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# Conflicts:
#	.github/workflows/build.yml
#	kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt
#	kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/TensorLinearStructure.kt
This commit is contained in:
Iaroslav Postovalov 2021-07-13 00:53:37 +07:00
commit 3d8390a130
443 changed files with 23848 additions and 9524 deletions
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122
.github/workflows/build.yml vendored
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@ -1,110 +1,42 @@
name: Gradle build
on: [ push ]
on:
push:
pull_request:
types: [opened, edited]
jobs:
build-ubuntu:
runs-on: ubuntu-20.04
build:
strategy:
matrix:
os: [ macOS-latest, windows-latest ]
runs-on: ${{matrix.os}}
timeout-minutes: 30
steps:
- uses: actions/checkout@v2
- name: Checkout the repo
uses: actions/checkout@v2
- name: Set up JDK 11
uses: actions/setup-java@v1
uses: DeLaGuardo/setup-graalvm@4.0
with:
java-version: 11
- name: Install build-essential
run: |
sudo apt install -y build-essential
- name: Grant execute permission for gradlew
run: chmod +x gradlew
- name: Install Chrome
run: |
sudo apt install -y libappindicator1 fonts-liberation
wget https://dl.google.com/linux/direct/google-chrome-stable_current_amd64.deb
sudo dpkg -i google-chrome*.deb
- name: Cache gradle
uses: actions/cache@v2
with:
path: |
.gradle
build
~/.gradle
key: gradle
restore-keys: gradle
- name: Cache konan
uses: actions/cache@v2
with:
path: |
~/.konan/dependencies
~/.konan/kotlin-native-prebuilt-linux-*
key: ${{ runner.os }}-konan
restore-keys: ${{ runner.os }}-konan
- name: Build with Gradle
run: ./gradlew -Dorg.gradle.daemon=false --build-cache build
build-osx:
runs-on: macos-latest
steps:
- uses: actions/checkout@v2
- name: Set up JDK 11
uses: actions/setup-java@v1
with:
java-version: 11
- name: Grant execute permission for gradlew
run: chmod +x gradlew
- name: Cache gradle
uses: actions/cache@v2
with:
path: |
.gradle
build
~/.gradle
key: gradle
restore-keys: gradle
- name: Cache konan
uses: actions/cache@v2
with:
path: |
~/.konan/dependencies
~/.konan/kotlin-native-prebuilt-macos-*
key: ${{ runner.os }}-konan
restore-keys: ${{ runner.os }}-konan
- name: Build with Gradle
run: sudo ./gradlew -Dorg.gradle.daemon=false --build-cache build
build-windows:
runs-on: windows-latest
steps:
- uses: actions/checkout@v2
- name: Set up JDK 11
uses: actions/setup-java@v1
with:
java-version: 11
- name: Grant execute permission for gradlew
run: chmod +x gradlew
graalvm: 21.1.0
java: java11
arch: amd64
- name: Add msys to path
if: matrix.os == 'windows-latest'
run: SETX PATH "%PATH%;C:\msys64\mingw64\bin"
- name: Cache gradle
uses: actions/cache@v2
with:
path: |
.gradle
build
~/.gradle
key: ${{ runner.os }}-gradle
restore-keys: ${{ runner.os }}-gradle
path: ~/.gradle/caches
key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
restore-keys: |
${{ runner.os }}-gradle-
- name: Cache konan
uses: actions/cache@v2
with:
path: |
~/.konan/dependencies
~/.konan/kotlin-native-prebuilt-mingw-*
key: ${{ runner.os }}-konan
restore-keys: ${{ runner.os }}-konan
- name: Build with Gradle
run: ./gradlew --build-cache build
path: ~/.konan
key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
restore-keys: |
${{ runner.os }}-gradle-
- name: Build
run: ./gradlew build --no-daemon --stacktrace

24
.github/workflows/pages.yml vendored Normal file
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@ -0,0 +1,24 @@
name: Dokka publication
on:
push:
branches:
- master
jobs:
build:
runs-on: ubuntu-20.04
steps:
- name: Checkout the repo
uses: actions/checkout@v2
- name: Set up JDK 11
uses: actions/setup-java@v1
with:
java-version: 11
- name: Build
run: ./gradlew dokkaHtmlMultiModule --no-daemon --no-parallel --stacktrace
- name: Deploy to GitHub Pages
uses: JamesIves/github-pages-deploy-action@4.1.0
with:
branch: gh-pages
folder: build/dokka/htmlMultiModule

61
.github/workflows/publish.yml vendored Normal file
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@ -0,0 +1,61 @@
name: Gradle publish
on:
workflow_dispatch:
release:
types:
- created
jobs:
publish:
environment:
name: publish
strategy:
matrix:
os: [ macOS-latest, windows-latest ]
runs-on: ${{matrix.os}}
steps:
- name: Checkout the repo
uses: actions/checkout@v2
- name: Set up JDK 11
uses: DeLaGuardo/setup-graalvm@4.0
with:
graalvm: 21.1.0
java: java11
arch: amd64
- name: Add msys to path
if: matrix.os == 'windows-latest'
run: SETX PATH "%PATH%;C:\msys64\mingw64\bin"
- name: Cache gradle
uses: actions/cache@v2
with:
path: ~/.gradle/caches
key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
restore-keys: |
${{ runner.os }}-gradle-
- name: Cache konan
uses: actions/cache@v2
with:
path: ~/.konan
key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
restore-keys: |
${{ runner.os }}-gradle-
- name: Publish Windows Artifacts
if: matrix.os == 'windows-latest'
run: >
./gradlew release --no-daemon
-Ppublishing.enabled=true
-Ppublishing.github.user=${{ secrets.PUBLISHING_GITHUB_USER }}
-Ppublishing.github.token=${{ secrets.PUBLISHING_GITHUB_TOKEN }}
-Ppublishing.space.user=${{ secrets.PUBLISHING_SPACE_USER }}
-Ppublishing.space.token=${{ secrets.PUBLISHING_SPACE_TOKEN }}
- name: Publish Mac Artifacts
if: matrix.os == 'macOS-latest'
run: >
./gradlew release --no-daemon
-Ppublishing.enabled=true
-Ppublishing.platform=macosX64
-Ppublishing.github.user=${{ secrets.PUBLISHING_GITHUB_USER }}
-Ppublishing.github.token=${{ secrets.PUBLISHING_GITHUB_TOKEN }}
-Ppublishing.space.user=${{ secrets.PUBLISHING_SPACE_USER }}
-Ppublishing.space.token=${{ secrets.PUBLISHING_SPACE_TOKEN }}

117
.github/workflows/release.yml vendored
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@ -1,117 +0,0 @@
name: Gradle release
on:
release:
types:
- created
jobs:
build-ubuntu:
runs-on: ubuntu-20.04
steps:
- uses: actions/checkout@v2
- name: Set up JDK 11
uses: actions/setup-java@v1
with:
java-version: 11
- name: Grant execute permission for gradlew
run: chmod +x gradlew
- name: Install Chrome
run: |
sudo apt install -y libappindicator1 fonts-liberation
wget https://dl.google.com/linux/direct/google-chrome-stable_current_amd64.deb
sudo dpkg -i google-chrome*.deb
- name: Cache gradle
uses: actions/cache@v2
with:
path: |
.gradle
build
~/.gradle
key: gradle
restore-keys: gradle
- name: Cache konan
uses: actions/cache@v2
with:
path: |
~/.konan/dependencies
~/.konan/kotlin-native-prebuilt-linux-*
key: ${{ runner.os }}-konan
restore-keys: ${{ runner.os }}-konan
- name: Build with Gradle
run: ./gradlew -Dorg.gradle.daemon=false --build-cache build
- name: Run release task
run: ./gradlew release -PbintrayUser=${{ secrets.BINTRAY_USER }} -PbintrayApiKey=${{ secrets.BINTRAY_KEY }}
build-osx:
runs-on: macos-latest
steps:
- uses: actions/checkout@v2
- name: Set up JDK 11
uses: actions/setup-java@v1
with:
java-version: 11
- name: Grant execute permission for gradlew
run: chmod +x gradlew
- name: Cache gradle
uses: actions/cache@v2
with:
path: |
.gradle
build
~/.gradle
key: gradle
restore-keys: gradle
- name: Cache konan
uses: actions/cache@v2
with:
path: |
~/.konan/dependencies
~/.konan/kotlin-native-prebuilt-macos-*
key: ${{ runner.os }}-konan
restore-keys: ${{ runner.os }}-konan
- name: Build with Gradle
run: sudo ./gradlew -Dorg.gradle.daemon=false --build-cache build
- name: Run release task
run: ./gradlew release -PbintrayUser=${{ secrets.BINTRAY_USER }} -PbintrayApiKey=${{ secrets.BINTRAY_KEY }}
build-windows:
runs-on: windows-latest
steps:
- uses: actions/checkout@v2
- name: Set up JDK 11
uses: actions/setup-java@v1
with:
java-version: 11
- name: Grant execute permission for gradlew
run: chmod +x gradlew
- name: Add msys to path
run: SETX PATH "%PATH%;C:\msys64\mingw64\bin"
- name: Cache gradle
uses: actions/cache@v2
with:
path: |
.gradle
build
~/.gradle
key: ${{ runner.os }}-gradle
restore-keys: ${{ runner.os }}-gradle
- name: Cache konan
uses: actions/cache@v2
with:
path: |
~/.konan/dependencies
~/.konan/kotlin-native-prebuilt-mingw-*
key: ${{ runner.os }}-konan
restore-keys: ${{ runner.os }}-konan
- name: Build with Gradle
run: ./gradlew --build-cache build
- name: Run release task
run: ./gradlew release -PbintrayUser=${{ secrets.BINTRAY_USER }} -PbintrayApiKey=${{ secrets.BINTRAY_KEY }}

5
.gitignore vendored
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@ -1,7 +1,12 @@
.gradle
build/
out/
.idea/
!.idea/copyright/
!.idea/scopes/
.vscode/
# Avoid ignoring Gradle wrapper jar file (.jar files are usually ignored)

6
.idea/copyright/kmath.xml Normal file
View File

@ -0,0 +1,6 @@
<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>

21
.idea/copyright/profiles_settings.xml Normal file
View File

@ -0,0 +1,21 @@
<component name="CopyrightManager">
<settings default="kmath">
<module2copyright>
<element module="Apply copyright" copyright="kmath" />
</module2copyright>
<LanguageOptions name="Groovy">
<option name="fileTypeOverride" value="1" />
</LanguageOptions>
<LanguageOptions name="HTML">
<option name="fileTypeOverride" value="1" />
<option name="prefixLines" value="false" />
</LanguageOptions>
<LanguageOptions name="Properties">
<option name="fileTypeOverride" value="1" />
</LanguageOptions>
<LanguageOptions name="XML">
<option name="fileTypeOverride" value="1" />
<option name="prefixLines" value="false" />
</LanguageOptions>
</settings>
</component>

4
.idea/scopes/Apply_copyright.xml Normal file
View File

@ -0,0 +1,4 @@
<component name="DependencyValidationManager">
<scope name="Apply copyright"
pattern="!file[*]:*//testData//*&amp;&amp;!file[*]:testData//*&amp;&amp;!file[*]:*.gradle.kts&amp;&amp;!file[*]:*.gradle&amp;&amp;!file[group:kotlin-ultimate]:*/&amp;&amp;!file[kotlin.libraries]:stdlib/api//*"/>
</component>

42
CHANGELOG.md
View File

@ -2,14 +2,55 @@
## [Unreleased]
### Added
- `ScaleOperations` interface
- `Field` extends `ScaleOperations`
- Basic integration API
- Basic MPP distributions and samplers
- `bindSymbolOrNull`
- Blocking chains and Statistics
- Multiplatform integration
- Integration for any Field element
- Extended operations for ND4J fields
- Jupyter Notebook integration module (kmath-jupyter)
- `@PerformancePitfall` annotation to mark possibly slow API
- `BigInt` operation performance improvement and fixes by @zhelenskiy (#328)
- Integration between `MST` and Symja `IExpr`
### Changed
- Exponential operations merged with hyperbolic functions
- Space is replaced by Group. Space is reserved for vector spaces.
- VectorSpace is now a vector space
- Buffer factories for primitives moved to MutableBuffer.Companion
- Rename `NDStructure` and `NDAlgebra` to `StructureND` and `AlgebraND` respectively
- `Real` -> `Double`
- DataSets are moved from functions to core
- Redesign advanced Chain API
- Redesign `MST`. Remove `MstExpression`.
- Move `MST` to core
- Separated benchmarks and examples
- Rewrite `kmath-ejml` without `ejml-simple` artifact, support sparse matrices
- Promote stability of kmath-ast and kmath-kotlingrad to EXPERIMENTAL.
- ColumnarData returns nullable column
- `MST` is made sealed interface
- Replace `MST.Symbolic` by `Symbol`, `Symbol` now implements MST
- Remove Any restriction on polynomials
- Add `out` variance to type parameters of `StructureND` and its implementations where possible
- Rename `DifferentiableMstExpression` to `KotlingradExpression`
### Deprecated
### Removed
- Nearest in Domain. To be implemented in geometry package.
- Number multiplication and division in main Algebra chain
- `contentEquals` from Buffer. It moved to the companion.
- MSTExpression
- Expression algebra builders
- Complex and Quaternion no longer are elements.
- Second generic from DifferentiableExpression
### Fixed
- Ring inherits RingOperations, not GroupOperations
- Univariate histogram filling
### Security
@ -65,6 +106,7 @@
- `toGrid` method.
- Public visibility of `BufferAccessor2D`
- `Real` class
- StructureND identity and equals
### Fixed
- `symbol` method in `MstExtendedField` (https://github.com/mipt-npm/kmath/pull/140)

201
LICENSE
View File

@ -1,201 +0,0 @@
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131
README.md
View File

@ -1,11 +1,8 @@
[![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)
Bintray: [ ![Download](https://api.bintray.com/packages/mipt-npm/kscience/kmath-core/images/download.svg) ](https://bintray.com/mipt-npm/kscience/kmath-core/_latestVersion)
Bintray-dev: [ ![Download](https://api.bintray.com/packages/mipt-npm/dev/kmath-core/images/download.svg) ](https://bintray.com/mipt-npm/dev/kmath-core/_latestVersion)
[![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/)
# KMath
@ -14,6 +11,8 @@ Python's NumPy library. Later we found that kotlin is much more flexible languag
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/)
## Publications and talks
* [A conceptual article about context-oriented design](https://proandroiddev.com/an-introduction-context-oriented-programming-in-kotlin-2e79d316b0a2)
@ -41,7 +40,7 @@ KMath is a modular library. Different modules provide different features with di
* **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.
* **DEVELOPMENT**. API breaking genrally 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.
* **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.
* **STABLE**. The API stabilized. Breaking changes are allowed only in major releases.
<!--Current feature list is [here](/docs/features.md)-->
@ -77,6 +76,12 @@ KMath is a modular library. Different modules provide different features with di
<hr/>
* ### [benchmarks](benchmarks)
>
>
> **Maturity**: EXPERIMENTAL
<hr/>
* ### [examples](examples)
>
>
@ -86,15 +91,13 @@ KMath is a modular library. Different modules provide different features with di
* ### [kmath-ast](kmath-ast)
>
>
> **Maturity**: PROTOTYPE
> **Maturity**: EXPERIMENTAL
>
> **Features:**
> - [expression-language](kmath-ast/src/jvmMain/kotlin/kscience/kmath/ast/parser.kt) : Expression language and its parser
> - [mst](kmath-ast/src/commonMain/kotlin/kscience/kmath/ast/MST.kt) : MST (Mathematical Syntax Tree) as expression language's syntax intermediate representation
> - [mst-building](kmath-ast/src/commonMain/kotlin/kscience/kmath/ast/MstAlgebra.kt) : MST building algebraic structure
> - [mst-interpreter](kmath-ast/src/commonMain/kotlin/kscience/kmath/ast/MST.kt) : MST interpreter
> - [mst-jvm-codegen](kmath-ast/src/jvmMain/kotlin/kscience/kmath/asm/asm.kt) : Dynamic MST to JVM bytecode compiler
> - [mst-js-codegen](kmath-ast/src/jsMain/kotlin/kscience/kmath/estree/estree.kt) : Dynamic MST to JS compiler
> - [expression-language](kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt) : Expression language and its parser
> - [mst-jvm-codegen](kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/asm.kt) : Dynamic MST to JVM bytecode compiler
> - [mst-js-codegen](kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt) : Dynamic MST to JS compiler
> - [rendering](kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt) : Extendable MST rendering
<hr/>
@ -110,8 +113,8 @@ KMath is a modular library. Different modules provide different features with di
> **Maturity**: PROTOTYPE
>
> **Features:**
> - [complex](kmath-complex/src/commonMain/kotlin/kscience/kmath/complex/Complex.kt) : Complex Numbers
> - [quaternion](kmath-complex/src/commonMain/kotlin/kscience/kmath/complex/Quaternion.kt) : Quaternions
> - [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
<hr/>
@ -121,15 +124,15 @@ KMath is a modular library. Different modules provide different features with di
> **Maturity**: DEVELOPMENT
>
> **Features:**
> - [algebras](kmath-core/src/commonMain/kotlin/kscience/kmath/operations/Algebra.kt) : Algebraic structures like rings, spaces and fields.
> - [nd](kmath-core/src/commonMain/kotlin/kscience/kmath/structures/NDStructure.kt) : Many-dimensional structures and operations on them.
> - [linear](kmath-core/src/commonMain/kotlin/kscience/kmath/operations/Algebra.kt) : Basic linear algebra operations (sums, products, etc.), backed by the `Space` API. Advanced linear algebra operations like matrix inversion and LU decomposition.
> - [buffers](kmath-core/src/commonMain/kotlin/kscience/kmath/structures/Buffers.kt) : One-dimensional structure
> - [expressions](kmath-core/src/commonMain/kotlin/kscience/kmath/expressions) : By writing a single mathematical expression once, users will be able to apply different types of
> - [algebras](kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Algebraic structures like rings, spaces and fields.
> - [nd](kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/StructureND.kt) : Many-dimensional structures and operations on them.
> - [linear](kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Basic linear algebra operations (sums, products, etc.), backed by the `Space` API. Advanced linear algebra operations like matrix inversion and LU decomposition.
> - [buffers](kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffers.kt) : One-dimensional structure
> - [expressions](kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions) : By writing a single mathematical expression once, users will be able to apply different types of
objects to the expression by providing a context. Expressions can be used for a wide variety of purposes from high
performance calculations to code generation.
> - [domains](kmath-core/src/commonMain/kotlin/kscience/kmath/domains) : Domains
> - [autodif](kmath-core/src/commonMain/kotlin/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic differentiation
> - [domains](kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains) : Domains
> - [autodif](kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic differentiation
<hr/>
@ -149,6 +152,12 @@ performance calculations to code generation.
>
>
> **Maturity**: PROTOTYPE
>
> **Features:**
> - [ejml-vector](kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlVector.kt) : Point implementations.
> - [ejml-matrix](kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlMatrix.kt) : Matrix implementation.
> - [ejml-linear-space](kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt) : LinearSpace implementations.
<hr/>
* ### [kmath-for-real](kmath-for-real)
@ -159,22 +168,23 @@ One can still use generic algebras though.
> **Maturity**: EXPERIMENTAL
>
> **Features:**
> - [RealVector](kmath-for-real/src/commonMain/kotlin/kscience/kmath/real/RealVector.kt) : Numpy-like operations for Buffers/Points
> - [RealMatrix](kmath-for-real/src/commonMain/kotlin/kscience/kmath/real/RealMatrix.kt) : Numpy-like operations for 2d real structures
> - [grids](kmath-for-real/src/commonMain/kotlin/kscience/kmath/structures/grids.kt) : Uniform grid generators
> - [DoubleVector](kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt) : Numpy-like operations for Buffers/Points
> - [DoubleMatrix](kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/DoubleMatrix.kt) : Numpy-like operations for 2d real structures
> - [grids](kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/structures/grids.kt) : Uniform grid generators
<hr/>
* ### [kmath-functions](kmath-functions)
> Functions and interpolation
>
>
> **Maturity**: PROTOTYPE
> **Maturity**: EXPERIMENTAL
>
> **Features:**
> - [piecewise](kmath-functions/Piecewise functions.) : src/commonMain/kotlin/kscience/kmath/functions/Piecewise.kt
> - [polynomials](kmath-functions/Polynomial functions.) : src/commonMain/kotlin/kscience/kmath/functions/Polynomial.kt
> - [linear interpolation](kmath-functions/Linear XY interpolator.) : src/commonMain/kotlin/kscience/kmath/interpolation/LinearInterpolator.kt
> - [spline interpolation](kmath-functions/Cubic spline XY interpolator.) : src/commonMain/kotlin/kscience/kmath/interpolation/SplineInterpolator.kt
> - [piecewise](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/Piecewise.kt) : Piecewise functions.
> - [polynomials](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/Polynomial.kt) : Polynomial functions.
> - [linear interpolation](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/LinearInterpolator.kt) : Linear XY interpolator.
> - [spline interpolation](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/SplineInterpolator.kt) : Cubic spline XY interpolator.
> - [integration](kmath-functions/#) : Univariate and multivariate quadratures
<hr/>
@ -190,27 +200,48 @@ One can still use generic algebras though.
> **Maturity**: PROTOTYPE
<hr/>
* ### [kmath-kotlingrad](kmath-kotlingrad)
* ### [kmath-jafama](kmath-jafama)
>
>
> **Maturity**: PROTOTYPE
>
> **Features:**
> - [jafama-double](kmath-jafama/src/main/kotlin/space/kscience/kmath/jafama/) : Double ExtendedField implementations based on Jafama
<hr/>
* ### [kmath-jupyter](kmath-jupyter)
>
>
> **Maturity**: PROTOTYPE
<hr/>
* ### [kmath-kotlingrad](kmath-kotlingrad)
>
>
> **Maturity**: EXPERIMENTAL
>
> **Features:**
> - [differentiable-mst-expression](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/DifferentiableMstExpression.kt) : MST based DifferentiableExpression.
> - [differentiable-mst-expression](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/DifferentiableMstExpression.kt) : Conversions between Kotlin∇'s SFun and MST
<hr/>
* ### [kmath-memory](kmath-memory)
> An API and basic implementation for arranging objects in a continous memory block.
> An API and basic implementation for arranging objects in a continuous memory block.
>
> **Maturity**: DEVELOPMENT
<hr/>
* ### [kmath-nd4j](kmath-nd4j)
> ND4J NDStructure implementation and according NDAlgebra classes
>
>
> **Maturity**: EXPERIMENTAL
>
> **Features:**
> - [nd4jarraystructure](kmath-nd4j/src/commonMain/kotlin/kscience/kmath/operations/Algebra.kt) : NDStructure wrapper for INDArray
> - [nd4jarrayrings](kmath-nd4j/src/commonMain/kotlin/kscience/kmath/structures/NDStructure.kt) : Rings over Nd4jArrayStructure of Int and Long
> - [nd4jarrayfields](kmath-nd4j/src/commonMain/kotlin/kscience/kmath/structures/Buffers.kt) : Fields over Nd4jArrayStructure of Float and Double
> - [nd4jarraystructure](kmath-nd4j/#) : NDStructure wrapper for INDArray
> - [nd4jarrayrings](kmath-nd4j/#) : Rings over Nd4jArrayStructure of Int and Long
> - [nd4jarrayfields](kmath-nd4j/#) : Fields over Nd4jArrayStructure of Float and Double
<hr/>
@ -220,6 +251,24 @@ One can still use generic algebras though.
> **Maturity**: EXPERIMENTAL
<hr/>
* ### [kmath-symja](kmath-symja)
>
>
> **Maturity**: PROTOTYPE
<hr/>
* ### [kmath-tensors](kmath-tensors)
>
>
> **Maturity**: PROTOTYPE
>
> **Features:**
> - [tensor algebra](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt) : Basic linear algebra operations on tensors (plus, dot, etc.)
> - [tensor algebra with broadcasting](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/BroadcastDoubleTensorAlgebra.kt) : Basic linear algebra operations implemented with broadcasting.
> - [linear algebra operations](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt) : Advanced linear algebra operations like LU decomposition, SVD, etc.
<hr/>
* ### [kmath-viktor](kmath-viktor)
>
>
@ -245,6 +294,10 @@ cases. We expect the worst KMath benchmarks will perform better than native Pyth
native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be
better than SciPy.
## Requirements
KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend to use GraalVM-CE 11 for execution in order to get better performance.
### Repositories
Release and development artifacts are accessible from mipt-npm [Space](https://www.jetbrains.com/space/) repository `https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven` (see documentation of
@ -256,8 +309,8 @@ repositories {
}
dependencies {
api("kscience.kmath:kmath-core:() -> kotlin.Any")
// api("kscience.kmath:kmath-core-jvm:() -> kotlin.Any") for jvm-specific version
api("space.kscience:kmath-core:0.3.0-dev-14")
// api("space.kscience:kmath-core-jvm:0.3.0-dev-14") for jvm-specific version
}
```

138
benchmarks/build.gradle.kts Normal file
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@ -0,0 +1,138 @@
@file:Suppress("UNUSED_VARIABLE")
import space.kscience.kmath.benchmarks.addBenchmarkProperties
plugins {
kotlin("multiplatform")
kotlin("plugin.allopen")
id("org.jetbrains.kotlinx.benchmark")
}
allOpen.annotation("org.openjdk.jmh.annotations.State")
sourceSets.register("benchmarks")
repositories {
mavenCentral()
maven("https://repo.kotlin.link")
maven("https://clojars.org/repo")
maven("https://jitpack.io")
maven("http://logicrunch.research.it.uu.se/maven") {
isAllowInsecureProtocol = true
}
}
kotlin {
jvm()
sourceSets {
val commonMain by getting {
dependencies {
implementation(project(":kmath-ast"))
implementation(project(":kmath-core"))
implementation(project(":kmath-coroutines"))
implementation(project(":kmath-complex"))
implementation(project(":kmath-stat"))
implementation(project(":kmath-dimensions"))
implementation(project(":kmath-for-real"))
implementation(project(":kmath-jafama"))
implementation("org.jetbrains.kotlinx:kotlinx-benchmark-runtime:0.3.1")
}
}
val jvmMain by getting {
dependencies {
implementation(project(":kmath-commons"))
implementation(project(":kmath-ejml"))
implementation(project(":kmath-nd4j"))
implementation(project(":kmath-kotlingrad"))
implementation(project(":kmath-viktor"))
implementation("org.nd4j:nd4j-native:1.0.0-M1")
// uncomment if your system supports AVX2
// val os = System.getProperty("os.name")
//
// if (System.getProperty("os.arch") in arrayOf("x86_64", "amd64")) when {
// os.startsWith("Windows") -> implementation("org.nd4j:nd4j-native:1.0.0-beta7:windows-x86_64-avx2")
// os == "Linux" -> implementation("org.nd4j:nd4j-native:1.0.0-beta7:linux-x86_64-avx2")
// os == "Mac OS X" -> implementation("org.nd4j:nd4j-native:1.0.0-beta7:macosx-x86_64-avx2")
// } else
// implementation("org.nd4j:nd4j-native-platform:1.0.0-beta7")
}
}
}
}
// Configure benchmark
benchmark {
// Setup configurations
targets {
register("jvm")
}
fun kotlinx.benchmark.gradle.BenchmarkConfiguration.commonConfiguration() {
warmups = 1
iterations = 5
iterationTime = 1000
iterationTimeUnit = "ms"
}
configurations.register("buffer") {
commonConfiguration()
include("BufferBenchmark")
}
configurations.register("dot") {
commonConfiguration()
include("DotBenchmark")
}
configurations.register("expressions") {
commonConfiguration()
include("ExpressionsInterpretersBenchmark")
}
configurations.register("matrixInverse") {
commonConfiguration()
include("MatrixInverseBenchmark")
}
configurations.register("bigInt") {
commonConfiguration()
include("BigIntBenchmark")
}
configurations.register("jafamaDouble") {
commonConfiguration()
include("JafamaBenchmark")
}
}
// Fix kotlinx-benchmarks bug
afterEvaluate {
val jvmBenchmarkJar by tasks.getting(org.gradle.jvm.tasks.Jar::class) {
duplicatesStrategy = DuplicatesStrategy.EXCLUDE
}
}
kotlin.sourceSets.all {
with(languageSettings) {
useExperimentalAnnotation("kotlin.contracts.ExperimentalContracts")
useExperimentalAnnotation("kotlin.ExperimentalUnsignedTypes")
useExperimentalAnnotation("space.kscience.kmath.misc.UnstableKMathAPI")
}
}
tasks.withType<org.jetbrains.kotlin.gradle.tasks.KotlinCompile> {
kotlinOptions {
jvmTarget = "11"
freeCompilerArgs = freeCompilerArgs + "-Xjvm-default=all"
}
}
readme {
maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL
}
addBenchmarkProperties()

43
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ArrayBenchmark.kt Normal file
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@ -0,0 +1,43 @@
/*
* 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.
*/
package space.kscience.kmath.benchmarks
import kotlinx.benchmark.Benchmark
import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import java.nio.IntBuffer
@State(Scope.Benchmark)
internal class ArrayBenchmark {
@Benchmark
fun benchmarkArrayRead(blackhole: Blackhole) {
var res = 0
for (i in 1..size) res += array[size - i]
blackhole.consume(res)
}
@Benchmark
fun benchmarkBufferRead(blackhole: Blackhole) {
var res = 0
for (i in 1..size) res += arrayBuffer[size - i]
blackhole.consume(res)
}
@Benchmark
fun nativeBufferRead(blackhole: Blackhole) {
var res = 0
for (i in 1..size) res += nativeBuffer[size - i]
blackhole.consume(res)
}
private companion object {
private const val size = 1000
private val array = IntArray(size) { it }
private val arrayBuffer = IntBuffer.wrap(array)
private val nativeBuffer = IntBuffer.allocate(size).also { for (i in 0 until size) it.put(i, i) }
}
}

97
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BigIntBenchmark.kt Normal file
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@ -0,0 +1,97 @@
/*
* 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.
*/
package space.kscience.kmath.benchmarks
import kotlinx.benchmark.Blackhole
import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.operations.*
import java.math.BigInteger
@UnstableKMathAPI
@State(Scope.Benchmark)
internal class BigIntBenchmark {
val kmNumber = BigIntField.number(Int.MAX_VALUE)
val jvmNumber = JBigIntegerField.number(Int.MAX_VALUE)
val largeKmNumber = BigIntField { number(11).pow(100_000U) }
val largeJvmNumber: BigInteger = JBigIntegerField { number(11).pow(100_000) }
val bigExponent = 50_000
@Benchmark
fun kmAdd(blackhole: Blackhole) = BigIntField {
blackhole.consume(kmNumber + kmNumber + kmNumber)
}
@Benchmark
fun jvmAdd(blackhole: Blackhole) = JBigIntegerField {
blackhole.consume(jvmNumber + jvmNumber + jvmNumber)
}
@Benchmark
fun kmAddLarge(blackhole: Blackhole) = BigIntField {
blackhole.consume(largeKmNumber + largeKmNumber + largeKmNumber)
}
@Benchmark
fun jvmAddLarge(blackhole: Blackhole) = JBigIntegerField {
blackhole.consume(largeJvmNumber + largeJvmNumber + largeJvmNumber)
}
@Benchmark
fun kmMultiply(blackhole: Blackhole) = BigIntField {
blackhole.consume(kmNumber * kmNumber * kmNumber)
}
@Benchmark
fun kmMultiplyLarge(blackhole: Blackhole) = BigIntField {
blackhole.consume(largeKmNumber*largeKmNumber)
}
@Benchmark
fun jvmMultiply(blackhole: Blackhole) = JBigIntegerField {
blackhole.consume(jvmNumber * jvmNumber * jvmNumber)
}
@Benchmark
fun jvmMultiplyLarge(blackhole: Blackhole) = JBigIntegerField {
blackhole.consume(largeJvmNumber*largeJvmNumber)
}
@Benchmark
fun kmPower(blackhole: Blackhole) = BigIntField {
blackhole.consume(kmNumber.pow(bigExponent.toUInt()))
}
@Benchmark
fun jvmPower(blackhole: Blackhole) = JBigIntegerField {
blackhole.consume(jvmNumber.pow(bigExponent))
}
@Benchmark
fun kmParsing16(blackhole: Blackhole) = JBigIntegerField {
blackhole.consume("0x7f57ed8b89c29a3b9a85c7a5b84ca3929c7b7488593".parseBigInteger())
}
@Benchmark
fun kmParsing10(blackhole: Blackhole) = JBigIntegerField {
blackhole.consume("236656783929183747565738292847574838922010".parseBigInteger())
}
@Benchmark
fun jvmParsing10(blackhole: Blackhole) = JBigIntegerField {
blackhole.consume("236656783929183747565738292847574838922010".toBigInteger(10))
}
@Benchmark
fun jvmParsing16(blackhole: Blackhole) = JBigIntegerField {
blackhole.consume("7f57ed8b89c29a3b9a85c7a5b84ca3929c7b7488593".toBigInteger(16))
}
}

21
examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/BufferBenchmark.kt → benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BufferBenchmark.kt
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@ -1,18 +1,23 @@
/*
* 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.
*/
package space.kscience.kmath.benchmarks
import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import kotlinx.benchmark.Benchmark
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import space.kscience.kmath.complex.Complex
import space.kscience.kmath.complex.complex
import space.kscience.kmath.structures.DoubleBuffer
import space.kscience.kmath.structures.MutableBuffer
import space.kscience.kmath.structures.RealBuffer
@State(Scope.Benchmark)
internal class BufferBenchmark {
@Benchmark
fun genericRealBufferReadWrite() {
val buffer = RealBuffer(size) { it.toDouble() }
fun genericDoubleBufferReadWrite() {
val buffer = DoubleBuffer(size) { it.toDouble() }
(0 until size).forEach {
buffer[it]
@ -28,7 +33,7 @@ internal class BufferBenchmark {
}
}
companion object {
const val size: Int = 100
private companion object {
private const val size = 100
}
}

70
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt Normal file
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@ -0,0 +1,70 @@
/*
* 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.
*/
package space.kscience.kmath.benchmarks
import kotlinx.benchmark.Benchmark
import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import space.kscience.kmath.commons.linear.CMLinearSpace
import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
import space.kscience.kmath.linear.LinearSpace
import space.kscience.kmath.linear.invoke
import space.kscience.kmath.operations.DoubleField
import kotlin.random.Random
@State(Scope.Benchmark)
internal class DotBenchmark {
companion object {
val random = Random(12224)
const val dim = 1000
//creating invertible matrix
val matrix1 = LinearSpace.real.buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
val matrix2 = LinearSpace.real.buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
val cmMatrix1 = CMLinearSpace { matrix1.toCM() }
val cmMatrix2 = CMLinearSpace { matrix2.toCM() }
val ejmlMatrix1 = EjmlLinearSpaceDDRM { matrix1.toEjml() }
val ejmlMatrix2 = EjmlLinearSpaceDDRM { matrix2.toEjml() }
}
@Benchmark
fun cmDot(blackhole: Blackhole) {
CMLinearSpace.run {
blackhole.consume(cmMatrix1 dot cmMatrix2)
}
}
@Benchmark
fun ejmlDot(blackhole: Blackhole) {
EjmlLinearSpaceDDRM {
blackhole.consume(ejmlMatrix1 dot ejmlMatrix2)
}
}
@Benchmark
fun ejmlDotWithConversion(blackhole: Blackhole) {
EjmlLinearSpaceDDRM {
blackhole.consume(matrix1 dot matrix2)
}
}
@Benchmark
fun bufferedDot(blackhole: Blackhole) {
LinearSpace.auto(DoubleField).invoke {
blackhole.consume(matrix1 dot matrix2)
}
}
@Benchmark
fun realDot(blackhole: Blackhole) {
LinearSpace.real {
blackhole.consume(matrix1 dot matrix2)
}
}
}

94
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt Normal file
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@ -0,0 +1,94 @@
/*
* 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.
*/
package space.kscience.kmath.benchmarks
import kotlinx.benchmark.Benchmark
import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import space.kscience.kmath.asm.compileToExpression
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.operations.invoke
import kotlin.math.sin
import kotlin.random.Random
@State(Scope.Benchmark)
internal class ExpressionsInterpretersBenchmark {
/**
* Benchmark case for [Expression] created with [expressionInExtendedField].
*/
@Benchmark
fun functionalExpression(blackhole: Blackhole) = invokeAndSum(functional, blackhole)
/**
* Benchmark case for [Expression] created with [toExpression].
*/
@Benchmark
fun mstExpression(blackhole: Blackhole) = invokeAndSum(mst, blackhole)
/**
* Benchmark case for [Expression] created with [compileToExpression].
*/
@Benchmark
fun asmExpression(blackhole: Blackhole) = invokeAndSum(asm, blackhole)
/**
* Benchmark case for [Expression] implemented manually with `kotlin.math` functions.
*/
@Benchmark
fun rawExpression(blackhole: Blackhole) = invokeAndSum(raw, blackhole)
/**
* Benchmark case for direct computation w/o [Expression].
*/
@Benchmark
fun justCalculate(blackhole: Blackhole) {
val random = Random(0)
var sum = 0.0
repeat(times) {
val x = random.nextDouble()
sum += x * 2.0 + 2.0 / x - 16.0 / sin(x)
}
blackhole.consume(sum)
}
private fun invokeAndSum(expr: Expression<Double>, blackhole: Blackhole) {
val random = Random(0)
var sum = 0.0
repeat(times) {
sum += expr(x to random.nextDouble())
}
blackhole.consume(sum)
}
private companion object {
private val x by symbol
private val algebra = DoubleField
private const val times = 1_000_000
private val functional = DoubleField.expressionInExtendedField {
bindSymbol(x) * number(2.0) + number(2.0) / bindSymbol(x) - number(16.0) / sin(bindSymbol(x))
}
private val node = MstExtendedField {
x * 2.0 + number(2.0) / x - number(16.0) / sin(x)
}
private val mst = node.toExpression(DoubleField)
private val asm = node.compileToExpression(DoubleField)
private val raw = Expression<Double> { args ->
val x = args[x]!!
x * 2.0 + 2.0 / x - 16.0 / sin(x)
}
}
}

39
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/JafamaBenchmark.kt Normal file
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@ -0,0 +1,39 @@
/*
* 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.
*/
package space.kscience.kmath.benchmarks
import kotlinx.benchmark.Blackhole
import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import space.kscience.kmath.jafama.JafamaDoubleField
import space.kscience.kmath.jafama.StrictJafamaDoubleField
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
import kotlin.random.Random
@State(Scope.Benchmark)
internal class JafamaBenchmark {
@Benchmark
fun jafama(blackhole: Blackhole) = invokeBenchmarks(blackhole) { x ->
JafamaDoubleField { x * power(x, 4) * exp(x) / cos(x) + sin(x) }
}
@Benchmark
fun core(blackhole: Blackhole) = invokeBenchmarks(blackhole) { x ->
DoubleField { x * power(x, 4) * exp(x) / cos(x) + sin(x) }
}
@Benchmark
fun strictJafama(blackhole: Blackhole) = invokeBenchmarks(blackhole) { x ->
StrictJafamaDoubleField { x * power(x, 4) * exp(x) / cos(x) + sin(x) }
}
private inline fun invokeBenchmarks(blackhole: Blackhole, expr: (Double) -> Double) {
val rng = Random(0)
repeat(1000000) { blackhole.consume(expr(rng.nextDouble())) }
}
}

54
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/MatrixInverseBenchmark.kt Normal file
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@ -0,0 +1,54 @@
/*
* 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.
*/
package space.kscience.kmath.benchmarks
import kotlinx.benchmark.Benchmark
import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import space.kscience.kmath.commons.linear.CMLinearSpace
import space.kscience.kmath.commons.linear.inverse
import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
import space.kscience.kmath.linear.InverseMatrixFeature
import space.kscience.kmath.linear.LinearSpace
import space.kscience.kmath.linear.inverseWithLup
import space.kscience.kmath.linear.invoke
import space.kscience.kmath.nd.getFeature
import kotlin.random.Random
@State(Scope.Benchmark)
internal class MatrixInverseBenchmark {
private companion object {
private val random = Random(1224)
private const val dim = 100
private val space = LinearSpace.real
//creating invertible matrix
private val u = space.buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
private val l = space.buildMatrix(dim, dim) { i, j -> if (i >= j) random.nextDouble() else 0.0 }
private val matrix = space { l dot u }
}
@Benchmark
fun kmathLupInversion(blackhole: Blackhole) {
blackhole.consume(LinearSpace.real.inverseWithLup(matrix))
}
@Benchmark
fun cmLUPInversion(blackhole: Blackhole) {
with(CMLinearSpace) {
blackhole.consume(inverse(matrix))
}
}
@Benchmark
fun ejmlInverse(blackhole: Blackhole) {
with(EjmlLinearSpaceDDRM) {
blackhole.consume(matrix.getFeature<InverseMatrixFeature<Double>>()?.inverse)
}
}
}

53
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt Normal file
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@ -0,0 +1,53 @@
/*
* 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.
*/
package space.kscience.kmath.benchmarks
import kotlinx.benchmark.Benchmark
import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.structures.Buffer
@State(Scope.Benchmark)
internal class NDFieldBenchmark {
@Benchmark
fun autoFieldAdd(blackhole: Blackhole) {
with(autoField) {
var res: StructureND<Double> = one
repeat(n) { res += one }
blackhole.consume(res)
}
}
@Benchmark
fun specializedFieldAdd(blackhole: Blackhole) {
with(specializedField) {
var res: StructureND<Double> = one
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
}
@Benchmark
fun boxingFieldAdd(blackhole: Blackhole) {
with(genericField) {
var res: StructureND<Double> = one
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
}
private companion object {
private const val dim = 1000
private const val n = 100
private val autoField = AlgebraND.auto(DoubleField, dim, dim)
private val specializedField = AlgebraND.real(dim, dim)
private val genericField = AlgebraND.field(DoubleField, Buffer.Companion::boxing, dim, dim)
}
}

66
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorBenchmark.kt Normal file
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/*
* 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.
*/
package space.kscience.kmath.benchmarks
import kotlinx.benchmark.Benchmark
import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import org.jetbrains.bio.viktor.F64Array
import space.kscience.kmath.nd.AlgebraND
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.auto
import space.kscience.kmath.nd.real
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.viktor.ViktorNDField
@State(Scope.Benchmark)
internal class ViktorBenchmark {
@Benchmark
fun automaticFieldAddition(blackhole: Blackhole) {
with(autoField) {
var res: StructureND<Double> = one
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
}
@Benchmark
fun realFieldAddition(blackhole: Blackhole) {
with(realField) {
var res: StructureND<Double> = one
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
}
@Benchmark
fun viktorFieldAddition(blackhole: Blackhole) {
with(viktorField) {
var res = one
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
}
@Benchmark
fun rawViktor(blackhole: Blackhole) {
val one = F64Array.full(init = 1.0, shape = intArrayOf(dim, dim))
var res = one
repeat(n) { res = res + one }
blackhole.consume(res)
}
private companion object {
private const val dim = 1000
private const val n = 100
// automatically build context most suited for given type.
private val autoField = AlgebraND.auto(DoubleField, dim, dim)
private val realField = AlgebraND.real(dim, dim)
private val viktorField = ViktorNDField(dim, dim)
}
}

58
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorLogBenchmark.kt Normal file
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@ -0,0 +1,58 @@
/*
* 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.
*/
package space.kscience.kmath.benchmarks
import kotlinx.benchmark.Benchmark
import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import org.jetbrains.bio.viktor.F64Array
import space.kscience.kmath.nd.AlgebraND
import space.kscience.kmath.nd.auto
import space.kscience.kmath.nd.real
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.viktor.ViktorFieldND
@State(Scope.Benchmark)
internal class ViktorLogBenchmark {
@Benchmark
fun realFieldLog(blackhole: Blackhole) {
with(realNdField) {
val fortyTwo = produce { 42.0 }
var res = one
repeat(n) { res = ln(fortyTwo) }
blackhole.consume(res)
}
}
@Benchmark
fun viktorFieldLog(blackhole: Blackhole) {
with(viktorField) {
val fortyTwo = produce { 42.0 }
var res = one
repeat(n) { res = ln(fortyTwo) }
blackhole.consume(res)
}
}
@Benchmark
fun rawViktorLog(blackhole: Blackhole) {
val fortyTwo = F64Array.full(dim, dim, init = 42.0)
lateinit var res: F64Array
repeat(n) { res = fortyTwo.log() }
blackhole.consume(res)
}
private companion object {
private const val dim = 1000
private const val n = 100
// automatically build context most suited for given type.
private val autoField = AlgebraND.auto(DoubleField, dim, dim)
private val realNdField = AlgebraND.real(dim, dim)
private val viktorField = ViktorFieldND(intArrayOf(dim, dim))
}
}

48
build.gradle.kts
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@ -1,28 +1,46 @@
plugins {
id("ru.mipt.npm.gradle.project")
kotlin("jupyter.api") apply false
}
allprojects {
repositories {
jcenter()
maven("https://clojars.org/repo")
maven("https://dl.bintray.com/egor-bogomolov/astminer/")
maven("https://dl.bintray.com/hotkeytlt/maven")
maven("https://dl.bintray.com/kotlin/kotlin-eap")
maven("https://dl.bintray.com/kotlin/kotlinx")
maven("https://dl.bintray.com/mipt-npm/dev")
maven("https://dl.bintray.com/mipt-npm/kscience")
maven("https://jitpack.io")
maven("http://logicrunch.research.it.uu.se/maven/")
maven("http://logicrunch.research.it.uu.se/maven") {
isAllowInsecureProtocol = true
}
maven("https://oss.sonatype.org/content/repositories/snapshots")
mavenCentral()
}
group = "space.kscience"
version = "0.2.0"
version = "0.3.0-dev-14"
}
subprojects {
if (name.startsWith("kmath")) apply<ru.mipt.npm.gradle.KSciencePublishingPlugin>()
if (name.startsWith("kmath")) apply<MavenPublishPlugin>()
afterEvaluate {
tasks.withType<org.jetbrains.dokka.gradle.DokkaTaskPartial> {
dependsOn(tasks.getByName("assemble"))
dokkaSourceSets.all {
val readmeFile = File(this@subprojects.projectDir, "README.md")
if (readmeFile.exists()) includes.from(readmeFile.absolutePath)
externalDocumentationLink("https://ejml.org/javadoc/")
externalDocumentationLink("https://commons.apache.org/proper/commons-math/javadocs/api-3.6.1/")
externalDocumentationLink("https://deeplearning4j.org/api/latest/")
externalDocumentationLink("https://axelclk.bitbucket.io/symja/javadoc/")
externalDocumentationLink("https://kotlin.github.io/kotlinx.coroutines/kotlinx-coroutines-core/")
externalDocumentationLink(
"https://breandan.net/kotlingrad/kotlingrad/",
"https://breandan.net/kotlingrad/kotlingrad/kotlingrad/package-list",
)
}
}
}
}
readme {
@ -30,11 +48,11 @@ readme {
}
ksciencePublish {
spaceRepo = "https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven"
bintrayRepo = "kscience"
githubProject = "kmath"
github("kmath")
space()
sonatype()
}
apiValidation{
apiValidation {
nonPublicMarkers.add("space.kscience.kmath.misc.UnstableKMathAPI")
}
}

20
buildSrc/build.gradle.kts Normal file
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@ -0,0 +1,20 @@
plugins {
`kotlin-dsl`
kotlin("plugin.serialization") version "1.4.31"
}
repositories {
maven("https://repo.kotlin.link")
mavenCentral()
gradlePluginPortal()
}
dependencies {
api("org.jetbrains.kotlinx:kotlinx-serialization-json:1.1.0")
api("ru.mipt.npm:gradle-tools:0.10.0")
api("org.jetbrains.kotlinx:kotlinx-benchmark-plugin:0.3.1")
}
kotlin.sourceSets.all {
languageSettings.useExperimentalAnnotation("kotlin.ExperimentalStdlibApi")
}

60
buildSrc/src/main/kotlin/space/kscience/kmath/benchmarks/JmhReport.kt Normal file
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/*
* 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.
*/
package space.kscience.kmath.benchmarks
import kotlinx.serialization.Serializable
@Serializable
data class JmhReport(
val jmhVersion: String,
val benchmark: String,
val mode: String,
val threads: Int,
val forks: Int,
val jvm: String,
val jvmArgs: List<String>,
val jdkVersion: String,
val vmName: String,
val vmVersion: String,
val warmupIterations: Int,
val warmupTime: String,
val warmupBatchSize: Int,
val measurementIterations: Int,
val measurementTime: String,
val measurementBatchSize: Int,
val params: Map<String, String> = emptyMap(),
val primaryMetric: PrimaryMetric,
val secondaryMetrics: Map<String, SecondaryMetric>,
) {
interface Metric {
val score: Double
val scoreError: Double
val scoreConfidence: List<Double>
val scorePercentiles: Map<Double, Double>
val scoreUnit: String
}
@Serializable
data class PrimaryMetric(
override val score: Double,
override val scoreError: Double,
override val scoreConfidence: List<Double>,
override val scorePercentiles: Map<Double, Double>,
override val scoreUnit: String,
val rawDataHistogram: List<List<List<List<Double>>>>? = null,
val rawData: List<List<Double>>? = null,
) : Metric
@Serializable
data class SecondaryMetric(
override val score: Double,
override val scoreError: Double,
override val scoreConfidence: List<Double>,
override val scorePercentiles: Map<Double, Double>,
override val scoreUnit: String,
val rawData: List<List<Double>>,
) : Metric
}

100
buildSrc/src/main/kotlin/space/kscience/kmath/benchmarks/addBenchmarkProperties.kt Normal file
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/*
* 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.
*/
package space.kscience.kmath.benchmarks
import kotlinx.benchmark.gradle.BenchmarksExtension
import kotlinx.serialization.*
import kotlinx.serialization.json.*
import org.gradle.api.Project
import ru.mipt.npm.gradle.KScienceReadmeExtension
import java.time.*
import java.time.format.*
import java.time.temporal.ChronoField.*
private val ISO_DATE_TIME: DateTimeFormatter = DateTimeFormatterBuilder().run {
parseCaseInsensitive()
appendValue(YEAR, 4, 10, SignStyle.EXCEEDS_PAD)
appendLiteral('-')
appendValue(MONTH_OF_YEAR, 2)
appendLiteral('-')
appendValue(DAY_OF_MONTH, 2)
appendLiteral('T')
appendValue(HOUR_OF_DAY, 2)
appendLiteral('.')
appendValue(MINUTE_OF_HOUR, 2)
optionalStart()
appendLiteral('.')
appendValue(SECOND_OF_MINUTE, 2)
optionalStart()
appendFraction(NANO_OF_SECOND, 0, 9, true)
optionalStart()
appendOffsetId()
optionalStart()
appendLiteral('[')
parseCaseSensitive()
appendZoneRegionId()
appendLiteral(']')
toFormatter()
}
private fun noun(number: Number, singular: String, plural: String) = if (number.toLong() == 1L) singular else plural
fun Project.addBenchmarkProperties() {
val benchmarksProject = this
rootProject.subprojects.forEach { p ->
p.extensions.findByType(KScienceReadmeExtension::class.java)?.run {
benchmarksProject.extensions.findByType(BenchmarksExtension::class.java)?.configurations?.forEach { cfg ->
property("benchmark${cfg.name.replaceFirstChar(Char::uppercase)}") {
val launches = benchmarksProject.buildDir.resolve("reports/benchmarks/${cfg.name}")
val resDirectory = launches.listFiles()?.maxByOrNull {
LocalDateTime.parse(it.name, ISO_DATE_TIME).atZone(ZoneId.systemDefault()).toInstant()
}
if (resDirectory == null) {
"> **Can't find appropriate benchmark data. Try generating readme files after running benchmarks**."
} else {
val reports =
Json.decodeFromString<List<JmhReport>>(resDirectory.resolve("jvm.json").readText())
buildString {
appendLine("<details>")
appendLine("<summary>")
appendLine("Report for benchmark configuration <code>${cfg.name}</code>")
appendLine("</summary>")
appendLine()
val first = reports.first()
appendLine("* Run on ${first.vmName} (build ${first.vmVersion}) with Java process:")
appendLine()
appendLine("```")
appendLine("${first.jvm} ${
first.jvmArgs.joinToString(" ")
}")
appendLine("```")
appendLine("* JMH ${first.jmhVersion} was used in `${first.mode}` mode with ${first.warmupIterations} warmup ${
noun(first.warmupIterations, "iteration", "iterations")
} by ${first.warmupTime} and ${first.measurementIterations} measurement ${
noun(first.measurementIterations, "iteration", "iterations")
} by ${first.measurementTime}.")
appendLine()
appendLine("| Benchmark | Score |")
appendLine("|:---------:|:-----:|")
reports.forEach { report ->
appendLine("|`${report.benchmark}`|${report.primaryMetric.score} &plusmn; ${report.primaryMetric.scoreError} ${report.primaryMetric.scoreUnit}|")
}
appendLine("</details>")
}
}
}
}
}
}
}

425
buildSrc/src/main/kotlin/space/kscience/kmath/ejml/codegen/ejmlCodegen.kt Normal file
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@ -0,0 +1,425 @@
/*
* 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.
*/
@file:Suppress("KDocUnresolvedReference")
package space.kscience.kmath.ejml.codegen
import org.intellij.lang.annotations.Language
import java.io.File
private fun Appendable.appendEjmlVector(type: String, ejmlMatrixType: String) {
@Language("kotlin") val text = """/**
* [EjmlVector] specialization for [$type].
*/
public class Ejml${type}Vector<out M : $ejmlMatrixType>(public override val origin: M) : EjmlVector<$type, M>(origin) {
init {
require(origin.numRows == 1) { "The origin matrix must have only one row to form a vector" }
}
public override operator fun get(index: Int): $type = origin[0, index]
}"""
appendLine(text)
appendLine()
}
private fun Appendable.appendEjmlMatrix(type: String, ejmlMatrixType: String) {
val text = """/**
* [EjmlMatrix] specialization for [$type].
*/
public class Ejml${type}Matrix<out M : $ejmlMatrixType>(public override val origin: M) : EjmlMatrix<$type, M>(origin) {
public override operator fun get(i: Int, j: Int): $type = origin[i, j]
}"""
appendLine(text)
appendLine()
}
private fun Appendable.appendEjmlLinearSpace(
type: String,
kmathAlgebra: String,
ejmlMatrixParentTypeMatrix: String,
ejmlMatrixType: String,
ejmlMatrixDenseType: String,
ops: String,
denseOps: String,
isDense: Boolean,
) {
@Language("kotlin") val text = """/**
* [EjmlLinearSpace] implementation based on [CommonOps_$ops], [DecompositionFactory_${ops}] operations and
* [${ejmlMatrixType}] matrices.
*/
public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra}, $ejmlMatrixType>() {
/**
* The [${kmathAlgebra}] reference.
*/
public override val elementAlgebra: $kmathAlgebra get() = $kmathAlgebra
@Suppress("UNCHECKED_CAST")
public override fun Matrix<${type}>.toEjml(): Ejml${type}Matrix<${ejmlMatrixType}> = when {
this is Ejml${type}Matrix<*> && origin is $ejmlMatrixType -> this as Ejml${type}Matrix<${ejmlMatrixType}>
else -> buildMatrix(rowNum, colNum) { i, j -> get(i, j) }
}
@Suppress("UNCHECKED_CAST")
public override fun Point<${type}>.toEjml(): Ejml${type}Vector<${ejmlMatrixType}> = when {
this is Ejml${type}Vector<*> && origin is $ejmlMatrixType -> this as Ejml${type}Vector<${ejmlMatrixType}>
else -> Ejml${type}Vector(${ejmlMatrixType}(size, 1).also {
(0 until it.numRows).forEach { row -> it[row, 0] = get(row) }
})
}
public override fun buildMatrix(
rows: Int,
columns: Int,
initializer: ${kmathAlgebra}.(i: Int, j: Int) -> ${type},
): Ejml${type}Matrix<${ejmlMatrixType}> = ${ejmlMatrixType}(rows, columns).also {
(0 until rows).forEach { row ->
(0 until columns).forEach { col -> it[row, col] = elementAlgebra.initializer(row, col) }
}
}.wrapMatrix()
public override fun buildVector(
size: Int,
initializer: ${kmathAlgebra}.(Int) -> ${type},
): Ejml${type}Vector<${ejmlMatrixType}> = Ejml${type}Vector(${ejmlMatrixType}(size, 1).also {
(0 until it.numRows).forEach { row -> it[row, 0] = elementAlgebra.initializer(row) }
})
private fun <T : ${ejmlMatrixParentTypeMatrix}> T.wrapMatrix() = Ejml${type}Matrix(this)
private fun <T : ${ejmlMatrixParentTypeMatrix}> T.wrapVector() = Ejml${type}Vector(this)
public override fun Matrix<${type}>.unaryMinus(): Matrix<${type}> = this * elementAlgebra { -one }
public override fun Matrix<${type}>.dot(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
val out = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.mult(toEjml().origin, other.toEjml().origin, out)
return out.wrapMatrix()
}
public override fun Matrix<${type}>.dot(vector: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
val out = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.mult(toEjml().origin, vector.toEjml().origin, out)
return out.wrapVector()
}
public override operator fun Matrix<${type}>.minus(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
val out = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.add(
elementAlgebra.one,
toEjml().origin,
elementAlgebra { -one },
other.toEjml().origin,
out,${
if (isDense) "" else
"""
null,
null,"""
}
)
return out.wrapMatrix()
}
public override operator fun Matrix<${type}>.times(value: ${type}): Ejml${type}Matrix<${ejmlMatrixType}> {
val res = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.scale(value, toEjml().origin, res)
return res.wrapMatrix()
}
public override fun Point<${type}>.unaryMinus(): Ejml${type}Vector<${ejmlMatrixType}> {
val res = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.changeSign(toEjml().origin, res)
return res.wrapVector()
}
public override fun Matrix<${type}>.plus(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
val out = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.add(
elementAlgebra.one,
toEjml().origin,
elementAlgebra.one,
other.toEjml().origin,
out,${
if (isDense) "" else
"""
null,
null,"""
}
)
return out.wrapMatrix()
}
public override fun Point<${type}>.plus(other: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
val out = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.add(
elementAlgebra.one,
toEjml().origin,
elementAlgebra.one,
other.toEjml().origin,
out,${
if (isDense) "" else
"""
null,
null,"""
}
)
return out.wrapVector()
}
public override fun Point<${type}>.minus(other: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
val out = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.add(
elementAlgebra.one,
toEjml().origin,
elementAlgebra { -one },
other.toEjml().origin,
out,${
if (isDense) "" else
"""
null,
null,"""
}
)
return out.wrapVector()
}
public override fun ${type}.times(m: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> = m * this
public override fun Point<${type}>.times(value: ${type}): Ejml${type}Vector<${ejmlMatrixType}> {
val res = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.scale(value, toEjml().origin, res)
return res.wrapVector()
}
public override fun ${type}.times(v: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> = v * this
@UnstableKMathAPI
public override fun <F : StructureFeature> getFeature(structure: Matrix<${type}>, type: KClass<out F>): F? {
structure.getFeature(type)?.let { return it }
val origin = structure.toEjml().origin
return when (type) {
${
if (isDense)
""" InverseMatrixFeature::class -> object : InverseMatrixFeature<${type}> {
override val inverse: Matrix<${type}> by lazy {
val res = origin.copy()
CommonOps_${ops}.invert(res)
res.wrapMatrix()
}
}
DeterminantFeature::class -> object : DeterminantFeature<${type}> {
override val determinant: $type by lazy { CommonOps_${ops}.det(origin) }
}
SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature<${type}> {
private val svd by lazy {
DecompositionFactory_${ops}.svd(origin.numRows, origin.numCols, true, true, false)
.apply { decompose(origin.copy()) }
}
override val u: Matrix<${type}> by lazy { svd.getU(null, false).wrapMatrix() }
override val s: Matrix<${type}> by lazy { svd.getW(null).wrapMatrix() }
override val v: Matrix<${type}> by lazy { svd.getV(null, false).wrapMatrix() }
override val singularValues: Point<${type}> by lazy { ${type}Buffer(svd.singularValues) }
}
QRDecompositionFeature::class -> object : QRDecompositionFeature<${type}> {
private val qr by lazy {
DecompositionFactory_${ops}.qr().apply { decompose(origin.copy()) }
}
override val q: Matrix<${type}> by lazy {
qr.getQ(null, false).wrapMatrix() + OrthogonalFeature
}
override val r: Matrix<${type}> by lazy { qr.getR(null, false).wrapMatrix() + UFeature }
}
CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<${type}> {
override val l: Matrix<${type}> by lazy {
val cholesky =
DecompositionFactory_${ops}.chol(structure.rowNum, true).apply { decompose(origin.copy()) }
cholesky.getT(null).wrapMatrix() + LFeature
}
}
LupDecompositionFeature::class -> object : LupDecompositionFeature<${type}> {
private val lup by lazy {
DecompositionFactory_${ops}.lu(origin.numRows, origin.numCols).apply { decompose(origin.copy()) }
}
override val l: Matrix<${type}> by lazy {
lup.getLower(null).wrapMatrix() + LFeature
}
override val u: Matrix<${type}> by lazy {
lup.getUpper(null).wrapMatrix() + UFeature
}
override val p: Matrix<${type}> by lazy { lup.getRowPivot(null).wrapMatrix() }
}""" else """ QRDecompositionFeature::class -> object : QRDecompositionFeature<$type> {
private val qr by lazy {
DecompositionFactory_${ops}.qr(FillReducing.NONE).apply { decompose(origin.copy()) }
}
override val q: Matrix<${type}> by lazy {
qr.getQ(null, false).wrapMatrix() + OrthogonalFeature
}
override val r: Matrix<${type}> by lazy { qr.getR(null, false).wrapMatrix() + UFeature }
}
CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<${type}> {
override val l: Matrix<${type}> by lazy {
val cholesky =
DecompositionFactory_${ops}.cholesky().apply { decompose(origin.copy()) }
(cholesky.getT(null) as ${ejmlMatrixParentTypeMatrix}).wrapMatrix() + LFeature
}
}
LUDecompositionFeature::class, DeterminantFeature::class, InverseMatrixFeature::class -> object :
LUDecompositionFeature<${type}>, DeterminantFeature<${type}>, InverseMatrixFeature<${type}> {
private val lu by lazy {
DecompositionFactory_${ops}.lu(FillReducing.NONE).apply { decompose(origin.copy()) }
}
override val l: Matrix<${type}> by lazy {
lu.getLower(null).wrapMatrix() + LFeature
}
override val u: Matrix<${type}> by lazy {
lu.getUpper(null).wrapMatrix() + UFeature
}
override val inverse: Matrix<${type}> by lazy {
var a = origin
val inverse = ${ejmlMatrixDenseType}(1, 1)
val solver = LinearSolverFactory_${ops}.lu(FillReducing.NONE)
if (solver.modifiesA()) a = a.copy()
val i = CommonOps_${denseOps}.identity(a.numRows)
solver.solve(i, inverse)
inverse.wrapMatrix()
}
override val determinant: $type by lazy { elementAlgebra.number(lu.computeDeterminant().real) }
}"""
}
else -> null
}?.let(type::cast)
}
/**
* Solves for *x* in the following equation: *x = [a] <sup>-1</sup> &middot; [b]*.
*
* @param a the base matrix.
* @param b n by p matrix.
* @return the solution for *x* that is n by p.
*/
public fun solve(a: Matrix<${type}>, b: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
val res = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.solve(${ejmlMatrixType}(a.toEjml().origin), ${ejmlMatrixType}(b.toEjml().origin), res)
return res.wrapMatrix()
}
/**
* Solves for *x* in the following equation: *x = [a] <sup>-1</sup> &middot; [b]*.
*
* @param a the base matrix.
* @param b n by p vector.
* @return the solution for *x* that is n by p.
*/
public fun solve(a: Matrix<${type}>, b: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
val res = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.solve(${ejmlMatrixType}(a.toEjml().origin), ${ejmlMatrixType}(b.toEjml().origin), res)
return Ejml${type}Vector(res)
}
}"""
appendLine(text)
appendLine()
}
/**
* Generates routine EJML classes.
*/
fun ejmlCodegen(outputFile: String): Unit = File(outputFile).run {
parentFile.mkdirs()
writer().use {
it.appendLine("/*")
it.appendLine(" * Copyright 2018-2021 KMath contributors.")
it.appendLine(" * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.")
it.appendLine(" */")
it.appendLine()
it.appendLine("/* This file is generated with buildSrc/src/main/kotlin/space/kscience/kmath/ejml/codegen/ejmlCodegen.kt */")
it.appendLine()
it.appendLine("package space.kscience.kmath.ejml")
it.appendLine()
it.appendLine("""import org.ejml.data.*
import org.ejml.dense.row.CommonOps_DDRM
import org.ejml.dense.row.CommonOps_FDRM
import org.ejml.dense.row.factory.DecompositionFactory_DDRM
import org.ejml.dense.row.factory.DecompositionFactory_FDRM
import org.ejml.sparse.FillReducing
import org.ejml.sparse.csc.CommonOps_DSCC
import org.ejml.sparse.csc.CommonOps_FSCC
import org.ejml.sparse.csc.factory.DecompositionFactory_DSCC
import org.ejml.sparse.csc.factory.DecompositionFactory_FSCC
import org.ejml.sparse.csc.factory.LinearSolverFactory_DSCC
import org.ejml.sparse.csc.factory.LinearSolverFactory_FSCC
import space.kscience.kmath.linear.*
import space.kscience.kmath.linear.Matrix
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.StructureFeature
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.FloatField
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.DoubleBuffer
import space.kscience.kmath.structures.FloatBuffer
import kotlin.reflect.KClass
import kotlin.reflect.cast""")
it.appendLine()
it.appendEjmlVector("Double", "DMatrix")
it.appendEjmlVector("Float", "FMatrix")
it.appendEjmlMatrix("Double", "DMatrix")
it.appendEjmlMatrix("Float", "FMatrix")
it.appendEjmlLinearSpace("Double", "DoubleField", "DMatrix", "DMatrixRMaj", "DMatrixRMaj", "DDRM", "DDRM", true)
it.appendEjmlLinearSpace("Float", "FloatField", "FMatrix", "FMatrixRMaj", "FMatrixRMaj", "FDRM", "FDRM", true)
it.appendEjmlLinearSpace(
type = "Double",
kmathAlgebra = "DoubleField",
ejmlMatrixParentTypeMatrix = "DMatrix",
ejmlMatrixType = "DMatrixSparseCSC",
ejmlMatrixDenseType = "DMatrixRMaj",
ops = "DSCC",
denseOps = "DDRM",
isDense = false,
)
it.appendEjmlLinearSpace(
type = "Float",
kmathAlgebra = "FloatField",
ejmlMatrixParentTypeMatrix = "FMatrix",
ejmlMatrixType = "FMatrixSparseCSC",
ejmlMatrixDenseType = "FMatrixRMaj",
ops = "FSCC",
denseOps = "FDRM",
isDense = false,
)
}
}

2
docs/algebra.md
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@ -31,7 +31,7 @@ multiplication;
- [Ring](http://mathworld.wolfram.com/Ring.html) adds multiplication and its neutral element (i.e. 1);
- [Field](http://mathworld.wolfram.com/Field.html) adds division operation.
A typical implementation of `Field<T>` is the `RealField` which works on doubles, and `VectorSpace` for `Space<T>`.
A typical implementation of `Field<T>` is the `DoubleField` which works on doubles, and `VectorSpace` for `Space<T>`.
In some cases algebra context can hold additional operations like `exp` or `sin`, and then it inherits appropriate
interface. Also, contexts may have operations, which produce elements outside of the context. For example, `Matrix.dot`

29
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4
docs/nd-structure.md
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@ -10,11 +10,11 @@ structures. In `kmath` performance depends on which particular context was used
Let us consider following contexts:
```kotlin
// automatically build context most suited for given type.
val autoField = NDField.auto(RealField, dim, dim)
val autoField = NDField.auto(DoubleField, dim, dim)
// specialized nd-field for Double. It works as generic Double field as well
val specializedField = NDField.real(dim, dim)
//A generic boxing field. It should be used for objects, not primitives.
val genericField = NDField.buffered(RealField, dim, dim)
val genericField = NDField.buffered(DoubleField, dim, dim)
```
Now let us perform several tests and see which implementation is best suited for each case:

66
docs/templates/ARTIFACT-TEMPLATE.md vendored
View File

@ -1,40 +1,26 @@
> #### Artifact:
>
> This module artifact: `${group}:${name}:${version}`.
>
> Bintray release version: [ ![Download](https://api.bintray.com/packages/mipt-npm/kscience/${name}/images/download.svg) ](https://bintray.com/mipt-npm/kscience/${name}/_latestVersion)
>
> Bintray development version: [ ![Download](https://api.bintray.com/packages/mipt-npm/dev/${name}/images/download.svg) ](https://bintray.com/mipt-npm/dev/${name}/_latestVersion)
>
> **Gradle:**
>
> ```gradle
> repositories {
> maven { url 'https://repo.kotlin.link' }
> maven { url 'https://dl.bintray.com/hotkeytlt/maven' }
> maven { url "https://dl.bintray.com/kotlin/kotlin-eap" } // include for builds based on kotlin-eap
>// Uncomment if repo.kotlin.link is unavailable
>// maven { url 'https://dl.bintray.com/mipt-npm/kscience' }
>// maven { url 'https://dl.bintray.com/mipt-npm/dev' }
> }
>
> dependencies {
> implementation '${group}:${name}:${version}'
> }
> ```
> **Gradle Kotlin DSL:**
>
> ```kotlin
> repositories {
> maven("https://repo.kotlin.link")
> maven("https://dl.bintray.com/kotlin/kotlin-eap") // include for builds based on kotlin-eap
> maven("https://dl.bintray.com/hotkeytlt/maven") // required for a
>// Uncomment if repo.kotlin.link is unavailable
>// maven("https://dl.bintray.com/mipt-npm/kscience")
>// maven("https://dl.bintray.com/mipt-npm/dev")
> }
>
> dependencies {
> implementation("${group}:${name}:${version}")
> }
> ```
## Artifact:
The Maven coordinates of this project are `${group}:${name}:${version}`.
**Gradle:**
```gradle
repositories {
maven { url 'https://repo.kotlin.link' }
mavenCentral()
}
dependencies {
implementation '${group}:${name}:${version}'
}
```
**Gradle Kotlin DSL:**
```kotlin
repositories {
maven("https://repo.kotlin.link")
mavenCentral()
}
dependencies {
implementation("${group}:${name}:${version}")
}
```

19
docs/templates/README-TEMPLATE.md vendored
View File

@ -1,11 +1,8 @@
[![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)
Bintray: [ ![Download](https://api.bintray.com/packages/mipt-npm/kscience/kmath-core/images/download.svg) ](https://bintray.com/mipt-npm/kscience/kmath-core/_latestVersion)
Bintray-dev: [ ![Download](https://api.bintray.com/packages/mipt-npm/dev/kmath-core/images/download.svg) ](https://bintray.com/mipt-npm/dev/kmath-core/_latestVersion)
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# KMath
@ -14,6 +11,8 @@ Python's NumPy library. Later we found that kotlin is much more flexible languag
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/)
## Publications and talks
* [A conceptual article about context-oriented design](https://proandroiddev.com/an-introduction-context-oriented-programming-in-kotlin-2e79d316b0a2)
@ -41,7 +40,7 @@ KMath is a modular library. Different modules provide different features with di
* **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.
* **DEVELOPMENT**. API breaking genrally 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.
* **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.
* **STABLE**. The API stabilized. Breaking changes are allowed only in major releases.
<!--Current feature list is [here](/docs/features.md)-->
@ -95,6 +94,10 @@ cases. We expect the worst KMath benchmarks will perform better than native Pyth
native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be
better than SciPy.
## Requirements
KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend to use GraalVM-CE 11 for execution in order to get better performance.
### Repositories
Release and development artifacts are accessible from mipt-npm [Space](https://www.jetbrains.com/space/) repository `https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven` (see documentation of
@ -106,8 +109,8 @@ repositories {
}
dependencies {
api("kscience.kmath:kmath-core:$version")
// api("kscience.kmath:kmath-core-jvm:$version") for jvm-specific version
api("${group}:kmath-core:$version")
// api("${group}:kmath-core-jvm:$version") for jvm-specific version
}
```

76
examples/build.gradle.kts
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@ -1,27 +1,16 @@
import org.jetbrains.kotlin.gradle.tasks.KotlinCompile
plugins {
kotlin("jvm")
kotlin("plugin.allopen")
id("kotlinx.benchmark")
}
allOpen.annotation("org.openjdk.jmh.annotations.State")
sourceSets.register("benchmarks")
repositories {
jcenter()
mavenCentral()
maven("https://repo.kotlin.link")
maven("https://clojars.org/repo")
maven("https://dl.bintray.com/egor-bogomolov/astminer/")
maven("https://dl.bintray.com/hotkeytlt/maven")
maven("https://dl.bintray.com/kotlin/kotlin-eap")
maven("https://dl.bintray.com/kotlin/kotlinx")
maven("https://dl.bintray.com/mipt-npm/dev")
maven("https://dl.bintray.com/mipt-npm/kscience")
maven("https://jitpack.io")
maven("http://logicrunch.research.it.uu.se/maven/")
mavenCentral()
maven("https://maven.pkg.jetbrains.space/kotlin/p/kotlin/kotlin-js-wrappers")
maven("http://logicrunch.research.it.uu.se/maven") {
isAllowInsecureProtocol = true
}
}
dependencies {
@ -36,10 +25,10 @@ dependencies {
implementation(project(":kmath-dimensions"))
implementation(project(":kmath-ejml"))
implementation(project(":kmath-nd4j"))
implementation(project(":kmath-tensors"))
implementation(project(":kmath-symja"))
implementation(project(":kmath-for-real"))
implementation("org.deeplearning4j:deeplearning4j-core:1.0.0-beta7")
implementation("org.nd4j:nd4j-native:1.0.0-beta7")
// uncomment if your system supports AVX2
@ -52,59 +41,28 @@ dependencies {
// } else
implementation("org.nd4j:nd4j-native-platform:1.0.0-beta7")
implementation("org.jetbrains.kotlinx:kotlinx-io:0.2.0-npm-dev-11")
implementation("org.jetbrains.kotlinx:kotlinx.benchmark.runtime:0.2.0-dev-20")
implementation("org.slf4j:slf4j-simple:1.7.30")
// plotting
implementation("kscience.plotlykt:plotlykt-server:0.3.1-dev")
"benchmarksImplementation"("org.jetbrains.kotlinx:kotlinx.benchmark.runtime-jvm:0.2.0-dev-20")
"benchmarksImplementation"(sourceSets.main.get().output + sourceSets.main.get().runtimeClasspath)
}
// Configure benchmark
benchmark {
// Setup configurations
targets.register("benchmarks")
// This one matches sourceSet name above
configurations.register("buffer") {
warmups = 1 // number of warmup iterations
iterations = 3 // number of iterations
iterationTime = 500 // time in seconds per iteration
iterationTimeUnit = "ms" // time unity for iterationTime, default is seconds
include("BufferBenchmark")
}
configurations.register("dot") {
warmups = 1 // number of warmup iterations
iterations = 3 // number of iterations
iterationTime = 500 // time in seconds per iteration
iterationTimeUnit = "ms" // time unity for iterationTime, default is seconds
include("DotBenchmark")
}
configurations.register("expressions") {
warmups = 1 // number of warmup iterations
iterations = 3 // number of iterations
iterationTime = 500 // time in seconds per iteration
iterationTimeUnit = "ms" // time unity for iterationTime, default is seconds
include("ExpressionsInterpretersBenchmark")
}
implementation("space.kscience:plotlykt-server:0.4.0")
//jafama
implementation(project(":kmath-jafama"))
}
kotlin.sourceSets.all {
with(languageSettings) {
useExperimentalAnnotation("kotlin.contracts.ExperimentalContracts")
useExperimentalAnnotation("kotlin.ExperimentalUnsignedTypes")
useExperimentalAnnotation("space.kscience.kmath.misc.UnstableKMathAPI")
}
}
tasks.withType<KotlinCompile> {
kotlinOptions.jvmTarget = "11"
tasks.withType<org.jetbrains.kotlin.gradle.tasks.KotlinCompile> {
kotlinOptions{
jvmTarget = "11"
freeCompilerArgs = freeCompilerArgs + "-Xjvm-default=all" + "-Xopt-in=kotlin.RequiresOptIn"
}
}
readme{
readme {
maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL
}

34
examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/ArrayBenchmark.kt
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@ -1,34 +0,0 @@
package space.kscience.kmath.benchmarks
import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import java.nio.IntBuffer
@State(Scope.Benchmark)
internal class ArrayBenchmark {
@Benchmark
fun benchmarkArrayRead() {
var res = 0
for (i in 1..space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.size) res += space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.array[space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.size - i]
}
@Benchmark
fun benchmarkBufferRead() {
var res = 0
for (i in 1..space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.size) res += space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.arrayBuffer[space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.size - i]
}
@Benchmark
fun nativeBufferRead() {
var res = 0
for (i in 1..space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.size) res += space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.nativeBuffer[space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.size - i]
}
companion object {
const val size: Int = 1000
val array: IntArray = IntArray(space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.size) { it }
val arrayBuffer: IntBuffer = IntBuffer.wrap(space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.array)
val nativeBuffer: IntBuffer = IntBuffer.allocate(space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.size).also { for (i in 0 until space.kscience.kmath.benchmarks.ArrayBenchmark.Companion.size) it.put(i, i) }
}
}

67
examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt
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@ -1,67 +0,0 @@
package space.kscience.kmath.benchmarks
import kotlinx.benchmark.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import space.kscience.kmath.commons.linear.CMMatrixContext
import space.kscience.kmath.ejml.EjmlMatrixContext
import space.kscience.kmath.linear.BufferMatrixContext
import space.kscience.kmath.linear.Matrix
import space.kscience.kmath.linear.RealMatrixContext
import space.kscience.kmath.operations.RealField
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import kotlin.random.Random
@State(Scope.Benchmark)
internal class DotBenchmark {
companion object {
val random = Random(12224)
val dim = 1000
//creating invertible matrix
val matrix1 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
val matrix2 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
val cmMatrix1 = CMMatrixContext { matrix1.toCM() }
val cmMatrix2 = CMMatrixContext { matrix2.toCM() }
val ejmlMatrix1 = EjmlMatrixContext { matrix1.toEjml() }
val ejmlMatrix2 = EjmlMatrixContext { matrix2.toEjml() }
}
@Benchmark
fun cmDot() {
CMMatrixContext {
cmMatrix1 dot cmMatrix2
}
}
@Benchmark
fun ejmlDot() {
EjmlMatrixContext {
ejmlMatrix1 dot ejmlMatrix2
}
}
@Benchmark
fun ejmlDotWithConversion() {
EjmlMatrixContext {
matrix1 dot matrix2
}
}
@Benchmark
fun bufferedDot() {
BufferMatrixContext(RealField, Buffer.Companion::real).invoke {
matrix1 dot matrix2
}
}
@Benchmark
fun realDot() {
RealMatrixContext {
matrix1 dot matrix2
}
}
}

71
examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt
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@ -1,71 +0,0 @@
package space.kscience.kmath.benchmarks
import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import space.kscience.kmath.asm.compile
import space.kscience.kmath.ast.mstInField
import space.kscience.kmath.expressions.Expression
import space.kscience.kmath.expressions.expressionInField
import space.kscience.kmath.expressions.invoke
import space.kscience.kmath.expressions.symbol
import space.kscience.kmath.operations.Field
import space.kscience.kmath.operations.RealField
import space.kscience.kmath.operations.bindSymbol
import kotlin.random.Random
@State(Scope.Benchmark)
internal class ExpressionsInterpretersBenchmark {
private val algebra: Field<Double> = RealField
val x by symbol
@Benchmark
fun functionalExpression() {
val expr = algebra.expressionInField {
val x = bindSymbol(x)
x * const(2.0) + const(2.0) / x - const(16.0)
}
invokeAndSum(expr)
}
@Benchmark
fun mstExpression() {
val expr = algebra.mstInField {
val x = bindSymbol(x)
x * 2.0 + 2.0 / x - 16.0
}
invokeAndSum(expr)
}
@Benchmark
fun asmExpression() {
val expr = algebra.mstInField {
val x = bindSymbol(x)
x * 2.0 + 2.0 / x - 16.0
}.compile()
invokeAndSum(expr)
}
@Benchmark
fun rawExpression() {
val expr = Expression<Double> { args ->
val x = args.getValue(x)
x * 2.0 + 2.0 / x - 16.0
}
invokeAndSum(expr)
}
private fun invokeAndSum(expr: Expression<Double>) {
val random = Random(0)
var sum = 0.0
repeat(1000000) {
sum += expr(x to random.nextDouble())
}
println(sum)
}
}

47
examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/LinearAlgebraBenchmark.kt
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@ -1,47 +0,0 @@
package space.kscience.kmath.benchmarks
import kotlinx.benchmark.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import space.kscience.kmath.commons.linear.CMMatrixContext
import space.kscience.kmath.commons.linear.CMMatrixContext.dot
import space.kscience.kmath.commons.linear.inverse
import space.kscience.kmath.ejml.EjmlMatrixContext
import space.kscience.kmath.ejml.inverse
import space.kscience.kmath.linear.Matrix
import space.kscience.kmath.linear.MatrixContext
import space.kscience.kmath.linear.inverseWithLup
import space.kscience.kmath.linear.real
import kotlin.random.Random
@State(Scope.Benchmark)
internal class LinearAlgebraBenchmark {
companion object {
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
}
@Benchmark
fun kmathLupInversion() {
MatrixContext.real.inverseWithLup(matrix)
}
@Benchmark
fun cmLUPInversion() {
with(CMMatrixContext) {
inverse(matrix)
}
}
@Benchmark
fun ejmlInverse() {
with(EjmlMatrixContext) {
inverse(matrix)
}
}
}

44
examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt
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@ -1,44 +0,0 @@
package space.kscience.kmath.benchmarks
import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.RealField
import space.kscience.kmath.structures.Buffer
@State(Scope.Benchmark)
internal class NDFieldBenchmark {
@Benchmark
fun autoFieldAdd() {
with(autoField) {
var res: NDStructure<Double> = one
repeat(n) { res += one }
}
}
@Benchmark
fun specializedFieldAdd() {
with(specializedField) {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
}
@Benchmark
fun boxingFieldAdd() {
with(genericField) {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
}
companion object {
const val dim: Int = 1000
const val n: Int = 100
val autoField = NDAlgebra.auto(RealField, dim, dim)
val specializedField: RealNDField = NDAlgebra.real(dim, dim)
val genericField = NDAlgebra.field(RealField, Buffer.Companion::boxing, dim, dim)
}
}

51
examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/ViktorBenchmark.kt
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@ -1,51 +0,0 @@
package space.kscience.kmath.benchmarks
import org.jetbrains.bio.viktor.F64Array
import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.RealField
import space.kscience.kmath.viktor.ViktorNDField
@State(Scope.Benchmark)
internal class ViktorBenchmark {
final val dim: Int = 1000
final val n: Int = 100
// automatically build context most suited for given type.
final val autoField: NDField<Double, RealField> = NDAlgebra.auto(RealField, dim, dim)
final val realField: RealNDField = NDAlgebra.real(dim, dim)
final val viktorField: ViktorNDField = ViktorNDField(dim, dim)
@Benchmark
fun automaticFieldAddition() {
with(autoField) {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
}
@Benchmark
fun realFieldAddition() {
with(realField) {
var res: NDStructure<Double> = one
repeat(n) { res += 1.0 }
}
}
@Benchmark
fun viktorFieldAddition() {
with(viktorField) {
var res = one
repeat(n) { res += 1.0 }
}
}
@Benchmark
fun rawViktor() {
val one = F64Array.full(init = 1.0, shape = intArrayOf(dim, dim))
var res = one
repeat(n) { res = res + one }
}
}

48
examples/src/benchmarks/kotlin/space/kscience/kmath/benchmarks/ViktorLogBenchmark.kt
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@ -1,48 +0,0 @@
package space.kscience.kmath.benchmarks
import org.jetbrains.bio.viktor.F64Array
import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.RealField
import space.kscience.kmath.viktor.ViktorNDField
@State(Scope.Benchmark)
internal class ViktorLogBenchmark {
final val dim: Int = 1000
final val n: Int = 100
// automatically build context most suited for given type.
final val autoField: NDField<Double, RealField> = NDAlgebra.auto(RealField, dim, dim)
final val realField: RealNDField = NDAlgebra.real(dim, dim)
final val viktorField: ViktorNDField = ViktorNDField(intArrayOf(dim, dim))
@Benchmark
fun realFieldLog() {
with(realField) {
val fortyTwo = produce { 42.0 }
var res = one
repeat(n) { res = ln(fortyTwo) }
}
}
@Benchmark
fun viktorFieldLog() {
with(viktorField) {
val fortyTwo = produce { 42.0 }
var res = one
repeat(n) { res = ln(fortyTwo) }
}
}
@Benchmark
fun rawViktorLog() {
val fortyTwo = F64Array.full(dim, dim, init = 42.0)
var res: F64Array
repeat(n) {
res = fortyTwo.log()
}
}
}

26
examples/src/main/kotlin/space/kscience/kmath/ast/astRendering.kt Normal file
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@ -0,0 +1,26 @@
/*
* 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.
*/
package space.kscience.kmath.ast
import space.kscience.kmath.ast.rendering.FeaturedMathRendererWithPostProcess
import space.kscience.kmath.ast.rendering.LatexSyntaxRenderer
import space.kscience.kmath.ast.rendering.MathMLSyntaxRenderer
import space.kscience.kmath.ast.rendering.renderWithStringBuilder
public fun main() {
val mst = "exp(sqrt(x))-asin(2*x)/(2e10+x^3)/(-12)".parseMath()
val syntax = FeaturedMathRendererWithPostProcess.Default.render(mst)
println("MathSyntax:")
println(syntax)
println()
val latex = LatexSyntaxRenderer.renderWithStringBuilder(syntax)
println("LaTeX:")
println(latex)
println()
val mathML = MathMLSyntaxRenderer.renderWithStringBuilder(syntax)
println("MathML:")
println(mathML)
}

21
examples/src/main/kotlin/space/kscience/kmath/ast/expressions.kt
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@ -1,15 +1,22 @@
/*
* 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.
*/
package space.kscience.kmath.ast
import space.kscience.kmath.expressions.invoke
import space.kscience.kmath.operations.RealField
import space.kscience.kmath.expressions.MstField
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.interpret
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
fun main() {
val expr = RealField.mstInField {
val x = bindSymbol("x")
x * 2.0 + 2.0 / x - 16.0
val expr = MstField {
x * 2.0 + number(2.0) / x - 16.0
}
repeat(10000000){
expr.invoke("x" to 1.0)
repeat(10000000) {
expr.interpret(DoubleField, x to 1.0)
}
}

27
examples/src/main/kotlin/space/kscience/kmath/ast/kotlingradSupport.kt
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@ -1,24 +1,27 @@
/*
* 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.
*/
package space.kscience.kmath.ast
import space.kscience.kmath.asm.compile
import space.kscience.kmath.expressions.derivative
import space.kscience.kmath.expressions.invoke
import space.kscience.kmath.expressions.symbol
import space.kscience.kmath.kotlingrad.differentiable
import space.kscience.kmath.operations.RealField
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.toExpression
import space.kscience.kmath.kotlingrad.toKotlingradExpression
import space.kscience.kmath.operations.DoubleField
/**
* In this example, x^2-4*x-44 function is differentiated with Kotlin∇, and the autodiff result is compared with
* valid derivative.
* valid derivative in a certain point.
*/
fun main() {
val x by symbol
val actualDerivative = MstExpression(RealField, "x^2-4*x-44".parseMath())
.differentiable()
val actualDerivative = "x^2-4*x-44"
.parseMath()
.toKotlingradExpression(DoubleField)
.derivative(x)
.compile()
val expectedDerivative = MstExpression(RealField, "2*x-4".parseMath()).compile()
assert(actualDerivative("x" to 123.0) == expectedDerivative("x" to 123.0))
val expectedDerivative = "2*x-4".parseMath().toExpression(DoubleField)
check(actualDerivative(x to 123.0) == expectedDerivative(x to 123.0))
}

27
examples/src/main/kotlin/space/kscience/kmath/ast/symjaSupport.kt Normal file
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@ -0,0 +1,27 @@
/*
* 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.
*/
package space.kscience.kmath.ast
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.derivative
import space.kscience.kmath.expressions.invoke
import space.kscience.kmath.expressions.toExpression
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.symja.toSymjaExpression
/**
* In this example, x^2-4*x-44 function is differentiated with Symja, and the autodiff result is compared with
* valid derivative in a certain point.
*/
fun main() {
val actualDerivative = "x^2-4*x-44"
.parseMath()
.toSymjaExpression(DoubleField)
.derivative(x)
val expectedDerivative = "2*x-4".parseMath().toExpression(DoubleField)
check(actualDerivative(x to 123.0) == expectedDerivative(x to 123.0))
}

39
examples/src/main/kotlin/space/kscience/kmath/commons/fit/fitWithAutoDiff.kt
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@ -1,19 +1,27 @@
/*
* 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.
*/
package space.kscience.kmath.commons.fit
import kotlinx.html.br
import kotlinx.html.h3
import kscience.plotly.*
import kscience.plotly.models.ScatterMode
import kscience.plotly.models.TraceValues
import space.kscience.kmath.commons.optimization.chiSquared
import space.kscience.kmath.commons.optimization.minimize
import space.kscience.kmath.distributions.NormalDistribution
import space.kscience.kmath.expressions.symbol
import space.kscience.kmath.real.RealVector
import space.kscience.kmath.optimization.FunctionOptimization
import space.kscience.kmath.optimization.OptimizationResult
import space.kscience.kmath.real.DoubleVector
import space.kscience.kmath.real.map
import space.kscience.kmath.real.step
import space.kscience.kmath.stat.*
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.kmath.structures.asIterable
import space.kscience.kmath.structures.toList
import space.kscience.plotly.*
import space.kscience.plotly.models.ScatterMode
import space.kscience.plotly.models.TraceValues
import kotlin.math.pow
import kotlin.math.sqrt
@ -26,17 +34,16 @@ private val c by symbol
/**
* Shortcut to use buffers in plotly
*/
operator fun TraceValues.invoke(vector: RealVector) {
operator fun TraceValues.invoke(vector: DoubleVector) {
numbers = vector.asIterable()
}
/**
* Least squares fie with auto-differentiation. Uses `kmath-commons` and `kmath-for-real` modules.
*/
fun main() {
suspend fun main() {
//A generator for a normally distributed values
val generator = Distribution.normal()
val generator = NormalDistribution(2.0, 7.0)
//A chain/flow of random values with the given seed
val chain = generator.sample(RandomGenerator.default(112667))
@ -49,7 +56,7 @@ fun main() {
//Perform an operation on each x value (much more effective, than numpy)
val y = x.map {
val value = it.pow(2) + it + 1
value + chain.nextDouble() * sqrt(value)
value + chain.next() * sqrt(value)
}
// this will also work, but less effective:
// val y = x.pow(2)+ x + 1 + chain.nextDouble()
@ -58,10 +65,10 @@ fun main() {
val yErr = y.map { sqrt(it) }//RealVector.same(x.size, sigma)
// compute differentiable chi^2 sum for given model ax^2 + bx + c
val chi2 = Fitting.chiSquared(x, y, yErr) { x1 ->
val chi2 = FunctionOptimization.chiSquared(x, y, yErr) { x1 ->
//bind variables to autodiff context
val a = bind(a)
val b = bind(b)
val a = bindSymbol(a)
val b = bindSymbol(b)
//Include default value for c if it is not provided as a parameter
val c = bindSymbolOrNull(c) ?: one
a * x1.pow(2) + b * x1 + c
@ -90,13 +97,13 @@ fun main() {
}
}
br()
h3{
h3 {
+"Fit result: $result"
}
h3{
h3 {
+"Chi2/dof = ${result.value / (x.size - 3)}"
}
}
page.makeFile()
}
}

23
examples/src/main/kotlin/space/kscience/kmath/functions/integrate.kt Normal file
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@ -0,0 +1,23 @@
/*
* 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.
*/
package space.kscience.kmath.functions
import space.kscience.kmath.integration.gaussIntegrator
import space.kscience.kmath.integration.integrate
import space.kscience.kmath.integration.value
import space.kscience.kmath.operations.DoubleField
import kotlin.math.pow
fun main() {
//Define a function
val function: UnivariateFunction<Double> = { x -> 3 * x.pow(2) + 2 * x + 1 }
//get the result of the integration
val result = DoubleField.gaussIntegrator.integrate(0.0..10.0, function = function)
//the value is nullable because in some cases the integration could not succeed
println(result.value)
}

54
examples/src/main/kotlin/space/kscience/kmath/functions/interpolate.kt Normal file
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@ -0,0 +1,54 @@
/*
* 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.
*/
package space.kscience.kmath.functions
import space.kscience.kmath.interpolation.SplineInterpolator
import space.kscience.kmath.interpolation.interpolatePolynomials
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.structures.DoubleBuffer
import space.kscience.plotly.Plotly
import space.kscience.plotly.UnstablePlotlyAPI
import space.kscience.plotly.makeFile
import space.kscience.plotly.models.functionXY
import space.kscience.plotly.scatter
import kotlin.math.PI
import kotlin.math.sin
@OptIn(UnstablePlotlyAPI::class)
fun main() {
val data = (0..10).map {
val x = it.toDouble() / 5 * PI
x to sin(x)
}
val polynomial: PiecewisePolynomial<Double> = SplineInterpolator(
DoubleField, ::DoubleBuffer
).interpolatePolynomials(data)
val function = polynomial.asFunction(DoubleField, 0.0)
val cmInterpolate = org.apache.commons.math3.analysis.interpolation.SplineInterpolator().interpolate(
data.map { it.first }.toDoubleArray(),
data.map { it.second }.toDoubleArray()
)
Plotly.plot {
scatter {
name = "interpolated"
x.numbers = data.map { it.first }
y.numbers = x.doubles.map { function(it) }
}
scatter {
name = "original"
functionXY(0.0..(2 * PI), 0.1) { sin(it) }
}
scatter {
name = "cm"
x.numbers = data.map { it.first }
y.numbers = x.doubles.map { cmInterpolate.value(it) }
}
}.makeFile()
}

45
examples/src/main/kotlin/space/kscience/kmath/functions/interpolateSquare.kt Normal file
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@ -0,0 +1,45 @@
/*
* 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.
*/
package space.kscience.kmath.functions
import space.kscience.kmath.interpolation.SplineInterpolator
import space.kscience.kmath.interpolation.interpolatePolynomials
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.real.step
import space.kscience.kmath.structures.map
import space.kscience.plotly.Plotly
import space.kscience.plotly.UnstablePlotlyAPI
import space.kscience.plotly.makeFile
import space.kscience.plotly.models.functionXY
import space.kscience.plotly.scatter
@OptIn(UnstablePlotlyAPI::class)
fun main() {
val function: UnivariateFunction<Double> = { x ->
if (x in 30.0..50.0) {
1.0
} else {
0.0
}
}
val xs = 0.0..100.0 step 0.5
val ys = xs.map(function)
val polynomial: PiecewisePolynomial<Double> = SplineInterpolator.double.interpolatePolynomials(xs, ys)
val polyFunction = polynomial.asFunction(DoubleField, 0.0)
Plotly.plot {
scatter {
name = "interpolated"
functionXY(25.0..55.0, 0.1) { polyFunction(it) }
}
scatter {
name = "original"
functionXY(25.0..55.0, 0.1) { function(it) }
}
}.makeFile()
}

33
examples/src/main/kotlin/space/kscience/kmath/functions/matrixIntegration.kt Normal file
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@ -0,0 +1,33 @@
/*
* 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.
*/
package space.kscience.kmath.functions
import space.kscience.kmath.integration.gaussIntegrator
import space.kscience.kmath.integration.integrate
import space.kscience.kmath.integration.value
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.nd
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
fun main(): Unit = DoubleField {
nd(2, 2) {
//Produce a diagonal StructureND
fun diagonal(v: Double) = produce { (i, j) ->
if (i == j) v else 0.0
}
//Define a function in a nd space
val function: (Double) -> StructureND<Double> = { x: Double -> 3 * number(x).pow(2) + 2 * diagonal(x) + 1 }
//get the result of the integration
val result = gaussIntegrator.integrate(0.0..10.0, function = function)
//the value is nullable because in some cases the integration could not succeed
println(result.value)
}
}

17
examples/src/main/kotlin/space/kscience/kmath/jafama/KMathaJafamaDemo.kt Normal file
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@ -0,0 +1,17 @@
/*
* 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.
*/
package space.kscience.kmath.jafama
import net.jafama.FastMath
fun main(){
val a = JafamaDoubleField.number(2.0)
val b = StrictJafamaDoubleField.power(FastMath.E,a)
println(JafamaDoubleField.add(b,a))
println(StrictJafamaDoubleField.ln(b))
}

33
examples/src/main/kotlin/space/kscience/kmath/linear/gradient.kt Normal file
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@ -0,0 +1,33 @@
/*
* 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.
*/
package space.kscience.kmath.linear
import space.kscience.kmath.real.*
import space.kscience.kmath.structures.DoubleBuffer
fun main() {
val x0 = DoubleVector(0.0, 0.0, 0.0)
val sigma = DoubleVector(1.0, 1.0, 1.0)
val gaussian: (Point<Double>) -> Double = { x ->
require(x.size == x0.size)
kotlin.math.exp(-((x - x0) / sigma).square().sum())
}
fun ((Point<Double>) -> Double).grad(x: Point<Double>): Point<Double> {
require(x.size == x0.size)
return DoubleBuffer(x.size) { i ->
val h = sigma[i] / 5
val dVector = DoubleBuffer(x.size) { if (it == i) h else 0.0 }
val f1 = this(x + dVector / 2)
val f0 = this(x - dVector / 2)
(f1 - f0) / h
}
}
println(gaussian.grad(x0))
}

5
examples/src/main/kotlin/space/kscience/kmath/operations/BigIntDemo.kt
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@ -1,3 +1,8 @@
/*
* 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.
*/
package space.kscience.kmath.operations
fun main() {

11
examples/src/main/kotlin/space/kscience/kmath/operations/ComplexDemo.kt
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@ -1,18 +1,23 @@
/*
* 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.
*/
package space.kscience.kmath.operations
import space.kscience.kmath.complex.Complex
import space.kscience.kmath.complex.complex
import space.kscience.kmath.nd.NDAlgebra
import space.kscience.kmath.nd.AlgebraND
fun main() {
// 2d element
val element = NDAlgebra.complex(2, 2).produce { (i, j) ->
val element = AlgebraND.complex(2, 2).produce { (i, j) ->
Complex(i.toDouble() - j.toDouble(), i.toDouble() + j.toDouble())
}
println(element)
// 1d element operation
val result = with(NDAlgebra.complex(8)) {
val result = with(AlgebraND.complex(8)) {
val a = produce { (it) -> i * it - it.toDouble() }
val b = 3
val c = Complex(1.0, 1.0)

42
examples/src/main/kotlin/space/kscience/kmath/stat/DistributionBenchmark.kt
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@ -1,23 +1,29 @@
package kscience.kmath.commons.prob
/*
* 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.
*/
package space.kscience.kmath.stat
import kotlinx.coroutines.Dispatchers
import kotlinx.coroutines.async
import kotlinx.coroutines.runBlocking
import org.apache.commons.rng.sampling.distribution.ZigguratNormalizedGaussianSampler
import org.apache.commons.rng.sampling.distribution.BoxMullerNormalizedGaussianSampler
import org.apache.commons.rng.simple.RandomSource
import space.kscience.kmath.stat.*
import space.kscience.kmath.samplers.GaussianSampler
import java.time.Duration
import java.time.Instant
import org.apache.commons.rng.sampling.distribution.GaussianSampler as CMGaussianSampler
private fun runChain(): Duration {
private suspend fun runKMathChained(): Duration {
val generator = RandomGenerator.fromSource(RandomSource.MT, 123L)
val normal = Distribution.normal(NormalSamplerMethod.Ziggurat)
val normal = GaussianSampler(7.0, 2.0)
val chain = normal.sample(generator)
val startTime = Instant.now()
var sum = 0.0
repeat(10000001) { counter ->
sum += chain.nextDouble()
sum += chain.next()
if (counter % 100000 == 0) {
val duration = Duration.between(startTime, Instant.now())
@ -29,9 +35,15 @@ private fun runChain(): Duration {
return Duration.between(startTime, Instant.now())
}
private fun runDirect(): Duration {
val provider = RandomSource.create(RandomSource.MT, 123L)
val sampler = ZigguratNormalizedGaussianSampler(provider)
private fun runApacheDirect(): Duration {
val rng = RandomSource.create(RandomSource.MT, 123L)
val sampler = CMGaussianSampler.of(
BoxMullerNormalizedGaussianSampler.of(rng),
7.0,
2.0
)
val startTime = Instant.now()
var sum = 0.0
@ -51,11 +63,9 @@ private fun runDirect(): Duration {
/**
* Comparing chain sampling performance with direct sampling performance
*/
fun main() {
runBlocking(Dispatchers.Default) {
val chainJob = async { runChain() }
val directJob = async { runDirect() }
println("Chain: ${chainJob.await()}")
println("Direct: ${directJob.await()}")
}
fun main(): Unit = runBlocking(Dispatchers.Default) {
val directJob = async { runApacheDirect() }
val chainJob = async { runKMathChained() }
println("KMath Chained: ${chainJob.await()}")
println("Apache Direct: ${directJob.await()}")
}

14
examples/src/main/kotlin/space/kscience/kmath/stat/DistributionDemo.kt
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@ -1,16 +1,22 @@
/*
* 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.
*/
package space.kscience.kmath.stat
import kotlinx.coroutines.runBlocking
import space.kscience.kmath.chains.Chain
import space.kscience.kmath.chains.collectWithState
import space.kscience.kmath.distributions.NormalDistribution
/**
* The state of distribution averager
* The state of distribution averager.
*/
private data class AveragingChainState(var num: Int = 0, var value: Double = 0.0)
/**
* Averaging
* Averaging.
*/
private fun Chain<Double>.mean(): Chain<Double> = collectWithState(AveragingChainState(), { it.copy() }) { chain ->
val next = chain.next()
@ -21,7 +27,7 @@ private fun Chain<Double>.mean(): Chain<Double> = collectWithState(AveragingChai
fun main() {
val normal = Distribution.normal()
val normal = NormalDistribution(0.0, 2.0)
val chain = normal.sample(RandomGenerator.default).mean()
runBlocking {
@ -32,4 +38,4 @@ fun main() {
}
}
}
}
}

17
examples/src/main/kotlin/space/kscience/kmath/structures/ComplexND.kt
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@ -1,11 +1,16 @@
/*
* 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.
*/
@file:Suppress("unused")
package space.kscience.kmath.structures
import space.kscience.kmath.complex.*
import space.kscience.kmath.linear.transpose
import space.kscience.kmath.nd.NDAlgebra
import space.kscience.kmath.nd.NDStructure
import space.kscience.kmath.nd.AlgebraND
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.real
import space.kscience.kmath.operations.invoke
@ -15,12 +20,12 @@ fun main() {
val dim = 1000
val n = 1000
val realField = NDAlgebra.real(dim, dim)
val complexField: ComplexNDField = NDAlgebra.complex(dim, dim)
val realField = AlgebraND.real(dim, dim)
val complexField: ComplexFieldND = AlgebraND.complex(dim, dim)
val realTime = measureTimeMillis {
realField {
var res: NDStructure<Double> = one
var res: StructureND<Double> = one
repeat(n) {
res += 1.0
}
@ -31,7 +36,7 @@ fun main() {
val complexTime = measureTimeMillis {
complexField {
var res: NDStructure<Complex> = one
var res: StructureND<Complex> = one
repeat(n) {
res += 1.0
}

31
examples/src/main/kotlin/space/kscience/kmath/structures/NDField.kt
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@ -1,10 +1,16 @@
/*
* 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.
*/
package space.kscience.kmath.structures
import kotlinx.coroutines.DelicateCoroutinesApi
import kotlinx.coroutines.GlobalScope
import org.nd4j.linalg.factory.Nd4j
import space.kscience.kmath.nd.*
import space.kscience.kmath.nd4j.Nd4jArrayField
import space.kscience.kmath.operations.RealField
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.viktor.ViktorNDField
import kotlin.contracts.InvocationKind
@ -17,6 +23,7 @@ internal inline fun measureAndPrint(title: String, block: () -> Unit) {
println("$title completed in $time millis")
}
@OptIn(DelicateCoroutinesApi::class)
fun main() {
// initializing Nd4j
Nd4j.zeros(0)
@ -24,56 +31,56 @@ fun main() {
val n = 1000
// automatically build context most suited for given type.
val autoField = NDAlgebra.auto(RealField, dim, dim)
val autoField = AlgebraND.auto(DoubleField, dim, dim)
// specialized nd-field for Double. It works as generic Double field as well
val realField = NDAlgebra.real(dim, dim)
val realField = AlgebraND.real(dim, dim)
//A generic boxing field. It should be used for objects, not primitives.
val boxingField = NDAlgebra.field(RealField, Buffer.Companion::boxing, dim, dim)
val boxingField = AlgebraND.field(DoubleField, Buffer.Companion::boxing, dim, dim)
// Nd4j specialized field.
val nd4jField = Nd4jArrayField.real(dim, dim)
//viktor field
val viktorField = ViktorNDField(dim,dim)
val viktorField = ViktorNDField(dim, dim)
//parallel processing based on Java Streams
val parallelField = NDAlgebra.realWithStream(dim,dim)
val parallelField = AlgebraND.realWithStream(dim, dim)
measureAndPrint("Boxing addition") {
boxingField {
var res: NDStructure<Double> = one
var res: StructureND<Double> = one
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Specialized addition") {
realField {
var res: NDStructure<Double> = one
var res: StructureND<Double> = one
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Nd4j specialized addition") {
nd4jField {
var res: NDStructure<Double> = one
var res: StructureND<Double> = one
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Viktor addition") {
viktorField {
var res: NDStructure<Double> = one
var res: StructureND<Double> = one
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Parallel stream addition") {
parallelField {
var res: NDStructure<Double> = one
var res: StructureND<Double> = one
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Automatic field addition") {
autoField {
var res: NDStructure<Double> = one
var res: StructureND<Double> = one
repeat(n) { res += 1.0 }
}
}

103
examples/src/main/kotlin/space/kscience/kmath/structures/ParallelRealNDField.kt
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@ -1,103 +0,0 @@
package space.kscience.kmath.structures
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.ExtendedField
import space.kscience.kmath.operations.RealField
import space.kscience.kmath.operations.RingWithNumbers
import java.util.*
import java.util.stream.IntStream
/**
* A demonstration implementation of NDField over Real using Java [DoubleStream] for parallel execution
*/
@OptIn(UnstableKMathAPI::class)
class StreamRealNDField(
override val shape: IntArray,
) : NDField<Double, RealField>,
RingWithNumbers<NDStructure<Double>>,
ExtendedField<NDStructure<Double>> {
private val strides = DefaultStrides(shape)
override val elementContext: RealField get() = RealField
override val zero: NDBuffer<Double> by lazy { produce { zero } }
override val one: NDBuffer<Double> by lazy { produce { one } }
override fun number(value: Number): NDBuffer<Double> {
val d = value.toDouble() // minimize conversions
return produce { d }
}
private val NDStructure<Double>.buffer: RealBuffer
get() = when {
!shape.contentEquals(this@StreamRealNDField.shape) -> throw ShapeMismatchException(
this@StreamRealNDField.shape,
shape
)
this is NDBuffer && this.strides == this@StreamRealNDField.strides -> this.buffer as RealBuffer
else -> RealBuffer(strides.linearSize) { offset -> get(strides.index(offset)) }
}
override fun produce(initializer: RealField.(IntArray) -> Double): NDBuffer<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
val index = strides.index(offset)
RealField.initializer(index)
}.toArray()
return NDBuffer(strides, array.asBuffer())
}
override fun NDStructure<Double>.map(
transform: RealField.(Double) -> Double,
): NDBuffer<Double> {
val array = Arrays.stream(buffer.array).parallel().map { RealField.transform(it) }.toArray()
return NDBuffer(strides, array.asBuffer())
}
override fun NDStructure<Double>.mapIndexed(
transform: RealField.(index: IntArray, Double) -> Double,
): NDBuffer<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
RealField.transform(
strides.index(offset),
buffer.array[offset]
)
}.toArray()
return NDBuffer(strides, array.asBuffer())
}
override fun combine(
a: NDStructure<Double>,
b: NDStructure<Double>,
transform: RealField.(Double, Double) -> Double,
): NDBuffer<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
RealField.transform(a.buffer.array[offset], b.buffer.array[offset])
}.toArray()
return NDBuffer(strides, array.asBuffer())
}
override fun power(arg: NDStructure<Double>, pow: Number): NDBuffer<Double> = arg.map() { power(it, pow) }
override fun exp(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { exp(it) }
override fun ln(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { ln(it) }
override fun sin(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { sin(it) }
override fun cos(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { cos(it) }
override fun tan(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { tan(it) }
override fun asin(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { asin(it) }
override fun acos(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { acos(it) }
override fun atan(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { atan(it) }
override fun sinh(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { sinh(it) }
override fun cosh(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { cosh(it) }
override fun tanh(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { tanh(it) }
override fun asinh(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { asinh(it) }
override fun acosh(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { acosh(it) }
override fun atanh(arg: NDStructure<Double>): NDBuffer<Double> = arg.map() { atanh(it) }
}
fun NDAlgebra.Companion.realWithStream(vararg shape: Int): StreamRealNDField = StreamRealNDField(shape)

108
examples/src/main/kotlin/space/kscience/kmath/structures/StreamDoubleFieldND.kt Normal file
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@ -0,0 +1,108 @@
/*
* 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.
*/
package space.kscience.kmath.structures
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.ExtendedField
import space.kscience.kmath.operations.NumbersAddOperations
import java.util.*
import java.util.stream.IntStream
/**
* A demonstration implementation of NDField over Real using Java [java.util.stream.DoubleStream] for parallel
* execution.
*/
class StreamDoubleFieldND(override val shape: IntArray) : FieldND<Double, DoubleField>,
NumbersAddOperations<StructureND<Double>>,
ExtendedField<StructureND<Double>> {
private val strides = DefaultStrides(shape)
override val elementContext: DoubleField get() = DoubleField
override val zero: BufferND<Double> by lazy { produce { zero } }
override val one: BufferND<Double> by lazy { produce { one } }
override fun number(value: Number): BufferND<Double> {
val d = value.toDouble() // minimize conversions
return produce { d }
}
private val StructureND<Double>.buffer: DoubleBuffer
get() = when {
!shape.contentEquals(this@StreamDoubleFieldND.shape) -> throw ShapeMismatchException(
this@StreamDoubleFieldND.shape,
shape
)
this is BufferND && this.strides == this@StreamDoubleFieldND.strides -> this.buffer as DoubleBuffer
else -> DoubleBuffer(strides.linearSize) { offset -> get(strides.index(offset)) }
}
override fun produce(initializer: DoubleField.(IntArray) -> Double): BufferND<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
val index = strides.index(offset)
DoubleField.initializer(index)
}.toArray()
return BufferND(strides, array.asBuffer())
}
override fun StructureND<Double>.map(
transform: DoubleField.(Double) -> Double,
): BufferND<Double> {
val array = Arrays.stream(buffer.array).parallel().map { DoubleField.transform(it) }.toArray()
return BufferND(strides, array.asBuffer())
}
override fun StructureND<Double>.mapIndexed(
transform: DoubleField.(index: IntArray, Double) -> Double,
): BufferND<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
DoubleField.transform(
strides.index(offset),
buffer.array[offset]
)
}.toArray()
return BufferND(strides, array.asBuffer())
}
override fun combine(
a: StructureND<Double>,
b: StructureND<Double>,
transform: DoubleField.(Double, Double) -> Double,
): BufferND<Double> {
val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
DoubleField.transform(a.buffer.array[offset], b.buffer.array[offset])
}.toArray()
return BufferND(strides, array.asBuffer())
}
override fun StructureND<Double>.unaryMinus(): StructureND<Double> = map { -it }
override fun scale(a: StructureND<Double>, value: Double): StructureND<Double> = a.map { it * value }
override fun power(arg: StructureND<Double>, pow: Number): BufferND<Double> = arg.map { power(it, pow) }
override fun exp(arg: StructureND<Double>): BufferND<Double> = arg.map { exp(it) }
override fun ln(arg: StructureND<Double>): BufferND<Double> = arg.map { ln(it) }
override fun sin(arg: StructureND<Double>): BufferND<Double> = arg.map { sin(it) }
override fun cos(arg: StructureND<Double>): BufferND<Double> = arg.map { cos(it) }
override fun tan(arg: StructureND<Double>): BufferND<Double> = arg.map { tan(it) }
override fun asin(arg: StructureND<Double>): BufferND<Double> = arg.map { asin(it) }
override fun acos(arg: StructureND<Double>): BufferND<Double> = arg.map { acos(it) }
override fun atan(arg: StructureND<Double>): BufferND<Double> = arg.map { atan(it) }
override fun sinh(arg: StructureND<Double>): BufferND<Double> = arg.map { sinh(it) }
override fun cosh(arg: StructureND<Double>): BufferND<Double> = arg.map { cosh(it) }
override fun tanh(arg: StructureND<Double>): BufferND<Double> = arg.map { tanh(it) }
override fun asinh(arg: StructureND<Double>): BufferND<Double> = arg.map { asinh(it) }
override fun acosh(arg: StructureND<Double>): BufferND<Double> = arg.map { acosh(it) }
override fun atanh(arg: StructureND<Double>): BufferND<Double> = arg.map { atanh(it) }
}
fun AlgebraND.Companion.realWithStream(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(shape)

13
examples/src/main/kotlin/space/kscience/kmath/structures/StructureReadBenchmark.kt
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@ -1,16 +1,21 @@
/*
* 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.
*/
package space.kscience.kmath.structures
import space.kscience.kmath.nd.BufferND
import space.kscience.kmath.nd.DefaultStrides
import space.kscience.kmath.nd.NDBuffer
import kotlin.system.measureTimeMillis
@Suppress("ASSIGNED_BUT_NEVER_ACCESSED_VARIABLE")
fun main() {
val n = 6000
val array = DoubleArray(n * n) { 1.0 }
val buffer = RealBuffer(array)
val buffer = DoubleBuffer(array)
val strides = DefaultStrides(intArrayOf(n, n))
val structure = NDBuffer(strides, buffer)
val structure = BufferND(strides, buffer)
measureTimeMillis {
var res = 0.0
@ -34,4 +39,4 @@ fun main() {
strides.indices().forEach { res = array[strides.offset(it)] }
}
println("Array reading finished in $time3 millis")
}
}

13
examples/src/main/kotlin/space/kscience/kmath/structures/StructureWriteBenchmark.kt
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@ -1,13 +1,18 @@
/*
* 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.
*/
package space.kscience.kmath.structures
import space.kscience.kmath.nd.NDStructure
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.mapToBuffer
import kotlin.system.measureTimeMillis
@Suppress("UNUSED_VARIABLE")
fun main() {
val n = 6000
val structure = NDStructure.build(intArrayOf(n, n), Buffer.Companion::auto) { 1.0 }
val structure = StructureND.buffered(intArrayOf(n, n), Buffer.Companion::auto) { 1.0 }
structure.mapToBuffer { it + 1 } // warm-up
val time1 = measureTimeMillis { val res = structure.mapToBuffer { it + 1 } }
println("Structure mapping finished in $time1 millis")
@ -20,10 +25,10 @@ fun main() {
println("Array mapping finished in $time2 millis")
val buffer = RealBuffer(DoubleArray(n * n) { 1.0 })
val buffer = DoubleBuffer(DoubleArray(n * n) { 1.0 })
val time3 = measureTimeMillis {
val target = RealBuffer(DoubleArray(n * n))
val target = DoubleBuffer(DoubleArray(n * n))
val res = array.forEachIndexed { index, value ->
target[index] = value + 1
}

9
examples/src/main/kotlin/space/kscience/kmath/structures/typeSafeDimensions.kt
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@ -1,3 +1,8 @@
/*
* 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.
*/
package space.kscience.kmath.structures
import space.kscience.kmath.dimensions.D2
@ -5,7 +10,7 @@ import space.kscience.kmath.dimensions.D3
import space.kscience.kmath.dimensions.DMatrixContext
import space.kscience.kmath.dimensions.Dimension
private fun DMatrixContext<Double>.simple() {
private fun DMatrixContext<Double, *>.simple() {
val m1 = produce<D2, D3> { i, j -> (i + j).toDouble() }
val m2 = produce<D3, D2> { i, j -> (i + j).toDouble() }
@ -17,7 +22,7 @@ private object D5 : Dimension {
override val dim: UInt = 5u
}
private fun DMatrixContext<Double>.custom() {
private fun DMatrixContext<Double, *>.custom() {
val m1 = produce<D2, D5> { i, j -> (i + j).toDouble() }
val m2 = produce<D5, D2> { i, j -> (i - j).toDouble() }
val m3 = produce<D2, D2> { i, j -> (i - j).toDouble() }

42
examples/src/main/kotlin/space/kscience/kmath/tensors/DataSetNormalization.kt Normal file
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@ -0,0 +1,42 @@
/*
* 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.
*/
package space.kscience.kmath.tensors
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
// Dataset normalization
fun main() = BroadcastDoubleTensorAlgebra { // work in context with broadcast methods
// take dataset of 5-element vectors from normal distribution
val dataset = randomNormal(intArrayOf(100, 5)) * 1.5 // all elements from N(0, 1.5)
dataset += fromArray(
intArrayOf(5),
doubleArrayOf(0.0, 1.0, 1.5, 3.0, 5.0) // rows means
)
// find out mean and standard deviation of each column
val mean = dataset.mean(0, false)
val std = dataset.std(0, false)
println("Mean:\n$mean")
println("Standard deviation:\n$std")
// also we can calculate other statistic as minimum and maximum of rows
println("Minimum:\n${dataset.min(0, false)}")
println("Maximum:\n${dataset.max(0, false)}")
// now we can scale dataset with mean normalization
val datasetScaled = (dataset - mean) / std
// find out mean and std of scaled dataset
println("Mean of scaled:\n${datasetScaled.mean(0, false)}")
println("Mean of scaled:\n${datasetScaled.std(0, false)}")
}

93
examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt Normal file
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@ -0,0 +1,93 @@
/*
* 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.
*/
package space.kscience.kmath.tensors
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
import space.kscience.kmath.tensors.core.DoubleTensor
// solving linear system with LUP decomposition
fun main() = BroadcastDoubleTensorAlgebra {// work in context with linear operations
// set true value of x
val trueX = fromArray(
intArrayOf(4),
doubleArrayOf(-2.0, 1.5, 6.8, -2.4)
)
// and A matrix
val a = fromArray(
intArrayOf(4, 4),
doubleArrayOf(
0.5, 10.5, 4.5, 1.0,
8.5, 0.9, 12.8, 0.1,
5.56, 9.19, 7.62, 5.45,
1.0, 2.0, -3.0, -2.5
)
)
// calculate y value
val b = a dot trueX
// check out A and b
println("A:\n$a")
println("b:\n$b")
// solve `Ax = b` system using LUP decomposition
// get P, L, U such that PA = LU
val (p, l, u) = a.lu()
// check that P is permutation matrix
println("P:\n$p")
// L is lower triangular matrix and U is upper triangular matrix
println("L:\n$l")
println("U:\n$u")
// and PA = LU
println("PA:\n${p dot a}")
println("LU:\n${l dot u}")
/* Ax = b;
PAx = Pb;
LUx = Pb;
let y = Ux, then
Ly = Pb -- this system can be easily solved, since the matrix L is lower triangular;
Ux = y can be solved the same way, since the matrix L is upper triangular
*/
// this function returns solution x of a system lx = b, l should be lower triangular
fun solveLT(l: DoubleTensor, b: DoubleTensor): DoubleTensor {
val n = l.shape[0]
val x = zeros(intArrayOf(n))
for (i in 0 until n) {
x[intArrayOf(i)] = (b[intArrayOf(i)] - l[i].dot(x).value()) / l[intArrayOf(i, i)]
}
return x
}
val y = solveLT(l, p dot b)
// solveLT(l, b) function can be easily adapted for upper triangular matrix by the permutation matrix revMat
// create it by placing ones on side diagonal
val revMat = u.zeroesLike()
val n = revMat.shape[0]
for (i in 0 until n) {
revMat[intArrayOf(i, n - 1 - i)] = 1.0
}
// solution of system ux = b, u should be upper triangular
fun solveUT(u: DoubleTensor, b: DoubleTensor): DoubleTensor = revMat dot solveLT(
revMat dot u dot revMat, revMat dot b
)
val x = solveUT(u, y)
println("True x:\n$trueX")
println("x founded with LU method:\n$x")
}

239
examples/src/main/kotlin/space/kscience/kmath/tensors/NeuralNetwork.kt Normal file
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@ -0,0 +1,239 @@
/*
* 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.
*/
package space.kscience.kmath.tensors
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.toDoubleArray
import kotlin.math.sqrt
const val seed = 100500L
// Simple feedforward neural network with backpropagation training
// interface of network layer
interface Layer {
fun forward(input: DoubleTensor): DoubleTensor
fun backward(input: DoubleTensor, outputError: DoubleTensor): DoubleTensor
}
// activation layer
open class Activation(
val activation: (DoubleTensor) -> DoubleTensor,
val activationDer: (DoubleTensor) -> DoubleTensor,
) : Layer {
override fun forward(input: DoubleTensor): DoubleTensor {
return activation(input)
}
override fun backward(input: DoubleTensor, outputError: DoubleTensor): DoubleTensor {
return DoubleTensorAlgebra { outputError * activationDer(input) }
}
}
fun relu(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
x.map { if (it > 0) it else 0.0 }
}
fun reluDer(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
x.map { if (it > 0) 1.0 else 0.0 }
}
// activation layer with relu activator
class ReLU : Activation(::relu, ::reluDer)
fun sigmoid(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
1.0 / (1.0 + (-x).exp())
}
fun sigmoidDer(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
sigmoid(x) * (1.0 - sigmoid(x))
}
// activation layer with sigmoid activator
class Sigmoid : Activation(::sigmoid, ::sigmoidDer)
// dense layer
class Dense(
private val inputUnits: Int,
private val outputUnits: Int,
private val learningRate: Double = 0.1,
) : Layer {
private val weights: DoubleTensor = DoubleTensorAlgebra {
randomNormal(
intArrayOf(inputUnits, outputUnits),
seed
) * sqrt(2.0 / (inputUnits + outputUnits))
}
private val bias: DoubleTensor = DoubleTensorAlgebra { zeros(intArrayOf(outputUnits)) }
override fun forward(input: DoubleTensor): DoubleTensor = BroadcastDoubleTensorAlgebra {
(input dot weights) + bias
}
override fun backward(input: DoubleTensor, outputError: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
val gradInput = outputError dot weights.transpose()
val gradW = input.transpose() dot outputError
val gradBias = outputError.mean(dim = 0, keepDim = false) * input.shape[0].toDouble()
weights -= learningRate * gradW
bias -= learningRate * gradBias
gradInput
}
}
// simple accuracy equal to the proportion of correct answers
fun accuracy(yPred: DoubleTensor, yTrue: DoubleTensor): Double {
check(yPred.shape contentEquals yTrue.shape)
val n = yPred.shape[0]
var correctCnt = 0
for (i in 0 until n) {
if (yPred[intArrayOf(i, 0)] == yTrue[intArrayOf(i, 0)]) {
correctCnt += 1
}
}
return correctCnt.toDouble() / n.toDouble()
}
// 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.toDoubleArray().forEachIndexed { index, labelDouble ->
val label = labelDouble.toInt()
onesForAnswers[intArrayOf(index, label)] = 1.0
}
val softmaxValue = yPred.exp() / yPred.exp().sum(dim = 1, keepDim = true)
(-onesForAnswers + softmaxValue) / (yPred.shape[0].toDouble())
}
private fun forward(x: DoubleTensor): List<DoubleTensor> {
var input = x
return buildList {
layers.forEach { layer ->
val output = layer.forward(input)
add(output)
input = output
}
}
}
private fun train(xTrain: DoubleTensor, yTrain: DoubleTensor) {
val layerInputs = buildList {
add(xTrain)
addAll(forward(xTrain))
}
var lossGrad = softMaxLoss(layerInputs.last(), yTrain)
layers.zip(layerInputs).reversed().forEach { (layer, input) ->
lossGrad = layer.backward(input, lossGrad)
}
}
fun fit(xTrain: DoubleTensor, yTrain: DoubleTensor, batchSize: Int, epochs: Int) = DoubleTensorAlgebra {
fun iterBatch(x: DoubleTensor, y: DoubleTensor): Sequence<Pair<DoubleTensor, DoubleTensor>> = sequence {
val n = x.shape[0]
val shuffledIndices = (0 until n).shuffled()
for (i in 0 until n step batchSize) {
val excerptIndices = shuffledIndices.drop(i).take(batchSize).toIntArray()
val batch = x.rowsByIndices(excerptIndices) to y.rowsByIndices(excerptIndices)
yield(batch)
}
}
for (epoch in 0 until epochs) {
println("Epoch ${epoch + 1}/$epochs")
for ((xBatch, yBatch) in iterBatch(xTrain, yTrain)) {
train(xBatch, yBatch)
}
println("Accuracy:${accuracy(yTrain, predict(xTrain).argMax(1, true))}")
}
}
fun predict(x: DoubleTensor): DoubleTensor {
return forward(x).last()
}
}
@OptIn(ExperimentalStdlibApi::class)
fun main() = BroadcastDoubleTensorAlgebra {
val features = 5
val sampleSize = 250
val trainSize = 180
//val testSize = sampleSize - trainSize
// take sample of features from normal distribution
val x = randomNormal(intArrayOf(sampleSize, features), seed) * 2.5
x += fromArray(
intArrayOf(5),
doubleArrayOf(0.0, -1.0, -2.5, -3.0, 5.5) // rows means
)
// define class like '1' if the sum of features > 0 and '0' otherwise
val y = fromArray(
intArrayOf(sampleSize, 1),
DoubleArray(sampleSize) { i ->
if (x[i].sum() > 0.0) {
1.0
} else {
0.0
}
}
)
// split train ans test
val trainIndices = (0 until trainSize).toList().toIntArray()
val testIndices = (trainSize until sampleSize).toList().toIntArray()
val xTrain = x.rowsByIndices(trainIndices)
val yTrain = y.rowsByIndices(trainIndices)
val xTest = x.rowsByIndices(testIndices)
val yTest = y.rowsByIndices(testIndices)
// build model
val layers = buildList {
add(Dense(features, 64))
add(ReLU())
add(Dense(64, 16))
add(ReLU())
add(Dense(16, 2))
add(Sigmoid())
}
val model = NeuralNetwork(layers)
// fit it with train data
model.fit(xTrain, yTrain, batchSize = 20, epochs = 10)
// make prediction
val prediction = model.predict(xTest)
// process raw prediction via argMax
val predictionLabels = prediction.argMax(1, true)
// find out accuracy
val acc = accuracy(yTest, predictionLabels)
println("Test accuracy:$acc")
}

68
examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt Normal file
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@ -0,0 +1,68 @@
/*
* 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.
*/
package space.kscience.kmath.tensors
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
import kotlin.math.abs
// OLS estimator using SVD
fun main() {
//seed for random
val randSeed = 100500L
// work in context with linear operations
DoubleTensorAlgebra {
// take coefficient vector from normal distribution
val alpha = randomNormal(
intArrayOf(5),
randSeed
) + fromArray(
intArrayOf(5),
doubleArrayOf(1.0, 2.5, 3.4, 5.0, 10.1)
)
println("Real alpha:\n$alpha")
// also take sample of size 20 from normal distribution for x
val x = randomNormal(
intArrayOf(20, 5),
randSeed
)
// calculate y and add gaussian noise (N(0, 0.05))
val y = x dot alpha
y += y.randomNormalLike(randSeed) * 0.05
// now restore the coefficient vector with OSL estimator with SVD
val (u, singValues, v) = x.svd()
// we have to make sure the singular values of the matrix are not close to zero
println("Singular values:\n$singValues")
// 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
println("Estimated alpha:\n" +
"$alphaOLS")
// figure out MSE of approximation
fun mse(yTrue: DoubleTensor, yPred: DoubleTensor): Double {
require(yTrue.shape.size == 1)
require(yTrue.shape contentEquals yPred.shape)
val diff = yTrue - yPred
return diff.dot(diff).sqrt().value()
}
println("MSE: ${mse(alpha, alphaOLS)}")
}
}

74
examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt Normal file
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@ -0,0 +1,74 @@
/*
* 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.
*/
package space.kscience.kmath.tensors
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
// simple PCA
fun main(): Unit = BroadcastDoubleTensorAlgebra { // work in context with broadcast methods
val seed = 100500L
// assume x is range from 0 until 10
val x = fromArray(
intArrayOf(10),
(0 until 10).toList().map { it.toDouble() }.toDoubleArray()
)
// take y dependent on x with noise
val y = 2.0 * x + (3.0 + x.randomNormalLike(seed) * 1.5)
println("x:\n$x")
println("y:\n$y")
// stack them into single dataset
val dataset = stack(listOf(x, y)).transpose()
// normalize both x and y
val xMean = x.mean()
val yMean = y.mean()
val xStd = x.std()
val yStd = y.std()
val xScaled = (x - xMean) / xStd
val yScaled = (y - yMean) / yStd
// save means ans standard deviations for further recovery
val mean = fromArray(
intArrayOf(2),
doubleArrayOf(xMean, yMean)
)
println("Means:\n$mean")
val std = fromArray(
intArrayOf(2),
doubleArrayOf(xStd, yStd)
)
println("Standard deviations:\n$std")
// calculate the covariance matrix of scaled x and y
val covMatrix = cov(listOf(xScaled, yScaled))
println("Covariance matrix:\n$covMatrix")
// and find out eigenvector of it
val (_, evecs) = covMatrix.symEig()
val v = evecs[0]
println("Eigenvector:\n$v")
// reduce dimension of dataset
val datasetReduced = v dot stack(listOf(xScaled, yScaled))
println("Reduced data:\n$datasetReduced")
// we can restore original data from reduced data.
// for example, find 7th element of dataset
val n = 7
val restored = (datasetReduced[n] dot v.view(intArrayOf(1, 2))) * std + mean
println("Original value:\n${dataset[n]}")
println("Restored value:\n$restored")
}

9
gradle.properties
View File

@ -1,8 +1,13 @@
#
# 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
kotlin.mpp.enableGranularSourceSetsMetadata=true
kotlin.mpp.stability.nowarn=true
kotlin.native.enableDependencyPropagation=false
kotlin.parallel.tasks.in.project=true
org.gradle.jvmargs=-XX:MaxMetaspaceSize=512m
org.gradle.configureondemand=true
org.gradle.jvmargs=-XX:MaxMetaspaceSize=2G
org.gradle.parallel=true
systemProp.org.gradle.internal.publish.checksums.insecure=true

BIN
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-6.8.2-bin.zip
distributionUrl=https\://services.gradle.org/distributions/gradle-7.1.1-bin.zip
zipStoreBase=GRADLE_USER_HOME
zipStorePath=wrapper/dists

2
gradlew vendored
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@ -72,7 +72,7 @@ case "`uname`" in
Darwin* )
darwin=true
;;
MINGW* )
MSYS* | MINGW* )
msys=true
;;
NONSTOP* )

247
kmath-ast/README.md
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@ -1,75 +1,65 @@
# Abstract Syntax Tree Expression Representation and Operations (`kmath-ast`)
# Module kmath-ast
This subproject implements the following features:
Performance and visualization extensions to MST API.
- [expression-language](src/jvmMain/kotlin/kscience/kmath/ast/parser.kt) : Expression language and its parser
- [mst](src/commonMain/kotlin/kscience/kmath/ast/MST.kt) : MST (Mathematical Syntax Tree) as expression language's syntax intermediate representation
- [mst-building](src/commonMain/kotlin/kscience/kmath/ast/MstAlgebra.kt) : MST building algebraic structure
- [mst-interpreter](src/commonMain/kotlin/kscience/kmath/ast/MST.kt) : MST interpreter
- [mst-jvm-codegen](src/jvmMain/kotlin/kscience/kmath/asm/asm.kt) : Dynamic MST to JVM bytecode compiler
- [mst-js-codegen](src/jsMain/kotlin/kscience/kmath/estree/estree.kt) : Dynamic MST to JS compiler
- [expression-language](src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt) : Expression language and its parser
- [mst-jvm-codegen](src/jvmMain/kotlin/space/kscience/kmath/asm/asm.kt) : Dynamic MST to JVM bytecode compiler
- [mst-js-codegen](src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt) : Dynamic MST to JS compiler
- [rendering](src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt) : Extendable MST rendering
> #### Artifact:
>
> This module artifact: `space.kscience:kmath-ast:0.2.0`.
>
> Bintray release version: [ ![Download](https://api.bintray.com/packages/mipt-npm/kscience/kmath-ast/images/download.svg) ](https://bintray.com/mipt-npm/kscience/kmath-ast/_latestVersion)
>
> Bintray development version: [ ![Download](https://api.bintray.com/packages/mipt-npm/dev/kmath-ast/images/download.svg) ](https://bintray.com/mipt-npm/dev/kmath-ast/_latestVersion)
>
> **Gradle:**
>
> ```gradle
> repositories {
> maven { url 'https://repo.kotlin.link' }
> maven { url 'https://dl.bintray.com/hotkeytlt/maven' }
> maven { url "https://dl.bintray.com/kotlin/kotlin-eap" } // include for builds based on kotlin-eap
>// Uncomment if repo.kotlin.link is unavailable
>// maven { url 'https://dl.bintray.com/mipt-npm/kscience' }
>// maven { url 'https://dl.bintray.com/mipt-npm/dev' }
> }
>
> dependencies {
> implementation 'space.kscience:kmath-ast:0.2.0'
> }
> ```
> **Gradle Kotlin DSL:**
>
> ```kotlin
> repositories {
> maven("https://repo.kotlin.link")
> maven("https://dl.bintray.com/kotlin/kotlin-eap") // include for builds based on kotlin-eap
> maven("https://dl.bintray.com/hotkeytlt/maven") // required for a
>// Uncomment if repo.kotlin.link is unavailable
>// maven("https://dl.bintray.com/mipt-npm/kscience")
>// maven("https://dl.bintray.com/mipt-npm/dev")
> }
>
> dependencies {
> implementation("space.kscience:kmath-ast:0.2.0")
> }
> ```
## Artifact:
The Maven coordinates of this project are `space.kscience:kmath-ast:0.3.0-dev-14`.
**Gradle:**
```gradle
repositories {
maven { url 'https://repo.kotlin.link' }
mavenCentral()
}
dependencies {
implementation 'space.kscience:kmath-ast:0.3.0-dev-14'
}
```
**Gradle Kotlin DSL:**
```kotlin
repositories {
maven("https://repo.kotlin.link")
mavenCentral()
}
dependencies {
implementation("space.kscience:kmath-ast:0.3.0-dev-14")
}
```
## Dynamic expression code generation
### On JVM
`kmath-ast` JVM module supports runtime code generation to eliminate overhead of tree traversal. Code generator builds
a special implementation of `Expression<T>` with implemented `invoke` function.
`kmath-ast` JVM module supports runtime code generation to eliminate overhead of tree traversal. Code generator builds a
special implementation of `Expression<T>` with implemented `invoke` function.
For example, the following builder:
```kotlin
RealField.mstInField { symbol("x") + 2 }.compile()
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.*
import space.kscience.kmath.asm.*
MstField { x + 2 }.compileToExpression(DoubleField)
```
… leads to generation of bytecode, which can be decompiled to the following Java class:
... leads to generation of bytecode, which can be decompiled to the following Java class:
```java
package space.kscience.kmath.asm.generated;
import java.util.Map;
import kotlin.jvm.functions.Function2;
import space.kscience.kmath.asm.internal.MapIntrinsics;
import space.kscience.kmath.expressions.Expression;
@ -79,7 +69,7 @@ public final class AsmCompiledExpression_45045_0 implements Expression<Double> {
private final Object[] constants;
public final Double invoke(Map<Symbol, ? extends Double> arguments) {
return (Double)((Function2)this.constants[0]).invoke((Double)MapIntrinsics.getOrFail(arguments, "x"), 2);
return (Double) ((Function2) this.constants[0]).invoke((Double) MapIntrinsics.getOrFail(arguments, "x"), 2);
}
public AsmCompiledExpression_45045_0(Object[] constants) {
@ -89,19 +79,10 @@ public final class AsmCompiledExpression_45045_0 implements Expression<Double> {
```
### Example Usage
This API extends MST and MstExpression, so you may optimize as both of them:
```kotlin
RealField.mstInField { symbol("x") + 2 }.compile()
RealField.expression("x+2".parseMath())
```
#### Known issues
- The same classes may be generated and loaded twice, so it is recommended to cache compiled expressions to avoid
class loading overhead.
- The same classes may be generated and loaded twice, so it is recommended to cache compiled expressions to avoid class
loading overhead.
- This API is not supported by non-dynamic JVM implementations (like TeaVM and GraalVM) because of using class loaders.
### On JS
@ -109,17 +90,149 @@ RealField.expression("x+2".parseMath())
A similar feature is also available on JS.
```kotlin
RealField.mstInField { symbol("x") + 2 }.compile()
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.*
import space.kscience.kmath.estree.*
MstField { x + 2 }.compileToExpression(DoubleField)
```
The code above returns expression implemented with such a JS function:
```js
var executable = function (constants, arguments) {
return constants[1](constants[0](arguments, "x"), 2);
return constants[1](constants[0](arguments, "x"), 2);
};
```
JS also supports very experimental expression optimization with [WebAssembly](https://webassembly.org/) IR generation.
Currently, only expressions inside `DoubleField` and `IntRing` are supported.
```kotlin
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.*
import space.kscience.kmath.wasm.*
MstField { x + 2 }.compileToExpression(DoubleField)
```
An example of emitted Wasm IR in the form of WAT:
```lisp
(func $executable (param $0 f64) (result f64)
(f64.add
(local.get $0)
(f64.const 2)
)
)
```
#### Known issues
- This feature uses `eval` which can be unavailable in several environments.
- ESTree expression compilation uses `eval` which can be unavailable in several environments.
- WebAssembly isn't supported by old versions of browsers (see https://webassembly.org/roadmap/).
## Rendering expressions
kmath-ast also includes an extensible engine to display expressions in LaTeX or MathML syntax.
Example usage:
```kotlin
import space.kscience.kmath.ast.*
import space.kscience.kmath.ast.rendering.*
import space.kscience.kmath.misc.*
@OptIn(UnstableKMathAPI::class)
public fun main() {
val mst = "exp(sqrt(x))-asin(2*x)/(2e10+x^3)/(12)+x^(2/3)".parseMath()
val syntax = FeaturedMathRendererWithPostProcess.Default.render(mst)
val latex = LatexSyntaxRenderer.renderWithStringBuilder(syntax)
println("LaTeX:")
println(latex)
println()
val mathML = MathMLSyntaxRenderer.renderWithStringBuilder(syntax)
println("MathML:")
println(mathML)
}
```
Result LaTeX:
![](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})
Result MathML (can be used with MathJax or other renderers):
<details>
```html
<math xmlns="https://www.w3.org/1998/Math/MathML">
<mrow>
<mo>exp</mo>
<mspace width="0.167em"></mspace>
<mfenced open="(" close=")" separators="">
<msqrt>
<mi>x</mi>
</msqrt>
</mfenced>
<mo>-</mo>
<mfrac>
<mrow>
<mfrac>
<mrow>
<mo>arcsin</mo>
<mspace width="0.167em"></mspace>
<mfenced open="(" close=")" separators="">
<mn>2</mn>
<mspace width="0.167em"></mspace>
<mi>x</mi>
</mfenced>
</mrow>
<mrow>
<mn>2</mn>
<mo>&times;</mo>
<msup>
<mrow>
<mn>10</mn>
</mrow>
<mrow>
<mn>10</mn>
</mrow>
</msup>
<mo>+</mo>
<msup>
<mrow>
<mi>x</mi>
</mrow>
<mrow>
<mn>3</mn>
</mrow>
</msup>
</mrow>
</mfrac>
</mrow>
<mrow>
<mn>12</mn>
</mrow>
</mfrac>
<mo>+</mo>
<msup>
<mrow>
<mi>x</mi>
</mrow>
<mrow>
<mn>2</mn>
<mo>/</mo>
<mn>3</mn>
</mrow>
</msup>
</mrow>
</math>
```
</details>
It is also possible to create custom algorithms of render, and even add support of other markup languages
(see API reference).

58
kmath-ast/build.gradle.kts
View File

@ -1,7 +1,6 @@
import ru.mipt.npm.gradle.Maturity
plugins {
id("ru.mipt.npm.gradle.mpp")
kotlin("multiplatform")
id("ru.mipt.npm.gradle.common")
}
kotlin.js {
@ -19,8 +18,13 @@ kotlin.js {
}
kotlin.sourceSets {
filter { it.name.contains("test", true) }
.map(org.jetbrains.kotlin.gradle.plugin.KotlinSourceSet::languageSettings)
.forEach { it.useExperimentalAnnotation("space.kscience.kmath.misc.UnstableKMathAPI") }
commonMain {
dependencies {
api("com.github.h0tk3y.betterParse:better-parse:0.4.2")
api(project(":kmath-core"))
}
}
@ -33,15 +37,15 @@ kotlin.sourceSets {
jsMain {
dependencies {
implementation(npm("astring", "1.7.0"))
implementation(npm("astring", "1.7.5"))
implementation(npm("binaryen", "101.0.0"))
implementation(npm("js-base64", "3.6.1"))
}
}
jvmMain {
dependencies {
api("com.github.h0tk3y.betterParse:better-parse:0.4.1")
implementation("org.ow2.asm:asm:9.1")
implementation("org.ow2.asm:asm-commons:9.1")
implementation("org.ow2.asm:asm-commons:9.2")
}
}
}
@ -52,42 +56,26 @@ tasks.dokkaHtml {
}
readme {
maturity = Maturity.PROTOTYPE
maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL
propertyByTemplate("artifact", rootProject.file("docs/templates/ARTIFACT-TEMPLATE.md"))
feature(
id = "expression-language",
description = "Expression language and its parser",
ref = "src/jvmMain/kotlin/kscience/kmath/ast/parser.kt"
)
feature(
id = "mst",
description = "MST (Mathematical Syntax Tree) as expression language's syntax intermediate representation",
ref = "src/commonMain/kotlin/kscience/kmath/ast/MST.kt"
)
feature(
id = "mst-building",
description = "MST building algebraic structure",
ref = "src/commonMain/kotlin/kscience/kmath/ast/MstAlgebra.kt"
)
feature(
id = "mst-interpreter",
description = "MST interpreter",
ref = "src/commonMain/kotlin/kscience/kmath/ast/MST.kt"
)
ref = "src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt"
) { "Expression language and its parser" }
feature(
id = "mst-jvm-codegen",
description = "Dynamic MST to JVM bytecode compiler",
ref = "src/jvmMain/kotlin/kscience/kmath/asm/asm.kt"
)
ref = "src/jvmMain/kotlin/space/kscience/kmath/asm/asm.kt"
) { "Dynamic MST to JVM bytecode compiler" }
feature(
id = "mst-js-codegen",
description = "Dynamic MST to JS compiler",
ref = "src/jsMain/kotlin/kscience/kmath/estree/estree.kt"
)
ref = "src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt"
) { "Dynamic MST to JS compiler" }
feature(
id = "rendering",
ref = "src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt"
) { "Extendable MST rendering" }
}

171
kmath-ast/docs/README-TEMPLATE.md
View File

@ -1,6 +1,6 @@
# Abstract Syntax Tree Expression Representation and Operations (`kmath-ast`)
# Module kmath-ast
This subproject implements the following features:
Performance and visualization extensions to MST API.
${features}
@ -10,21 +10,27 @@ ${artifact}
### On JVM
`kmath-ast` JVM module supports runtime code generation to eliminate overhead of tree traversal. Code generator builds
a special implementation of `Expression<T>` with implemented `invoke` function.
`kmath-ast` JVM module supports runtime code generation to eliminate overhead of tree traversal. Code generator builds a
special implementation of `Expression<T>` with implemented `invoke` function.
For example, the following builder:
```kotlin
RealField.mstInField { symbol("x") + 2 }.compile()
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.*
import space.kscience.kmath.asm.*
MstField { x + 2 }.compileToExpression(DoubleField)
```
… leads to generation of bytecode, which can be decompiled to the following Java class:
... leads to generation of bytecode, which can be decompiled to the following Java class:
```java
package space.kscience.kmath.asm.generated;
import java.util.Map;
import kotlin.jvm.functions.Function2;
import space.kscience.kmath.asm.internal.MapIntrinsics;
import space.kscience.kmath.expressions.Expression;
@ -34,7 +40,7 @@ public final class AsmCompiledExpression_45045_0 implements Expression<Double> {
private final Object[] constants;
public final Double invoke(Map<Symbol, ? extends Double> arguments) {
return (Double)((Function2)this.constants[0]).invoke((Double)MapIntrinsics.getOrFail(arguments, "x"), 2);
return (Double) ((Function2) this.constants[0]).invoke((Double) MapIntrinsics.getOrFail(arguments, "x"), 2);
}
public AsmCompiledExpression_45045_0(Object[] constants) {
@ -44,19 +50,10 @@ public final class AsmCompiledExpression_45045_0 implements Expression<Double> {
```
### Example Usage
This API extends MST and MstExpression, so you may optimize as both of them:
```kotlin
RealField.mstInField { symbol("x") + 2 }.compile()
RealField.expression("x+2".parseMath())
```
#### Known issues
- The same classes may be generated and loaded twice, so it is recommended to cache compiled expressions to avoid
class loading overhead.
- The same classes may be generated and loaded twice, so it is recommended to cache compiled expressions to avoid class
loading overhead.
- This API is not supported by non-dynamic JVM implementations (like TeaVM and GraalVM) because of using class loaders.
### On JS
@ -64,17 +61,149 @@ RealField.expression("x+2".parseMath())
A similar feature is also available on JS.
```kotlin
RealField.mstInField { symbol("x") + 2 }.compile()
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.*
import space.kscience.kmath.estree.*
MstField { x + 2 }.compileToExpression(DoubleField)
```
The code above returns expression implemented with such a JS function:
```js
var executable = function (constants, arguments) {
return constants[1](constants[0](arguments, "x"), 2);
return constants[1](constants[0](arguments, "x"), 2);
};
```
JS also supports very experimental expression optimization with [WebAssembly](https://webassembly.org/) IR generation.
Currently, only expressions inside `DoubleField` and `IntRing` are supported.
```kotlin
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.*
import space.kscience.kmath.wasm.*
MstField { x + 2 }.compileToExpression(DoubleField)
```
An example of emitted Wasm IR in the form of WAT:
```lisp
(func \$executable (param \$0 f64) (result f64)
(f64.add
(local.get \$0)
(f64.const 2)
)
)
```
#### Known issues
- This feature uses `eval` which can be unavailable in several environments.
- ESTree expression compilation uses `eval` which can be unavailable in several environments.
- WebAssembly isn't supported by old versions of browsers (see https://webassembly.org/roadmap/).
## Rendering expressions
kmath-ast also includes an extensible engine to display expressions in LaTeX or MathML syntax.
Example usage:
```kotlin
import space.kscience.kmath.ast.*
import space.kscience.kmath.ast.rendering.*
import space.kscience.kmath.misc.*
@OptIn(UnstableKMathAPI::class)
public fun main() {
val mst = "exp(sqrt(x))-asin(2*x)/(2e10+x^3)/(12)+x^(2/3)".parseMath()
val syntax = FeaturedMathRendererWithPostProcess.Default.render(mst)
val latex = LatexSyntaxRenderer.renderWithStringBuilder(syntax)
println("LaTeX:")
println(latex)
println()
val mathML = MathMLSyntaxRenderer.renderWithStringBuilder(syntax)
println("MathML:")
println(mathML)
}
```
Result LaTeX:
![](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})
Result MathML (can be used with MathJax or other renderers):
<details>
```html
<math xmlns="https://www.w3.org/1998/Math/MathML">
<mrow>
<mo>exp</mo>
<mspace width="0.167em"></mspace>
<mfenced open="(" close=")" separators="">
<msqrt>
<mi>x</mi>
</msqrt>
</mfenced>
<mo>-</mo>
<mfrac>
<mrow>
<mfrac>
<mrow>
<mo>arcsin</mo>
<mspace width="0.167em"></mspace>
<mfenced open="(" close=")" separators="">
<mn>2</mn>
<mspace width="0.167em"></mspace>
<mi>x</mi>
</mfenced>
</mrow>
<mrow>
<mn>2</mn>
<mo>&times;</mo>
<msup>
<mrow>
<mn>10</mn>
</mrow>
<mrow>
<mn>10</mn>
</mrow>
</msup>
<mo>+</mo>
<msup>
<mrow>
<mi>x</mi>
</mrow>
<mrow>
<mn>3</mn>
</mrow>
</msup>
</mrow>
</mfrac>
</mrow>
<mrow>
<mn>12</mn>
</mrow>
</mfrac>
<mo>+</mo>
<msup>
<mrow>
<mi>x</mi>
</mrow>
<mrow>
<mn>2</mn>
<mo>/</mo>
<mn>3</mn>
</mrow>
</msup>
</mrow>
</math>
```
</details>
It is also possible to create custom algorithms of render, and even add support of other markup languages
(see API reference).

136
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/MstExpression.kt
View File

@ -1,136 +0,0 @@
package space.kscience.kmath.ast
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.*
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
/**
* The expression evaluates MST on-flight. Should be much faster than functional expression, but slower than
* ASM-generated expressions.
*
* @property algebra the algebra that provides operations.
* @property mst the [MST] node.
* @author Alexander Nozik
*/
public class MstExpression<T, out A : Algebra<T>>(public val algebra: A, public val mst: MST) : Expression<T> {
private inner class InnerAlgebra(val arguments: Map<Symbol, T>) : NumericAlgebra<T> {
override fun bindSymbol(value: String): T = try {
algebra.bindSymbol(value)
} catch (ignored: IllegalStateException) {
null
} ?: arguments.getValue(StringSymbol(value))
override fun unaryOperation(operation: String, arg: T): T =
algebra.unaryOperation(operation, arg)
override fun binaryOperation(operation: String, left: T, right: T): T =
algebra.binaryOperation(operation, left, right)
override fun unaryOperationFunction(operation: String): (arg: T) -> T =
algebra.unaryOperationFunction(operation)
override fun binaryOperationFunction(operation: String): (left: T, right: T) -> T =
algebra.binaryOperationFunction(operation)
@Suppress("UNCHECKED_CAST")
override fun number(value: Number): T = if (algebra is NumericAlgebra<*>)
(algebra as NumericAlgebra<T>).number(value)
else
error("Numeric nodes are not supported by $this")
}
override operator fun invoke(arguments: Map<Symbol, T>): T = InnerAlgebra(arguments).evaluate(mst)
}
/**
* Builds [MstExpression] over [Algebra].
*
* @author Alexander Nozik
*/
public inline fun <reified T : Any, A : Algebra<T>, E : Algebra<MST>> A.mst(
mstAlgebra: E,
block: E.() -> MST,
): MstExpression<T, A> = MstExpression(this, mstAlgebra.block())
/**
* Builds [MstExpression] over [Space].
*
* @author Alexander Nozik
*/
public inline fun <reified T : Any, A : Space<T>> A.mstInSpace(block: MstSpace.() -> MST): MstExpression<T, A> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return MstExpression(this, MstSpace.block())
}
/**
* Builds [MstExpression] over [Ring].
*
* @author Alexander Nozik
*/
public inline fun <reified T : Any, A : Ring<T>> A.mstInRing(block: MstRing.() -> MST): MstExpression<T, A> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return MstExpression(this, MstRing.block())
}
/**
* Builds [MstExpression] over [Field].
*
* @author Alexander Nozik
*/
public inline fun <reified T : Any, A : Field<T>> A.mstInField(block: MstField.() -> MST): MstExpression<T, A> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return MstExpression(this, MstField.block())
}
/**
* Builds [MstExpression] over [ExtendedField].
*
* @author Iaroslav Postovalov
*/
public inline fun <reified T : Any, A : ExtendedField<T>> A.mstInExtendedField(block: MstExtendedField.() -> MST): MstExpression<T, A> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return MstExpression(this, MstExtendedField.block())
}
/**
* Builds [MstExpression] over [FunctionalExpressionSpace].
*
* @author Alexander Nozik
*/
public inline fun <reified T : Any, A : Space<T>> FunctionalExpressionSpace<T, A>.mstInSpace(block: MstSpace.() -> MST): MstExpression<T, A> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return algebra.mstInSpace(block)
}
/**
* Builds [MstExpression] over [FunctionalExpressionRing].
*
* @author Alexander Nozik
*/
public inline fun <reified T : Any, A : Ring<T>> FunctionalExpressionRing<T, A>.mstInRing(block: MstRing.() -> MST): MstExpression<T, A> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return algebra.mstInRing(block)
}
/**
* Builds [MstExpression] over [FunctionalExpressionField].
*
* @author Alexander Nozik
*/
public inline fun <reified T : Any, A : Field<T>> FunctionalExpressionField<T, A>.mstInField(block: MstField.() -> MST): MstExpression<T, A> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return algebra.mstInField(block)
}
/**
* Builds [MstExpression] over [FunctionalExpressionExtendedField].
*
* @author Iaroslav Postovalov
*/
public inline fun <reified T : Any, A : ExtendedField<T>> FunctionalExpressionExtendedField<T, A>.mstInExtendedField(
block: MstExtendedField.() -> MST,
): MstExpression<T, A> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return algebra.mstInExtendedField(block)
}

21
kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/ast/parser.kt → kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt
View File

@ -1,4 +1,7 @@
// TODO move to common when https://github.com/h0tk3y/better-parse/pull/37 is merged
/*
* 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.
*/
package space.kscience.kmath.ast
@ -13,18 +16,20 @@ import com.github.h0tk3y.betterParse.lexer.literalToken
import com.github.h0tk3y.betterParse.lexer.regexToken
import com.github.h0tk3y.betterParse.parser.ParseResult
import com.github.h0tk3y.betterParse.parser.Parser
import space.kscience.kmath.expressions.MST
import space.kscience.kmath.expressions.StringSymbol
import space.kscience.kmath.operations.FieldOperations
import space.kscience.kmath.operations.GroupOperations
import space.kscience.kmath.operations.PowerOperations
import space.kscience.kmath.operations.RingOperations
import space.kscience.kmath.operations.SpaceOperations
/**
* better-parse implementation of grammar defined in the ArithmeticsEvaluator.g4.
*
* @author Alexander Nozik and Iaroslav Postovalov
* @author Alexander Nozik
* @author Iaroslav Postovalov
*/
public object ArithmeticsEvaluator : Grammar<MST>() {
// TODO replace with "...".toRegex() when better-parse 0.4.1 is released
private val num: Token by regexToken("[\\d.]+(?:[eE][-+]?\\d+)?".toRegex())
private val id: Token by regexToken("[a-z_A-Z][\\da-z_A-Z]*".toRegex())
private val lpar: Token by literalToken("(")
@ -38,7 +43,7 @@ public object ArithmeticsEvaluator : Grammar<MST>() {
private val ws: Token by regexToken("\\s+".toRegex(), ignore = true)
private val number: Parser<MST> by num use { MST.Numeric(text.toDouble()) }
private val singular: Parser<MST> by id use { MST.Symbolic(text) }
private val singular: Parser<MST> by id use { StringSymbol(text) }
private val unaryFunction: Parser<MST> by (id and -lpar and parser(ArithmeticsEvaluator::subSumChain) and -rpar)
.map { (id, term) -> MST.Unary(id.text, term) }
@ -55,7 +60,7 @@ public object ArithmeticsEvaluator : Grammar<MST>() {
.or(binaryFunction)
.or(unaryFunction)
.or(singular)
.or(-minus and parser(ArithmeticsEvaluator::term) map { MST.Unary(SpaceOperations.MINUS_OPERATION, it) })
.or(-minus and parser(ArithmeticsEvaluator::term) map { MST.Unary(GroupOperations.MINUS_OPERATION, it) })
.or(-lpar and parser(ArithmeticsEvaluator::subSumChain) and -rpar)
private val powChain: Parser<MST> by leftAssociative(term = term, operator = pow) { a, _, b ->
@ -77,9 +82,9 @@ public object ArithmeticsEvaluator : Grammar<MST>() {
operator = plus or minus use TokenMatch::type
) { a, op, b ->
if (op == plus)
MST.Binary(SpaceOperations.PLUS_OPERATION, a, b)
MST.Binary(GroupOperations.PLUS_OPERATION, a, b)
else
MST.Binary(SpaceOperations.MINUS_OPERATION, a, b)
MST.Binary(GroupOperations.MINUS_OPERATION, a, b)
}
override val rootParser: Parser<MST> by subSumChain

150
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/LatexSyntaxRenderer.kt Normal file
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@ -0,0 +1,150 @@
/*
* 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.
*/
package space.kscience.kmath.ast.rendering
import space.kscience.kmath.misc.UnstableKMathAPI
/**
* [SyntaxRenderer] implementation for LaTeX.
*
* The generated string is a valid LaTeX fragment to be used in the Math Mode.
*
* Example usage:
*
* ```
* \documentclass{article}
* \begin{document}
* \begin{equation}
* %code generated by the syntax renderer
* \end{equation}
* \end{document}
* ```
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public object LatexSyntaxRenderer : SyntaxRenderer {
public override fun render(node: MathSyntax, output: Appendable): Unit = output.run {
fun render(syntax: MathSyntax) = render(syntax, output)
when (node) {
is NumberSyntax -> append(node.string)
is SymbolSyntax -> append(node.string)
is OperatorNameSyntax -> {
append("\\operatorname{")
append(node.name)
append('}')
}
is SpecialSymbolSyntax -> when (node.kind) {
SpecialSymbolSyntax.Kind.INFINITY -> append("\\infty")
SpecialSymbolSyntax.Kind.SMALL_PI -> append("\\pi")
}
is OperandSyntax -> {
if (node.parentheses) append("\\left(")
render(node.operand)
if (node.parentheses) append("\\right)")
}
is UnaryOperatorSyntax -> {
render(node.prefix)
append("\\,")
render(node.operand)
}
is UnaryPlusSyntax -> {
append('+')
render(node.operand)
}
is UnaryMinusSyntax -> {
append('-')
render(node.operand)
}
is RadicalSyntax -> {
append("\\sqrt")
append('{')
render(node.operand)
append('}')
}
is ExponentSyntax -> if (node.useOperatorForm) {
append("\\operatorname{exp}\\,")
render(node.operand)
} else {
append("e^{")
render(node.operand)
append('}')
}
is SuperscriptSyntax -> {
render(node.left)
append("^{")
render(node.right)
append('}')
}
is SubscriptSyntax -> {
render(node.left)
append("_{")
render(node.right)
append('}')
}
is BinaryOperatorSyntax -> {
render(node.prefix)
append("\\left(")
render(node.left)
append(',')
render(node.right)
append("\\right)")
}
is BinaryPlusSyntax -> {
render(node.left)
append('+')
render(node.right)
}
is BinaryMinusSyntax -> {
render(node.left)
append('-')
render(node.right)
}
is FractionSyntax -> if (node.infix) {
render(node.left)
append('/')
render(node.right)
} else {
append("\\frac{")
render(node.left)
append("}{")
render(node.right)
append('}')
}
is RadicalWithIndexSyntax -> {
append("\\sqrt")
append('[')
render(node.left)
append(']')
append('{')
render(node.right)
append('}')
}
is MultiplicationSyntax -> {
render(node.left)
append(if (node.times) "\\times" else "\\,")
render(node.right)
}
}
}
}

157
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathMLSyntaxRenderer.kt Normal file
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@ -0,0 +1,157 @@
/*
* 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.
*/
package space.kscience.kmath.ast.rendering
import space.kscience.kmath.misc.UnstableKMathAPI
/**
* [SyntaxRenderer] implementation for MathML.
*
* The generated XML string is a valid MathML instance.
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public object MathMLSyntaxRenderer : SyntaxRenderer {
public override fun render(node: MathSyntax, output: Appendable) {
output.append("<math xmlns=\"https://www.w3.org/1998/Math/MathML\"><mrow>")
renderPart(node, output)
output.append("</mrow></math>")
}
/**
* Renders a part of syntax returning a correct MathML tag not the whole MathML instance.
*/
public fun renderPart(node: MathSyntax, output: Appendable): Unit = output.run {
fun tag(tagName: String, vararg attr: Pair<String, String>, block: () -> Unit = {}) {
append('<')
append(tagName)
if (attr.isNotEmpty()) {
append(' ')
var count = 0
for ((name, value) in attr) {
if (++count > 1) append(' ')
append(name)
append("=\"")
append(value)
append('"')
}
}
append('>')
block()
append("</")
append(tagName)
append('>')
}
fun render(syntax: MathSyntax) = renderPart(syntax, output)
when (node) {
is NumberSyntax -> tag("mn") { append(node.string) }
is SymbolSyntax -> tag("mi") { append(node.string) }
is OperatorNameSyntax -> tag("mo") { append(node.name) }
is SpecialSymbolSyntax -> when (node.kind) {
SpecialSymbolSyntax.Kind.INFINITY -> tag("mo") { append("&infin;") }
SpecialSymbolSyntax.Kind.SMALL_PI -> tag("mo") { append("&pi;") }
}
is OperandSyntax -> if (node.parentheses) {
tag("mfenced", "open" to "(", "close" to ")", "separators" to "") {
render(node.operand)
}
} else {
render(node.operand)
}
is UnaryOperatorSyntax -> {
render(node.prefix)
tag("mspace", "width" to "0.167em")
render(node.operand)
}
is UnaryPlusSyntax -> {
tag("mo") { append('+') }
render(node.operand)
}
is UnaryMinusSyntax -> {
tag("mo") { append("-") }
render(node.operand)
}
is RadicalSyntax -> tag("msqrt") { render(node.operand) }
is ExponentSyntax -> if (node.useOperatorForm) {
tag("mo") { append("exp") }
tag("mspace", "width" to "0.167em")
render(node.operand)
} else {
tag("msup") {
tag("mrow") {
tag("mi") { append("e") }
}
tag("mrow") { render(node.operand) }
}
}
is SuperscriptSyntax -> tag("msup") {
tag("mrow") { render(node.left) }
tag("mrow") { render(node.right) }
}
is SubscriptSyntax -> tag("msub") {
tag("mrow") { render(node.left) }
tag("mrow") { render(node.right) }
}
is BinaryOperatorSyntax -> {
render(node.prefix)
tag("mfenced", "open" to "(", "close" to ")", "separators" to "") {
render(node.left)
tag("mo") { append(',') }
render(node.right)
}
}
is BinaryPlusSyntax -> {
render(node.left)
tag("mo") { append('+') }
render(node.right)
}
is BinaryMinusSyntax -> {
render(node.left)
tag("mo") { append('-') }
render(node.right)
}
is FractionSyntax -> if (node.infix) {
render(node.left)
tag("mo") { append('/') }
render(node.right)
} else tag("mfrac") {
tag("mrow") { render(node.left) }
tag("mrow") { render(node.right) }
}
is RadicalWithIndexSyntax -> tag("mroot") {
tag("mrow") { render(node.right) }
tag("mrow") { render(node.left) }
}
is MultiplicationSyntax -> {
render(node.left)
if (node.times) tag("mo") { append("&times;") } else tag("mspace", "width" to "0.167em")
render(node.right)
}
}
}
}

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kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt Normal file
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@ -0,0 +1,115 @@
/*
* 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.
*/
package space.kscience.kmath.ast.rendering
import space.kscience.kmath.expressions.MST
import space.kscience.kmath.misc.UnstableKMathAPI
/**
* Renders [MST] to [MathSyntax].
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public fun interface MathRenderer {
/**
* Renders [MST] to [MathSyntax].
*/
public fun render(mst: MST): MathSyntax
}
/**
* Implements [MST] render process with sequence of features.
*
* @property features The applied features.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public open class FeaturedMathRenderer(public val features: List<RenderFeature>) : MathRenderer {
public override fun render(mst: MST): MathSyntax {
for (feature in features) feature.render(this, mst)?.let { return it }
throw UnsupportedOperationException("Renderer $this has no appropriate feature to render node $mst.")
}
/**
* Logical unit of [MST] rendering.
*/
public fun interface RenderFeature {
/**
* Renders [MST] to [MathSyntax] in the context of owning renderer.
*/
public fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax?
}
}
/**
* Extends [FeaturedMathRenderer] by adding post-processing stages.
*
* @property stages The applied stages.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public open class FeaturedMathRendererWithPostProcess(
features: List<RenderFeature>,
public val stages: List<PostProcessPhase>,
) : FeaturedMathRenderer(features) {
public override fun render(mst: MST): MathSyntax {
val res = super.render(mst)
for (stage in stages) stage.perform(res)
return res
}
/**
* Logical unit of [MathSyntax] post-processing.
*/
public fun interface PostProcessPhase {
/**
* Performs the specified action over [MathSyntax].
*/
public fun perform(node: MathSyntax)
}
public companion object {
/**
* The default setup of [FeaturedMathRendererWithPostProcess].
*/
public val Default: FeaturedMathRendererWithPostProcess = FeaturedMathRendererWithPostProcess(
listOf(
// Printing known operations
BinaryPlus.Default,
BinaryMinus.Default,
UnaryPlus.Default,
UnaryMinus.Default,
Multiplication.Default,
Fraction.Default,
Power.Default,
SquareRoot.Default,
Exponent.Default,
InverseTrigonometricOperations.Default,
InverseHyperbolicOperations.Default,
// Fallback option for unknown operations - printing them as operator
BinaryOperator.Default,
UnaryOperator.Default,
// Pretty printing for some objects
PrettyPrintFloats.Default,
PrettyPrintIntegers.Default,
PrettyPrintPi.Default,
// Printing terminal nodes as string
PrintNumeric,
PrintSymbol,
),
listOf(
BetterExponent,
BetterFraction,
SimplifyParentheses.Default,
BetterMultiplication,
),
)
}
}

381
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@ -0,0 +1,381 @@
/*
* 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.
*/
package space.kscience.kmath.ast.rendering
import space.kscience.kmath.misc.UnstableKMathAPI
/**
* Mathematical typography syntax node.
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public sealed class MathSyntax {
/**
* The parent node of this syntax node.
*/
public var parent: MathSyntax? = null
}
/**
* Terminal node, which should not have any children nodes.
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public sealed class TerminalSyntax : MathSyntax()
/**
* Node containing a certain operation.
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public sealed class OperationSyntax : MathSyntax() {
/**
* The operation token.
*/
public abstract val operation: String
}
/**
* Unary node, which has only one child.
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public sealed class UnarySyntax : OperationSyntax() {
/**
* The operand of this node.
*/
public abstract val operand: MathSyntax
}
/**
* Binary node, which has only two children.
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public sealed class BinarySyntax : OperationSyntax() {
/**
* The left-hand side operand.
*/
public abstract val left: MathSyntax
/**
* The right-hand side operand.
*/
public abstract val right: MathSyntax
}
/**
* Represents a number.
*
* @property string The digits of number.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public data class NumberSyntax(public var string: String) : TerminalSyntax()
/**
* Represents a symbol.
*
* @property string The symbol.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public data class SymbolSyntax(public var string: String) : TerminalSyntax()
/**
* Represents special typing for operator name.
*
* @property name The operator name.
* @see BinaryOperatorSyntax
* @see UnaryOperatorSyntax
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public data class OperatorNameSyntax(public var name: String) : TerminalSyntax()
/**
* Represents a usage of special symbols (e.g., *&infin;*).
*
* @property kind The kind of symbol.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public data class SpecialSymbolSyntax(public var kind: Kind) : TerminalSyntax() {
/**
* The kind of symbol.
*/
public enum class Kind {
/**
* The infinity (&infin;) symbol.
*/
INFINITY,
/**
* The Pi (&pi;) symbol.
*/
SMALL_PI;
}
}
/**
* Represents operand of a certain operator wrapped with parentheses or not.
*
* @property operand The operand.
* @property parentheses Whether the operand should be wrapped with parentheses.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public data class OperandSyntax(
public val operand: MathSyntax,
public var parentheses: Boolean,
) : MathSyntax() {
init {
operand.parent = this
}
}
/**
* Represents unary, prefix operator syntax (like *f(x)*).
*
* @property prefix The prefix.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public data class UnaryOperatorSyntax(
public override val operation: String,
public var prefix: MathSyntax,
public override val operand: OperandSyntax,
) : UnarySyntax() {
init {
operand.parent = this
}
}
/**
* Represents prefix, unary plus operator (*+x*).
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public data class UnaryPlusSyntax(
public override val operation: String,
public override val operand: OperandSyntax,
) : UnarySyntax() {
init {
operand.parent = this
}
}
/**
* Represents prefix, unary minus operator (*-x*).
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public data class UnaryMinusSyntax(
public override val operation: String,
public override val operand: OperandSyntax,
) : UnarySyntax() {
init {
operand.parent = this
}
}
/**
* Represents radical with a node inside it (*&radic;x*).
*
* @property operand The radicand.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public data class RadicalSyntax(
public override val operation: String,
public override val operand: MathSyntax,
) : UnarySyntax() {
init {
operand.parent = this
}
}
/**
* Represents exponential function.
*
* @property operand The argument of function.
* @property useOperatorForm `true` if operator form is used (*exp (x)*), `false` if exponentiation form is used
* (*e<sup>x</sup>*).
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public data class ExponentSyntax(
public override val operation: String,
public override val operand: OperandSyntax,
public var useOperatorForm: Boolean,
) : UnarySyntax() {
init {
operand.parent = this
}
}
/**
* Represents a syntax node with superscript (*x<sup>2</sup>*).
*
* @property left The node.
* @property right The superscript.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public data class SuperscriptSyntax(
public override val operation: String,
public override val left: MathSyntax,
public override val right: MathSyntax,
) : BinarySyntax() {
init {
left.parent = this
right.parent = this
}
}
/**
* Represents a syntax node with subscript (*x<sub>i</sup>*).
*
* @property left The node.
* @property right The subscript.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public data class SubscriptSyntax(
public override val operation: String,
public override val left: MathSyntax,
public override val right: MathSyntax,
) : BinarySyntax() {
init {
left.parent = this
right.parent = this
}
}
/**
* Represents binary, prefix operator syntax (like *f(a, b)*).
*
* @property prefix The prefix.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public data class BinaryOperatorSyntax(
public override val operation: String,
public var prefix: MathSyntax,
public override val left: MathSyntax,
public override val right: MathSyntax,
) : BinarySyntax() {
init {
left.parent = this
right.parent = this
}
}
/**
* Represents binary, infix addition (*42 + 42*).
*
* @param left The augend.
* @param right The addend.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public data class BinaryPlusSyntax(
public override val operation: String,
public override val left: OperandSyntax,
public override val right: OperandSyntax,
) : BinarySyntax() {
init {
left.parent = this
right.parent = this
}
}
/**
* Represents binary, infix subtraction (*42 - 42*).
*
* @param left The minuend.
* @param right The subtrahend.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public data class BinaryMinusSyntax(
public override val operation: String,
public override val left: OperandSyntax,
public override val right: OperandSyntax,
) : BinarySyntax() {
init {
left.parent = this
right.parent = this
}
}
/**
* Represents fraction with numerator and denominator.
*
* @property left The numerator.
* @property right The denominator.
* @property infix Whether infix (*1 / 2*) or normal (*&frac12;*) fraction should be made.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public data class FractionSyntax(
public override val operation: String,
public override val left: OperandSyntax,
public override val right: OperandSyntax,
public var infix: Boolean,
) : BinarySyntax() {
init {
left.parent = this
right.parent = this
}
}
/**
* Represents radical syntax with index (*<sup>3</sup>&radic;x*).
*
* @property left The index.
* @property right The radicand.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public data class RadicalWithIndexSyntax(
public override val operation: String,
public override val left: MathSyntax,
public override val right: MathSyntax,
) : BinarySyntax() {
init {
left.parent = this
right.parent = this
}
}
/**
* Represents binary, infix multiplication in the form of coefficient (*2 x*) or with operator (*x &times; 2*).
*
* @property left The multiplicand.
* @property right The multiplier.
* @property times Whether the times (&times;) symbol should be used.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public data class MultiplicationSyntax(
public override val operation: String,
public override val left: OperandSyntax,
public override val right: OperandSyntax,
public var times: Boolean,
) : BinarySyntax() {
init {
left.parent = this
right.parent = this
}
}

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/*
* 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.
*/
package space.kscience.kmath.ast.rendering
import space.kscience.kmath.misc.UnstableKMathAPI
/**
* Abstraction of writing [MathSyntax] as a string of an actual markup language. Typical implementation should
* involve traversal of MathSyntax with handling each its subtype.
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public fun interface SyntaxRenderer {
/**
* Renders the [MathSyntax] to [output].
*/
public fun render(node: MathSyntax, output: Appendable)
}
/**
* Calls [SyntaxRenderer.render] with given [node] and a new [StringBuilder] instance, and returns its content.
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public fun SyntaxRenderer.renderWithStringBuilder(node: MathSyntax): String {
val sb = StringBuilder()
render(node, sb)
return sb.toString()
}

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/*
* 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.
*/
package space.kscience.kmath.ast.rendering
import space.kscience.kmath.ast.rendering.FeaturedMathRenderer.RenderFeature
import space.kscience.kmath.expressions.MST
import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.operations.*
import kotlin.reflect.KClass
/**
* Prints any [Symbol] as a [SymbolSyntax] containing the [Symbol.value] of it.
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public val PrintSymbol: RenderFeature = RenderFeature { _, node ->
if (node !is Symbol) null
else SymbolSyntax(string = node.identity)
}
/**
* Prints any [MST.Numeric] as a [NumberSyntax] containing the [Any.toString] result of it.
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public val PrintNumeric: RenderFeature = RenderFeature { _, node ->
if (node !is MST.Numeric)
null
else
NumberSyntax(string = node.value.toString())
}
@UnstableKMathAPI
private fun printSignedNumberString(s: String): MathSyntax = if (s.startsWith('-'))
UnaryMinusSyntax(
operation = GroupOperations.MINUS_OPERATION,
operand = OperandSyntax(
operand = NumberSyntax(string = s.removePrefix("-")),
parentheses = true,
),
)
else
NumberSyntax(string = s)
/**
* Special printing for numeric types which are printed in form of
* *('-'? (DIGIT+ ('.' DIGIT+)? ('E' '-'? DIGIT+)? | 'Infinity')) | 'NaN'*.
*
* @property types The suitable types.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public class PrettyPrintFloats(public val types: Set<KClass<out Number>>) : RenderFeature {
public override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? {
if (node !is MST.Numeric || node.value::class !in types) return null
val toString = when (val v = node.value) {
is Float -> v.multiplatformToString()
is Double -> v.multiplatformToString()
else -> v.toString()
}.removeSuffix(".0")
if (toString.contains('E', ignoreCase = true)) {
val (beforeE, afterE) = toString.split('E', ignoreCase = true)
val significand = beforeE.toDouble().toString().removeSuffix(".0")
val exponent = afterE.toDouble().toString().removeSuffix(".0")
return MultiplicationSyntax(
operation = RingOperations.TIMES_OPERATION,
left = OperandSyntax(operand = NumberSyntax(significand), parentheses = true),
right = OperandSyntax(
operand = SuperscriptSyntax(
operation = PowerOperations.POW_OPERATION,
left = NumberSyntax(string = "10"),
right = printSignedNumberString(exponent),
),
parentheses = true,
),
times = true,
)
}
if (toString.endsWith("Infinity")) {
val infty = SpecialSymbolSyntax(SpecialSymbolSyntax.Kind.INFINITY)
if (toString.startsWith('-'))
return UnaryMinusSyntax(
operation = GroupOperations.MINUS_OPERATION,
operand = OperandSyntax(operand = infty, parentheses = true),
)
return infty
}
return printSignedNumberString(toString)
}
public companion object {
/**
* The default instance containing [Float], and [Double].
*/
public val Default: PrettyPrintFloats = PrettyPrintFloats(setOf(Float::class, Double::class))
}
}
/**
* Special printing for numeric types which are printed in form of *'-'? DIGIT+*.
*
* @property types The suitable types.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public class PrettyPrintIntegers(public val types: Set<KClass<out Number>>) : RenderFeature {
public override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? =
if (node !is MST.Numeric || node.value::class !in types)
null
else
printSignedNumberString(node.value.toString())
public companion object {
/**
* The default instance containing [Byte], [Short], [Int], and [Long].
*/
public val Default: PrettyPrintIntegers =
PrettyPrintIntegers(setOf(Byte::class, Short::class, Int::class, Long::class))
}
}
/**
* Special printing for symbols meaning Pi.
*
* @property symbols The allowed symbols.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public class PrettyPrintPi(public val symbols: Set<String>) : RenderFeature {
public override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? =
if (node !is Symbol || node.identity !in symbols)
null
else
SpecialSymbolSyntax(kind = SpecialSymbolSyntax.Kind.SMALL_PI)
public companion object {
/**
* The default instance containing `pi`.
*/
public val Default: PrettyPrintPi = PrettyPrintPi(setOf("pi"))
}
}
/**
* Abstract printing of unary operations which discards [MST] if their operation is not in [operations] or its type is
* not [MST.Unary].
*
* @param operations the allowed operations. If `null`, any operation is accepted.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public abstract class Unary(public val operations: Collection<String>?) : RenderFeature {
/**
* The actual render function specialized for [MST.Unary].
*/
protected abstract fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax?
public final override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? =
if (node !is MST.Unary || operations != null && node.operation !in operations)
null
else
renderUnary(renderer, node)
}
/**
* Abstract printing of unary operations which discards [MST] if their operation is not in [operations] or its type is
* not [MST.Binary].
*
* @property operations the allowed operations. If `null`, any operation is accepted.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public abstract class Binary(public val operations: Collection<String>?) : RenderFeature {
/**
* The actual render function specialized for [MST.Binary].
*/
protected abstract fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax?
public final override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? {
if (node !is MST.Binary || operations != null && node.operation !in operations) return null
return renderBinary(renderer, node)
}
}
/**
* Handles binary nodes by producing [BinaryPlusSyntax].
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public class BinaryPlus(operations: Collection<String>?) : Binary(operations) {
public override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax =
BinaryPlusSyntax(
operation = node.operation,
left = OperandSyntax(parent.render(node.left), true),
right = OperandSyntax(parent.render(node.right), true),
)
public companion object {
/**
* The default instance configured with [GroupOperations.PLUS_OPERATION].
*/
public val Default: BinaryPlus = BinaryPlus(setOf(GroupOperations.PLUS_OPERATION))
}
}
/**
* Handles binary nodes by producing [BinaryMinusSyntax].
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public class BinaryMinus(operations: Collection<String>?) : Binary(operations) {
public override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax =
BinaryMinusSyntax(
operation = node.operation,
left = OperandSyntax(operand = parent.render(node.left), parentheses = true),
right = OperandSyntax(operand = parent.render(node.right), parentheses = true),
)
public companion object {
/**
* The default instance configured with [GroupOperations.MINUS_OPERATION].
*/
public val Default: BinaryMinus = BinaryMinus(setOf(GroupOperations.MINUS_OPERATION))
}
}
/**
* Handles unary nodes by producing [UnaryPlusSyntax].
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public class UnaryPlus(operations: Collection<String>?) : Unary(operations) {
public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax = UnaryPlusSyntax(
operation = node.operation,
operand = OperandSyntax(operand = parent.render(node.value), parentheses = true),
)
public companion object {
/**
* The default instance configured with [GroupOperations.PLUS_OPERATION].
*/
public val Default: UnaryPlus = UnaryPlus(setOf(GroupOperations.PLUS_OPERATION))
}
}
/**
* Handles binary nodes by producing [UnaryMinusSyntax].
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public class UnaryMinus(operations: Collection<String>?) : Unary(operations) {
public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax = UnaryMinusSyntax(
operation = node.operation,
operand = OperandSyntax(operand = parent.render(node.value), parentheses = true),
)
public companion object {
/**
* The default instance configured with [GroupOperations.MINUS_OPERATION].
*/
public val Default: UnaryMinus = UnaryMinus(setOf(GroupOperations.MINUS_OPERATION))
}
}
/**
* Handles binary nodes by producing [FractionSyntax].
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public class Fraction(operations: Collection<String>?) : Binary(operations) {
public override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax = FractionSyntax(
operation = node.operation,
left = OperandSyntax(operand = parent.render(node.left), parentheses = true),
right = OperandSyntax(operand = parent.render(node.right), parentheses = true),
infix = true,
)
public companion object {
/**
* The default instance configured with [FieldOperations.DIV_OPERATION].
*/
public val Default: Fraction = Fraction(setOf(FieldOperations.DIV_OPERATION))
}
}
/**
* Handles binary nodes by producing [BinaryOperatorSyntax].
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public class BinaryOperator(operations: Collection<String>?) : Binary(operations) {
public override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax =
BinaryOperatorSyntax(
operation = node.operation,
prefix = OperatorNameSyntax(name = node.operation),
left = parent.render(node.left),
right = parent.render(node.right),
)
public companion object {
/**
* The default instance configured with `null`.
*/
public val Default: BinaryOperator = BinaryOperator(null)
}
}
/**
* Handles unary nodes by producing [UnaryOperatorSyntax].
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public class UnaryOperator(operations: Collection<String>?) : Unary(operations) {
public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax =
UnaryOperatorSyntax(
operation = node.operation,
prefix = OperatorNameSyntax(node.operation),
operand = OperandSyntax(parent.render(node.value), true),
)
public companion object {
/**
* The default instance configured with `null`.
*/
public val Default: UnaryOperator = UnaryOperator(null)
}
}
/**
* Handles binary nodes by producing [SuperscriptSyntax].
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public class Power(operations: Collection<String>?) : Binary(operations) {
public override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax =
SuperscriptSyntax(
operation = node.operation,
left = OperandSyntax(parent.render(node.left), true),
right = OperandSyntax(parent.render(node.right), true),
)
public companion object {
/**
* The default instance configured with [PowerOperations.POW_OPERATION].
*/
public val Default: Power = Power(setOf(PowerOperations.POW_OPERATION))
}
}
/**
* Handles binary nodes by producing [RadicalSyntax] with no index.
*/
@UnstableKMathAPI
public class SquareRoot(operations: Collection<String>?) : Unary(operations) {
public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax =
RadicalSyntax(operation = node.operation, operand = parent.render(node.value))
public companion object {
/**
* The default instance configured with [PowerOperations.SQRT_OPERATION].
*/
public val Default: SquareRoot = SquareRoot(setOf(PowerOperations.SQRT_OPERATION))
}
}
/**
* Handles unary nodes by producing [ExponentSyntax].
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public class Exponent(operations: Collection<String>?) : Unary(operations) {
public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax = ExponentSyntax(
operation = node.operation,
operand = OperandSyntax(operand = parent.render(node.value), parentheses = true),
useOperatorForm = true,
)
public companion object {
/**
* The default instance configured with [ExponentialOperations.EXP_OPERATION].
*/
public val Default: Exponent = Exponent(setOf(ExponentialOperations.EXP_OPERATION))
}
}
/**
* Handles binary nodes by producing [MultiplicationSyntax].
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public class Multiplication(operations: Collection<String>?) : Binary(operations) {
public override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax =
MultiplicationSyntax(
operation = node.operation,
left = OperandSyntax(operand = parent.render(node.left), parentheses = true),
right = OperandSyntax(operand = parent.render(node.right), parentheses = true),
times = true,
)
public companion object {
/**
* The default instance configured with [RingOperations.TIMES_OPERATION].
*/
public val Default: Multiplication = Multiplication(setOf(RingOperations.TIMES_OPERATION))
}
}
/**
* Handles binary nodes by producing inverse [UnaryOperatorSyntax] with *arc* prefix instead of *a*.
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public class InverseTrigonometricOperations(operations: Collection<String>?) : Unary(operations) {
public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax =
UnaryOperatorSyntax(
operation = node.operation,
prefix = OperatorNameSyntax(name = node.operation.replaceFirst("a", "arc")),
operand = OperandSyntax(operand = parent.render(node.value), parentheses = true),
)
public companion object {
/**
* The default instance configured with [TrigonometricOperations.ACOS_OPERATION],
* [TrigonometricOperations.ASIN_OPERATION], [TrigonometricOperations.ATAN_OPERATION].
*/
public val Default: InverseTrigonometricOperations = InverseTrigonometricOperations(setOf(
TrigonometricOperations.ACOS_OPERATION,
TrigonometricOperations.ASIN_OPERATION,
TrigonometricOperations.ATAN_OPERATION,
))
}
}
/**
* Handles binary nodes by producing inverse [UnaryOperatorSyntax] with *ar* prefix instead of *a*.
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public class InverseHyperbolicOperations(operations: Collection<String>?) : Unary(operations) {
public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax =
UnaryOperatorSyntax(
operation = node.operation,
prefix = OperatorNameSyntax(name = node.operation.replaceFirst("a", "ar")),
operand = OperandSyntax(operand = parent.render(node.value), parentheses = true),
)
public companion object {
/**
* The default instance configured with [ExponentialOperations.ACOSH_OPERATION],
* [ExponentialOperations.ASINH_OPERATION], and [ExponentialOperations.ATANH_OPERATION].
*/
public val Default: InverseHyperbolicOperations = InverseHyperbolicOperations(setOf(
ExponentialOperations.ACOSH_OPERATION,
ExponentialOperations.ASINH_OPERATION,
ExponentialOperations.ATANH_OPERATION,
))
}
}

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kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/multiplatformToString.kt Normal file
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/*
* 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.
*/
package space.kscience.kmath.ast.rendering
internal expect fun Double.multiplatformToString(): String
internal expect fun Float.multiplatformToString(): String

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kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/phases.kt Normal file
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/*
* 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.
*/
package space.kscience.kmath.ast.rendering
import space.kscience.kmath.ast.rendering.FeaturedMathRendererWithPostProcess.PostProcessPhase
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.operations.FieldOperations
import space.kscience.kmath.operations.GroupOperations
import space.kscience.kmath.operations.PowerOperations
import space.kscience.kmath.operations.RingOperations
/**
* Removes unnecessary times (&times;) symbols from [MultiplicationSyntax].
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public val BetterMultiplication: PostProcessPhase = PostProcessPhase { node ->
fun perform(node: MathSyntax): Unit = when (node) {
is NumberSyntax -> Unit
is SymbolSyntax -> Unit
is OperatorNameSyntax -> Unit
is SpecialSymbolSyntax -> Unit
is OperandSyntax -> perform(node.operand)
is UnaryOperatorSyntax -> {
perform(node.prefix)
perform(node.operand)
}
is UnaryPlusSyntax -> perform(node.operand)
is UnaryMinusSyntax -> perform(node.operand)
is RadicalSyntax -> perform(node.operand)
is ExponentSyntax -> perform(node.operand)
is SuperscriptSyntax -> {
perform(node.left)
perform(node.right)
}
is SubscriptSyntax -> {
perform(node.left)
perform(node.right)
}
is BinaryOperatorSyntax -> {
perform(node.prefix)
perform(node.left)
perform(node.right)
}
is BinaryPlusSyntax -> {
perform(node.left)
perform(node.right)
}
is BinaryMinusSyntax -> {
perform(node.left)
perform(node.right)
}
is FractionSyntax -> {
perform(node.left)
perform(node.right)
}
is RadicalWithIndexSyntax -> {
perform(node.left)
perform(node.right)
}
is MultiplicationSyntax -> {
node.times = node.right.operand is NumberSyntax && !node.right.parentheses
|| node.left.operand is NumberSyntax && node.right.operand is FractionSyntax
|| node.left.operand is NumberSyntax && node.right.operand is NumberSyntax
|| node.left.operand is NumberSyntax && node.right.operand is SuperscriptSyntax && node.right.operand.left is NumberSyntax
perform(node.left)
perform(node.right)
}
}
perform(node)
}
/**
* Chooses [FractionSyntax.infix] depending on the context.
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public val BetterFraction: PostProcessPhase = PostProcessPhase { node ->
fun perform(node: MathSyntax, infix: Boolean = false): Unit = when (node) {
is NumberSyntax -> Unit
is SymbolSyntax -> Unit
is OperatorNameSyntax -> Unit
is SpecialSymbolSyntax -> Unit
is OperandSyntax -> perform(node.operand, infix)
is UnaryOperatorSyntax -> {
perform(node.prefix, infix)
perform(node.operand, infix)
}
is UnaryPlusSyntax -> perform(node.operand, infix)
is UnaryMinusSyntax -> perform(node.operand, infix)
is RadicalSyntax -> perform(node.operand, infix)
is ExponentSyntax -> perform(node.operand, infix)
is SuperscriptSyntax -> {
perform(node.left, true)
perform(node.right, true)
}
is SubscriptSyntax -> {
perform(node.left, true)
perform(node.right, true)
}
is BinaryOperatorSyntax -> {
perform(node.prefix, infix)
perform(node.left, infix)
perform(node.right, infix)
}
is BinaryPlusSyntax -> {
perform(node.left, infix)
perform(node.right, infix)
}
is BinaryMinusSyntax -> {
perform(node.left, infix)
perform(node.right, infix)
}
is FractionSyntax -> {
node.infix = infix
perform(node.left, infix)
perform(node.right, infix)
}
is RadicalWithIndexSyntax -> {
perform(node.left, true)
perform(node.right, true)
}
is MultiplicationSyntax -> {
perform(node.left, infix)
perform(node.right, infix)
}
}
perform(node)
}
/**
* Applies [ExponentSyntax.useOperatorForm] to [ExponentSyntax] when the operand contains a fraction, a
* superscript or a subscript to improve readability.
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public val BetterExponent: PostProcessPhase = PostProcessPhase { node ->
fun perform(node: MathSyntax): Boolean {
return when (node) {
is NumberSyntax -> false
is SymbolSyntax -> false
is OperatorNameSyntax -> false
is SpecialSymbolSyntax -> false
is OperandSyntax -> perform(node.operand)
is UnaryOperatorSyntax -> perform(node.prefix) || perform(node.operand)
is UnaryPlusSyntax -> perform(node.operand)
is UnaryMinusSyntax -> perform(node.operand)
is RadicalSyntax -> true
is ExponentSyntax -> {
val r = perform(node.operand)
node.useOperatorForm = r
r
}
is SuperscriptSyntax -> true
is SubscriptSyntax -> true
is BinaryOperatorSyntax -> perform(node.prefix) || perform(node.left) || perform(node.right)
is BinaryPlusSyntax -> perform(node.left) || perform(node.right)
is BinaryMinusSyntax -> perform(node.left) || perform(node.right)
is FractionSyntax -> true
is RadicalWithIndexSyntax -> true
is MultiplicationSyntax -> perform(node.left) || perform(node.right)
}
}
perform(node)
}
/**
* Removes unnecessary parentheses from [OperandSyntax].
*
* @property precedenceFunction Returns the precedence number for syntax node. Higher number is lower priority.
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public class SimplifyParentheses(public val precedenceFunction: (MathSyntax) -> Int) :
PostProcessPhase {
public override fun perform(node: MathSyntax): Unit = when (node) {
is NumberSyntax -> Unit
is SymbolSyntax -> Unit
is OperatorNameSyntax -> Unit
is SpecialSymbolSyntax -> Unit
is OperandSyntax -> {
val isRightOfSuperscript =
(node.parent is SuperscriptSyntax) && (node.parent as SuperscriptSyntax).right === node
val precedence = precedenceFunction(node.operand)
val needParenthesesByPrecedence = when (val parent = node.parent) {
null -> false
is BinarySyntax -> {
val parentPrecedence = precedenceFunction(parent)
parentPrecedence < precedence ||
parentPrecedence == precedence && parentPrecedence != 0 && node === parent.right
}
else -> precedence > precedenceFunction(parent)
}
val isInsideExpOperator =
node.parent is ExponentSyntax && (node.parent as ExponentSyntax).useOperatorForm
val isOnOrUnderNormalFraction = node.parent is FractionSyntax && !((node.parent as FractionSyntax).infix)
node.parentheses = !isRightOfSuperscript
&& (needParenthesesByPrecedence || node.parent is UnaryOperatorSyntax || isInsideExpOperator)
&& !isOnOrUnderNormalFraction
perform(node.operand)
}
is UnaryOperatorSyntax -> {
perform(node.prefix)
perform(node.operand)
}
is UnaryPlusSyntax -> perform(node.operand)
is UnaryMinusSyntax -> perform(node.operand)
is RadicalSyntax -> perform(node.operand)
is ExponentSyntax -> perform(node.operand)
is SuperscriptSyntax -> {
perform(node.left)
perform(node.right)
}
is SubscriptSyntax -> {
perform(node.left)
perform(node.right)
}
is BinaryOperatorSyntax -> {
perform(node.prefix)
perform(node.left)
perform(node.right)
}
is BinaryPlusSyntax -> {
perform(node.left)
perform(node.right)
}
is BinaryMinusSyntax -> {
perform(node.left)
perform(node.right)
}
is FractionSyntax -> {
perform(node.left)
perform(node.right)
}
is MultiplicationSyntax -> {
perform(node.left)
perform(node.right)
}
is RadicalWithIndexSyntax -> {
perform(node.left)
perform(node.right)
}
}
public companion object {
/**
* The default configuration of [SimplifyParentheses] where power is 1, multiplicative operations are 2,
* additive operations are 3.
*/
public val Default: SimplifyParentheses = SimplifyParentheses {
when (it) {
is TerminalSyntax -> 0
is UnarySyntax -> 2
is BinarySyntax -> when (it.operation) {
PowerOperations.POW_OPERATION -> 1
RingOperations.TIMES_OPERATION -> 3
FieldOperations.DIV_OPERATION -> 3
GroupOperations.MINUS_OPERATION -> 4
GroupOperations.PLUS_OPERATION -> 4
else -> 0
}
else -> 0
}
}
}
}

56
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerConsistencyWithInterpreter.kt Normal file
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/*
* 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.
*/
package space.kscience.kmath.ast
import space.kscience.kmath.expressions.MstField
import space.kscience.kmath.expressions.MstRing
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.interpret
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.IntRing
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.operations.invoke
import kotlin.test.Test
import kotlin.test.assertEquals
internal class TestCompilerConsistencyWithInterpreter {
@Test
fun intRing() = runCompilerTest {
val mst = MstRing {
binaryOperationFunction("+")(
unaryOperationFunction("+")(
(x - (2.toByte() + (scale(
add(number(1), number(1)),
2.0,
) + 1.toByte()))) * 3.0 - 1.toByte()
),
number(1),
) * number(2)
}
assertEquals(
mst.interpret(IntRing, x to 3),
mst.compile(IntRing, x to 3),
)
}
@Test
fun doubleField() = runCompilerTest {
val mst = MstField {
+(3 - 2 + 2 * number(1) + 1.0) + binaryOperationFunction("+")(
(3.0 - (x + (scale(add(number(1.0), number(1.0)), 2.0) + 1.0))) * 3 - 1.0
+ number(1),
number(1) / 2 + number(2.0) * one,
) + zero
}
assertEquals(
mst.interpret(DoubleField, x to 2.0),
mst.compile(DoubleField, x to 2.0),
)
}
}

64
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerOperations.kt Normal file
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/*
* 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.
*/
package space.kscience.kmath.ast
import space.kscience.kmath.expressions.MstExtendedField
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.invoke
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
import kotlin.test.Test
import kotlin.test.assertEquals
internal class TestCompilerOperations {
@Test
fun testUnaryPlus() = runCompilerTest {
val expr = MstExtendedField { +x }.compileToExpression(DoubleField)
assertEquals(2.0, expr(x to 2.0))
}
@Test
fun testUnaryMinus() = runCompilerTest {
val expr = MstExtendedField { -x }.compileToExpression(DoubleField)
assertEquals(-2.0, expr(x to 2.0))
}
@Test
fun testAdd() = runCompilerTest {
val expr = MstExtendedField { x + x }.compileToExpression(DoubleField)
assertEquals(4.0, expr(x to 2.0))
}
@Test
fun testSine() = runCompilerTest {
val expr = MstExtendedField { sin(x) }.compileToExpression(DoubleField)
assertEquals(0.0, expr(x to 0.0))
}
@Test
fun testCosine() = runCompilerTest {
val expr = MstExtendedField { cos(x) }.compileToExpression(DoubleField)
assertEquals(1.0, expr(x to 0.0))
}
@Test
fun testSubtract() = runCompilerTest {
val expr = MstExtendedField { x - x }.compileToExpression(DoubleField)
assertEquals(0.0, expr(x to 2.0))
}
@Test
fun testDivide() = runCompilerTest {
val expr = MstExtendedField { x / x }.compileToExpression(DoubleField)
assertEquals(1.0, expr(x to 2.0))
}
@Test
fun testPower() = runCompilerTest {
val expr = MstExtendedField { x pow 2 }.compileToExpression(DoubleField)
assertEquals(4.0, expr(x to 2.0))
}
}

30
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerVariables.kt Normal file
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@ -0,0 +1,30 @@
/*
* 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.
*/
package space.kscience.kmath.ast
import space.kscience.kmath.expressions.MstRing
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.invoke
import space.kscience.kmath.operations.IntRing
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.operations.invoke
import kotlin.test.Test
import kotlin.test.assertEquals
import kotlin.test.assertFailsWith
internal class TestCompilerVariables {
@Test
fun testVariable() = runCompilerTest {
val expr = MstRing { x }.compileToExpression(IntRing)
assertEquals(1, expr(x to 1))
}
@Test
fun testUndefinedVariableFails() = runCompilerTest {
val expr = MstRing { x }.compileToExpression(IntRing)
assertFailsWith<NoSuchElementException> { expr() }
}
}

29
kmath-ast/src/jvmTest/kotlin/space/kscience/kmath/ast/ParserTest.kt → kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestParser.kt
View File

@ -1,29 +1,28 @@
/*
* 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.
*/
package space.kscience.kmath.ast
import space.kscience.kmath.complex.Complex
import space.kscience.kmath.complex.ComplexField
import space.kscience.kmath.expressions.invoke
import space.kscience.kmath.expressions.evaluate
import space.kscience.kmath.operations.Algebra
import space.kscience.kmath.operations.RealField
import space.kscience.kmath.operations.DoubleField
import kotlin.test.Test
import kotlin.test.assertEquals
internal class ParserTest {
internal class TestParser {
@Test
fun `evaluate MST`() {
fun evaluateParsedMst() {
val mst = "2+2*(2+2)".parseMath()
val res = ComplexField.evaluate(mst)
assertEquals(Complex(10.0, 0.0), res)
}
@Test
fun `evaluate MSTExpression`() {
val res = ComplexField.mstInField { number(2) + number(2) * (number(2) + number(2)) }()
assertEquals(Complex(10.0, 0.0), res)
}
@Test
fun `evaluate MST with singular`() {
fun evaluateMstSymbol() {
val mst = "i".parseMath()
val res = ComplexField.evaluate(mst)
assertEquals(ComplexField.i, res)
@ -31,16 +30,16 @@ internal class ParserTest {
@Test
fun `evaluate MST with unary function`() {
fun evaluateMstUnary() {
val mst = "sin(0)".parseMath()
val res = RealField.evaluate(mst)
val res = DoubleField.evaluate(mst)
assertEquals(0.0, res)
}
@Test
fun `evaluate MST with binary function`() {
fun evaluateMstBinary() {
val magicalAlgebra = object : Algebra<String> {
override fun bindSymbol(value: String): String = value
override fun bindSymbolOrNull(value: String): String = value
override fun unaryOperationFunction(operation: String): (arg: String) -> String {
throw NotImplementedError()

17
kmath-ast/src/jvmTest/kotlin/space/kscience/kmath/ast/ParserPrecedenceTest.kt → kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestParserPrecedence.kt
View File

@ -1,13 +1,16 @@
/*
* 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.
*/
package space.kscience.kmath.ast
import space.kscience.kmath.operations.Field
import space.kscience.kmath.operations.RealField
import space.kscience.kmath.expressions.evaluate
import space.kscience.kmath.operations.DoubleField
import kotlin.test.Test
import kotlin.test.assertEquals
internal class ParserPrecedenceTest {
private val f: Field<Double> = RealField
internal class TestParserPrecedence {
@Test
fun test1(): Unit = assertEquals(6.0, f.evaluate("2*2+2".parseMath()))
@ -31,4 +34,8 @@ internal class ParserPrecedenceTest {
@Test
fun test8(): Unit = assertEquals(18.0, f.evaluate("2*2^3+2".parseMath()))
private companion object {
private val f = DoubleField
}
}

120
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestFeatures.kt Normal file
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@ -0,0 +1,120 @@
/*
* 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.
*/
package space.kscience.kmath.ast.rendering
import space.kscience.kmath.ast.rendering.TestUtils.testLatex
import space.kscience.kmath.expressions.MST.Numeric
import kotlin.test.Test
internal class TestFeatures {
@Test
fun printSymbolic() = testLatex("x", "x")
@Test
fun printNumeric() {
val num = object : Number() {
override fun toByte(): Byte = throw UnsupportedOperationException()
override fun toChar(): Char = throw UnsupportedOperationException()
override fun toDouble(): Double = throw UnsupportedOperationException()
override fun toFloat(): Float = throw UnsupportedOperationException()
override fun toInt(): Int = throw UnsupportedOperationException()
override fun toLong(): Long = throw UnsupportedOperationException()
override fun toShort(): Short = throw UnsupportedOperationException()
override fun toString(): String = "foo"
}
testLatex(Numeric(num), "foo")
}
@Test
fun prettyPrintFloats() {
testLatex(Numeric(Double.NaN), "NaN")
testLatex(Numeric(Double.POSITIVE_INFINITY), "\\infty")
testLatex(Numeric(Double.NEGATIVE_INFINITY), "-\\infty")
testLatex(Numeric(1.0), "1")
testLatex(Numeric(-1.0), "-1")
testLatex(Numeric(1.42), "1.42")
testLatex(Numeric(-1.42), "-1.42")
testLatex(Numeric(1.1e10), "1.1\\times10^{10}")
testLatex(Numeric(1.1e-10), "1.1\\times10^{-10}")
testLatex(Numeric(-1.1e-10), "-1.1\\times10^{-10}")
testLatex(Numeric(-1.1e10), "-1.1\\times10^{10}")
testLatex(Numeric(0.001), "0.001")
testLatex(Numeric(0.0000001), "1\\times10^{-7}")
testLatex(Numeric(Float.NaN), "NaN")
testLatex(Numeric(Float.POSITIVE_INFINITY), "\\infty")
testLatex(Numeric(Float.NEGATIVE_INFINITY), "-\\infty")
testLatex(Numeric(1.0f), "1")
testLatex(Numeric(-1.0f), "-1")
testLatex(Numeric(1.42f), "1.42")
testLatex(Numeric(-1.42f), "-1.42")
testLatex(Numeric(1e10f), "1\\times10^{10}")
testLatex(Numeric(1e-10f), "1\\times10^{-10}")
testLatex(Numeric(-1e-10f), "-1\\times10^{-10}")
testLatex(Numeric(-1e10f), "-1\\times10^{10}")
testLatex(Numeric(0.001f), "0.001")
testLatex(Numeric(0.0000001f), "1\\times10^{-7}")
}
@Test
fun prettyPrintIntegers() {
testLatex(Numeric(42), "42")
testLatex(Numeric(-42), "-42")
}
@Test
fun prettyPrintPi() {
testLatex("pi", "\\pi")
}
@Test
fun binaryPlus() = testLatex("2+2", "2+2")
@Test
fun binaryMinus() = testLatex("2-2", "2-2")
@Test
fun fraction() = testLatex("2/2", "\\frac{2}{2}")
@Test
fun binaryOperator() = testLatex("f(x, y)", "\\operatorname{f}\\left(x,y\\right)")
@Test
fun unaryOperator() = testLatex("f(x)", "\\operatorname{f}\\,\\left(x\\right)")
@Test
fun power() = testLatex("x^y", "x^{y}")
@Test
fun squareRoot() = testLatex("sqrt(x)", "\\sqrt{x}")
@Test
fun exponential() = testLatex("exp(x)", "e^{x}")
@Test
fun multiplication() = testLatex("x*1", "x\\times1")
@Test
fun inverseTrigonometric() {
testLatex("asin(x)", "\\operatorname{arcsin}\\,\\left(x\\right)")
testLatex("acos(x)", "\\operatorname{arccos}\\,\\left(x\\right)")
testLatex("atan(x)", "\\operatorname{arctan}\\,\\left(x\\right)")
}
@Test
fun inverseHyperbolic() {
testLatex("asinh(x)", "\\operatorname{arsinh}\\,\\left(x\\right)")
testLatex("acosh(x)", "\\operatorname{arcosh}\\,\\left(x\\right)")
testLatex("atanh(x)", "\\operatorname{artanh}\\,\\left(x\\right)")
}
// @Test
// fun unaryPlus() {
// testLatex("+1", "+1")
// testLatex("+1", "++1")
// }
}

73
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestLatex.kt Normal file
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@ -0,0 +1,73 @@
/*
* 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.
*/
package space.kscience.kmath.ast.rendering
import space.kscience.kmath.ast.rendering.TestUtils.testLatex
import space.kscience.kmath.expressions.MST
import space.kscience.kmath.operations.GroupOperations
import kotlin.test.Test
internal class TestLatex {
@Test
fun number() = testLatex("42", "42")
@Test
fun symbol() = testLatex("x", "x")
@Test
fun operatorName() = testLatex("sin(1)", "\\operatorname{sin}\\,\\left(1\\right)")
@Test
fun specialSymbol() {
testLatex(MST.Numeric(Double.POSITIVE_INFINITY), "\\infty")
testLatex("pi", "\\pi")
}
@Test
fun operand() {
testLatex("sin(1)", "\\operatorname{sin}\\,\\left(1\\right)")
testLatex("1+1", "1+1")
}
@Test
fun unaryOperator() = testLatex("sin(1)", "\\operatorname{sin}\\,\\left(1\\right)")
@Test
fun unaryPlus() = testLatex(MST.Unary(GroupOperations.PLUS_OPERATION, MST.Numeric(1)), "+1")
@Test
fun unaryMinus() = testLatex("-x", "-x")
@Test
fun radical() = testLatex("sqrt(x)", "\\sqrt{x}")
@Test
fun superscript() = testLatex("x^y", "x^{y}")
@Test
fun subscript() = testLatex(SubscriptSyntax("", SymbolSyntax("x"), NumberSyntax("123")), "x_{123}")
@Test
fun binaryOperator() = testLatex("f(x, y)", "\\operatorname{f}\\left(x,y\\right)")
@Test
fun binaryPlus() = testLatex("x+x", "x+x")
@Test
fun binaryMinus() = testLatex("x-x", "x-x")
@Test
fun fraction() = testLatex("x/x", "\\frac{x}{x}")
@Test
fun radicalWithIndex() = testLatex(RadicalWithIndexSyntax("", SymbolSyntax("x"), SymbolSyntax("y")), "\\sqrt[x]{y}")
@Test
fun multiplication() {
testLatex("x*1", "x\\times1")
testLatex("1*x", "1\\,x")
}
}

92
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestMathML.kt Normal file
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@ -0,0 +1,92 @@
/*
* 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.
*/
package space.kscience.kmath.ast.rendering
import space.kscience.kmath.ast.rendering.TestUtils.testMathML
import space.kscience.kmath.expressions.MST
import space.kscience.kmath.operations.GroupOperations
import kotlin.test.Test
internal class TestMathML {
@Test
fun number() = testMathML("42", "<mn>42</mn>")
@Test
fun symbol() = testMathML("x", "<mi>x</mi>")
@Test
fun operatorName() = testMathML(
"sin(1)",
"<mo>sin</mo><mspace width=\"0.167em\"></mspace><mfenced open=\"(\" close=\")\" separators=\"\"><mn>1</mn></mfenced>",
)
@Test
fun specialSymbol() {
testMathML(MST.Numeric(Double.POSITIVE_INFINITY), "<mo>&infin;</mo>")
testMathML("pi", "<mo>&pi;</mo>")
}
@Test
fun operand() {
testMathML(
"sin(1)",
"<mo>sin</mo><mspace width=\"0.167em\"></mspace><mfenced open=\"(\" close=\")\" separators=\"\"><mn>1</mn></mfenced>",
)
testMathML("1+1", "<mn>1</mn><mo>+</mo><mn>1</mn>")
}
@Test
fun unaryOperator() = testMathML(
"sin(1)",
"<mo>sin</mo><mspace width=\"0.167em\"></mspace><mfenced open=\"(\" close=\")\" separators=\"\"><mn>1</mn></mfenced>",
)
@Test
fun unaryPlus() =
testMathML(MST.Unary(GroupOperations.PLUS_OPERATION, MST.Numeric(1)), "<mo>+</mo><mn>1</mn>")
@Test
fun unaryMinus() = testMathML("-x", "<mo>-</mo><mi>x</mi>")
@Test
fun radical() = testMathML("sqrt(x)", "<msqrt><mi>x</mi></msqrt>")
@Test
fun superscript() = testMathML("x^y", "<msup><mrow><mi>x</mi></mrow><mrow><mi>y</mi></mrow></msup>")
@Test
fun subscript() = testMathML(
SubscriptSyntax("", SymbolSyntax("x"), NumberSyntax("123")),
"<msub><mrow><mi>x</mi></mrow><mrow><mn>123</mn></mrow></msub>",
)
@Test
fun binaryOperator() = testMathML(
"f(x, y)",
"<mo>f</mo><mfenced open=\"(\" close=\")\" separators=\"\"><mi>x</mi><mo>,</mo><mi>y</mi></mfenced>",
)
@Test
fun binaryPlus() = testMathML("x+x", "<mi>x</mi><mo>+</mo><mi>x</mi>")
@Test
fun binaryMinus() = testMathML("x-x", "<mi>x</mi><mo>-</mo><mi>x</mi>")
@Test
fun fraction() = testMathML("x/x", "<mfrac><mrow><mi>x</mi></mrow><mrow><mi>x</mi></mrow></mfrac>")
@Test
fun radicalWithIndex() =
testMathML(RadicalWithIndexSyntax("", SymbolSyntax("x"), SymbolSyntax("y")),
"<mroot><mrow><mi>y</mi></mrow><mrow><mi>x</mi></mrow></mroot>")
@Test
fun multiplication() {
testMathML("x*1", "<mi>x</mi><mo>&times;</mo><mn>1</mn>")
testMathML("1*x", "<mn>1</mn><mspace width=\"0.167em\"></mspace><mi>x</mi>")
}
}

46
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestStages.kt Normal file
View File

@ -0,0 +1,46 @@
/*
* 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.
*/
package space.kscience.kmath.ast.rendering
import space.kscience.kmath.ast.rendering.TestUtils.testLatex
import kotlin.test.Test
internal class TestStages {
@Test
fun betterMultiplication() {
testLatex("a*1", "a\\times1")
testLatex("1*(2/3)", "1\\times\\left(\\frac{2}{3}\\right)")
testLatex("1*1", "1\\times1")
testLatex("2e10", "2\\times10^{10}")
testLatex("2*x", "2\\,x")
testLatex("2*(x+1)", "2\\,\\left(x+1\\right)")
testLatex("x*y", "x\\,y")
}
@Test
fun parentheses() {
testLatex("(x+1)", "x+1")
testLatex("x*x*x", "x\\,x\\,x")
testLatex("(x+x)*x", "\\left(x+x\\right)\\,x")
testLatex("x+x*x", "x+x\\,x")
testLatex("x+x^x*x+x", "x+x^{x}\\,x+x")
testLatex("(x+x)^x+x*x", "\\left(x+x\\right)^{x}+x\\,x")
testLatex("x^(x+x)", "x^{x+x}")
}
@Test
fun exponent() {
testLatex("exp(x)", "e^{x}")
testLatex("exp(x/2)", "\\operatorname{exp}\\,\\left(\\frac{x}{2}\\right)")
testLatex("exp(x^2)", "\\operatorname{exp}\\,\\left(x^{2}\\right)")
}
@Test
fun fraction() {
testLatex("x/y", "\\frac{x}{y}")
testLatex("x^(x/y)", "x^{x/y}")
}
}

46
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/rendering/TestUtils.kt Normal file
View File

@ -0,0 +1,46 @@
/*
* 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.
*/
package space.kscience.kmath.ast.rendering
import space.kscience.kmath.ast.parseMath
import space.kscience.kmath.expressions.MST
import kotlin.test.assertEquals
internal object TestUtils {
private fun mathSyntax(mst: MST) = FeaturedMathRendererWithPostProcess.Default.render(mst)
private fun latex(mst: MST) = LatexSyntaxRenderer.renderWithStringBuilder(mathSyntax(mst))
private fun mathML(mst: MST) = MathMLSyntaxRenderer.renderWithStringBuilder(mathSyntax(mst))
internal fun testLatex(mst: MST, expectedLatex: String) = assertEquals(
expected = expectedLatex,
actual = latex(mst),
)
internal fun testLatex(expression: String, expectedLatex: String) = assertEquals(
expected = expectedLatex,
actual = latex(expression.parseMath()),
)
internal fun testLatex(expression: MathSyntax, expectedLatex: String) = assertEquals(
expected = expectedLatex,
actual = LatexSyntaxRenderer.renderWithStringBuilder(expression),
)
internal fun testMathML(mst: MST, expectedMathML: String) = assertEquals(
expected = "<math xmlns=\"https://www.w3.org/1998/Math/MathML\"><mrow>$expectedMathML</mrow></math>",
actual = mathML(mst),
)
internal fun testMathML(expression: String, expectedMathML: String) = assertEquals(
expected = "<math xmlns=\"https://www.w3.org/1998/Math/MathML\"><mrow>$expectedMathML</mrow></math>",
actual = mathML(expression.parseMath()),
)
internal fun testMathML(expression: MathSyntax, expectedMathML: String) = assertEquals(
expected = "<math xmlns=\"https://www.w3.org/1998/Math/MathML\"><mrow>$expectedMathML</mrow></math>",
actual = MathMLSyntaxRenderer.renderWithStringBuilder(expression),
)
}

25
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/utils.kt Normal file
View File

@ -0,0 +1,25 @@
/*
* 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.
*/
package space.kscience.kmath.ast
import space.kscience.kmath.expressions.Expression
import space.kscience.kmath.expressions.MST
import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.IntRing
internal interface CompilerTestContext {
fun MST.compileToExpression(algebra: IntRing): Expression<Int>
fun MST.compile(algebra: IntRing, arguments: Map<Symbol, Int>): Int
fun MST.compile(algebra: IntRing, vararg arguments: Pair<Symbol, Int>): Int = compile(algebra, mapOf(*arguments))
fun MST.compileToExpression(algebra: DoubleField): Expression<Double>
fun MST.compile(algebra: DoubleField, arguments: Map<Symbol, Double>): Double
fun MST.compile(algebra: DoubleField, vararg arguments: Pair<Symbol, Double>): Double =
compile(algebra, mapOf(*arguments))
}
internal expect inline fun runCompilerTest(action: CompilerTestContext.() -> Unit)

18
kmath-ast/src/jsMain/kotlin/space/kscience/kmath/ast/rendering/multiplatformToString.kt Normal file
View File

@ -0,0 +1,18 @@
/*
* 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.
*/
package space.kscience.kmath.ast.rendering
internal actual fun Double.multiplatformToString(): String {
val d = this
if (d >= 1e7 || d <= -1e7) return js("d.toExponential()") as String
return toString()
}
internal actual fun Float.multiplatformToString(): String {
val d = this
if (d >= 1e7f || d <= -1e7f) return js("d.toExponential()") as String
return toString()
}

56
kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt
View File

@ -1,44 +1,48 @@
/*
* 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.
*/
package space.kscience.kmath.estree
import space.kscience.kmath.ast.MST
import space.kscience.kmath.ast.MST.*
import space.kscience.kmath.ast.MstExpression
import space.kscience.kmath.estree.internal.ESTreeBuilder
import space.kscience.kmath.estree.internal.estree.BaseExpression
import space.kscience.kmath.expressions.Expression
import space.kscience.kmath.expressions.MST
import space.kscience.kmath.expressions.MST.*
import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.expressions.invoke
import space.kscience.kmath.internal.estree.BaseExpression
import space.kscience.kmath.operations.Algebra
import space.kscience.kmath.operations.NumericAlgebra
import space.kscience.kmath.operations.bindSymbolOrNull
@PublishedApi
internal fun <T> MST.compileWith(algebra: Algebra<T>): Expression<T> {
fun ESTreeBuilder<T>.visit(node: MST): BaseExpression = when (node) {
is Symbolic -> {
val symbol = try {
algebra.bindSymbol(node.value)
} catch (ignored: IllegalStateException) {
null
}
is Symbol -> {
val symbol = algebra.bindSymbolOrNull(node)
if (symbol != null)
constant(symbol)
else
variable(node.value)
variable(node.identity)
}
is Numeric -> constant(node.value)
is Unary -> when {
algebra is NumericAlgebra && node.value is Numeric -> constant(
algebra.unaryOperationFunction(node.operation)(algebra.number(node.value.value)))
algebra.unaryOperationFunction(node.operation)(algebra.number((node.value as Numeric).value)))
else -> call(algebra.unaryOperationFunction(node.operation), visit(node.value))
}
is Binary -> when {
algebra is NumericAlgebra && node.left is Numeric && node.right is Numeric -> constant(
algebra
.binaryOperationFunction(node.operation)
.invoke(algebra.number(node.left.value), algebra.number(node.right.value))
algebra.binaryOperationFunction(node.operation).invoke(
algebra.number((node.left as Numeric).value),
algebra.number((node.right as Numeric).value)
)
)
algebra is NumericAlgebra && node.left is Numeric -> call(
@ -64,19 +68,21 @@ internal fun <T> MST.compileWith(algebra: Algebra<T>): Expression<T> {
return ESTreeBuilder<T> { visit(this@compileWith) }.instance
}
/**
* Create a compiled expression with given [MST] and given [algebra].
*/
public fun <T : Any> MST.compileToExpression(algebra: Algebra<T>): Expression<T> = compileWith(algebra)
/**
* Compiles an [MST] to ESTree generated expression using given algebra.
*
* @author Alexander Nozik.
* Compile given MST to expression and evaluate it against [arguments]
*/
public fun <T : Any> Algebra<T>.expression(mst: MST): Expression<T> =
mst.compileWith(this)
public inline fun <reified T : Any> MST.compile(algebra: Algebra<T>, arguments: Map<Symbol, T>): T =
compileToExpression(algebra).invoke(arguments)
/**
* Optimizes performance of an [MstExpression] by compiling it into ESTree generated expression.
*
* @author Alexander Nozik.
* Compile given MST to expression and evaluate it against [arguments]
*/
public fun <T : Any> MstExpression<T, Algebra<T>>.compile(): Expression<T> =
mst.compileWith(algebra)
public inline fun <reified T : Any> MST.compile(algebra: Algebra<T>, vararg arguments: Pair<Symbol, T>): T =
compileToExpression(algebra).invoke(*arguments)

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