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Merge pull request #59 from mipt-npm/dev

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Merge dev into master
This commit is contained in:
Alexander Nozik 2019-05-31 12:35:57 +03:00 committed by GitHub
commit 82e4ea8897
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70 changed files with 2151 additions and 1416 deletions
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5
.gitignore vendored
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@ -1,6 +1,7 @@
.gradle
**/build/
/.idea/
build/
out/
.idea/
# Avoid ignoring Gradle wrapper jar file (.jar files are usually ignored)
!gradle-wrapper.jar

2
benchmarks/build.gradle
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@ -21,7 +21,7 @@ dependencies {
//jmh project(':kmath-core')
}
jmh{
jmh {
warmupIterations = 1
}

11
benchmarks/src/jmh/kotlin/scientifik/kmath/structures/BufferBenchmark.kt
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@ -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)
(0 until size).forEach {
buffer[it] = it.toDouble()
}
val buffer = DoubleBuffer(size){it.toDouble()}
(0 until size).forEach {
buffer[it]
@ -22,10 +20,7 @@ open class BufferBenchmark {
@Benchmark
fun complexBufferReadWrite() {
val buffer = MutableBuffer.complex(size / 2)
(0 until size / 2).forEach {
buffer[it] = Complex(it.toDouble(), -it.toDouble())
}
val buffer = MutableBuffer.complex(size / 2){Complex(it.toDouble(), -it.toDouble())}
(0 until size / 2).forEach {
buffer[it]

18
benchmarks/src/jmh/kotlin/scientifik/kmath/structures/NDFieldBenchmark.kt
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@ -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 specializedField = NDField.real(intArrayOf(dim, dim))
val bufferedField = NDField.auto(RealField, dim, dim)
val specializedField = NDField.real(dim, dim)
val genericField = NDField.buffered(intArrayOf(dim, dim), RealField)
val lazyNDField = NDField.lazy(intArrayOf(dim, dim), RealField)
}
}

37
benchmarks/src/main/kotlin/scientifik/kmath/linear/LinearAlgebraBenchmark.kt
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@ -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 inverseTime = measureTimeMillis {
repeat(n) {
val res = solver.inverse(matrix)
repeat(50) {
val res = inverse(matrix)
}
}
println("[kmath] Inversion of $n matrices $dim x $dim finished in $inverseTime millis")
val inverseTime = measureTimeMillis {
repeat(n) {
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)

4
benchmarks/src/main/kotlin/scientifik/kmath/linear/MultiplicationBenchmark.kt
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@ -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()

2
benchmarks/src/main/kotlin/scientifik/kmath/structures/ComplexNDBenchmark.kt
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@ -23,7 +23,7 @@ fun main() {
val complexTime = measureTimeMillis {
complexField.run {
var res: ComplexNDElement = one
var res = one
repeat(n) {
res += 1.0
}

121
build.gradle.kts
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@ -1,110 +1,29 @@
import org.jetbrains.kotlin.gradle.dsl.KotlinMultiplatformExtension
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")
val kmathVersion by extra("0.1.2-dev-4")
allprojects {
repositories {
jcenter()
maven("https://kotlin.bintray.com/kotlinx")
}
group = "scientifik"
version = kmathVersion
repositories {
//maven("https://dl.bintray.com/kotlin/kotlin-eap")
jcenter()
}
apply(plugin = "maven")
apply(plugin = "maven-publish")
// 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
extensions.findByType<KotlinMultiplatformExtension>()?.apply {
jvm {
compilations.all {
kotlinOptions {
jvmTarget = "1.8"
}
}
}
targets.all {
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 }
// }
// }
}
}
}
// Actually, probably we should apply it to plugins explicitly
// We also can merge them to single kmath-publish plugin
if (name.startsWith("kmath")) {
apply(plugin = "bintray-config")
apply(plugin = "artifactory-config")
}
// dokka {
// outputFormat = "html"
// outputDirectory = javadoc.destinationDir
// }
//
// task dokkaJar (type: Jar, dependsOn: dokka) {
// from javadoc . destinationDir
// classifier = "javadoc"
// }
}

18
buildSrc/build.gradle.kts Normal file
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@ -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")
}

0
buildSrc/settings.gradle.kts Normal file
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10
buildSrc/src/main/kotlin/Versions.kt Normal file
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@ -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"
}

38
buildSrc/src/main/kotlin/artifactory-config.gradle.kts Normal file
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@ -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)
})
})
}

97
buildSrc/src/main/kotlin/bintray-config.gradle.kts Normal file
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@ -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
})
}
}
}
}

44
buildSrc/src/main/kotlin/js-test.gradle.kts Normal file
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@ -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)

118
buildSrc/src/main/kotlin/multiplatform-config.gradle.kts Normal file
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@ -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")
}
}

20
doc/linear.md
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@ -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

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gradle/wrapper/gradle-wrapper.jar vendored
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2
gradle/wrapper/gradle-wrapper.properties vendored
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@ -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

18
gradlew vendored
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@ -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"

18
gradlew.bat vendored
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@ -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

2
kmath-commons/build.gradle.kts
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@ -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")

36
kmath-commons/src/main/kotlin/scientifik/kmath/linear/CMMatrix.kt
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@ -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>) =
CMMatrix(this.toCM().origin.multiply(b.toCM().origin))
override fun add(a: Matrix<Double>, b: Matrix<Double>) =
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) =
CMMatrix(this.toCM().origin.scalarMultiply(value.toDouble()))
override fun multiply(a: Matrix<Double>, k: Number) =
CMMatrix(a.toCM().origin.scalarMultiply(k.toDouble()))
override fun Matrix<Double>.times(value: Double): Matrix<Double> = produce(rowNum,colNum){i,j-> get(i,j)*value}
}
operator fun CMMatrix.plus(other: CMMatrix): CMMatrix = CMMatrix(this.origin.add(other.origin))

39
kmath-commons/src/main/kotlin/scientifik/kmath/linear/CMSolver.kt Normal file
View File

@ -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()

53
kmath-commons/src/main/kotlin/scientifik/kmath/transform/Transformations.kt
View File

@ -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.sequential.Producer
import scientifik.kmath.sequential.map
import scientifik.kmath.streaming.chunked
import scientifik.kmath.streaming.spread
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}

52
kmath-core/build.gradle.kts
View File

@ -1,51 +1,13 @@
plugins {
kotlin("multiplatform")
`multiplatform-config`
}
val ioVersion: String by rootProject.extra
kotlin {
jvm()
js()
sourceSets {
val commonMain by getting {
dependencies {
api(project(":kmath-memory"))
api(kotlin("stdlib"))
}
kotlin.sourceSets {
commonMain {
dependencies {
api(project(":kmath-memory"))
}
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 {
// }
}
//mingwMain {}
//mingwTest {}
}

20
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/BufferMatrix.kt
View File

@ -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) {

96
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/FeaturedMatrix.kt Normal file
View File

@ -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) }

237
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/LUPDecomposition.kt
View File

@ -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.MutableBuffer.Companion.boxing
import scientifik.kmath.structures.MutableBufferFactory
import scientifik.kmath.structures.NDStructure
import scientifik.kmath.structures.get
import scientifik.kmath.structures.BufferAccessor2D
import scientifik.kmath.structures.Matrix
import scientifik.kmath.structures.Structure2D
import kotlin.reflect.KClass
class LUPDecomposition<T : Comparable<T>>(
private val elementContext: Ring<T>,
internal val lu: NDStructure<T>,
/**
* Common implementation of [LUPDecompositionFeature]
*/
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
}
@ -62,70 +67,86 @@ class LUPDecomposition<T : Comparable<T>>(
}
class LUSolver<T : Comparable<T>, F : Field<T>>(
val context: GenericMatrixContext<T, F>,
val bufferFactory: MutableBufferFactory<T> = ::boxing,
val singularityCheck: (T) -> Boolean
) : LinearSolver<T> {
fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.abs(value: T) =
if (value > elementContext.zero) value else with(elementContext) { -value }
private fun abs(value: T) =
if (value > context.elementContext.zero) value else with(context.elementContext) { -value }
/**
* Create a lup decomposition of generic matrix
*/
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")
}
fun buildDecomposition(matrix: Matrix<T>): LUPDecomposition<T> {
if (matrix.rowNum != matrix.colNum) {
error("LU decomposition supports only square matrices")
}
val m = matrix.colNum
val pivot = IntArray(matrix.rowNum)
val m = matrix.colNum
val pivot = IntArray(matrix.rowNum)
//TODO just waits for KEEP-176
BufferAccessor2D(type, matrix.rowNum, matrix.colNum).run {
elementContext.run {
val lu = Mutable2DStructure.create(matrix.rowNum, matrix.colNum, bufferFactory) { i, j ->
matrix[i, j]
}
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 sum = lu[row, col]
var max = col // permutation row
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)
} ?: col
// maintain best permutation choice
if (abs(sum) > largest) {
largest = abs(sum)
max = row
}
}
// Singularity check
if (singularityCheck(lu[max, col])) {
error("Singular matrix")
if (checkSingular(abs(lu[max, col]))) {
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]
lu[col, i] = lu[max, i]
val tmp = luMax[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)
fun GenericMatrixContext<Double, RealField>.lup(matrix: Matrix<Double>) = lup(Double::class, matrix) { it < 1e-11 }
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}")
}
BufferAccessor2D(type, matrix.rowNum, matrix.colNum).run {
elementContext.run {
override fun solve(a: Matrix<T>, b: Matrix<T>): Matrix<T> {
if (b.rowNum != a.colNum) {
error("Matrix dimension mismatch expected ${a.rowNum}, but got ${b.colNum}")
}
// Apply permutations to b
val bp = create { i, j -> zero }
// Use existing decomposition if it is provided by matrix
val decomposition = a.getFeature() ?: buildDecomposition(a)
with(decomposition) {
with(context.elementContext) {
// Apply permutations to b
val bp = Mutable2DStructure.create(a.rowNum, a.colNum, bufferFactory) { i, j ->
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 (i in col + 1 until a.rowNum) {
for (j in 0 until b.colNum) {
bp[i, j] -= bp[col, j] * lu[i, col]
}
}
}
// Solve UX = Y
for (col in a.rowNum - 1 downTo 0) {
for (j in 0 until b.colNum) {
bp[col, j] /= lu[col, col]
}
for (i in 0 until col) {
for (j in 0 until b.colNum) {
bp[i, j] -= bp[col, j] * lu[i, col]
}
}
}
return context.produce(a.rowNum, a.colNum) { i, j -> bp[i, j] }
}
// Solve LY = b
for (col in 0 until pivot.size) {
val bpCol = bp.row(col)
for (i in col + 1 until pivot.size) {
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 pivot.size - 1 downTo 0) {
val bpCol = bp.row(col)
val luDiag = lu[col, col]
for (j in 0 until matrix.colNum) {
bpCol[j] /= luDiag
}
for (i in 0 until col) {
val bpI = bp.row(i)
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] }
}
}
}
override fun inverse(a: Matrix<T>): Matrix<T> = solve(a, context.one(a.rowNum, a.colNum))
inline fun <reified T : Any> LUPDecomposition<T>.solve(matrix: Matrix<T>) = solve(T::class, matrix)
companion object {
val real = LUSolver(MatrixContext.real, MutableBuffer.Companion::auto) { it < 1e-11 }
}
}
/**
* 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 }

17
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/LinearAlgrebra.kt
View File

@ -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
@ -10,9 +11,9 @@ import scientifik.kmath.structures.asSequence
/**
* A group of methods to resolve equation A dot X = B, where A and B are matrices or vectors
*/
interface LinearSolver<T : Any> {
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) }
} else error("Can't convert matrix with more than one column to vector")
VirtualBuffer(rowNum) { get(it, 0) }
} 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) }

213
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/Matrix.kt
View File

@ -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) }

105
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/MatrixContext.kt Normal file
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@ -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 } }
}

8
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/MatrixFeatures.kt
View File

@ -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 u: Matrix<T>
val p: Matrix<T>
val l: FeaturedMatrix<T>
val u: FeaturedMatrix<T>
val p: FeaturedMatrix<T>
}
//TODO add sparse matrix feature

40
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/Mutable2DStructure.kt
View File

@ -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)
}
}
}

70
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/Vector.kt
View File

@ -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
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)
}
}
fun <T : Any, S : Space<T>> BufferVectorSpace<T, S>.produceElement(initializer: (Int) -> T): Vector<T, S> =
BufferVector(this, produce(initializer))
@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>>(
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))
}
@Deprecated("Use VectorContext instead")
data class BufferVector<T : Any, S : Space<T>>(override val context: VectorSpace<T, S>, val buffer: Buffer<T>) :
Vector<T, S> {

75
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/VectorSpace.kt Normal file
View File

@ -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))
}

18
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/VirtualMatrix.kt
View File

@ -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 {

2
kmath-sequential/src/commonMain/kotlin/scientifik/kmath/sequential/Cumulative.kt → kmath-core/src/commonMain/kotlin/scientifik/kmath/misc/Cumulative.kt
View File

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

218
kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/AutoDiff.kt Normal file
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@ -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)
}

33
kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/Complex.kt
View File

@ -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
/**
* 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)
override fun compareTo(other: Complex): Int = abs.compareTo(other.abs)
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> {

132
kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/NumberAlgebra.kt
View File

@ -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 fun multiply(a: Double, b: Double): Double = a * b
override fun multiply(a: Double, k: Number): Double = a * k.toDouble()
override inline fun add(a: Double, b: Double) = a + b
override inline fun multiply(a: Double, b: Double) = a * b
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 fun cos(arg: Double): Double = kotlin.math.cos(arg)
override inline fun sin(arg: Double) = kotlin.math.sin(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 fun ln(arg: Double): Double = kotlin.math.ln(arg)
override inline fun exp(arg: Double) = kotlin.math.exp(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 fun multiply(a: Int, b: Int): Int = a * b
override fun multiply(a: Int, k: Number): Int = (k * a)
override inline fun add(a: Int, b: Int) = a + b
override inline fun multiply(a: Int, b: Int) = a * b
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 fun multiply(a: Short, b: Short): Short = (a * b).toShort()
override fun multiply(a: Short, k: Number): Short = (a * k)
override inline fun add(a: Short, b: Short) = (a + b).toShort()
override inline fun multiply(a: Short, b: Short) = (a * b).toShort()
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 fun multiply(a: Byte, b: Byte): Byte = (a * b).toByte()
override fun multiply(a: Byte, k: Number): Byte = (a * k)
override inline fun add(a: Byte, b: Byte) = (a + b).toByte()
override inline fun multiply(a: Byte, b: Byte) = (a * b).toByte()
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 fun multiply(a: Long, b: Long): Long = (a * b)
override fun multiply(a: Long, k: Number): Long = (a * k)
override inline fun add(a: Long, b: Long) = (a + b)
override inline fun multiply(a: Long, b: Long) = (a * b)
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)
}

45
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/BufferAccessor2D.kt Normal file
View File

@ -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)
}

47
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Buffers.kt
View File

@ -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> {
return when (T::class) {
inline fun <T : Any> auto(type: KClass<out T>, size: Int, initializer: (Int) -> T): MutableBuffer<T> {
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()
@ -261,4 +282,6 @@ fun <T> Buffer<T>.asReadOnly(): Buffer<T> = if (this is MutableBuffer) {
/**
* Typealias for buffer transformations
*/
typealias BufferTransform<T, R> = (Buffer<T>) -> Buffer<R>
typealias BufferTransform<T, R> = (Buffer<T>) -> Buffer<R>
typealias SuspendBufferTransform<T, R> = suspend (Buffer<T>) -> Buffer<R>

3
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/NDAlgebra.kt
View File

@ -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) }

18
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/NDStructure.kt
View File

@ -1,12 +1,14 @@
package scientifik.kmath.structures
import kotlin.jvm.JvmName
import kotlin.reflect.KClass
interface NDStructure<T> {
val shape: IntArray
val dimension
get() = shape.size
val dimension 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)
}

60
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/SpecializedStructures.kt → kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Structure1D.kt
View File

@ -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 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)
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 2d-structure from ${shape.size}d-structure")
}
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)

79
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Structure2D.kt Normal file
View File

@ -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>

29
kmath-core/src/commonTest/kotlin/scientifik/kmath/linear/MatrixTest.kt
View File

@ -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 matrix2 = vector2.toMatrix().transpose()
val matrix1 = vector1.asMatrix()
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
}
}

36
kmath-core/src/commonTest/kotlin/scientifik/kmath/linear/RealLUSolverTest.kt
View File

@ -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 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 inverted = MatrixContext.real.inverse(matrix)
val expected = Matrix.square(
0.375, -0.125,

1
kmath-sequential/src/commonTest/kotlin/scientifik/kmath/sequential/CumulativeKtTest.kt → kmath-core/src/commonTest/kotlin/scientifik/kmath/misc/CumulativeKtTest.kt
View File

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

176
kmath-core/src/commonTest/kotlin/scientifik/kmath/operations/AutoDiffTest.kt Normal file
View File

@ -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)
}
}

56
kmath-coroutines/build.gradle.kts
View File

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

102
kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/CoroutinesExtra.kt Normal file
View File

@ -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)
}

17
kmath-sequential/src/commonMain/kotlin/scientifik/kmath/sequential/Chain.kt → kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/chains/Chain.kt
View File

@ -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.ExperimentalCoroutinesApi
import kotlinx.coroutines.channels.ReceiveChannel
import kotlinx.coroutines.channels.produce
import kotlinx.coroutines.isActive
import kotlinx.coroutines.FlowPreview
/**
@ -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
fun <R> Chain<R>.asChannel(scope: CoroutineScope): ReceiveChannel<R> = scope.produce { while (isActive) send(next()) }
@FlowPreview
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()

80
kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/streaming/BufferFlow.kt Normal file
View File

@ -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())
}
}

18
kmath-sequential/src/commonMain/kotlin/scientifik/kmath/sequential/RingBuffer.kt → kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/streaming/RingBuffer.kt
View File

@ -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)
}

9
kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/structures/CoroutinesExtra.kt
View File

@ -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

2
kmath-sequential/src/jvmMain/kotlin/scientifik/kmath/sequential/ChainExt.kt → kmath-coroutines/src/jvmMain/kotlin/scientifik/kmath/chains/ChainExt.kt
View File

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

1
kmath-coroutines/src/jvmMain/kotlin/scientifik/kmath/structures/LazyNDStructure.kt
View File

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

48
kmath-coroutines/src/jvmTest/kotlin/scientifik/kmath/streaming/BufferFlowTest.kt Normal file
View File

@ -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}")
}
}
}
}

38
kmath-coroutines/src/jvmTest/kotlin/scientifik/kmath/streaming/RingBufferTest.kt Normal file
View File

@ -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])
}
}
}

34
kmath-histograms/build.gradle.kts
View File

@ -1,34 +1,10 @@
plugins {
kotlin("multiplatform")
`multiplatform-config`
}
kotlin {
jvm()
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"))
}
}
// Just an example how we can collapse nested DSL for simple declarations
kotlin.sourceSets.commonMain {
dependencies {
api(project(":kmath-core"))
}
}

66
kmath-koma/build.gradle.kts
View File

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

44
kmath-koma/src/commonMain/kotlin/scientifik.kmath.linear/KomaMatrix.kt
View File

@ -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>,
LinearSolver<T> {
class KomaMatrixContext<T : Any>(
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>) =
KomaMatrix(this.toKoma().origin + b.toKoma().origin)
override fun add(a: Matrix<T>, b: Matrix<T>) =
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) :
Matrix<T> {
fun <T : Any> KomaMatrixContext<T>.solve(a: Matrix<T>, b: 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 u: Matrix<T> get() = KomaMatrix(lup.third)
override val p: Matrix<T> get() = KomaMatrix(lup.first)
override val l: FeaturedMatrix<T> get() = KomaMatrix(lup.second)
override val u: FeaturedMatrix<T> get() = KomaMatrix(lup.third)
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)

49
kmath-memory/build.gradle.kts
View File

@ -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 {
// }
}
}

29
kmath-prob/build.gradle.kts Normal file
View File

@ -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"
}

53
kmath-sequential/build.gradle.kts
View File

@ -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"
}

82
kmath-sequential/src/commonMain/kotlin/scientifik/kmath/sequential/BufferStreaming.kt
View File

@ -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) }

275
kmath-sequential/src/commonMain/kotlin/scientifik/kmath/sequential/Streaming.kt
View File

@ -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) }

19
kmath-sequential/src/jvmTest/kotlin/scientifik.kmath.sequential/RingBufferTest.kt
View File

@ -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())
}
}
}

2
settings.gradle.kts
View File

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