmath.structures.Matrix
 import kotlin.test.Test
 import kotlin.test.assertEquals
 
@@ -16,7 +17,7 @@ class MatrixTest {
     @Test
     fun testVectorToMatrix() {
         val vector = Vector.real(5) { it.toDouble() }
-        val matrix = vector.toMatrix()
+        val matrix = vector.asMatrix()
         assertEquals(4.0, matrix[4, 0])
     }
 
@@ -33,8 +34,8 @@ class MatrixTest {
         val vector1 = Vector.real(5) { it.toDouble() }
         val vector2 = Vector.real(5) { 5 - it.toDouble() }
 
-        val matrix1 = vector1.toMatrix()
+        val matrix1 = vector1.asMatrix()
-        val matrix2 = vector2.toMatrix().transpose()
+        val matrix2 = vector2.asMatrix().transpose()
         val product = MatrixContext.real.run { matrix1 dot matrix2 }
 
 
@@ -51,4 +52,26 @@ class MatrixTest {
 
         assertEquals(2.0, matrix[1, 2])
     }
+
+    @Test
+    fun testMatrixExtension() {
+        val transitionMatrix: Matrix<Double> = VirtualMatrix(6, 6) { row, col ->
+            when {
+                col == 0 -> .50
+                row + 1 == col -> .50
+                row == 5 && col == 5 -> 1.0
+                else -> 0.0
+            }
+        }
+
+        infix fun Matrix<Double>.pow(power: Int): Matrix<Double> {
+            var res = this
+            repeat(power - 1) {
+                res = res dot this
+            }
+            return res
+        }
+
+        val toTenthPower = transitionMatrix pow 10
+    }
 }

kmath-core/src/commonTest/kotlin/scientifik/kmath/linear/RealLUSolverTest.kt

@@ -1,16 +1,37 @@
 package scientifik.kmath.linear
 
+import scientifik.kmath.structures.Matrix
+import kotlin.contracts.ExperimentalContracts
 import kotlin.test.Test
 import kotlin.test.assertEquals
 
+@ExperimentalContracts
 class RealLUSolverTest {
+
     @Test
     fun testInvertOne() {
         val matrix = MatrixContext.real.one(2, 2)
-        val inverted = LUSolver.real.inverse(matrix)
+        val inverted = MatrixContext.real.inverse(matrix)
         assertEquals(matrix, inverted)
     }
 
+    @Test
+    fun testDecomposition() {
+        val matrix = Matrix.square(
+            3.0, 1.0,
+            1.0, 3.0
+        )
+
+        MatrixContext.real.run {
+            val lup = lup(matrix)
+
+            //Check determinant
+            assertEquals(8.0, lup.determinant)
+
+            assertEquals(lup.p dot matrix, lup.l dot lup.u)
+        }
+    }
+
     @Test
     fun testInvert() {
         val matrix = Matrix.square(
@@ -18,18 +39,7 @@ class RealLUSolverTest {
             1.0, 3.0
         )
 
-        val decomposed = LUSolver.real.decompose(matrix)
+        val inverted = MatrixContext.real.inverse(matrix)
-        val decomposition = decomposed.getFeature<LUPDecomposition<Double>>()!!
-
-        //Check determinant
-        assertEquals(8.0, decomposition.determinant)
-
-        //Check decomposition
-        with(MatrixContext.real) {
-            assertEquals(decomposition.p dot matrix, decomposition.l dot decomposition.u)
-        }
-
-        val inverted = LUSolver.real.inverse(decomposed)
 
         val expected = Matrix.square(
             0.375, -0.125,

kmath-core/src/commonTest/kotlin/scientifik/kmath/misc/CumulativeKtTest.kt

@@ -1,6 +1,5 @@
 package scientifik.kmath.misc
 
-import scientifik.kmath.sequential.cumulativeSum
 import kotlin.test.Test
 import kotlin.test.assertEquals
 

kmath-core/src/commonTest/kotlin/scientifik/kmath/operations/AutoDiffTest.kt

@@ -0,0 +1,176 @@
+package scientifik.kmath.operations
+
+import scientifik.kmath.structures.asBuffer
+import kotlin.math.PI
+import kotlin.test.Test
+import kotlin.test.assertEquals
+import kotlin.test.assertTrue
+
+class AutoDiffTest {
+    @Test
+    fun testPlusX2() {
+        val x = Variable(3) // diff w.r.t this x at 3
+        val y = deriv { x + x }
+        assertEquals(6.0, y.value) //    y  = x + x = 6
+        assertEquals(2.0, y.deriv(x)) // dy/dx = 2
+    }
+
+    @Test
+    fun testPlus() {
+        // two variables
+        val x = Variable(2)
+        val y = Variable(3)
+        val z = deriv { x + y }
+        assertEquals(5.0, z.value) //    z  = x + y = 5
+        assertEquals(1.0, z.deriv(x)) // dz/dx = 1
+        assertEquals(1.0, z.deriv(y)) // dz/dy = 1
+    }
+
+    @Test
+    fun testMinus() {
+        // two variables
+        val x = Variable(7)
+        val y = Variable(3)
+        val z = deriv { x - y }
+        assertEquals(4.0, z.value)  //    z  = x - y = 4
+        assertEquals(1.0, z.deriv(x))  // dz/dx = 1
+        assertEquals(-1.0, z.deriv(y)) // dz/dy = -1
+    }
+
+    @Test
+    fun testMulX2() {
+        val x = Variable(3) // diff w.r.t this x at 3
+        val y = deriv { x * x }
+        assertEquals(9.0, y.value) //    y  = x * x = 9
+        assertEquals(6.0, y.deriv(x)) // dy/dx = 2 * x = 7
+    }
+
+    @Test
+    fun testSqr() {
+        val x = Variable(3)
+        val y = deriv { sqr(x) }
+        assertEquals(9.0, y.value) //    y  = x ^ 2 = 9
+        assertEquals(6.0, y.deriv(x)) // dy/dx = 2 * x = 7
+    }
+
+    @Test
+    fun testSqrSqr() {
+        val x = Variable(2)
+        val y = deriv { sqr(sqr(x)) }
+        assertEquals(16.0, y.value) //     y = x ^ 4   = 16
+        assertEquals(32.0, y.deriv(x)) // dy/dx = 4 * x^3 = 32
+    }
+
+    @Test
+    fun testX3() {
+        val x = Variable(2) // diff w.r.t this x at 2
+        val y = deriv { x * x * x }
+        assertEquals(8.0, y.value)  //    y  = x * x * x = 8
+        assertEquals(12.0, y.deriv(x)) // dy/dx = 3 * x * x = 12
+    }
+
+    @Test
+    fun testDiv() {
+        val x = Variable(5)
+        val y = Variable(2)
+        val z = deriv { x / y }
+        assertEquals(2.5, z.value)   //     z =  x / y   = 2.5
+        assertEquals(0.5, z.deriv(x))   // dz/dx =  1 / y   = 0.5
+        assertEquals(-1.25, z.deriv(y)) // dz/dy = -x / y^2 = -1.25
+    }
+
+    @Test
+    fun testPow3() {
+        val x = Variable(2) // diff w.r.t this x at 2
+        val y = deriv { pow(x, 3) }
+        assertEquals(8.0, y.value)  //    y  = x ^ 3     = 8
+        assertEquals(12.0, y.deriv(x)) // dy/dx = 3 * x ^ 2 = 12
+    }
+
+    @Test
+    fun testPowFull() {
+        val x = Variable(2)
+        val y = Variable(3)
+        val z = deriv { pow(x, y) }
+        assertApprox(8.0, z.value)           //     z = x ^ y = 8
+        assertApprox(12.0, z.deriv(x))          // dz/dx = y * x ^ (y - 1) = 12
+        assertApprox(8.0 * kotlin.math.ln(2.0), z.deriv(y)) // dz/dy = x ^ y * ln(x)
+    }
+
+    @Test
+    fun testFromPaper() {
+        val x = Variable(3)
+        val y = deriv { 2 * x + x * x * x }
+        assertEquals(33.0, y.value)  //     y = 2 * x + x * x * x = 33
+        assertEquals(29.0, y.deriv(x))  // dy/dx = 2 + 3 * x * x = 29
+    }
+
+    @Test
+    fun testInnerVariable() {
+        val x = Variable(1)
+        val y = deriv {
+            Variable(1) * x
+        }
+        assertEquals(1.0, y.value)          //     y = x ^ n = 1
+        assertEquals(1.0, y.deriv(x)) // dy/dx = n * x ^ (n - 1) = n - 1
+    }
+
+    @Test
+    fun testLongChain() {
+        val n = 10_000
+        val x = Variable(1)
+        val y = deriv {
+            var res = Variable(1)
+            for (i in 1..n) res *= x
+            res
+        }
+        assertEquals(1.0, y.value)          //     y = x ^ n = 1
+        assertEquals(n.toDouble(), y.deriv(x)) // dy/dx = n * x ^ (n - 1) = n - 1
+    }
+
+    @Test
+    fun testExample() {
+        val x = Variable(2)
+        val y = deriv { sqr(x) + 5 * x + 3 }
+        assertEquals(17.0, y.value) // the value of result (y)
+        assertEquals(9.0, y.deriv(x))  // dy/dx
+    }
+
+    @Test
+    fun testSqrt() {
+        val x = Variable(16)
+        val y = deriv { sqrt(x) }
+        assertEquals(4.0, y.value)     //     y = x ^ 1/2 = 4
+        assertEquals(1.0 / 8, y.deriv(x)) // dy/dx = 1/2 / x ^ 1/4 = 1/8
+    }
+
+    @Test
+    fun testSin() {
+        val x = Variable(PI / 6)
+        val y = deriv { sin(x) }
+        assertApprox(0.5, y.value)           //    y = sin(PI/6) = 0.5
+        assertApprox(kotlin.math.sqrt(3.0) / 2, y.deriv(x)) // dy/dx = cos(PI/6) = sqrt(3)/2
+    }
+
+    @Test
+    fun testCos() {
+        val x = Variable(PI / 6)
+        val y = deriv { cos(x) }
+        assertApprox(kotlin.math.sqrt(3.0) / 2, y.value) //     y =  cos(PI/6) = sqrt(3)/2
+        assertApprox(-0.5, y.deriv(x))          // dy/dx = -sin(PI/6) = -0.5
+    }
+
+    @Test
+    fun testDivGrad() {
+        val x = Variable(1.0)
+        val y = Variable(2.0)
+        val res = deriv { x * x + y * y }
+        assertEquals(6.0, res.div())
+        assertTrue(res.grad(x, y).contentEquals(doubleArrayOf(2.0, 4.0).asBuffer()))
+    }
+
+    private fun assertApprox(a: Double, b: Double) {
+        if ((a - b) > 1e-10) assertEquals(a, b)
+    }
+
+}

kmath-coroutines/build.gradle.kts

@@ -1,46 +1,26 @@
 plugins {
-    kotlin("multiplatform")
+    `multiplatform-config`
+    id("kotlinx-atomicfu") version Versions.atomicfuVersion
 }
 
-val coroutinesVersion: String by rootProject.extra
+kotlin.sourceSets {
-
+    commonMain {
-kotlin {
-    jvm()
-    js()
-
-    sourceSets {
-        val commonMain by getting {
         dependencies {
             api(project(":kmath-core"))
-                api("org.jetbrains.kotlinx:kotlinx-coroutines-core-common:$coroutinesVersion")
+            api("org.jetbrains.kotlinx:kotlinx-coroutines-core-common:${Versions.coroutinesVersion}")
+            compileOnly("org.jetbrains.kotlinx:atomicfu-common:${Versions.atomicfuVersion}")
         }
     }
-        val commonTest by getting {
+    jvmMain {
         dependencies {
-                implementation(kotlin("test-common"))
+            api("org.jetbrains.kotlinx:kotlinx-coroutines-core:${Versions.coroutinesVersion}")
-                implementation(kotlin("test-annotations-common"))
+            compileOnly("org.jetbrains.kotlinx:atomicfu:${Versions.atomicfuVersion}")
         }
     }
-        val jvmMain by getting {
+    jsMain {
         dependencies {
-                api("org.jetbrains.kotlinx:kotlinx-coroutines-core:$coroutinesVersion")
+            api("org.jetbrains.kotlinx:kotlinx-coroutines-core-js:${Versions.coroutinesVersion}")
-            }
+            compileOnly("org.jetbrains.kotlinx:atomicfu-js:${Versions.atomicfuVersion}")
-        }
-        val jvmTest by getting {
-            dependencies {
-                implementation(kotlin("test"))
-                implementation(kotlin("test-junit"))
-            }
-        }
-        val jsMain by getting {
-            dependencies {
-                api("org.jetbrains.kotlinx:kotlinx-coroutines-core-js:$coroutinesVersion")
-            }
-        }
-        val jsTest by getting {
-            dependencies {
-                implementation(kotlin("test-js"))
-            }
         }
     }
 }

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

@@ -0,0 +1,102 @@
+package scientifik.kmath
+
+import kotlinx.coroutines.*
+import kotlinx.coroutines.channels.produce
+import kotlinx.coroutines.flow.*
+
+val Dispatchers.Math: CoroutineDispatcher get() = Dispatchers.Default
+
+/**
+ * An imitator of [Deferred] which holds a suspended function block and dispatcher
+ */
+internal class LazyDeferred<T>(val dispatcher: CoroutineDispatcher, val block: suspend CoroutineScope.() -> T) {
+    private var deferred: Deferred<T>? = null
+
+    internal fun start(scope: CoroutineScope) {
+        if (deferred == null) {
+            deferred = scope.async(dispatcher, block = block)
+        }
+    }
+
+    suspend fun await(): T = deferred?.await() ?: error("Coroutine not started")
+}
+
+@FlowPreview
+class AsyncFlow<T> internal constructor(internal val deferredFlow: Flow<LazyDeferred<T>>) : Flow<T> {
+    override suspend fun collect(collector: FlowCollector<T>) {
+        deferredFlow.collect {
+            collector.emit((it.await()))
+        }
+    }
+}
+
+@FlowPreview
+fun <T, R> Flow<T>.async(
+    dispatcher: CoroutineDispatcher = Dispatchers.Default,
+    block: suspend CoroutineScope.(T) -> R
+): AsyncFlow<R> {
+    val flow = map {
+        LazyDeferred(dispatcher) { block(it) }
+    }
+    return AsyncFlow(flow)
+}
+
+@FlowPreview
+fun <T, R> AsyncFlow<T>.map(action: (T) -> R) = AsyncFlow(deferredFlow.map { input ->
+    //TODO add function composition
+    LazyDeferred(input.dispatcher) {
+        input.start(this)
+        action(input.await())
+    }
+})
+
+@ExperimentalCoroutinesApi
+@FlowPreview
+suspend fun <T> AsyncFlow<T>.collect(concurrency: Int, collector: FlowCollector<T>) {
+    require(concurrency >= 1) { "Buffer size should be more than 1, but was $concurrency" }
+    coroutineScope {
+        //Starting up to N deferred coroutines ahead of time
+        val channel = produce(capacity = concurrency - 1) {
+            deferredFlow.collect { value ->
+                value.start(this@coroutineScope)
+                send(value)
+            }
+        }
+
+        (channel as Job).invokeOnCompletion {
+            if (it is CancellationException && it.cause == null) cancel()
+        }
+
+        for (element in channel) {
+            collector.emit(element.await())
+        }
+
+        val producer = channel as Job
+        if (producer.isCancelled) {
+            producer.join()
+            //throw producer.getCancellationException()
+        }
+    }
+}
+
+@ExperimentalCoroutinesApi
+@FlowPreview
+suspend fun <T> AsyncFlow<T>.collect(concurrency: Int, action: suspend (value: T) -> Unit): Unit {
+    collect(concurrency, object : FlowCollector<T> {
+        override suspend fun emit(value: T) = action(value)
+    })
+}
+
+@FlowPreview
+fun <T, R> Flow<T>.map(
+    dispatcher: CoroutineDispatcher,
+    concurrencyLevel: Int = 16,
+    bufferSize: Int = concurrencyLevel,
+    transform: suspend (T) -> R
+): Flow<R> {
+    return flatMapMerge(concurrencyLevel, bufferSize) { value ->
+        flow { emit(transform(value)) }
+    }.flowOn(dispatcher)
+}
+
+

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

@@ -14,14 +14,10 @@
  *  limitations under the License.
  */
 
-package scientifik.kmath.sequential
+package scientifik.kmath.chains
 
 import kotlinx.atomicfu.atomic
-import kotlinx.coroutines.CoroutineScope
+import kotlinx.coroutines.FlowPreview
-import kotlinx.coroutines.ExperimentalCoroutinesApi
-import kotlinx.coroutines.channels.ReceiveChannel
-import kotlinx.coroutines.channels.produce
-import kotlinx.coroutines.isActive
 
 
 /**
@@ -30,7 +26,7 @@ import kotlinx.coroutines.isActive
  */
 interface Chain<out R> {
     /**
-     * Last value of the chain. Returns null if [next] was not called
+     * Last cached value of the chain. Returns null if [next] was not called
      */
     val value: R?
 
@@ -47,10 +43,11 @@ interface Chain<out R> {
 }
 
 /**
- * Chain as a coroutine receive channel
+ * Chain as a coroutine flow. The flow emit affects chain state and vice versa
  */
-@ExperimentalCoroutinesApi
+@FlowPreview
-fun <R> Chain<R>.asChannel(scope: CoroutineScope): ReceiveChannel<R> = scope.produce { while (isActive) send(next()) }
+val <R> Chain<R>.flow
+    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()

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

@@ -0,0 +1,80 @@
+package scientifik.kmath.streaming
+
+import kotlinx.coroutines.FlowPreview
+import kotlinx.coroutines.flow.*
+import scientifik.kmath.structures.Buffer
+import scientifik.kmath.structures.BufferFactory
+import scientifik.kmath.structures.DoubleBuffer
+
+/**
+ * Create a [Flow] from buffer
+ */
+@FlowPreview
+fun <T> Buffer<T>.asFlow() = iterator().asFlow()
+
+/**
+ * Flat map a [Flow] of [Buffer] into continuous [Flow] of elements
+ */
+@FlowPreview
+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 {
+    require(bufferSize > 0) { "Resulting chunk size must be more than zero" }
+    val list = ArrayList<T>(bufferSize)
+    var counter = 0
+
+    this@chunked.collect { element ->
+        list.add(element)
+        counter++
+        if (counter == bufferSize) {
+            val buffer = bufferFactory(bufferSize) { list[it] }
+            emit(buffer)
+            list.clear()
+            counter = 0
+        }
+    }
+    if (counter > 0) {
+        emit(bufferFactory(counter) { list[it] })
+    }
+}
+
+/**
+ * Specialized flow chunker for real buffer
+ */
+@FlowPreview
+fun Flow<Double>.chunked(bufferSize: Int) = flow {
+    require(bufferSize > 0) { "Resulting chunk size must be more than zero" }
+    val array = DoubleArray(bufferSize)
+    var counter = 0
+
+    this@chunked.collect { element ->
+        array[counter] = element
+        counter++
+        if (counter == bufferSize) {
+            val buffer = DoubleBuffer(array)
+            emit(buffer)
+            counter = 0
+        }
+    }
+    if (counter > 0) {
+        emit(DoubleBuffer(counter) { array[it] })
+    }
+}
+
+/**
+ * Map a flow to a moving window buffer. The window step is one.
+ * In order to get different steps, one could use skip operation.
+ */
+@FlowPreview
+fun <T> Flow<T>.windowed(window: Int): Flow<Buffer<T>> = flow {
+    require(window > 1) { "Window size must be more than one" }
+    val ringBuffer = RingBuffer.boxing<T>(window)
+    this@windowed.collect { element ->
+        ringBuffer.push(element)
+        emit(ringBuffer.snapshot())
+    }
+}

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

@@ -1,16 +1,18 @@
-package scientifik.kmath.sequential
+package scientifik.kmath.streaming
 
 import kotlinx.coroutines.sync.Mutex
 import kotlinx.coroutines.sync.withLock
 import scientifik.kmath.structures.Buffer
 import scientifik.kmath.structures.MutableBuffer
 import scientifik.kmath.structures.VirtualBuffer
+import kotlin.reflect.KClass
 
 /**
  * Thread-safe ring buffer
  */
+@Suppress("UNCHECKED_CAST")
 internal class RingBuffer<T>(
-    private val buffer: MutableBuffer<T>,
+    private val buffer: MutableBuffer<T?>,
     private var startIndex: Int = 0,
     size: Int = 0
 ) : Buffer<T> {
@@ -23,7 +25,7 @@ internal class RingBuffer<T>(
     override fun get(index: Int): T {
         require(index >= 0) { "Index must be positive" }
         require(index < size) { "Index $index is out of circular buffer size $size" }
-        return buffer[startIndex.forward(index)]
+        return buffer[startIndex.forward(index)] as T
     }
 
     fun isFull() = size == buffer.size
@@ -40,7 +42,7 @@ internal class RingBuffer<T>(
             if (count == 0) {
                 done()
             } else {
-                setNext(copy[index])
+                setNext(copy[index] as T)
                 index = index.forward(1)
                 count--
             }
@@ -53,7 +55,9 @@ internal class RingBuffer<T>(
     suspend fun snapshot(): Buffer<T> {
         mutex.withLock {
             val copy = buffer.copy()
-            return VirtualBuffer(size) { i -> copy[startIndex.forward(i)] }
+            return VirtualBuffer(size) { i ->
+                copy[startIndex.forward(i)] as T
+            }
         }
     }
 
@@ -74,14 +78,14 @@ internal class RingBuffer<T>(
 
     companion object {
         inline fun <reified T : Any> build(size: Int, empty: T): RingBuffer<T> {
-            val buffer = MutableBuffer.auto(size) { empty }
+            val buffer = MutableBuffer.auto(size) { empty } as MutableBuffer<T?>
             return RingBuffer(buffer)
         }
 
         /**
          * Slow yet universal buffer
          */
-        fun <T> boxing(size: Int): RingBuffer<T?> {
+        fun <T> boxing(size: Int): RingBuffer<T> {
             val buffer: MutableBuffer<T?> = MutableBuffer.boxing(size) { null }
             return RingBuffer(buffer)
         }

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

@@ -1,9 +0,0 @@
-package scientifik.kmath.structures
-
-import kotlinx.coroutines.CoroutineDispatcher
-import kotlinx.coroutines.CoroutineScope
-import kotlinx.coroutines.Dispatchers
-import kotlin.coroutines.CoroutineContext
-import kotlin.coroutines.EmptyCoroutineContext
-
-val Dispatchers.Math: CoroutineDispatcher get() = Dispatchers.Default

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

@@ -1,4 +1,4 @@
-package scientifik.kmath.sequential
+package scientifik.kmath.chains
 
 import kotlinx.coroutines.runBlocking
 import kotlin.sequences.Sequence

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

@@ -1,6 +1,7 @@
 package scientifik.kmath.structures
 
 import kotlinx.coroutines.*
+import scientifik.kmath.Math
 
 class LazyNDStructure<T>(
     val scope: CoroutineScope,

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

@@ -0,0 +1,48 @@
+package scientifik.kmath.streaming
+
+import kotlinx.coroutines.*
+import kotlinx.coroutines.flow.asFlow
+import kotlinx.coroutines.flow.collect
+import org.junit.Test
+import scientifik.kmath.async
+import scientifik.kmath.collect
+import scientifik.kmath.map
+import java.util.concurrent.Executors
+
+
+@ExperimentalCoroutinesApi
+@InternalCoroutinesApi
+@FlowPreview
+class BufferFlowTest {
+
+    val dispatcher = Executors.newFixedThreadPool(4).asCoroutineDispatcher()
+
+    @Test(timeout = 2000)
+    fun map() {
+        runBlocking {
+            (1..20).asFlow().map( dispatcher) {
+                //println("Started $it on ${Thread.currentThread().name}")
+                @Suppress("BlockingMethodInNonBlockingContext")
+                Thread.sleep(200)
+                it
+            }.collect {
+                println("Completed $it on ${Thread.currentThread().name}")
+            }
+        }
+    }
+
+    @Test(timeout = 2000)
+    fun async() {
+        runBlocking {
+            (1..20).asFlow().async(dispatcher) {
+                //println("Started $it on ${Thread.currentThread().name}")
+                @Suppress("BlockingMethodInNonBlockingContext")
+                Thread.sleep(200)
+                it
+            }.collect(4) {
+                println("Completed $it on ${Thread.currentThread().name}")
+            }
+        }
+    }
+
+}

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

@@ -0,0 +1,38 @@
+package scientifik.kmath.streaming
+
+import kotlinx.coroutines.flow.*
+import kotlinx.coroutines.runBlocking
+import org.junit.Test
+import scientifik.kmath.structures.asSequence
+import kotlin.test.assertEquals
+
+class RingBufferTest {
+    @Test
+    fun push() {
+        val buffer = RingBuffer.build(20, Double.NaN)
+        runBlocking {
+            for (i in 1..30) {
+                buffer.push(i.toDouble())
+            }
+            assertEquals(410.0, buffer.asSequence().sum())
+        }
+    }
+
+    @Test
+    fun windowed(){
+        val flow = flow{
+            var i = 0
+            while(true){
+                emit(i++)
+            }
+        }
+        val windowed = flow.windowed(10)
+        runBlocking {
+            val first = windowed.take(1).single()
+            val res = windowed.take(15).map { it -> it.asSequence().average() }.toList()
+            assertEquals(0.0, res[0])
+            assertEquals(4.5, res[9])
+            assertEquals(9.5, res[14])
+        }
+    }
+}

kmath-histograms/build.gradle.kts

@@ -1,34 +1,10 @@
 plugins {
-    kotlin("multiplatform")
+    `multiplatform-config`
 }
 
-kotlin {
+// Just an example how we can collapse nested DSL for simple declarations
-    jvm()
+kotlin.sourceSets.commonMain {
-    js()
-
-    sourceSets {
-
-        val commonMain by getting {
     dependencies {
         api(project(":kmath-core"))
     }
-        }
-        val commonTest by getting {
-            dependencies {
-                implementation(kotlin("test-common"))
-                implementation(kotlin("test-annotations-common"))
-            }
-        }
-        val jvmTest by getting {
-            dependencies {
-                implementation(kotlin("test"))
-                implementation(kotlin("test-junit"))
-            }
-        }
-        val jsTest by getting {
-            dependencies {
-                implementation(kotlin("test-js"))
-            }
-        }
-    }
 }

kmath-koma/build.gradle.kts

@@ -1,57 +1,31 @@
 plugins {
-    kotlin("multiplatform")
+    `multiplatform-config`
 }
 
 repositories {
     maven("http://dl.bintray.com/kyonifer/maven")
 }
 
-kotlin {
+kotlin.sourceSets {
-    jvm {
+    commonMain {
-        compilations.all {
-            kotlinOptions {
-                jvmTarget = "1.8"
-                freeCompilerArgs += "-progressive"
-            }
-        }
-    }
-    js()
-
-    sourceSets {
-
-        val commonMain by getting {
         dependencies {
             api(project(":kmath-core"))
             api("com.kyonifer:koma-core-api-common:0.12")
         }
     }
-        val commonTest by getting {
+    jvmMain {
-            dependencies {
-                implementation(kotlin("test-common"))
-                implementation(kotlin("test-annotations-common"))
-            }
-        }
-        val jvmMain by getting {
         dependencies {
             api("com.kyonifer:koma-core-api-jvm:0.12")
         }
     }
-        val jvmTest by getting {
+    jvmTest {
         dependencies {
-                implementation(kotlin("test"))
-                implementation(kotlin("test-junit"))
             implementation("com.kyonifer:koma-core-ejml:0.12")
         }
     }
-        val jsMain by getting {
+    jsMain {
         dependencies {
             api("com.kyonifer:koma-core-api-js:0.12")
         }
     }
-        val jsTest by getting {
-            dependencies {
-                implementation(kotlin("test-js"))
-            }
-        }
-    }
 }

kmath-koma/src/commonMain/kotlin/scientifik.kmath.linear/KomaMatrix.kt

@@ -2,9 +2,14 @@ package scientifik.kmath.linear
 
 import koma.extensions.fill
 import koma.matrix.MatrixFactory
+import scientifik.kmath.operations.Space
+import scientifik.kmath.structures.Matrix
 
-class KomaMatrixContext<T : Any>(val factory: MatrixFactory<koma.matrix.Matrix<T>>) : MatrixContext<T>,
+class KomaMatrixContext<T : Any>(
-    LinearSolver<T> {
+    private val factory: MatrixFactory<koma.matrix.Matrix<T>>,
+    private val space: Space<T>
+) :
+    MatrixContext<T> {
 
     override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T) =
         KomaMatrix(factory.zeros(rows, columns).fill(initializer))
@@ -31,41 +36,52 @@ class KomaMatrixContext<T : Any>(val factory: MatrixFactory<koma.matrix.Matrix<T
     override fun Matrix<T>.unaryMinus() =
         KomaMatrix(this.toKoma().origin.unaryMinus())
 
-    override fun Matrix<T>.plus(b: Matrix<T>) =
+    override fun add(a: Matrix<T>, b: Matrix<T>) =
-        KomaMatrix(this.toKoma().origin + b.toKoma().origin)
+        KomaMatrix(a.toKoma().origin + b.toKoma().origin)
 
     override fun Matrix<T>.minus(b: Matrix<T>) =
         KomaMatrix(this.toKoma().origin - b.toKoma().origin)
 
+    override fun multiply(a: Matrix<T>, k: Number): Matrix<T> =
+        produce(a.rowNum, a.colNum) { i, j -> space.run { a[i, j] * k } }
+
     override fun Matrix<T>.times(value: T) =
         KomaMatrix(this.toKoma().origin * value)
 
+    companion object {
 
-    override fun solve(a: Matrix<T>, b: Matrix<T>) =
+    }
-        KomaMatrix(a.toKoma().origin.solve(b.toKoma().origin))
 
-    override fun inverse(a: Matrix<T>) =
-        KomaMatrix(a.toKoma().origin.inv())
 }
 
-class KomaMatrix<T : Any>(val origin: koma.matrix.Matrix<T>, features: Set<MatrixFeature>? = null) :
+fun <T : Any> KomaMatrixContext<T>.solve(a: Matrix<T>, b: Matrix<T>) =
-    Matrix<T> {
+    KomaMatrix(a.toKoma().origin.solve(b.toKoma().origin))
+
+fun <T : Any> KomaMatrixContext<T>.solve(a: Matrix<T>, b: Point<T>) =
+    KomaVector(a.toKoma().origin.solve(b.toKoma().origin))
+
+fun <T : Any> KomaMatrixContext<T>.inverse(a: Matrix<T>) =
+    KomaMatrix(a.toKoma().origin.inv())
+
+class KomaMatrix<T : Any>(val origin: koma.matrix.Matrix<T>, features: Set<MatrixFeature>? = null) : FeaturedMatrix<T> {
     override val rowNum: Int get() = origin.numRows()
     override val colNum: Int get() = origin.numCols()
 
+    override val shape: IntArray get() = intArrayOf(origin.numRows(), origin.numCols())
+
     override val features: Set<MatrixFeature> = features ?: setOf(
         object : DeterminantFeature<T> {
             override val determinant: T get() = origin.det()
         },
         object : LUPDecompositionFeature<T> {
             private val lup by lazy { origin.LU() }
-            override val l: Matrix<T> get() = KomaMatrix(lup.second)
+            override val l: FeaturedMatrix<T> get() = KomaMatrix(lup.second)
-            override val u: Matrix<T> get() = KomaMatrix(lup.third)
+            override val u: FeaturedMatrix<T> get() = KomaMatrix(lup.third)
-            override val p: Matrix<T> get() = KomaMatrix(lup.first)
+            override val p: FeaturedMatrix<T> get() = KomaMatrix(lup.first)
         }
     )
 
-    override fun suggestFeature(vararg features: MatrixFeature): Matrix<T> =
+    override fun suggestFeature(vararg features: MatrixFeature): FeaturedMatrix<T> =
         KomaMatrix(this.origin, this.features + features)
 
     override fun get(i: Int, j: Int): T = origin.getGeneric(i, j)

kmath-memory/build.gradle.kts

@@ -1,50 +1,3 @@
 plugins {
-    kotlin("multiplatform")
+    `multiplatform-config`
-}
-
-val ioVersion: String by rootProject.extra
-
-
-kotlin {
-    jvm()
-    js()
-
-    sourceSets {
-        val commonMain by getting {
-            dependencies {
-                api(kotlin("stdlib"))
-            }
-        }
-        val commonTest by getting {
-            dependencies {
-                implementation(kotlin("test-common"))
-                implementation(kotlin("test-annotations-common"))
-            }
-        }
-        val jvmMain by getting {
-            dependencies {
-                api(kotlin("stdlib-jdk8"))
-            }
-        }
-        val jvmTest by getting {
-            dependencies {
-                implementation(kotlin("test"))
-                implementation(kotlin("test-junit"))
-            }
-        }
-        val jsMain by getting {
-            dependencies {
-                api(kotlin("stdlib-js"))
-            }
-        }
-        val jsTest by getting {
-            dependencies {
-                implementation(kotlin("test-js"))
-            }
-        }
-//        mingwMain {
-//        }
-//        mingwTest {
-//        }
-    }
 }

kmath-prob/build.gradle.kts

@@ -0,0 +1,29 @@
+plugins {
+    `multiplatform-config`
+    id("kotlinx-atomicfu") version Versions.atomicfuVersion
+}
+
+kotlin.sourceSets {
+    commonMain {
+        dependencies {
+            api(project(":kmath-core"))
+            api(project(":kmath-coroutines"))
+            compileOnly("org.jetbrains.kotlinx:atomicfu-common:${Versions.atomicfuVersion}")
+        }
+    }
+    jvmMain {
+        dependencies {
+            compileOnly("org.jetbrains.kotlinx:atomicfu:${Versions.atomicfuVersion}")
+        }
+    }
+    jsMain {
+        dependencies {
+            compileOnly("org.jetbrains.kotlinx:atomicfu-js:${Versions.atomicfuVersion}")
+        }
+    }
+
+}
+
+atomicfu {
+    variant = "VH"
+}

kmath-sequential/build.gradle.kts

@@ -1,53 +0,0 @@
-plugins {
-    kotlin("multiplatform")
-    id("kotlinx-atomicfu") version "0.12.1"
-}
-
-val atomicfuVersion: String by rootProject.extra
-
-kotlin {
-    jvm ()
-    //js()
-
-    sourceSets {
-        val commonMain by getting {
-            dependencies {
-                api(project(":kmath-core"))
-                api(project(":kmath-coroutines"))
-                compileOnly("org.jetbrains.kotlinx:atomicfu-common:$atomicfuVersion")
-            }
-        }
-        val commonTest by getting {
-            dependencies {
-                implementation(kotlin("test-common"))
-                implementation(kotlin("test-annotations-common"))
-            }
-        }
-        val jvmMain by getting {
-            dependencies {
-                compileOnly("org.jetbrains.kotlinx:atomicfu:$atomicfuVersion")
-            }
-        }
-        val jvmTest by getting {
-            dependencies {
-                implementation(kotlin("test"))
-                implementation(kotlin("test-junit"))
-            }
-        }
-//        val jsMain by getting {
-//            dependencies {
-//                compileOnly("org.jetbrains.kotlinx:atomicfu-js:$atomicfuVersion")
-//            }
-//        }
-//        val jsTest by getting {
-//            dependencies {
-//                implementation(kotlin("test-js"))
-//            }
-//        }
-
-    }
-}
-
-atomicfu {
-    variant = "VH"
-}

kmath-sequential/src/commonMain/kotlin/scientifik/kmath/sequential/BufferStreaming.kt

@@ -1,82 +0,0 @@
-package scientifik.kmath.sequential
-
-import kotlinx.coroutines.CoroutineScope
-import kotlinx.coroutines.ExperimentalCoroutinesApi
-import kotlinx.coroutines.channels.Channel
-import kotlinx.coroutines.channels.produce
-import kotlinx.coroutines.isActive
-import kotlinx.coroutines.sync.Mutex
-import kotlinx.coroutines.sync.withLock
-import scientifik.kmath.structures.Buffer
-import scientifik.kmath.structures.BufferFactory
-
-/**
- * A processor that collects incoming elements into fixed size buffers
- */
-@ExperimentalCoroutinesApi
-class JoinProcessor<T>(
-    scope: CoroutineScope,
-    bufferSize: Int,
-    bufferFactory: BufferFactory<T> = Buffer.Companion::boxing
-) : AbstractProcessor<T, Buffer<T>>(scope) {
-
-    private val input = Channel<T>(bufferSize)
-
-    private val output = produce(coroutineContext) {
-        val list = ArrayList<T>(bufferSize)
-        while (isActive) {
-            list.clear()
-            repeat(bufferSize) {
-                list.add(input.receive())
-            }
-            val buffer = bufferFactory(bufferSize) { list[it] }
-            send(buffer)
-        }
-    }
-
-    override suspend fun receive(): Buffer<T> = output.receive()
-
-    override suspend fun send(value: T) {
-        input.send(value)
-    }
-}
-
-/**
- * A processor that splits incoming buffers into individual elements
- */
-class SplitProcessor<T>(scope: CoroutineScope) : AbstractProcessor<Buffer<T>, T>(scope) {
-
-    private val input = Channel<Buffer<T>>()
-
-    private val mutex = Mutex()
-
-    private var currentBuffer: Buffer<T>? = null
-
-    private var pos = 0
-
-
-    override suspend fun receive(): T {
-        mutex.withLock {
-            while (currentBuffer == null || pos == currentBuffer!!.size) {
-                currentBuffer = input.receive()
-                pos = 0
-            }
-            return currentBuffer!![pos].also { pos++ }
-        }
-    }
-
-    override suspend fun send(value: Buffer<T>) {
-        input.send(value)
-    }
-}
-
-@ExperimentalCoroutinesApi
-fun <T> Producer<T>.chunked(chunkSize: Int, bufferFactory: BufferFactory<T>) =
-    JoinProcessor<T>(this, chunkSize, bufferFactory).also { connect(it) }
-
-@ExperimentalCoroutinesApi
-inline fun <reified T : Any> Producer<T>.chunked(chunkSize: Int) =
-    JoinProcessor<T>(this, chunkSize, Buffer.Companion::auto).also { connect(it) }
-
-
-

kmath-sequential/src/commonMain/kotlin/scientifik/kmath/sequential/Streaming.kt

@@ -1,275 +0,0 @@
-package scientifik.kmath.sequential
-
-import kotlinx.coroutines.CoroutineScope
-import kotlinx.coroutines.GlobalScope
-import kotlinx.coroutines.channels.*
-import kotlinx.coroutines.isActive
-import kotlinx.coroutines.launch
-import kotlinx.coroutines.sync.Mutex
-import kotlinx.coroutines.sync.withLock
-import scientifik.kmath.structures.Buffer
-import kotlin.coroutines.CoroutineContext
-
-/**
- * Initial chain block. Could produce an element sequence and be connected to single [Consumer]
- *
- * The general rule is that channel is created on first call. Also each element is responsible for its connection so
- * while the connections are symmetric, the scope, used for making the connection is responsible for cancelation.
- *
- * Also connections are not reversible. Once connected block stays faithful until it finishes processing.
- * Manually putting elements to connected block could lead to undetermined behavior and must be avoided.
- */
-interface Producer<T> : CoroutineScope {
-    fun connect(consumer: Consumer<T>)
-
-    suspend fun receive(): T
-
-    val consumer: Consumer<T>?
-
-    val outputIsConnected: Boolean get() = consumer != null
-
-    //fun close()
-}
-
-/**
- * Terminal chain block. Could consume an element sequence and be connected to signle [Producer]
- */
-interface Consumer<T> : CoroutineScope {
-    fun connect(producer: Producer<T>)
-
-    suspend fun send(value: T)
-
-    val producer: Producer<T>?
-
-    val inputIsConnected: Boolean get() = producer != null
-
-    //fun close()
-}
-
-interface Processor<T, R> : Consumer<T>, Producer<R>
-
-abstract class AbstractProducer<T>(scope: CoroutineScope) : Producer<T> {
-    override val coroutineContext: CoroutineContext = scope.coroutineContext
-
-    override var consumer: Consumer<T>? = null
-        protected set
-
-    override fun connect(consumer: Consumer<T>) {
-        //Ignore if already connected to specific consumer
-        if (consumer != this.consumer) {
-            if (outputIsConnected) error("The output slot of producer is occupied")
-            if (consumer.inputIsConnected) error("The input slot of consumer is occupied")
-            this.consumer = consumer
-            if (consumer.producer != null) {
-                //No need to save the job, it will be canceled on scope cancel
-                connectOutput(consumer)
-                // connect back, consumer is already set so no circular reference
-                consumer.connect(this)
-            } else error("Unreachable statement")
-        }
-    }
-
-    protected open fun connectOutput(consumer: Consumer<T>) {
-        launch {
-            while (this.isActive) {
-                consumer.send(receive())
-            }
-        }
-    }
-}
-
-abstract class AbstractConsumer<T>(scope: CoroutineScope) : Consumer<T> {
-    override val coroutineContext: CoroutineContext = scope.coroutineContext
-
-    override var producer: Producer<T>? = null
-        protected set
-
-    override fun connect(producer: Producer<T>) {
-        //Ignore if already connected to specific consumer
-        if (producer != this.producer) {
-            if (inputIsConnected) error("The input slot of consumer is occupied")
-            if (producer.outputIsConnected) error("The input slot of producer is occupied")
-            this.producer = producer
-            //No need to save the job, it will be canceled on scope cancel
-            if (producer.consumer != null) {
-                connectInput(producer)
-                // connect back
-                producer.connect(this)
-            } else error("Unreachable statement")
-        }
-    }
-
-    protected open fun connectInput(producer: Producer<T>) {
-        launch {
-            while (isActive) {
-                send(producer.receive())
-            }
-        }
-    }
-}
-
-abstract class AbstractProcessor<T, R>(scope: CoroutineScope) : Processor<T, R>, AbstractProducer<R>(scope) {
-
-    override var producer: Producer<T>? = null
-        protected set
-
-    override fun connect(producer: Producer<T>) {
-        //Ignore if already connected to specific consumer
-        if (producer != this.producer) {
-            if (inputIsConnected) error("The input slot of consumer is occupied")
-            if (producer.outputIsConnected) error("The input slot of producer is occupied")
-            this.producer = producer
-            //No need to save the job, it will be canceled on scope cancel
-            if (producer.consumer != null) {
-                connectInput(producer)
-                // connect back
-                producer.connect(this)
-            } else error("Unreachable statement")
-        }
-    }
-
-    protected open fun connectInput(producer: Producer<T>) {
-        launch {
-            while (isActive) {
-                send(producer.receive())
-            }
-        }
-    }
-}
-
-/**
- * A simple [produce]-based producer
- */
-class GenericProducer<T>(
-    scope: CoroutineScope,
-    capacity: Int = Channel.UNLIMITED,
-    block: suspend ProducerScope<T>.() -> Unit
-) : AbstractProducer<T>(scope) {
-
-    private val channel: ReceiveChannel<T> by lazy { produce(capacity = capacity, block = block) }
-
-    override suspend fun receive(): T = channel.receive()
-}
-
-/**
- * A simple pipeline [Processor] block
- */
-class PipeProcessor<T, R>(
-    scope: CoroutineScope,
-    capacity: Int = Channel.RENDEZVOUS,
-    process: suspend (T) -> R
-) : AbstractProcessor<T, R>(scope) {
-
-    private val input = Channel<T>(capacity)
-    private val output: ReceiveChannel<R> = input.map(coroutineContext, process)
-
-    override suspend fun receive(): R = output.receive()
-
-    override suspend fun send(value: T) {
-        input.send(value)
-    }
-}
-
-
-/**
- * A moving window [Processor] with circular buffer
- */
-class WindowedProcessor<T, R>(
-    scope: CoroutineScope,
-    window: Int,
-    val process: suspend (Buffer<T?>) -> R
-) : AbstractProcessor<T, R>(scope) {
-
-    private val ringBuffer = RingBuffer.boxing<T>(window)
-
-    private val channel = Channel<R>(Channel.RENDEZVOUS)
-
-    override suspend fun receive(): R {
-        return channel.receive()
-    }
-
-    override suspend fun send(value: T) {
-        ringBuffer.push(value)
-        channel.send(process(ringBuffer.snapshot()))
-    }
-}
-
-/**
- * Thread-safe aggregator of values from input. The aggregator does not store all incoming values, it uses fold procedure
- * to incorporate them into state on-arrival.
- * The current aggregated state could be accessed by [state]. The input channel is inactive unless requested
- * @param T - the type of the input element
- * @param S - the type of the aggregator
- */
-class Reducer<T, S>(
-    scope: CoroutineScope,
-    initialState: S,
-    val fold: suspend (S, T) -> S
-) : AbstractConsumer<T>(scope) {
-
-    var state: S = initialState
-        private set
-
-    private val mutex = Mutex()
-
-    override suspend fun send(value: T) = mutex.withLock {
-        state = fold(state, value)
-    }
-}
-
-/**
- * Collector that accumulates all values in a list. List could be accessed from non-suspending environment via [list] value.
- */
-class Collector<T>(scope: CoroutineScope) : AbstractConsumer<T>(scope) {
-
-    private val _list = ArrayList<T>()
-    private val mutex = Mutex()
-    val list: List<T> get() = _list
-
-    override suspend fun send(value: T) {
-        mutex.withLock {
-            _list.add(value)
-        }
-    }
-}
-
-/**
- * Convert a sequence to [Producer]
- */
-fun <T> Sequence<T>.produce(scope: CoroutineScope = GlobalScope) =
-    GenericProducer<T>(scope) { forEach { send(it) } }
-
-/**
- * Convert a [ReceiveChannel] to [Producer]
- */
-fun <T> ReceiveChannel<T>.produce(scope: CoroutineScope = GlobalScope) =
-    GenericProducer<T>(scope) { for (e in this@produce) send(e) }
-
-
-fun <T, C : Consumer<T>> Producer<T>.consumer(consumerFactory: () -> C): C =
-    consumerFactory().also { connect(it) }
-
-fun <T, R> Producer<T>.map(capacity: Int = Channel.RENDEZVOUS, process: suspend (T) -> R) =
-    PipeProcessor(this, capacity, process).also { connect(it) }
-
-/**
- * Create a reducer and connect this producer to reducer
- */
-fun <T, S> Producer<T>.reduce(initialState: S, fold: suspend (S, T) -> S) =
-    Reducer(this, initialState, fold).also { connect(it) }
-
-/**
- * Create a [Collector] and attach it to this [Producer]
- */
-fun <T> Producer<T>.collect() =
-    Collector<T>(this).also { connect(it) }
-
-fun <T, R, P : Processor<T, R>> Producer<T>.process(processorBuilder: () -> P): P =
-    processorBuilder().also { connect(it) }
-
-fun <T, R> Producer<T>.process(capacity: Int = Channel.RENDEZVOUS, process: suspend (T) -> R) =
-    PipeProcessor<T, R>(this, capacity, process).also { connect(it) }
-
-
-fun <T, R> Producer<T>.windowed(window: Int, process: suspend (Buffer<T?>) -> R) =
-    WindowedProcessor(this, window, process).also { connect(it) }

kmath-sequential/src/jvmTest/kotlin/scientifik.kmath.sequential/RingBufferTest.kt

@@ -1,19 +0,0 @@
-package scientifik.kmath.sequential
-
-import kotlinx.coroutines.runBlocking
-import scientifik.kmath.structures.asSequence
-import kotlin.test.Test
-import kotlin.test.assertEquals
-
-class RingBufferTest {
-    @Test
-    fun testPush() {
-        val buffer = RingBuffer.build(20, Double.NaN)
-        runBlocking {
-            for (i in 1..30) {
-                buffer.push(i.toDouble())
-            }
-            assertEquals(410.0, buffer.asSequence().sum())
-        }
-    }
-}

settings.gradle.kts

@@ -26,6 +26,6 @@ include(
     ":kmath-histograms",
     ":kmath-commons",
     ":kmath-koma",
-    ":kmath-sequential",
+    ":kmath-prob",
     ":benchmarks"
 )