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Merge pull request #504 from SciProgCentre/dev

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Merge to update docs and contributions
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3
.github/CODEOWNERS vendored Normal file
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@ -0,0 +1,3 @@
@altavir
/kmath-trajectory @ESchouten

11
.idea/copyright/kmath.xml
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@ -1,6 +1,7 @@
<component name="CopyrightManager">
<copyright>
<option name="notice" value="Copyright 2018-2021 KMath contributors.&#10;Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file." />
<option name="myName" value="kmath" />
</copyright>
</component>
<copyright>
<option name="allowReplaceRegexp" value="Copyright \d{4}-\d{4} KMath" />
<option name="notice" value="Copyright 2018-&amp;#36;today.year KMath contributors.&#10;Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file." />
<option name="myName" value="kmath" />
</copyright>
</component>

2
.idea/copyright/profiles_settings.xml
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@ -1,5 +1,5 @@
<component name="CopyrightManager">
<settings default="kmath">
<settings>
<module2copyright>
<element module="Apply copyright" copyright="kmath" />
</module2copyright>

2
.space.kts
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@ -1,3 +1,3 @@
job("Build") {
gradlew("openjdk:11", "build")
}
}

0
.space/CODEOWNERS Normal file
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8
CHANGELOG.md
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@ -2,8 +2,16 @@
## [Unreleased]
### Added
- 2D optimal trajectory computation in a separate module `kmath-trajectory`
- Autodiff for generic algebra elements in core!
- Algebra now has an obligatory `bufferFactory` (#477).
### Changed
- Major refactor of tensors (only minor API changes)
- Kotlin 1.7.20
- `LazyStructure` `deffered` -> `async` to comply with coroutines code style
- Default `dot` operation in tensor algebra no longer support broadcasting. Instead `matmul` operation is added to `DoubleTensorAlgebra`.
- Multik went MPP
### Deprecated

47
README.md
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@ -1,6 +1,6 @@
[![JetBrains Research](https://jb.gg/badges/research.svg)](https://confluence.jetbrains.com/display/ALL/JetBrains+on+GitHub)
[![DOI](https://zenodo.org/badge/129486382.svg)](https://zenodo.org/badge/latestdoi/129486382)
![Gradle build](https://github.com/mipt-npm/kmath/workflows/Gradle%20build/badge.svg)
![Gradle build](https://github.com/SciProgCentre/kmath/workflows/Gradle%20build/badge.svg)
[![Maven Central](https://img.shields.io/maven-central/v/space.kscience/kmath-core.svg?label=Maven%20Central)](https://search.maven.org/search?q=g:%22space.kscience%22)
[![Space](https://img.shields.io/badge/dynamic/xml?color=orange&label=Space&query=//metadata/versioning/latest&url=https%3A%2F%2Fmaven.pkg.jetbrains.space%2Fmipt-npm%2Fp%2Fsci%2Fmaven%2Fspace%2Fkscience%2Fkmath-core%2Fmaven-metadata.xml)](https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven/space/kscience/)
@ -11,7 +11,7 @@ analog to Python's NumPy library. Later we found that kotlin is much more flexib
architecture designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like
experience could be achieved with [kmath-for-real](/kmath-for-real) extension module.
[Documentation site (**WIP**)](https://mipt-npm.github.io/kmath/)
[Documentation site (**WIP**)](https://SciProgCentre.github.io/kmath/)
## Publications and talks
@ -44,7 +44,7 @@ module definitions below. The module stability could have the following levels:
* **PROTOTYPE**. On this level there are no compatibility guarantees. All methods and classes form those modules could
break any moment. You can still use it, but be sure to fix the specific version.
* **EXPERIMENTAL**. The general API is decided, but some changes could be made. Volatile API is marked
with `@UnstableKmathAPI` or other stability warning annotations.
with `@UnstableKMathAPI` or other stability warning annotations.
* **DEVELOPMENT**. API breaking generally follows semantic versioning ideology. There could be changes in minor
versions, but not in patch versions. API is protected
with [binary-compatibility-validator](https://github.com/Kotlin/binary-compatibility-validator) tool.
@ -86,8 +86,8 @@ module definitions below. The module stability could have the following levels:
> **Maturity**: PROTOTYPE
>
> **Features:**
> - [complex](kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Complex.kt) : Complex Numbers
> - [quaternion](kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt) : Quaternions
> - [complex](kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Complex.kt) : Complex numbers operations
> - [quaternion](kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt) : Quaternions and their composition
### [kmath-core](kmath-core)
@ -214,6 +214,28 @@ One can still use generic algebras though.
>
> **Maturity**: EXPERIMENTAL
### [kmath-polynomial](kmath-polynomial)
>
>
> **Maturity**: PROTOTYPE
>
> **Features:**
> - [polynomial abstraction](kmath-polynomial/src/commonMain/kotlin/space/kscience/kmath/functions/Polynomial.kt) : Abstraction for polynomial spaces.
> - [rational function abstraction](kmath-polynomial/src/commonMain/kotlin/space/kscience/kmath/functions/RationalFunction.kt) : Abstraction for rational functions spaces.
> - ["list" polynomials](kmath-polynomial/src/commonMain/kotlin/space/kscience/kmath/functions/ListRationalFunction.kt) : List implementation of univariate polynomials.
> - ["list" rational functions](kmath-polynomial/src/commonMain/kotlin/space/kscience/kmath/functions/ListPolynomial.kt) : List implementation of univariate rational functions.
> - ["list" polynomials and rational functions constructors](kmath-polynomial/src/commonMain/kotlin/space/kscience/kmath/functions/listConstructors.kt) : Constructors for list polynomials and rational functions.
> - ["list" polynomials and rational functions utilities](kmath-polynomial/src/commonMain/kotlin/space/kscience/kmath/functions/listUtil.kt) : Utilities for list polynomials and rational functions.
> - ["numbered" polynomials](kmath-polynomial/src/commonMain/kotlin/space/kscience/kmath/functions/NumberedRationalFunction.kt) : Numbered implementation of multivariate polynomials.
> - ["numbered" rational functions](kmath-polynomial/src/commonMain/kotlin/space/kscience/kmath/functions/NumberedPolynomial.kt) : Numbered implementation of multivariate rational functions.
> - ["numbered" polynomials and rational functions constructors](kmath-polynomial/src/commonMain/kotlin/space/kscience/kmath/functions/numberedConstructors.kt) : Constructors for numbered polynomials and rational functions.
> - ["numbered" polynomials and rational functions utilities](kmath-polynomial/src/commonMain/kotlin/space/kscience/kmath/functions/numberedUtil.kt) : Utilities for numbered polynomials and rational functions.
> - ["labeled" polynomials](kmath-polynomial/src/commonMain/kotlin/space/kscience/kmath/functions/LabeledRationalFunction.kt) : Labeled implementation of multivariate polynomials.
> - ["labeled" rational functions](kmath-polynomial/src/commonMain/kotlin/space/kscience/kmath/functions/LabeledPolynomial.kt) : Labeled implementation of multivariate rational functions.
> - ["labeled" polynomials and rational functions constructors](kmath-polynomial/src/commonMain/kotlin/space/kscience/kmath/functions/labeledConstructors.kt) : Constructors for labeled polynomials and rational functions.
> - ["labeled" polynomials and rational functions utilities](kmath-polynomial/src/commonMain/kotlin/space/kscience/kmath/functions/labeledUtil.kt) : Utilities for labeled polynomials and rational functions.
### [kmath-stat](kmath-stat)
>
>
@ -240,11 +262,21 @@ One can still use generic algebras though.
> - [linear algebra operations](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt) : Advanced linear algebra operations like LU decomposition, SVD, etc.
### [kmath-trajectory](kmath-trajectory)
> Path and trajectory optimization
>
> **Maturity**: PROTOTYPE
### [kmath-viktor](kmath-viktor)
>
>
> **Maturity**: DEVELOPMENT
### [test-utils](test-utils)
>
>
> **Maturity**: EXPERIMENTAL
## Multi-platform support
@ -261,8 +293,7 @@ performance and flexibility.
We expect to focus on creating convenient universal API first and then work on increasing performance for specific
cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized
native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be
better than SciPy.
native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be better than SciPy.
## Requirements
@ -294,4 +325,4 @@ Gradle `6.0+` is required for multiplatform artifacts.
The project requires a lot of additional work. The most important thing we need is a feedback about what features are
required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues
marked with
[waiting for a hero](https://github.com/mipt-npm/kmath/labels/waiting%20for%20a%20hero) label.
[waiting for a hero](https://github.com/SciProgCentre/kmath/labels/waiting%20for%20a%20hero) label.

12
benchmarks/build.gradle.kts
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@ -1,5 +1,6 @@
@file:Suppress("UNUSED_VARIABLE")
import org.jetbrains.kotlin.gradle.tasks.KotlinJvmCompile
import space.kscience.kmath.benchmarks.addBenchmarkProperties
plugins {
@ -15,6 +16,8 @@ repositories {
mavenCentral()
}
val multikVersion: String by rootProject.extra
kotlin {
jvm()
@ -39,7 +42,9 @@ kotlin {
implementation(project(":kmath-dimensions"))
implementation(project(":kmath-for-real"))
implementation(project(":kmath-tensors"))
implementation("org.jetbrains.kotlinx:kotlinx-benchmark-runtime:0.4.2")
implementation(project(":kmath-multik"))
implementation("org.jetbrains.kotlinx:multik-default:$multikVersion")
implementation(npmlibs.kotlinx.benchmark.runtime)
}
}
@ -51,7 +56,6 @@ kotlin {
implementation(project(":kmath-kotlingrad"))
implementation(project(":kmath-viktor"))
implementation(project(":kmath-jafama"))
implementation(project(":kmath-multik"))
implementation(projects.kmath.kmathTensorflow)
implementation("org.tensorflow:tensorflow-core-platform:0.4.0")
implementation("org.nd4j:nd4j-native:1.0.0-M1")
@ -155,7 +159,7 @@ kotlin.sourceSets.all {
}
}
tasks.withType<org.jetbrains.kotlin.gradle.dsl.KotlinJvmCompile> {
tasks.withType<KotlinJvmCompile> {
kotlinOptions {
jvmTarget = "11"
freeCompilerArgs = freeCompilerArgs + "-Xjvm-default=all" + "-Xlambdas=indy"
@ -163,7 +167,7 @@ tasks.withType<org.jetbrains.kotlin.gradle.dsl.KotlinJvmCompile> {
}
readme {
maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL
maturity = space.kscience.gradle.Maturity.EXPERIMENTAL
}
addBenchmarkProperties()

2
benchmarks/src/jsMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ArrayBenchmark.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BigIntBenchmark.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BufferBenchmark.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

8
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -13,10 +13,8 @@ import space.kscience.kmath.commons.linear.CMLinearSpace
import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
import space.kscience.kmath.linear.invoke
import space.kscience.kmath.linear.linearSpace
import space.kscience.kmath.multik.multikAlgebra
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.tensorflow.produceWithTF
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
import space.kscience.kmath.tensors.core.tensorAlgebra
@ -79,12 +77,12 @@ internal class DotBenchmark {
}
@Benchmark
fun multikDot(blackhole: Blackhole) = with(DoubleField.multikAlgebra) {
fun multikDot(blackhole: Blackhole) = with(multikAlgebra) {
blackhole.consume(matrix1 dot matrix2)
}
@Benchmark
fun bufferedDot(blackhole: Blackhole) = with(DoubleField.linearSpace(Buffer.Companion::auto)) {
fun bufferedDot(blackhole: Blackhole) = with(DoubleField.linearSpace) {
blackhole.consume(matrix1 dot matrix2)
}

2
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/JafamaBenchmark.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/MatrixInverseBenchmark.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

18
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -13,14 +13,12 @@ import org.jetbrains.kotlinx.multik.api.Multik
import org.jetbrains.kotlinx.multik.api.ones
import org.jetbrains.kotlinx.multik.ndarray.data.DN
import org.jetbrains.kotlinx.multik.ndarray.data.DataType
import space.kscience.kmath.multik.multikAlgebra
import space.kscience.kmath.nd.BufferedFieldOpsND
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.nd.one
import space.kscience.kmath.nd4j.nd4j
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.one
import space.kscience.kmath.tensors.core.tensorAlgebra
@ -28,12 +26,6 @@ import space.kscience.kmath.viktor.viktorAlgebra
@State(Scope.Benchmark)
internal class NDFieldBenchmark {
@Benchmark
fun autoFieldAdd(blackhole: Blackhole) = with(autoField) {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
@Benchmark
fun specializedFieldAdd(blackhole: Blackhole) = with(specializedField) {
@ -50,7 +42,7 @@ internal class NDFieldBenchmark {
}
@Benchmark
fun multikAdd(blackhole: Blackhole) = with(multikField) {
fun multikAdd(blackhole: Blackhole) = with(multikAlgebra) {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
@ -78,7 +70,7 @@ internal class NDFieldBenchmark {
}
@Benchmark
fun multikInPlaceAdd(blackhole: Blackhole) = with(DoubleField.multikAlgebra) {
fun multikInPlaceAdd(blackhole: Blackhole) = with(multikAlgebra) {
val res = Multik.ones<Double, DN>(shape, DataType.DoubleDataType).wrap()
repeat(n) { res += 1.0 }
blackhole.consume(res)
@ -95,11 +87,9 @@ internal class NDFieldBenchmark {
private const val dim = 1000
private const val n = 100
private val shape = intArrayOf(dim, dim)
private val autoField = BufferedFieldOpsND(DoubleField, Buffer.Companion::auto)
private val specializedField = DoubleField.ndAlgebra
private val genericField = BufferedFieldOpsND(DoubleField, Buffer.Companion::boxing)
private val genericField = BufferedFieldOpsND(DoubleField)
private val nd4jField = DoubleField.nd4j
private val multikField = DoubleField.multikAlgebra
private val viktorField = DoubleField.viktorAlgebra
}
}

2
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/TensorAlgebraBenchmark.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

23
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorBenchmark.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -10,25 +10,19 @@ import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import org.jetbrains.bio.viktor.F64Array
import space.kscience.kmath.nd.*
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.nd.one
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.viktor.ViktorFieldND
@State(Scope.Benchmark)
internal class ViktorBenchmark {
@Benchmark
fun automaticFieldAddition(blackhole: Blackhole) {
with(autoField) {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
}
@Benchmark
fun realFieldAddition(blackhole: Blackhole) {
with(realField) {
fun doubleFieldAddition(blackhole: Blackhole) {
with(doubleField) {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
@ -58,8 +52,7 @@ internal class ViktorBenchmark {
private val shape = Shape(dim, dim)
// automatically build context most suited for given type.
private val autoField = BufferedFieldOpsND(DoubleField, Buffer.Companion::auto)
private val realField = DoubleField.ndAlgebra
private val doubleField = DoubleField.ndAlgebra
private val viktorField = ViktorFieldND(dim, dim)
}
}

9
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorLogBenchmark.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -10,19 +10,17 @@ import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import org.jetbrains.bio.viktor.F64Array
import space.kscience.kmath.nd.BufferedFieldOpsND
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.nd.one
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.viktor.ViktorFieldND
@State(Scope.Benchmark)
internal class ViktorLogBenchmark {
@Benchmark
fun realFieldLog(blackhole: Blackhole) {
with(realField) {
with(doubleField) {
val fortyTwo = structureND(shape) { 42.0 }
var res = one(shape)
repeat(n) { res = ln(fortyTwo) }
@ -54,8 +52,7 @@ internal class ViktorLogBenchmark {
private val shape = Shape(dim, dim)
// automatically build context most suited for given type.
private val autoField = BufferedFieldOpsND(DoubleField, Buffer.Companion::auto)
private val realField = DoubleField.ndAlgebra
private val doubleField = DoubleField.ndAlgebra
private val viktorField = ViktorFieldND(dim, dim)
}
}

11
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/globals.kt Normal file
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@ -0,0 +1,11 @@
/*
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.benchmarks
import org.jetbrains.kotlinx.multik.default.DefaultEngine
import space.kscience.kmath.multik.MultikDoubleAlgebra
val multikAlgebra = MultikDoubleAlgebra(DefaultEngine())

42
build.gradle.kts
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@ -1,6 +1,10 @@
import space.kscience.gradle.isInDevelopment
import space.kscience.gradle.useApache2Licence
import space.kscience.gradle.useSPCTeam
plugins {
id("ru.mipt.npm.gradle.project")
id("org.jetbrains.kotlinx.kover") version "0.5.0"
id("space.kscience.gradle.project")
id("org.jetbrains.kotlinx.kover") version "0.6.0"
}
allprojects {
@ -11,13 +15,13 @@ allprojects {
}
group = "space.kscience"
version = "0.3.0"
version = "0.3.1-dev-4"
}
subprojects {
if (name.startsWith("kmath")) apply<MavenPublishPlugin>()
plugins.withId("org.jetbrains.dokka"){
plugins.withId("org.jetbrains.dokka") {
tasks.withType<org.jetbrains.dokka.gradle.DokkaTaskPartial> {
dependsOn(tasks["assemble"])
@ -31,7 +35,7 @@ subprojects {
localDirectory.set(kotlinDir)
remoteUrl.set(
java.net.URL("https://github.com/mipt-npm/kmath/tree/master/${this@subprojects.name}/$kotlinDirPath")
java.net.URL("https://github.com/SciProgCentre/kmath/tree/master/${this@subprojects.name}/$kotlinDirPath")
)
}
@ -51,14 +55,38 @@ subprojects {
}
}
}
plugins.withId("org.jetbrains.kotlin.multiplatform") {
configure<org.jetbrains.kotlin.gradle.dsl.KotlinMultiplatformExtension> {
sourceSets {
val commonTest by getting {
dependencies {
implementation(projects.testUtils)
}
}
}
}
}
}
readme.readmeTemplate = file("docs/templates/README-TEMPLATE.md")
ksciencePublish {
github("kmath", addToRelease = false)
space()
pom("https://github.com/SciProgCentre/kmath") {
useApache2Licence()
useSPCTeam()
}
github("kmath", "SciProgCentre")
space(
if (isInDevelopment) {
"https://maven.pkg.jetbrains.space/mipt-npm/p/sci/dev"
} else {
"https://maven.pkg.jetbrains.space/mipt-npm/p/sci/release"
}
)
sonatype()
}
apiValidation.nonPublicMarkers.add("space.kscience.kmath.misc.UnstableKMathAPI")
val multikVersion by extra("0.2.0")

13
buildSrc/build.gradle.kts
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@ -1,7 +1,7 @@
plugins {
`kotlin-dsl`
`version-catalog`
alias(miptNpmLibs.plugins.kotlin.plugin.serialization)
kotlin("plugin.serialization") version "1.6.21"
}
java.targetCompatibility = JavaVersion.VERSION_11
@ -13,17 +13,18 @@ repositories {
gradlePluginPortal()
}
val toolsVersion: String by extra
val kotlinVersion = miptNpmLibs.versions.kotlin.asProvider().get()
val benchmarksVersion = miptNpmLibs.versions.kotlinx.benchmark.get()
val toolsVersion = npmlibs.versions.tools.get()
val kotlinVersion = npmlibs.versions.kotlin.asProvider().get()
val benchmarksVersion = npmlibs.versions.kotlinx.benchmark.get()
dependencies {
api("ru.mipt.npm:gradle-tools:$toolsVersion")
api("space.kscience:gradle-tools:$toolsVersion")
api(npmlibs.atomicfu.gradle)
//plugins form benchmarks
api("org.jetbrains.kotlinx:kotlinx-benchmark-plugin:$benchmarksVersion")
api("org.jetbrains.kotlin:kotlin-allopen:$kotlinVersion")
//to be used inside build-script only
implementation(miptNpmLibs.kotlinx.serialization.json)
implementation(npmlibs.kotlinx.serialization.json)
}
kotlin.sourceSets.all {

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buildSrc/gradle.properties
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@ -1,7 +0,0 @@
#
# Copyright 2018-2021 KMath contributors.
# Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
#
kotlin.code.style=official
toolsVersion=0.11.2-kotlin-1.6.10

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@ -6,7 +6,17 @@
enableFeaturePreview("TYPESAFE_PROJECT_ACCESSORS")
dependencyResolutionManagement {
val toolsVersion: String by extra
val projectProperties = java.util.Properties()
file("../gradle.properties").inputStream().use {
projectProperties.load(it)
}
projectProperties.forEach { key, value ->
extra.set(key.toString(), value)
}
val toolsVersion: String = projectProperties["toolsVersion"].toString()
repositories {
mavenLocal()
@ -16,8 +26,8 @@ dependencyResolutionManagement {
}
versionCatalogs {
create("miptNpmLibs") {
from("ru.mipt.npm:version-catalog:$toolsVersion")
create("npmlibs") {
from("space.kscience:version-catalog:$toolsVersion")
}
}
}

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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

4
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -9,7 +9,7 @@ import kotlinx.benchmark.gradle.BenchmarksExtension
import kotlinx.serialization.decodeFromString
import kotlinx.serialization.json.Json
import org.gradle.api.Project
import ru.mipt.npm.gradle.KScienceReadmeExtension
import space.kscience.gradle.KScienceReadmeExtension
import java.time.LocalDateTime
import java.time.ZoneId
import java.time.format.DateTimeFormatter

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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -319,7 +319,9 @@ public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra},
}
else -> null
}?.let(type::cast)
}?.let{
type.cast(it)
}
}
/**

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@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!--
- Copyright 2018-2021 KMath contributors.
- Copyright 2018-2022 KMath contributors.
- Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
-->

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@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!--
- Copyright 2018-2021 KMath contributors.
- Copyright 2018-2022 KMath contributors.
- Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
-->

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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!--
- Copyright 2018-2021 KMath contributors.
- Copyright 2018-2022 KMath contributors.
- Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
-->

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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!--
- Copyright 2018-2021 KMath contributors.
- Copyright 2018-2022 KMath contributors.
- Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
-->

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@ -0,0 +1,172 @@
# Polynomials and Rational Functions
KMath provides a way to work with uni- and multivariate polynomials and rational functions. It includes full support of arithmetic operations of integers, **constants** (elements of ring polynomials are build over), variables (for certain multivariate implementations), polynomials and rational functions encapsulated in so-called **polynomial space** and **rational function space** and some other utilities such as algebraic differentiation and substitution.
## Concrete realizations
There are 3 approaches to represent polynomials:
1. For univariate polynomials one can represent and store polynomial as a list of coefficients for each power of the variable. I.e. polynomial $a_0 + \dots + a_n x^n $ can be represented as a finite sequence $(a_0; \dots; a_n)$. (Compare to sequential definition of polynomials.)
2. For multivariate polynomials one can represent and store polynomial as a matching (in programming it is called "map" or "dictionary", in math it is called [functional relation](https://en.wikipedia.org/wiki/Binary_relation#Special_types_of_binary_relations)) of each "**term signature**" (that describes what variables and in what powers appear in the term) with corresponding coefficient of the term. But there are 2 possible approaches of term signature representation:
1. One can number all the variables, so term signature can be represented as a sequence describing powers of the variables. I.e. signature of term $c \\; x_0^{d_0} \dots x_n^{d_n} $ (for natural or zero $d_i $) can be represented as a finite sequence $(d_0; \dots; d_n)$.
2. One can represent variables as objects ("**labels**"), so term signature can be also represented as a matching of each appeared variable with its power in the term. I.e. signature of term $c \\; x_0^{d_0} \dots x_n^{d_n} $ (for natural non-zero $d_i $) can be represented as a finite matching $(x_0 \to d_1; \dots; x_n \to d_n)$.
All that three approaches are implemented by "list", "numbered", and "labeled" versions of polynomials and polynomial spaces respectively. Whereas all rational functions are represented as fractions with corresponding polynomial numerator and denominator, and rational functions' spaces are implemented in the same way as usual field of rational numbers (or more precisely, as any field of fractions over integral domain) should be implemented.
So here are a bit of details. Let `C` by type of constants. Then:
1. `ListPolynomial`, `ListPolynomialSpace`, `ListRationalFunction` and `ListRationalFunctionSpace` implement the first scenario. `ListPolynomial` stores polynomial $a_0 + \dots + a_n x^n $ as a coefficients list `listOf(a_0, ..., a_n)` (of type `List<C>`).
They also have variation `ScalableListPolynomialSpace` that replaces former polynomials and implements `ScaleOperations`.
2. `NumberedPolynomial`, `NumberedPolynomialSpace`, `NumberedRationalFunction` and `NumberedRationalFunctionSpace` implement second scenario. `NumberedPolynomial` stores polynomials as structures of type `Map<List<UInt>, C>`. Signatures are stored as `List<UInt>`. To prevent ambiguity signatures should not end with zeros.
3. `LabeledPolynomial`, `LabeledPolynomialSpace`, `LabeledRationalFunction` and `LabeledRationalFunctionSpace` implement third scenario using common `Symbol` as variable type. `LabeledPolynomial` stores polynomials as structures of type `Map<Map<Symbol, UInt>, C>`. Signatures are stored as `Map<Symbol, UInt>`. To prevent ambiguity each signature should not map any variable to zero.
### Example: `ListPolynomial`
For example, polynomial $2 - 3x + x^2 $ (with `Int` coefficients) is represented
```kotlin
val polynomial: ListPolynomial<Int> = ListPolynomial(listOf(2, -3, 1))
// or
val polynomial: ListPolynomial<Int> = ListPolynomial(2, -3, 1)
```
All algebraic operations can be used in corresponding space:
```kotlin
val computationResult = Int.algebra.listPolynomialSpace {
ListPolynomial(2, -3, 1) + ListPolynomial(0, 6) == ListPolynomial(2, 3, 1)
}
println(computationResult) // true
```
For more see [examples](../examples/src/main/kotlin/space/kscience/kmath/functions/polynomials.kt).
### Example: `NumberedPolynomial`
For example, polynomial $3 + 5 x_1 - 7 x_0^2 x_2 $ (with `Int` coefficients) is represented
```kotlin
val polynomial: NumberedPolynomial<Int> = NumberedPolynomial(
mapOf(
listOf<UInt>() to 3,
listOf(0u, 1u) to 5,
listOf(2u, 0u, 1u) to -7,
)
)
// or
val polynomial: NumberedPolynomial<Int> = NumberedPolynomial(
listOf<UInt>() to 3,
listOf(0u, 1u) to 5,
listOf(2u, 0u, 1u) to -7,
)
```
All algebraic operations can be used in corresponding space:
```kotlin
val computationResult = Int.algebra.numberedPolynomialSpace {
NumberedPolynomial(
listOf<UInt>() to 3,
listOf(0u, 1u) to 5,
listOf(2u, 0u, 1u) to -7,
) + NumberedPolynomial(
listOf(0u, 1u) to -5,
listOf(0u, 0u, 0u, 4u) to 4,
) == NumberedPolynomial(
listOf<UInt>() to 3,
listOf(0u, 1u) to 0,
listOf(2u, 0u, 1u) to -7,
listOf(0u, 0u, 0u, 4u) to 4,
)
}
println(computationResult) // true
```
For more see [examples](../examples/src/main/kotlin/space/kscience/kmath/functions/polynomials.kt).
### Example: `LabeledPolynomial`
For example, polynomial $3 + 5 y - 7 x^2 z $ (with `Int` coefficients) is represented
```kotlin
val polynomial: LabeledPolynomial<Int> = LabeledPolynomial(
mapOf(
mapOf<Symbol, UInt>() to 3,
mapOf(y to 1u) to 5,
mapOf(x to 2u, z to 1u) to -7,
)
)
// or
val polynomial: LabeledPolynomial<Int> = LabeledPolynomial(
mapOf<Symbol, UInt>() to 3,
mapOf(y to 1u) to 5,
mapOf(x to 2u, z to 1u) to -7,
)
```
All algebraic operations can be used in corresponding space:
```kotlin
val computationResult = Int.algebra.labeledPolynomialSpace {
LabeledPolynomial(
listOf<UInt>() to 3,
listOf(0u, 1u) to 5,
listOf(2u, 0u, 1u) to -7,
) + LabeledPolynomial(
listOf(0u, 1u) to -5,
listOf(0u, 0u, 0u, 4u) to 4,
) == LabeledPolynomial(
listOf<UInt>() to 3,
listOf(0u, 1u) to 0,
listOf(2u, 0u, 1u) to -7,
listOf(0u, 0u, 0u, 4u) to 4,
)
}
println(computationResult) // true
```
For more see [examples](../examples/src/main/kotlin/space/kscience/kmath/functions/polynomials.kt).
## Abstract entities (interfaces and abstract classes)
```mermaid
classDiagram
Polynomial <|-- ListPolynomial
Polynomial <|-- NumberedPolynomial
Polynomial <|-- LabeledPolynomial
RationalFunction <|-- ListRationalFunction
RationalFunction <|-- NumberedRationalFunction
RationalFunction <|-- LabeledRationalFunction
Ring <|-- PolynomialSpace
PolynomialSpace <|-- MultivariatePolynomialSpace
PolynomialSpace <|-- PolynomialSpaceOverRing
Ring <|-- RationalFunctionSpace
RationalFunctionSpace <|-- MultivariateRationalFunctionSpace
RationalFunctionSpace <|-- RationalFunctionSpaceOverRing
RationalFunctionSpace <|-- RationalFunctionSpaceOverPolynomialSpace
RationalFunctionSpace <|-- PolynomialSpaceOfFractions
RationalFunctionSpaceOverPolynomialSpace <|-- MultivariateRationalFunctionSpaceOverMultivariatePolynomialSpace
MultivariateRationalFunctionSpace <|-- MultivariateRationalFunctionSpaceOverMultivariatePolynomialSpace
MultivariateRationalFunctionSpace <|-- MultivariatePolynomialSpaceOfFractions
PolynomialSpaceOfFractions <|-- MultivariatePolynomialSpaceOfFractions
```
There are implemented `Polynomial` and `RationalFunction` interfaces as abstractions of polynomials and rational functions respectively (although, there is not a lot of logic in them) and `PolynomialSpace` and `RationalFunctionSpace` (that implement `Ring` interface) as abstractions of polynomials' and rational functions' spaces respectively. More precisely, that means they allow to declare common logic of interaction with such objects and spaces:
- `Polynomial` does not provide any logic. It is marker interface.
- `RationalFunction` provides numerator and denominator of rational function and destructuring declaration for them.
- `PolynomialSpace` provides all possible arithmetic interactions of integers, constants (of type `C`), and polynomials (of type `P`) like addition, subtraction, multiplication, and some others and common properties like degree of polynomial.
- `RationalFunctionSpace` provides the same as `PolynomialSpace` but also for rational functions: all possible arithmetic interactions of integers, constants (of type `C`), polynomials (of type `P`), and rational functions (of type `R`) like addition, subtraction, multiplication, division (in some cases), and some others and common properties like degree of polynomial.
Then to add abstraction of similar behaviour with variables (in multivariate case) there are implemented `MultivariatePolynomialSpace` and `MultivariateRationalFunctionSpace`. They just include variables (of type `V`) in the interactions of the entities.
Also, to remove boilerplates there were provided helping subinterfaces and abstract subclasses:
- `PolynomialSpaceOverRing` allows to replace implementation of interactions of integers and constants with implementations from provided ring over constants (of type `A: Ring<C>`).
- `RationalFunctionSpaceOverRing` &mdash; the same but for `RationalFunctionSpace`.
- `RationalFunctionSpaceOverPolynomialSpace` &mdash; the same but "the inheritance" includes interactions with polynomials from provided `PolynomialSpace`.
- `PolynomialSpaceOfFractions` is actually abstract subclass of `RationalFunctionSpace` that implements all fractions boilerplates with provided (`protected`) constructor of rational functions by polynomial numerator and denominator.
- `MultivariateRationalFunctionSpaceOverMultivariatePolynomialSpace` and `MultivariatePolynomialSpaceOfFractions` &mdash; the same stories of operators inheritance and fractions boilerplates respectively but in multivariate case.
## Utilities
For all kinds of polynomials there are provided (implementation details depend on kind of polynomials) such common utilities as:
1. differentiation and anti-differentiation,
2. substitution, invocation and functional representation.

11
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@ -1,6 +1,6 @@
[![JetBrains Research](https://jb.gg/badges/research.svg)](https://confluence.jetbrains.com/display/ALL/JetBrains+on+GitHub)
[![DOI](https://zenodo.org/badge/129486382.svg)](https://zenodo.org/badge/latestdoi/129486382)
![Gradle build](https://github.com/mipt-npm/kmath/workflows/Gradle%20build/badge.svg)
![Gradle build](https://github.com/SciProgCentre/kmath/workflows/Gradle%20build/badge.svg)
[![Maven Central](https://img.shields.io/maven-central/v/space.kscience/kmath-core.svg?label=Maven%20Central)](https://search.maven.org/search?q=g:%22space.kscience%22)
[![Space](https://img.shields.io/badge/dynamic/xml?color=orange&label=Space&query=//metadata/versioning/latest&url=https%3A%2F%2Fmaven.pkg.jetbrains.space%2Fmipt-npm%2Fp%2Fsci%2Fmaven%2Fspace%2Fkscience%2Fkmath-core%2Fmaven-metadata.xml)](https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven/space/kscience/)
@ -11,7 +11,7 @@ analog to Python's NumPy library. Later we found that kotlin is much more flexib
architecture designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like
experience could be achieved with [kmath-for-real](/kmath-for-real) extension module.
[Documentation site (**WIP**)](https://mipt-npm.github.io/kmath/)
[Documentation site (**WIP**)](https://SciProgCentre.github.io/kmath/)
## Publications and talks
@ -44,7 +44,7 @@ module definitions below. The module stability could have the following levels:
* **PROTOTYPE**. On this level there are no compatibility guarantees. All methods and classes form those modules could
break any moment. You can still use it, but be sure to fix the specific version.
* **EXPERIMENTAL**. The general API is decided, but some changes could be made. Volatile API is marked
with `@UnstableKmathAPI` or other stability warning annotations.
with `@UnstableKMathAPI` or other stability warning annotations.
* **DEVELOPMENT**. API breaking generally follows semantic versioning ideology. There could be changes in minor
versions, but not in patch versions. API is protected
with [binary-compatibility-validator](https://github.com/Kotlin/binary-compatibility-validator) tool.
@ -69,8 +69,7 @@ performance and flexibility.
We expect to focus on creating convenient universal API first and then work on increasing performance for specific
cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized
native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be
better than SciPy.
native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be better than SciPy.
## Requirements
@ -102,4 +101,4 @@ Gradle `6.0+` is required for multiplatform artifacts.
The project requires a lot of additional work. The most important thing we need is a feedback about what features are
required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues
marked with
[waiting for a hero](https://github.com/mipt-npm/kmath/labels/waiting%20for%20a%20hero) label.
[waiting for a hero](https://github.com/mipt-npm/kmath/labels/waiting%20for%20a%20hero) label.

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@ -8,6 +8,8 @@ repositories {
maven("https://maven.pkg.jetbrains.space/kotlin/p/kotlin/kotlin-js-wrappers")
}
val multikVersion: String by rootProject.extra
dependencies {
implementation(project(":kmath-ast"))
implementation(project(":kmath-kotlingrad"))
@ -15,6 +17,8 @@ dependencies {
implementation(project(":kmath-coroutines"))
implementation(project(":kmath-commons"))
implementation(project(":kmath-complex"))
implementation(project(":kmath-functions"))
implementation(project(":kmath-polynomial"))
implementation(project(":kmath-optimization"))
implementation(project(":kmath-stat"))
implementation(project(":kmath-viktor"))
@ -28,6 +32,7 @@ dependencies {
implementation(project(":kmath-jafama"))
//multik
implementation(project(":kmath-multik"))
implementation("org.jetbrains.kotlinx:multik-default:$multikVersion")
implementation("org.nd4j:nd4j-native:1.0.0-beta7")
@ -66,5 +71,5 @@ tasks.withType<org.jetbrains.kotlin.gradle.dsl.KotlinJvmCompile> {
}
readme {
maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL
maturity = space.kscience.gradle.Maturity.EXPERIMENTAL
}

2
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

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@ -0,0 +1,399 @@
/*
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@file:Suppress("LocalVariableName")
package space.kscience.kmath.functions
import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.expressions.symbol
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.operations.invoke
/**
* Shows [ListPolynomial]s' and [ListRationalFunction]s' capabilities.
*/
fun listPolynomialsExample() {
// [ListPolynomial] is a representation of a univariate polynomial as a list of coefficients from the least term to
// the greatest term. For example,
val polynomial1: ListPolynomial<Int> = ListPolynomial(listOf(2, -3, 1))
// represents polynomial 2 + (-3) x + x^2
// There are also shortcut fabrics:
val polynomial2: ListPolynomial<Int> = ListPolynomial(2, -3, 1)
println(polynomial1 == polynomial2) // true
// and even
val polynomial3: ListPolynomial<Int> = 57.asListPolynomial()
val polynomial4: ListPolynomial<Int> = ListPolynomial(listOf(57))
println(polynomial3 == polynomial4) // true
val polynomial5: ListPolynomial<Int> = ListPolynomial(3, -1)
// For every ring there can be provided a polynomial ring:
Int.algebra.listPolynomialSpace {
println(-polynomial5 == ListPolynomial(-3, 1)) // true
println(polynomial1 + polynomial5 == ListPolynomial(5, -4, 1)) // true
println(polynomial1 - polynomial5 == ListPolynomial(-1, -2, 1)) // true
println(polynomial1 * polynomial5 == ListPolynomial(6, -11, 6, -1)) // true
}
// You can even write
val x: ListPolynomial<Double> = ListPolynomial(0.0, 1.0)
val polynomial6: ListPolynomial<Double> = ListPolynomial(2.0, -3.0, 1.0)
Double.algebra.listPolynomialSpace {
println(2 - 3 * x + x * x == polynomial6)
println(2.0 - 3.0 * x + x * x == polynomial6)
}
// Also there are some utilities for polynomials:
println(polynomial1.substitute(Int.algebra, 1) == 0) // true, because 2 + (-3) * 1 + 1^2 = 0
println(polynomial1.substitute(Int.algebra, polynomial5) == polynomial1) // true, because 2 + (-3) * (3-x) + (3-x)^2 = 2 - 3x + x^2
println(polynomial1.derivative(Int.algebra) == ListPolynomial(-3, 2)) // true, (2 - 3x + x^2)' = -3 + 2x
println(polynomial1.nthDerivative(Int.algebra, 2) == 2.asListPolynomial()) // true, (2 - 3x + x^2)'' = 2
// Lastly, there are rational functions and some other utilities:
Double.algebra.listRationalFunctionSpace {
val rationalFunction1: ListRationalFunction<Double> = ListRationalFunction(listOf(2.0, -3.0, 1.0), listOf(3.0, -1.0))
// It's just (2 - 3x + x^2)/(3 - x)
val rationalFunction2 : ListRationalFunction<Double> = ListRationalFunction(listOf(5.0, -4.0, 1.0), listOf(3.0, -1.0))
// It's just (5 - 4x + x^2)/(3 - x)
println(rationalFunction1 + 1 == rationalFunction2)
}
}
/**
* Shows [NumberedPolynomial]s' and [NumberedRationalFunction]s' capabilities.
*/
fun numberedPolynomialsExample() {
// Consider polynomial
// 3 + 5 x_2 - 7 x_1^2 x_3
// Consider, for example, its term -7 x_1^2 x_3. -7 is a coefficient of the term, whereas (2, 0, 1, 0, 0, ...) is
// description of degrees of variables x_1, x_2, ... in the term. Such description with removed leading zeros
// [2, 0, 1] is called "signature" of the term -7 x_1^2 x_3.
val polynomial1: NumberedPolynomial<Int>
with(Int.algebra) {
// [NumberedPolynomial] is a representation of a multivariate polynomial, that stores terms in a map with terms'
// signatures as the map's keys and terms' coefficients as corresponding values. For example,
polynomial1 = NumberedPolynomial(
mapOf(
listOf<UInt>() to 3,
listOf(0u, 1u) to 5,
listOf(2u, 0u, 1u) to -7,
)
)
// represents polynomial 3 + 5 x_2 - 7 x_1^2 x_3
// This `NumberedPolynomial` function needs context of either ring of constant (as `Int.algebra` in this example)
// or space of NumberedPolynomials over it. To understand why it is like this see documentations of functions
// NumberedPolynomial and NumberedPolynomialWithoutCheck
// There are also shortcut fabrics:
val polynomial2: NumberedPolynomial<Int> = NumberedPolynomial(
listOf<UInt>() to 3,
listOf(0u, 1u) to 5,
listOf(2u, 0u, 1u) to -7,
)
println(polynomial1 == polynomial2) // true
// and even
val polynomial3: NumberedPolynomial<Int> = 57.asNumberedPolynomial() // This one actually does not algebraic context!
val polynomial4: NumberedPolynomial<Int> = NumberedPolynomial(listOf<UInt>() to 57)
println(polynomial3 == polynomial4) // true
numberedPolynomialSpace {
// Also there is DSL for constructing NumberedPolynomials:
val polynomial5: NumberedPolynomial<Int> = NumberedPolynomialDSL1 {
3 {}
5 { 1 inPowerOf 1u }
-7 with { 0 pow 2u; 2 pow 1u }
// `pow` and `inPowerOf` are the same
// `with` is omittable
}
println(polynomial1 == polynomial5) // true
// Unfortunately the DSL does not work good in bare context of constants' ring, so for now it's disabled and
// works only in NumberedPolynomialSpace and NumberedRationalFunctionSpace
}
}
val polynomial6: NumberedPolynomial<Int> = Int.algebra {
NumberedPolynomial(
listOf<UInt>() to 7,
listOf(0u, 1u) to -5,
listOf(2u, 0u, 1u) to 0,
listOf(0u, 0u, 0u, 4u) to 4,
)
}
// For every ring there can be provided a polynomial ring:
Int.algebra.numberedPolynomialSpace {
println(
-polynomial6 == NumberedPolynomial(
listOf<UInt>() to -7,
listOf(0u, 1u) to 5,
listOf(2u, 0u, 1u) to 0,
listOf(0u, 0u, 0u, 4u) to (-4),
)
) // true
println(
polynomial1 + polynomial6 == NumberedPolynomial(
listOf<UInt>() to 10,
listOf(0u, 1u) to 0,
listOf(2u, 0u, 1u) to -7,
listOf(0u, 0u, 0u, 4u) to 4,
)
) // true
println(
polynomial1 - polynomial6 == NumberedPolynomial(
listOf<UInt>() to -4,
listOf(0u, 1u) to 10,
listOf(2u, 0u, 1u) to -7,
listOf(0u, 0u, 0u, 4u) to -4,
)
) // true
polynomial1 * polynomial6 // Multiplication works too
}
Double.algebra.numberedPolynomialSpace {
// You can even write
val x_1: NumberedPolynomial<Double> = NumberedPolynomial(listOf(1u) to 1.0)
val x_2: NumberedPolynomial<Double> = NumberedPolynomial(listOf(0u, 1u) to 1.0)
val x_3: NumberedPolynomial<Double> = NumberedPolynomial(listOf(0u, 0u, 1u) to 1.0)
val polynomial7: NumberedPolynomial<Double> = NumberedPolynomial(
listOf<UInt>() to 3.0,
listOf(0u, 1u) to 5.0,
listOf(2u, 0u, 1u) to -7.0,
)
Double.algebra.listPolynomialSpace {
println(3 + 5 * x_2 - 7 * x_1 * x_1 * x_3 == polynomial7)
println(3.0 + 5.0 * x_2 - 7.0 * x_1 * x_1 * x_3 == polynomial7)
}
}
Int.algebra.numberedPolynomialSpace {
val x_4: NumberedPolynomial<Int> = NumberedPolynomial(listOf(0u, 0u, 0u, 4u) to 1)
// Also there are some utilities for polynomials:
println(polynomial1.substitute(mapOf(0 to 1, 1 to -2, 2 to -1)) == 0.asNumberedPolynomial()) // true,
// because it's substitution x_1 -> 1, x_2 -> -2, x_3 -> -1,
// so 3 + 5 x_2 - 7 x_1^2 x_3 = 3 + 5 * (-2) - 7 * 1^2 * (-1) = 3 - 10 + 7 = 0
println(
polynomial1.substitute(mapOf(1 to x_4)) == NumberedPolynomial(
listOf<UInt>() to 3,
listOf(0u, 1u) to 5,
listOf(2u, 0u, 1u) to -7,
)
) // true, because it's substitution x_2 -> x_4, so result is 3 + 5 x_4 - 7 x_1^2 x_3
println(
polynomial1.derivativeWithRespectTo(Int.algebra, 1) ==
NumberedPolynomial(listOf<UInt>() to 5)
) // true, d/dx_2 (3 + 5 x_2 - 7 x_1^2 x_3) = 5
}
// Lastly, there are rational functions and some other utilities:
Double.algebra.numberedRationalFunctionSpace {
val rationalFunction1: NumberedRationalFunction<Double> = NumberedRationalFunction(
NumberedPolynomial(
listOf<UInt>() to 2.0,
listOf(1u) to -3.0,
listOf(2u) to 1.0,
),
NumberedPolynomial(
listOf<UInt>() to 3.0,
listOf(1u) to -1.0,
)
)
// It's just (2 - 3x + x^2)/(3 - x) where x = x_1
val rationalFunction2: NumberedRationalFunction<Double> = NumberedRationalFunction(
NumberedPolynomial(
listOf<UInt>() to 5.0,
listOf(1u) to -4.0,
listOf(2u) to 1.0,
),
NumberedPolynomial(
listOf<UInt>() to 3.0,
listOf(1u) to -1.0,
)
)
// It's just (5 - 4x + x^2)/(3 - x) where x = x_1
println(rationalFunction1 + 1 == rationalFunction2)
}
}
/**
* Shows [LabeledPolynomial]s' and [LabeledRationalFunction]s' capabilities.
*/
fun labeledPolynomialsExample() {
val x by symbol
val y by symbol
val z by symbol
val t by symbol
// Consider polynomial
// 3 + 5 y - 7 x^2 z
// Consider, for example, its term -7 x^2 z. -7 is a coefficient of the term, whereas matching (x -> 2, z -> 3) is
// description of degrees of variables x_1, x_2, ... in the term. Such description is called "signature" of the
// term -7 x_1^2 x_3.
val polynomial1: LabeledPolynomial<Int>
with(Int.algebra) {
// [LabeledPolynomial] is a representation of a multivariate polynomial, that stores terms in a map with terms'
// signatures as the map's keys and terms' coefficients as corresponding values. For example,
polynomial1 = LabeledPolynomial(
mapOf(
mapOf<Symbol, UInt>() to 3,
mapOf(y to 1u) to 5,
mapOf(x to 2u, z to 1u) to -7,
)
)
// represents polynomial 3 + 5 y - 7 x^2 z
// This `LabeledPolynomial` function needs context of either ring of constant (as `Int.algebra` in this example)
// or space of LabeledPolynomials over it. To understand why it is like this see documentations of functions
// LabeledPolynomial and LabeledPolynomialWithoutCheck
// There are also shortcut fabrics:
val polynomial2: LabeledPolynomial<Int> = LabeledPolynomial(
mapOf<Symbol, UInt>() to 3,
mapOf(y to 1u) to 5,
mapOf(x to 2u, z to 1u) to -7,
)
println(polynomial1 == polynomial2) // true
// and even
val polynomial3: LabeledPolynomial<Int> = 57.asLabeledPolynomial() // This one actually does not algebraic context!
val polynomial4: LabeledPolynomial<Int> = LabeledPolynomial(mapOf<Symbol, UInt>() to 57)
println(polynomial3 == polynomial4) // true
labeledPolynomialSpace {
// Also there is DSL for constructing NumberedPolynomials:
val polynomial5: LabeledPolynomial<Int> = LabeledPolynomialDSL1 {
3 {}
5 { y inPowerOf 1u }
-7 with { x pow 2u; z pow 1u }
// `pow` and `inPowerOf` are the same
// `with` is omittable
}
println(polynomial1 == polynomial5) // true
// Unfortunately the DSL does not work good in bare context of constants' ring, so for now it's disabled and
// works only in NumberedPolynomialSpace and NumberedRationalFunctionSpace
}
}
val polynomial6: LabeledPolynomial<Int> = Int.algebra {
LabeledPolynomial(
mapOf<Symbol, UInt>() to 7,
mapOf(y to 1u) to -5,
mapOf(x to 2u, z to 1u) to 0,
mapOf(t to 4u) to 4,
)
}
// For every ring there can be provided a polynomial ring:
Int.algebra.labeledPolynomialSpace {
println(
-polynomial6 == LabeledPolynomial(
mapOf<Symbol, UInt>() to -7,
mapOf(y to 1u) to 5,
mapOf(x to 2u, z to 1u) to 0,
mapOf(t to 4u) to -4,
)
) // true
println(
polynomial1 + polynomial6 == LabeledPolynomial(
mapOf<Symbol, UInt>() to 10,
mapOf(y to 1u) to 0,
mapOf(x to 2u, z to 1u) to -7,
mapOf(t to 4u) to 4,
)
) // true
println(
polynomial1 - polynomial6 == LabeledPolynomial(
mapOf<Symbol, UInt>() to -4,
mapOf(y to 1u) to 10,
mapOf(x to 2u, z to 1u) to -7,
mapOf(t to 4u) to -4,
)
) // true
polynomial1 * polynomial6 // Multiplication works too
}
Double.algebra.labeledPolynomialSpace {
// You can even write
val polynomial7: LabeledPolynomial<Double> = LabeledPolynomial(
mapOf<Symbol, UInt>() to 3.0,
mapOf(y to 1u) to 5.0,
mapOf(x to 2u, z to 1u) to -7.0,
)
Double.algebra.listPolynomialSpace {
println(3 + 5 * y - 7 * x * x * z == polynomial7)
println(3.0 + 5.0 * y - 7.0 * x * x * z == polynomial7)
}
}
Int.algebra.labeledPolynomialSpace {
// Also there are some utilities for polynomials:
println(polynomial1.substitute(mapOf(x to 1, y to -2, z to -1)) == 0.asLabeledPolynomial()) // true,
// because it's substitution x -> 1, y -> -2, z -> -1,
// so 3 + 5 y - 7 x^2 z = 3 + 5 * (-2) - 7 * 1^2 * (-1) = 3 - 10 + 7 = 0
println(
polynomial1.substitute(mapOf(y to t.asPolynomial())) == LabeledPolynomial(
mapOf<Symbol, UInt>() to 3,
mapOf(t to 1u) to 5,
mapOf(x to 2u, z to 1u) to -7,
)
) // true, because it's substitution y -> t, so result is 3 + 5 t - 7 x^2 z
println(
polynomial1.derivativeWithRespectTo(Int.algebra, y) == LabeledPolynomial(mapOf<Symbol, UInt>() to 5)
) // true, d/dy (3 + 5 y - 7 x^2 z) = 5
}
// Lastly, there are rational functions and some other utilities:
Double.algebra.labeledRationalFunctionSpace {
val rationalFunction1: LabeledRationalFunction<Double> = LabeledRationalFunction(
LabeledPolynomial(
mapOf<Symbol, UInt>() to 2.0,
mapOf(x to 1u) to -3.0,
mapOf(x to 2u) to 1.0,
),
LabeledPolynomial(
mapOf<Symbol, UInt>() to 3.0,
mapOf(x to 1u) to -1.0,
)
)
// It's just (2 - 3x + x^2)/(3 - x)
val rationalFunction2: LabeledRationalFunction<Double> = LabeledRationalFunction(
LabeledPolynomial(
mapOf<Symbol, UInt>() to 5.0,
mapOf(x to 1u) to -4.0,
mapOf(x to 2u) to 1.0,
),
LabeledPolynomial(
mapOf<Symbol, UInt>() to 3.0,
mapOf(x to 1u) to -1.0,
)
)
// It's just (5 - 4x + x^2)/(3 - x)
println(rationalFunction1 + 1 == rationalFunction2)
}
}
fun main() {
println("ListPolynomials:")
listPolynomialsExample()
println()
println("NumberedPolynomials:")
numberedPolynomialsExample()
println()
println("ListPolynomials:")
labeledPolynomialsExample()
println()
}

2
examples/src/main/kotlin/space/kscience/kmath/jafama/JafamaDemo.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
examples/src/main/kotlin/space/kscience/kmath/linear/dotPerformance.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
examples/src/main/kotlin/space/kscience/kmath/linear/gradient.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
examples/src/main/kotlin/space/kscience/kmath/operations/BigIntDemo.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

7
examples/src/main/kotlin/space/kscience/kmath/operations/complexDemo.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -7,7 +7,6 @@ package space.kscience.kmath.operations
import space.kscience.kmath.complex.Complex
import space.kscience.kmath.complex.algebra
import space.kscience.kmath.complex.bufferAlgebra
import space.kscience.kmath.complex.ndAlgebra
import space.kscience.kmath.nd.BufferND
import space.kscience.kmath.nd.StructureND
@ -18,7 +17,7 @@ fun main() = Complex.algebra {
println(complex * 8 - 5 * i)
//flat buffer
val buffer = with(bufferAlgebra){
val buffer = with(bufferAlgebra) {
buffer(8) { Complex(it, -it) }.map { Complex(it.im, it.re) }
}
println(buffer)
@ -30,7 +29,7 @@ fun main() = Complex.algebra {
println(element)
// 1d element operation
val result: StructureND<Complex> = ndAlgebra{
val result: StructureND<Complex> = ndAlgebra {
val a = structureND(8) { (it) -> i * it - it.toDouble() }
val b = 3
val c = Complex(1.0, 1.0)

2
examples/src/main/kotlin/space/kscience/kmath/operations/mixedNDOperations.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
examples/src/main/kotlin/space/kscience/kmath/stat/DistributionBenchmark.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
examples/src/main/kotlin/space/kscience/kmath/stat/DistributionDemo.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
examples/src/main/kotlin/space/kscience/kmath/structures/ComplexND.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

23
examples/src/main/kotlin/space/kscience/kmath/structures/NDField.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -32,12 +32,10 @@ fun main() {
val shape = Shape(dim, dim)
// automatically build context most suited for given type.
val autoField = BufferedFieldOpsND(DoubleField, Buffer.Companion::auto)
// specialized nd-field for Double. It works as generic Double field as well.
val realField = DoubleField.ndAlgebra
//A generic boxing field. It should be used for objects, not primitives.
val boxingField = BufferedFieldOpsND(DoubleField, Buffer.Companion::boxing)
val doubleField = DoubleField.ndAlgebra
//A generic field. It should be used for objects, not primitives.
val genericField = BufferedFieldOpsND(DoubleField)
// Nd4j specialized field.
val nd4jField = DoubleField.nd4j
//viktor field
@ -46,14 +44,14 @@ fun main() {
val parallelField = DoubleField.ndStreaming(dim, dim)
measureAndPrint("Boxing addition") {
boxingField {
genericField {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Specialized addition") {
realField {
doubleField {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
}
@ -80,15 +78,8 @@ fun main() {
}
}
measureAndPrint("Automatic field addition") {
autoField {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Lazy addition") {
val res = realField.one(shape).mapAsync(GlobalScope) {
val res = doubleField.one(shape).mapAsync(GlobalScope) {
var c = 0.0
repeat(n) {
c += 1.0

6
examples/src/main/kotlin/space/kscience/kmath/structures/StreamDoubleFieldND.kt
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@ -1,10 +1,11 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.structures
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.ExtendedField
@ -49,6 +50,7 @@ class StreamDoubleFieldND(override val shape: IntArray) : FieldND<Double, Double
return BufferND(strides, array.asBuffer())
}
@OptIn(PerformancePitfall::class)
override fun StructureND<Double>.map(
transform: DoubleField.(Double) -> Double,
): BufferND<Double> {
@ -56,6 +58,7 @@ class StreamDoubleFieldND(override val shape: IntArray) : FieldND<Double, Double
return BufferND(strides, array.asBuffer())
}
@OptIn(PerformancePitfall::class)
override fun StructureND<Double>.mapIndexed(
transform: DoubleField.(index: IntArray, Double) -> Double,
): BufferND<Double> {
@ -69,6 +72,7 @@ class StreamDoubleFieldND(override val shape: IntArray) : FieldND<Double, Double
return BufferND(strides, array.asBuffer())
}
@OptIn(PerformancePitfall::class)
override fun zip(
left: StructureND<Double>,
right: StructureND<Double>,

2
examples/src/main/kotlin/space/kscience/kmath/structures/StructureReadBenchmark.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
examples/src/main/kotlin/space/kscience/kmath/structures/StructureWriteBenchmark.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
examples/src/main/kotlin/space/kscience/kmath/structures/buffers.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
examples/src/main/kotlin/space/kscience/kmath/structures/typeSafeDimensions.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

6
examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
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@ -1,10 +1,11 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
@ -13,6 +14,7 @@ import kotlin.math.abs
// OLS estimator using SVD
@OptIn(PerformancePitfall::class)
fun main() {
//seed for random
val randSeed = 100500L
@ -50,7 +52,7 @@ fun main() {
// inverse Sigma matrix can be restored from singular values with diagonalEmbedding function
val sigma = diagonalEmbedding(singValues.map{ if (abs(it) < 1e-3) 0.0 else 1.0/it })
val alphaOLS = v dot sigma dot u.transpose() dot y
val alphaOLS = v dot sigma dot u.transposed() dot y
println("Estimated alpha:\n" +
"$alphaOLS")

10
examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -27,7 +27,7 @@ fun main(): Unit = Double.tensorAlgebra.withBroadcast { // work in context with
println("y:\n$y")
// stack them into single dataset
val dataset = stack(listOf(x, y)).transpose()
val dataset = stack(listOf(x, y)).transposed()
// normalize both x and y
val xMean = x.mean()
@ -58,7 +58,7 @@ fun main(): Unit = Double.tensorAlgebra.withBroadcast { // work in context with
// and find out eigenvector of it
val (_, evecs) = covMatrix.symEig()
val v = evecs[0]
val v = evecs.getTensor(0)
println("Eigenvector:\n$v")
// reduce dimension of dataset
@ -68,7 +68,7 @@ fun main(): Unit = Double.tensorAlgebra.withBroadcast { // work in context with
// we can restore original data from reduced data;
// for example, find 7th element of dataset.
val n = 7
val restored = (datasetReduced[n] dot v.view(intArrayOf(1, 2))) * std + mean
println("Original value:\n${dataset[n]}")
val restored = (datasetReduced.getTensor(n) dot v.view(intArrayOf(1, 2))) * std + mean
println("Original value:\n${dataset.getTensor(n)}")
println("Restored value:\n$restored")
}

2
examples/src/main/kotlin/space/kscience/kmath/tensors/dataSetNormalization.kt
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@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

6
examples/src/main/kotlin/space/kscience/kmath/tensors/linearSystemSolvingWithLUP.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -66,7 +66,7 @@ fun main() = Double.tensorAlgebra.withBroadcast {// work in context with linear
val n = l.shape[0]
val x = zeros(intArrayOf(n))
for (i in 0 until n) {
x[intArrayOf(i)] = (b[intArrayOf(i)] - l[i].dot(x).value()) / l[intArrayOf(i, i)]
x[intArrayOf(i)] = (b[intArrayOf(i)] - l.getTensor(i).dot(x).value()) / l[intArrayOf(i, i)]
}
return x
}
@ -75,7 +75,7 @@ fun main() = Double.tensorAlgebra.withBroadcast {// work in context with linear
// solveLT(l, b) function can be easily adapted for upper triangular matrix by the permutation matrix revMat
// create it by placing ones on side diagonal
val revMat = u.zeroesLike()
val revMat = zeroesLike(u)
val n = revMat.shape[0]
for (i in 0 until n) {
revMat[intArrayOf(i, n - 1 - i)] = 1.0

11
examples/src/main/kotlin/space/kscience/kmath/tensors/multik.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -7,11 +7,14 @@ package space.kscience.kmath.tensors
import org.jetbrains.kotlinx.multik.api.Multik
import org.jetbrains.kotlinx.multik.api.ndarray
import space.kscience.kmath.multik.multikAlgebra
import org.jetbrains.kotlinx.multik.default.DefaultEngine
import space.kscience.kmath.multik.MultikDoubleAlgebra
import space.kscience.kmath.nd.one
import space.kscience.kmath.operations.DoubleField
fun main(): Unit = with(DoubleField.multikAlgebra) {
val multikAlgebra = MultikDoubleAlgebra(DefaultEngine())
fun main(): Unit = with(multikAlgebra) {
val a = Multik.ndarray(intArrayOf(1, 2, 3)).asType<Double>().wrap()
val b = Multik.ndarray(doubleArrayOf(1.0, 2.0, 3.0)).wrap()
one(a.shape) - a + b * 3.0

20
examples/src/main/kotlin/space/kscience/kmath/tensors/neuralNetwork.kt
View File

@ -1,15 +1,16 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors
import space.kscience.kmath.operations.asIterable
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
import space.kscience.kmath.tensors.core.copyArray
import space.kscience.kmath.tensors.core.toDoubleTensor
import kotlin.math.sqrt
const val seed = 100500L
@ -79,9 +80,9 @@ class Dense(
}
override fun backward(input: DoubleTensor, outputError: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
val gradInput = outputError dot weights.transpose()
val gradInput = outputError dot weights.transposed()
val gradW = input.transpose() dot outputError
val gradW = input.transposed() dot outputError
val gradBias = outputError.mean(dim = 0, keepDim = false) * input.shape[0].toDouble()
weights -= learningRate * gradW
@ -106,12 +107,11 @@ fun accuracy(yPred: DoubleTensor, yTrue: DoubleTensor): Double {
}
// neural network class
@OptIn(ExperimentalStdlibApi::class)
class NeuralNetwork(private val layers: List<Layer>) {
private fun softMaxLoss(yPred: DoubleTensor, yTrue: DoubleTensor): DoubleTensor = BroadcastDoubleTensorAlgebra {
val onesForAnswers = yPred.zeroesLike()
yTrue.copyArray().forEachIndexed { index, labelDouble ->
val onesForAnswers = zeroesLike(yPred)
yTrue.source.asIterable().forEachIndexed { index, labelDouble ->
val label = labelDouble.toInt()
onesForAnswers[intArrayOf(index, label)] = 1.0
}
@ -163,7 +163,7 @@ class NeuralNetwork(private val layers: List<Layer>) {
for ((xBatch, yBatch) in iterBatch(xTrain, yTrain)) {
train(xBatch, yBatch)
}
println("Accuracy:${accuracy(yTrain, predict(xTrain).argMax(1, true).asDouble())}")
println("Accuracy:${accuracy(yTrain, predict(xTrain).argMax(1, true).toDoubleTensor())}")
}
}
@ -194,7 +194,7 @@ fun main() = BroadcastDoubleTensorAlgebra {
val y = fromArray(
intArrayOf(sampleSize, 1),
DoubleArray(sampleSize) { i ->
if (x[i].sum() > 0.0) {
if (x.getTensor(i).sum() > 0.0) {
1.0
} else {
0.0
@ -230,7 +230,7 @@ fun main() = BroadcastDoubleTensorAlgebra {
val prediction = model.predict(xTest)
// process raw prediction via argMax
val predictionLabels = prediction.argMax(1, true).asDouble()
val predictionLabels = prediction.argMax(1, true).toDoubleTensor()
// find out accuracy
val acc = accuracy(yTest, predictionLabels)

6
gradle.properties
View File

@ -6,8 +6,10 @@ kotlin.code.style=official
kotlin.jupyter.add.scanner=false
kotlin.mpp.stability.nowarn=true
kotlin.native.ignoreDisabledTargets=true
#kotlin.incremental.js.ir=true
kotlin.incremental.js.ir=true
org.gradle.configureondemand=true
org.gradle.jvmargs=-XX:MaxMetaspaceSize=1G
org.gradle.parallel=true
org.gradle.jvmargs=-Xmx4096m
toolsVersion=0.13.0-kotlin-1.7.20-Beta

2
gradle/wrapper/gradle-wrapper.properties vendored
View File

@ -1,5 +1,5 @@
distributionBase=GRADLE_USER_HOME
distributionPath=wrapper/dists
distributionUrl=https\://services.gradle.org/distributions/gradle-7.4.2-bin.zip
distributionUrl=https\://services.gradle.org/distributions/gradle-7.5-bin.zip
zipStoreBase=GRADLE_USER_HOME
zipStorePath=wrapper/dists

11
kmath-ast/README.md
View File

@ -10,7 +10,7 @@ Extensions to MST API: transformations, dynamic compilation and visualization.
## Artifact:
The Maven coordinates of this project are `space.kscience:kmath-ast:0.3.0`.
The Maven coordinates of this project are `space.kscience:kmath-ast:0.3.1-dev-1`.
**Gradle Groovy:**
```groovy
@ -20,7 +20,7 @@ repositories {
}
dependencies {
implementation 'space.kscience:kmath-ast:0.3.0'
implementation 'space.kscience:kmath-ast:0.3.1-dev-1'
}
```
**Gradle Kotlin DSL:**
@ -31,7 +31,7 @@ repositories {
}
dependencies {
implementation("space.kscience:kmath-ast:0.3.0")
implementation("space.kscience:kmath-ast:0.3.1-dev-1")
}
```
@ -199,10 +199,7 @@ public fun main() {
Result LaTeX:
<div style="background-color:white;">
![](https://latex.codecogs.com/gif.latex?%5Coperatorname{exp}%5C,%5Cleft(%5Csqrt{x}%5Cright)-%5Cfrac{%5Cfrac{%5Coperatorname{arcsin}%5C,%5Cleft(2%5C,x%5Cright)}{2%5Ctimes10^{10}%2Bx^{3}}}{12}+x^{2/3})
</div>
$$\operatorname{exp}\\,\left(\sqrt{x}\right)-\frac{\frac{\operatorname{arcsin}\\,\left(2\\,x\right)}{2\times10^{10}+x^{3}}}{12}+x^{2/3}$$
Result MathML (can be used with MathJax or other renderers):

13
kmath-ast/build.gradle.kts
View File

@ -1,6 +1,9 @@
plugins {
kotlin("multiplatform")
id("ru.mipt.npm.gradle.common")
id("space.kscience.gradle.mpp")
}
kscience{
native()
}
kotlin.js {
@ -24,7 +27,7 @@ kotlin.sourceSets {
commonMain {
dependencies {
api("com.github.h0tk3y.betterParse:better-parse:0.4.2")
api("com.github.h0tk3y.betterParse:better-parse:0.4.4")
api(project(":kmath-core"))
}
}
@ -57,11 +60,11 @@ tasks.dokkaHtml {
if (System.getProperty("space.kscience.kmath.ast.dump.generated.classes") == "1")
tasks.jvmTest {
jvmArgs = (jvmArgs ?: emptyList()) + listOf("-Dspace.kscience.kmath.ast.dump.generated.classes=1")
jvmArgs("-Dspace.kscience.kmath.ast.dump.generated.classes=1")
}
readme {
maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL
maturity = space.kscience.gradle.Maturity.EXPERIMENTAL
propertyByTemplate("artifact", rootProject.file("docs/templates/ARTIFACT-TEMPLATE.md"))
feature(

5
kmath-ast/docs/README-TEMPLATE.md
View File

@ -170,10 +170,7 @@ public fun main() {
Result LaTeX:
<div style="background-color:white;">
![](https://latex.codecogs.com/gif.latex?%5Coperatorname{exp}%5C,%5Cleft(%5Csqrt{x}%5Cright)-%5Cfrac{%5Cfrac{%5Coperatorname{arcsin}%5C,%5Cleft(2%5C,x%5Cright)}{2%5Ctimes10^{10}%2Bx^{3}}}{12}+x^{2/3})
</div>
$$\operatorname{exp}\\,\left(\sqrt{x}\right)-\frac{\frac{\operatorname{arcsin}\\,\left(2\\,x\right)}{2\times10^{10}+x^{3}}}{12}+x^{2/3}$$
Result MathML (can be used with MathJax or other renderers):

2
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/TypedMst.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/evaluateConstants.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/LatexSyntaxRenderer.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathMLSyntaxRenderer.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathSyntax.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/SyntaxRenderer.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/features.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/multiplatformToString.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/phases.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerConsistencyWithInterpreter.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerOperations.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerVariables.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestFolding.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestParser.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestParserPrecedence.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestFeatures.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestLatex.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestMathML.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestStages.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestUtils.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/utils.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/jsMain/kotlin/space/kscience/kmath/ast/rendering/multiplatformToString.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

4
kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -36,7 +36,7 @@ public fun <T : Any> MST.compileToExpression(algebra: Algebra<T>): Expression<T>
)
}
return ESTreeBuilder<T> { visit(typed) }.instance
return ESTreeBuilder { visit(typed) }.instance
}
/**

23
kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/internal/ESTreeBuilder.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@ -22,28 +22,20 @@ internal class ESTreeBuilder<T>(val bodyCallback: ESTreeBuilder<T>.() -> BaseExp
}
}
@Suppress("UNUSED_VARIABLE")
val instance: Expression<T> by lazy {
val node = Program(
sourceType = "script",
VariableDeclaration(
kind = "var",
VariableDeclarator(
id = Identifier("executable"),
init = FunctionExpression(
params = arrayOf(Identifier("constants"), Identifier("arguments")),
body = BlockStatement(ReturnStatement(bodyCallback())),
),
),
),
ReturnStatement(bodyCallback())
)
eval(generate(node))
GeneratedExpression(js("executable"), constants.toTypedArray())
val code = generate(node)
GeneratedExpression(js("new Function('constants', 'arguments_0', code)"), constants.toTypedArray())
}
private val constants = mutableListOf<Any>()
fun constant(value: Any?) = when {
fun constant(value: Any?): BaseExpression = when {
value == null || jsTypeOf(value) == "number" || jsTypeOf(value) == "string" || jsTypeOf(value) == "boolean" ->
SimpleLiteral(value)
@ -61,7 +53,8 @@ internal class ESTreeBuilder<T>(val bodyCallback: ESTreeBuilder<T>.() -> BaseExp
}
}
fun variable(name: Symbol): BaseExpression = call(getOrFail, Identifier("arguments"), SimpleLiteral(name.identity))
fun variable(name: Symbol): BaseExpression =
call(getOrFail, Identifier("arguments_0"), SimpleLiteral(name.identity))
fun call(function: Function<T>, vararg args: BaseExpression): BaseExpression = SimpleCallExpression(
optional = false,

2
kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/internal/astring/astring.typealises.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

2
kmath-ast/src/jsMain/kotlin/space/kscience/kmath/internal/astring/astring.kt
View File

@ -1,5 +1,5 @@
/*
* Copyright 2018-2021 KMath contributors.
* Copyright 2018-2022 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/

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